Model Projection

New scenario for models are defined in each round

Round 1

Scenario defined as of 2020-12-22
Model Projecting from Epiweek 53 to Epiweek 26


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

Notes:

Ensemble methods: The Scenario Modeling Hub ensembles individual projections using one method: Ensemble is obtained by calculating the median of each submitted quantile.
Ensemble projection includes only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 2

Scenario defined as of 2021-01-22
Model Projecting from Epiweek 3 to Epiweek 29


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

Notes:

Ensemble methods: The Scenario Modeling Hub ensembles individual projections using one method: Ensemble is obtained by calculating the median of each submitted quantile.
Ensemble projection includes only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 3

Scenario defined as of 2021-03-05
Model Projecting from Epiweek 9 to Epiweek 35


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR
Moderate NPI
(moderate NPI reduction)
Low NPI
(high NPI reduction)
High Vaccination
(high administration and VE, high vaccine coverage)
Scenario A
Vaccination
M/P 90%/95% VE
   35M 1st doses administered monthly, March-August


NPIs
50% reduction from Feb 2021 to Aug 2021

Scenario B
Vaccination
M/P 90%/95% VE
   35M 1st doses administered monthly, March-August


NPIs
80% reduction from Feb 2021 to Aug 2021

Low Vaccination
(low administration and VE, low vaccine coverage)
Scenario C
Vaccination
M/P 50%/75% VE against symptoms
   20M 1st doses administered monthly, March-August


NPIs
50% reduction from April 2021 to October 2021

Scenario D
Vaccination
M/P 50%/75% VE against symptoms
   20M 1st doses administered monthly, March-August


NPIs
80% reduction from Feb 2021 to Aug 2021




Notes:

Ensemble methods: The Scenario Modeling Hub ensembles individual projections using one method: Ensemble is obtained by calculating the median of each submitted quantile.
Ensemble projection includes only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Projection truncation: Since models were not required to incorporate behavioral feedbacks that would likely accompany large increases in COVID-19 cases, hospitalizations, or deaths, we truncate projected values beyond 80% of previously seen peaks. Within each location, truncation is evaluated separately for incident cases and deaths.
The truncation threshold is calculated as 80% of the highest observed weekly incidence, adjusted by population size. Once either threshold is crossed, all projections (incident and cumulative case, death, and hospitalization) are truncated using following the following rules:

  - If an individual model projects incident cases or deaths to be higher than the threshold, median projections are not plotted for that model beyond the week at which the threshold is crossed. Uncertainty intervals are retained for all weeks. This truncation is applied to all states, except those for which truncation would remove all projection points.
  - If the Ensemble crosses the threshold, no projections (median or uncertainty intervals) are plotted beyond the first week at which the threshold is crossed.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 4

Scenario defined as of 2021-03-28
Model Projecting from Epiweek 12 to Epiweek 38


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR
Moderate NPI
(moderate NPI reduction)
Low NPI
(high NPI reduction)
High Vaccination
(high administration and VE, high vaccine coverage)
Scenario A
Vaccination
M/P 75%/95% VE againt symptoms*
  50M 1st doses administered monthly, April-September**

J&J 70% VE againt symptoms
  10M-20M doses administered monthly (April: 10M, May: 15M, June-Sept: 20M)***

At most 90% vaccination coverage per group



NPIs
50% reduction from March 2021 to Sept 2021

Scenario B
Vaccination
M/P 75%/95% VE againt symptoms*
   50M 1st doses administered monthly, April-September**

J&J 70% VE againt symptoms
  10M-20M doses administered monthly (April: 10M, May: 15M, June-Sept: 20M)***

At most 90% vaccination coverage per group



NPIs
80% reduction from March 2021 to Sept 2021

Low Vaccination
(high hesistancy)
Scenario C
Vaccination
M/P 50%/85% VE againt symptoms*
   45M 1st doses administered monthly, April-September**

J&J 60% VE againt symptoms
  5M doses administered monthly, April-September

At most 90% vaccination coverage per group


NPIs
50% reduction from March 2021 to Sept 2021

Scenario D
Vaccination
M/P 50%/85% VE againt symptoms*
   45M 1st doses administered monthly, April-September**

J&J 60% VE againt symptoms
  5M doses administered monthly, April-September

At most 90% vaccination coverage per group



NPIs
80% reduction from March 2021 to Sept 20211



* VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death). Data on VE studies of infection and symptomatic disease are included below.

** If the maximum level of vaccination specified (e.g., 75%, 90%) is reached in a population group during the projection period, assume that no more vaccination occurs in that group (i.e., do not model new doses beyond this amount). Includes all available vaccines. Past empirical (i.e., true vaccination coverage) can exceed these levels.

*** Increasing J&J: Doses increase monthly due to manufacturing capacity increases.

Notes:

Ensemble methods: The Scenario Modeling Hub ensembles individual projections using one method: Ensemble is obtained by calculating the median of each submitted quantile.
Ensemble projection includes only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Projection truncation: Since models were not required to incorporate behavioral feedbacks that would likely accompany large increases in COVID-19 cases, hospitalizations, or deaths, we truncate projected values beyond 80% of previously seen peaks. Within each location, truncation is evaluated separately for incident cases and deaths.
The truncation threshold is calculated as 80% of the highest observed weekly incidence, adjusted by population size. Once either threshold is crossed, all projections (incident and cumulative case, death, and hospitalization) are truncated using following the following rules:

  - If an individual model projects incident cases or deaths to be higher than the threshold, median projections are not plotted for that model beyond the week at which the threshold is crossed. Uncertainty intervals are retained for all weeks. This truncation is applied to all states, except those for which truncation would remove all projection points.
  - If the Ensemble crosses the threshold, no projections (median or uncertainty intervals) are plotted beyond the first week at which the threshold is crossed.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 5

Scenario defined as of 2021-05-02
Model Projecting from Epiweek 17 to Epiweek 43


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP by averaging cumulative probabilities of a given value across submissions
Moderate NPI
(moderate NPI reduction)
Low NPI
(high NPI reduction)
High Vaccination
(low hesistancy)
Scenario A
Vaccination
Coverage saturates at 83% nationally among the vaccine-eligible population* by October 31, 2021**


NPIs
50% reduction from April 2021 to October 2021

Scenario B
Vaccination
Coverage saturates at 83% nationally among the vaccine-eligible population* by October 31, 2021**


NPIs
80% reduction from April 2021 to October 2021

Low Vaccination
(high hesistancy)
Scenario C
Vaccination
Coverage saturates at 68% nationally among the vaccine-eligible population* by October 31, 2021**


NPIs
50% reduction from April 2021 to October 2021

Scenario D
Vaccination
Coverage saturates at 68% nationally among the vaccine-eligible population* by October 31, 2021**


NPIs
80% reduction from April 2021 to October 2021



* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 16 years or older until June 1, 2021. On June 1, the eligible population is presumed to extend to individuals aged 12 years and older.

** Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 83% saturation is defined using the current estimates from the Delphi group (link) from March 13, 2021 data. The low saturation estimate of 68% is the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey from March 15, 2021 data (link). Both of these saturation levels are assumed to be among the population eligible for vaccination, not the full population.

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions.
(b) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Projection truncation: Since models were not required to incorporate behavioral feedbacks that would likely accompany large increases in COVID-19 cases, hospitalizations, or deaths, we truncate projected values beyond 80% of previously seen peaks. Within each location, truncation is evaluated separately for incident cases and deaths.
The truncation threshold is calculated as 80% of the highest observed weekly incidence, adjusted by population size. Once either threshold is crossed, all projections (incident and cumulative case, death, and hospitalization) are truncated using following the following rules:

  - If an individual model projects incident cases or deaths to be higher than the threshold, median projections are not plotted for that model beyond the week at which the threshold is crossed. Uncertainty intervals are retained for all weeks. This truncation is applied to all states, except those for which truncation would remove all projection points.
  - If the LOP Ensemble crosses the threshold, no projections (median or uncertainty intervals) are plotted beyond the first week at which the threshold is crossed.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 6

Scenario defined as of 2021-05-25
Model Projecting from Epiweek 21 to Epiweek 47


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP by averaging cumulative probabilities of a given value across submissions
Low Impact Variant
(low transmissibility increase, no immune escape)
High Impact Variant
(high transmissibility increase, no immune escape)
High Vaccination
(low hesistancy)
Scenario A
Vaccination
- Coverage saturates at 86% nationally among the vaccine-eligible population* by November 30, 2021**
- VE is 50%/90% for Pfizer/Moderna against currently circulating variants (1st/2nd dose)
- J&J no longer used


Variant
20% increased transmissibility as compared by B.1.1.7 for B.1.617+ variant. 5% prevalence of B.1.617+ nationally on May 29.

Scenario B
Vaccination
- Coverage saturates at 86% nationally among the vaccine-eligible population* by November 30, 2021**
- VE is 50%/90% for Pfizer/Moderna against currently circulating variants (1st/2nd dose)
- J&J no longer used


Variant
60% increased transmissibility as compared by B.1.1.7 for B.1.617+ variant. 5% prevalence of B.1.617+ nationally on May 29.

Low Vaccination
(high hesistancy)
Scenario C
Vaccination
- Coverage saturates at 75% nationally among the vaccine-eligible population* by November 30, 2021**
- VE is 50%/90% for Pfizer/Moderna against currently circulating variants (1st/2nd dose) and 60% for JJ (1 dose)
- J&J no longer used


Variant
20% increased transmissibility as compared by B.1.1.7 for B.1.617+ variant. 5% prevalence of B.1.617+ nationally on May 29.

Scenario D
Vaccination
- Coverage saturates at 75% nationally among the vaccine-eligible population* by November 30, 2021**
- VE is 50%/90% for Pfizer/Moderna against currently circulating variants (1st/2nd dose) and 60% for JJ (1 dose)
- J&J no longer used


Variant
60% increased transmissibility as compared by B.1.1.7 for B.1.617+ variant. 5% prevalence of B.1.617+ nationally on May 29.



* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 16 years or older until May 12, 2021. On May 12, the eligible population is extended to individuals aged 12 years and older, through the end of the projection period.

** Vaccine hesitancy expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 86% saturation is defined using the current estimates from the Delphi group (link, updated from Round 5). The low saturation estimate of 75% is the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey from Apr 14-26, 2021 data (link), which is updated from Round 5

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions.
(b) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Projection truncation: Since models were not required to incorporate behavioral feedbacks that would likely accompany large increases in COVID-19 cases, hospitalizations, or deaths, we truncate projected values beyond 80% of previously seen peaks. Within each location, truncation is evaluated separately for incident cases and deaths.
The truncation threshold is calculated as 80% of the highest observed weekly incidence, adjusted by population size. Once either threshold is crossed, all projections (incident and cumulative case, death, and hospitalization) are truncated using following the following rules:

  - If an individual model projects incident cases or deaths to be higher than the threshold, median projections are not plotted for that model beyond the week at which the threshold is crossed. Uncertainty intervals are retained for all weeks. This truncation is applied to all states, except those for which truncation would remove all projection points.
  - If the LOP Ensemble crosses the threshold, no projections (median or uncertainty intervals) are plotted beyond the first week at which the threshold is crossed.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 7

Scenario defined as of 2021-07-06
Model Projecting from Epiweek 27 to Epiweek 52


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP by averaging cumulative probabilities of a given value across submissions
Low Impact Variant
(low transmissibility increase, no immune escape)
High Impact Variant
(high transmissibility increase, no immune escape)
High Vaccination
(low hesistancy)
Scenario A
Vaccination
- Coverage saturates at 80% nationally among the vaccine-eligible population* by December 31, 2021**
- VE is 50%/90% for Pfizer/Moderna against the Delta variant, against symptoms (1st/2nd dose)
- J&J no longer used


Variant
40% increased transmissibility as compared with Alpha for Delta variant. Initial prevalence estimated at state-level by teams.

Scenario B
Vaccination
- Coverage saturates at 80% nationally among the vaccine-eligible population* by December 31, 2021**
- VE is 35%/85% for Pfizer/Moderna against the Delta variant, against symptoms (1st/2nd dose)
- J&J no longer used


Variant
60% increased transmissibility as compared with Alpha for Delta variant. Initial prevalence estimated at state-level by teams.

Low Vaccination
(high hesistancy)
Scenario C
Vaccination
- Coverage saturates at 70% nationally among the vaccine-eligible population* by December 31, 2021**
- VE is 50%/90% for Pfizer/Moderna against the Delta variant, against symptoms (1st/2nd dose)
- J&J no longer used


Variant
40% increased transmissibility as compared with Alpha for Delta variant. Initial prevalence estimated at state-level by teams.

Scenario D
Vaccination
- Coverage saturates at 70% nationally among the vaccine-eligible population* by December 31, 2021**
- VE is 35%/85% for Pfizer/Moderna against the Delta variant, against symptoms (1st/2nd dose)
- J&J no longer used


Variant
60% increased transmissibility as compared with Alpha for Delta variant. Initial prevalence estimated at state-level by teams.



* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 12 years and older through the end of the projection period.

** Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 80% saturation is defined crudely as using the current estimates from the Delphi group, adjusted for potential bias in respondents, who tend to be more highly vaccinated that the general US population (link, updated from Round 6). The low saturation estimate of 70% is based on an adjustment of the Pulse Survey overall estimate, adjusted for survey participant vaccination coverage. This number also mirrors the lowest county-level estimate (73.3%) from the U.S. Census Bureau’s Pulse Survey from May 26-June 7, 2021 data (link), which is updated from Round 6.

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions.
(b) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 8

Scenario defined as of 2021-08-02
Model Projecting from Epiweek 33 to Epiweek 6


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)


Round 8 is an internal training round; the results will not be published.

Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
Slow waning of natural and vaccine-induced immunity
(from no waning to exponential waning with mean of 3 years)
Fast waning of natural and vaccine-induced immunity
(exponential waning with mean of 1 year)
High protection against infection and severe disease after waning
Scenario A
No waning
- Vaccine-induced and natural immunity retain their initial protection throughout the simulation period

Scenario B
Waning
- Exponentially distributed immune waning with mean of 1 year (time to transition to partially immune state)


In partially immune state

- Protection from infection is:
 - 70% ≤ 65 years
 - 35% > 65 years
- Protection from hospitalization and death is 90%

Low protection against infection and severe disease after waning Scenario C
Waning
- Exponentially distributed immune waning with mean of 3 year (time to transition to partially immune state)


In partially immune state
- Protection from infection is:
 - 50% ≤ 65 years
 - 25% > 65 years
- Protection from hospitalization and death is 80%

Scenario D
Waning
- Exponentially distributed immune waning with mean of 1 year (time to transition to partially immune state)


In partially immune state
- Protection from infection is:
 - 50% ≤ 65 years
 - 25% > 65 years
- Protection from hospitalization and death is 80%


Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 9

Scenario defined as of 2021-08-30
Model Projecting from Epiweek 36 to Epiweek 9


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
Childhood Vaccination No Childhood Vaccination
No Variant
Scenario A
Vaccination
- Vaccination among 5-11 years old is approved and immunization begins on Nov 1.
- Each state's uptake rate reflects the percent coverage increases observed for 12-17 years olds since distribution began an May 13.

Variant
- The same mix of variants circulate throughout the projection period. No change in virus transmissibility

Scenario B
Vaccination


- No vaccination for children under 12 years old.


Variant
- The same mix of variants circulate throughout the projection period. No change in virus transmissibility

New Variant
Scenario C
Vaccination
- Vaccination among 5-11 years old is approved and immunization begins on Nov 1.
- Each state's uptake rate reflects the percent coverage increases observed for 12-17 years olds since distribution began an May 13.

Variant
- A more transmissible variant emerges, comprising 1% of circulating viruses on Nov 15.

- The new variant is 1.5X as transmissible as viruses circulating at the beginning of the projection period.

Scenario D
Vaccination


- No vaccination for children under 12 years old.


Variant
- A more transmissible variant emerges, comprising 1% of circulating viruses on Nov 15.

- The new variant is 1.5X as transmissible as viruses circulating at the beginning of the projection period.


Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 10

Scenario defined as of 2021-10-27
Model Projecting from Epiweek 46 to Epiweek 45


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)


Due to the Omicron variant, Round 10 results are no longer pertinent.

Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
High Booster Coverage Low Booster Coverage
Optimistic Waning
Scenario A
Booster
- Booster coverage saturates at 70 % of adults who previously received a full vaccine course.



Waning
- Slow immune waning, average transition time to partially immune state = 1 year.

- In partially immune state,
Protection from < 65 years >= 65 years
Infection 60 % 40 %
Hospitalization 90 % 80 %
Death 95 % 90 %

Scenario B
Booster
- Booster coverage saturates at 40 % of adults who previously received a full vaccine course.



Waning
- Slow immune waning, average transition time to partially immune state = 1 year.

- In partially immune state,
Protection from < 65 years >= 65 years
Infection 60 % 40 %
Hospitalization 90 % 80 %
Death 95 % 90 %

Pessimistic Waning
Scenario C
Booster
- Booster coverage saturates at 70 % of adults who previously received a full vaccine course.



Waning
- Fast immune waning, average transition time to partially immune state = 6 months.

- In partially immune state,
Protection from < 65 years >= 65 years
Infection 50 % 30 %
Hospitalization 80 % 70 %
Death 90 % 85 %

Scenario D
Booster
- Booster coverage saturates at 40 % of adults who previously received a full vaccine course.



Waning
- Fast immune waning, average transition time to partially immune state = 6 months.

- In partially immune state,
Protection from < 65 years >= 65 years
Infection 50 % 30 %
Hospitalization 80 % 70 %
Death 90 % 85 %


Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 11

Scenario defined as of 2021-12-13
Model Projecting from Epiweek 51 to Epiweek 10


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
High Immune Escape / Low Transmissibility Increase Low Immune Escape / High Transmissibility Increase
Optimistic severity
Scenario A

Immune Escape / Transmissibility

- Advantage Omicron over Delta in South Africa, Rt ratio: 2.8 (Rt = 2.5)

- Intrinsic transmissibility Omicron = 1 x seasonally-adjusted R0 of Delta

- Immune escape = 80%

Severity
- Among naive individuals, 50% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death divided by two; age-specific risks at teams discretion).

- Among previously infected or vaccinated, residual protection for Omicron cases:
 - Hospitalization: 85% [i.e., Pr(hosp|any immunity) = 0.15 * Pr(hosp|naive)]
 - Death: 95% [i.e.. Pr(death|any immunity) = 0.05*Pr(death|naive)]
Scenario B
Immune Escape / Transmissibility

- Advantage Omicron over Delta in South Africa, Rt ratio: 2.8 (Rt = 2.5)

- Intrinsic transmissibility Omicron = 1.66 x seasonally-adjusted R0 of Delta*

- Immune escape = 50%

Severity
- Among naive individuals, 50% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death divided by two; age-specific risks at teams discretion).

- Among previously infected or vaccinated, residual protection for Omicron cases:
 - Hospitalization: 85% [i.e., Pr(hosp|any immunity) = 0.15 * Pr(hosp|naive)]
 - Death: 95% [i.e.. Pr(death|any immunity) = 0.05*Pr(death|naive)]
Pessimistic Severity
Scenario C
Immune Escape / Transmissibility

- Advantage Omicron over Delta in South Africa, Rt ratio: 2.8 (Rt = 2.5)

- Intrinsic transmissibility Omicron = 1 x seasonally-adjusted R0 of Delta

- Immune escape = 80%

Severity
- Among naive individuals, no change in severity of omicron infection, relative to Delta.

- Among previously infected or vaccinated, residual protection for Omicron cases:
 - Hospitalization: 70%
 - Death: 85%

Scenario D
Immune Escape / Transmissibility

- Advantage Omicron over Delta in South Africa, Rt ratio: 2.8 (Rt = 2.5)

- Intrinsic transmissibility Omicron = 1.66 x seasonally-adjusted R0 of Delta*

- Immune escape = 50%

Severity
- Among naive individuals, no change in severity of omicron infection, relative to Delta.

- Among previously infected or vaccinated, residual protection for Omicron cases:
 - Hospitalization: 70%
 - Death: 85%

* Assuming Delta R0 = 6, Omicron R0 = 6 or R0 = 10.

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 12

Scenario defined as of 2022-01-09
Model Projecting from Epiweek 2 to Epiweek 12


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
High Immune Escape Low Immune Escape
Optimistic severity
Scenario A
Immune Escape


- Immune escape = 80% of previously immune are susceptible to infection
- All other transmission characteristics at discretion of teams
- Lower levels of escape can be assumed for recently boosted individuals at discretion

Severity
- 70% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death times 0.3; age-specific risks at teams discretion) among all immune classes

Example: A vaccinated person infected with Omicron has 30% the probability of death of a vaccinated person with Delta. Similarly, a naive person infected with Omicron has 30% the probability of death of a naive person infected with Delta.
Scenario B
Immune Escape


- Immune escape = 50% of previously immune are susceptible to infection
- All other transmission characteristics at discretion of teams
- Lower levels of escape can be assumed for recently boosted individuals at discretion

Severity
- 70% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death times 0.3; age-specific risks at teams discretion) among all immune classes

Example: A vaccinated person infected with Omicron has 30% the probability of death of a vaccinated person with Delta. Similarly, a naive person infected with Omicron has 30% the probability of death of a naive person infected with Delta.
Pessimistic Severity
Scenario C
Immune Escape


- Immune escape = 80% of previously immune are susceptible to infection
- All other transmission characteristics at discretion of teams
- Lower levels of escape can be assumed for recently boosted individuals at discretion

Severity
- 30% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death times 0.7; age-specific risks at teams discretion) among all immune classes.

Example: Similar to above, but with 70% of the probability of death as compared to the Delta infection.
Scenario D
Immune Escape


- Immune escape = 50% of previously immune are susceptible to infection
- All other transmission characteristics at discretion of teams
- Lower levels of escape can be assumed for recently boosted individuals at discretion

Severity
- 30% reduction in severity of omicron infection, relative to Delta (all-age risk of hospitalization and death times 0.7; age-specific risks at teams discretion) among all immune classes.

Example: Similar to above, but with 70% of the probability of death as compared to the Delta infection.


* Note that it's expected that with lower immune escape, transmissibility should be higher and vice versa.

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations


Model Projection

New scenario for models are defined in each round

Round 13

Scenario defined as of 2022-02-22
Model Projecting from Epiweek 7 to Epiweek 7


Special regions (American Samoa, Guam, Northern Marianas Island, Virgin Islands) not included
'multi' displays 95%, 90%, 80%, and 50% uncertainty intervals, shaded from lightest (95%) to darkest (50%)

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Definitions


NPI: NonPharmaceutical Interventions (e.g. masks, social distancing)

Epiweek: Epidemiological Week as defined by MMWR

LOP: Linear Opinion Pool; method used to calculate Ensemble_LOP and Ensemble_LOP_untrimmed by averaging cumulative probabilities of a given value across submissions. See Notes for more details.
No Immune Escape Variant New Immune Escape Variant
Optimistic Waning
Scenario A
No new Variant:


Projections are initialized with the mix of strains circulating at the start of the projection period.




Optimistic waning of projection against infection:
- Slow immune waning, median transition time to partially immune state = 10 months

- In the partially immune state, there is a 40% reduction in protection from baseline levels reported immediately after exposure (vaccination or infection)

Scenario B
New variant X emerges on May 1st, 2022


- There is a continuous influx of 50 weekly infections of varaint X for the following 16 weeks
- Variant X has 30% immune escape, and the same intrinsic transmissibility and severity as Omicron

Optimistic waning of projection against infection:
- Slow immune waning, median transition time to partially immune state = 10 months

- In the partially immune state, there is a 40% reduction in protection from baseline levels reported immediately after exposure (vaccination or infection)

Pessimistic Waning
Scenario C
No new Variant:


Projections are initialized with the mix of strains circulating at the start of the projection period.




Pessimistic waning of protection against infection:
- Fast immune waning, median transition time to partially immune state = 4 months

- In the partially immune state, there is a 60% reduction in protection from baseline levels reported immediately after exposure (vaccination or infection)

Scenario D
New variant X emerges on May 1st, 2022


- There is a continuous influx of 50 weekly infections of varaint X for the following 16 weeks
- Variant X has 30% immune escape, and the same intrinsic transmissibility and severity as Omicron

Pessimistic waning of protection against infection:
- Fast immune waning, median transition time to partially immune state = 4 months

- In the partially immune state, there is a 60% reduction in protection from baseline levels reported immediately after exposure (vaccination or infection)



Risk of severe disease, conditional on infection, does not wane with time and does not change with variant X .

Notes:

Ensemble methods: Currently, the Scenario Modeling Hub ensembles individual projections using two methods:
(a) Ensemble_LOP is calculated by averaging cumulative probabilities of a given value across weighted submissions. At each value, the highest and lowest probability is removed before averaging.
(b) Ensemble_LOP_untrimmed is calculated by averaging cumulative probabilities of a given value across weighted submissions. All values are included in the average.
(c) Ensemble is obtained by calculating the weighted median of each submitted quantile.
Ensembles projection include only those submissions that reported all quantiles and the four scenarios for their targets. Individual model and ensemble projections are available in the GitHub Repository.

Ground truth data: The model projections for cumulative hospitalizations start at zero, so no observed data are shown.

Licensing: Models projection are available by default under a CC BY 4.0 license. Some models have specific license. See repository for details.

Disclaimer: The content of the COVID-19 Scenario Modeling Hub is solely the responsability of the participating teams and the Hub maintainers and does not represent the official views of any related funding organizations

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Scenario Definition


Scenario Fullname Scenario Id Scenario Name Social Distancing Measures Testing-Trace-Isolate Masking Vaccine Efficacy Vaccine Availability Vaccine Hesitancy Vaccine
“Optimistic” Scenario A-2020-12-22 optimistic baseline state orders with regards to NPIs continue for six weeks from their start date (i.e., the date each individual state started the policy regime in place at baseline), interventions step down from baseline to the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps constant at baseline levels maintained at baseline levels indefinitely 95% after two doses, 50% after one dose, doses 3.5 weeks apart Actually distributed doses in December (approx.), 25 million courses (50 million doses) in January, 25 million courses per month thereafter NA NA
Business as Usual + Moderate Vaccine Scenario B-2020-12-22 moderate current elevated state orders with regards to NPIs continue for stated length or three weeks after the NPI is started if length is unstated; thereafter interventions step down from baseline to the lowest levels seen since May 2020 in a particular jurisdiction over two one-month steps constant at baseline levels maintained at baseline levels indefinitely 70% after two doses, 20% after one dose, doses 3.5 weeks apart Actually distributed doses in December (approx.), 12.5 million courses in January, 25 million courses per month thereafter NA NA
Fatigue and Hesitancy Scenario C-2020-12-22 fatigue current elevated state orders with regards to NPIs continue for stated length or three weeks after the NPI is started if length is unstated; thereafter interventions step down from baseline to an additional 5% below the lowest levels seen since May 2020 in a particular jurisdiction over two one-month steps constant at baseline levels adherence to these measures steps down from baseline to an additional 5% below the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps 95% after two doses, 50% after one dose, doses 3.5 weeks apart. Actually distributed doses in December (approx.), 12.5 million courses in January, 25 million courses per month thereafter no more than 50% of any priority group accepts the vaccine NA
Counterfactual Scenario D-2020-12-22 counterfactual current elevated state orders with regards to NPIs continue for stated length or three weeks after the NPI is started if length is unstated; thereafter interventions step down from baseline to the lowest levels seen since May 2020 in a particular jurisdiction over two one-month steps constant at baseline levels maintained at baseline levels indefinitely NA NA NA no vaccine



Common Assumptions


- Submission date: January 8, 2021 (approx.)

- Baseline date: December 15, 2020 - date of baseline intervention levels

- Start date for first-round scenarios: January 3, 2021 (week ending January 9) - first date of simulated outcomes; model should not be fit to data from after this date

- Simulation end date: at least through week ending April 3, 2021 (13-week horizon); preferably July 3, 2021 (26-week horizon)

- Transmission assumptions: models fit to US state-specific dynamic up until time of submission – no proscribed R0, interventions, etc.

- Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects

- Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team

- Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team

- Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it

- Vaccine uptake: It is unlikely vaccine uptake will be 100% within prioritized groups, however sufficient data are not available to specify this; we will leave this to team discretion, but we ask that they include these assumptions in their meta-data file

- Vaccine rollout: rollout to follow ACIP recommendations unless known to be contradicted by state recommendations

 - Phase 1a: health care workers, long-term care facilities

 - Phase 1b: frontline essential workers, adults 75+

 - Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74

- NPI assumptions: phased reductions of NPIs in 2021 that align with patterns observed at different times in the course of the epidemic in 2020 (see scenario-specific guidance); teams have some liberty on how to implement these reductions within the guidelines

- Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 - Coronavirus Government Response Tracker | Blavatnik School of Government

 - Coronavirus State Actions - National Governors Association

- Geographic scope: state-level and national projections

- Results: some subset of the following

 - Weekly incident deaths

 - Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident cases

 - Weekly cumulative cases since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident hospitalizations

 - Weekly cumulative hospitalizations since simulation start

 - Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week

- “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.

- Metadata: We will require a brief meta-data form, TBD, from all teams.

- Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles


Scenario Definition


Scenario Fullname Scenario Id Scenario Name Social Distancing Measures Testing-Trace-Isolate Masking Vaccine Efficacy Vaccine Availability Variant Strain Vaccine Hesitancy
“Optimistic” Scenario, No Variant Strain A-2021-01-22 optimistic_no_var baseline state orders with regards to NPIs continue for six weeks from their start date (i.e., the date each individual state started the policy regime in place at baseline), interventions step down from baseline to the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps constant at baseline levels maintained at baseline levels indefinitely 95% after two doses, 50% after one dose, doses 3.5 weeks apart Actually administered doses in December and January, vaccine administration rate observed so far in January persists through the end of the month, 25 million courses distibuted per month thereafter (NOTE: administration refers to actual receipt by an individual, distribution to the doses being sent to states) no variant strain NA
“Optimistic” Scenario, Variant Strain B-2021-01-22 optimistic_var baseline state orders with regards to NPIs continue for six weeks from their start date (i.e., the date each individual state started the policy regime in place at baseline), interventions step down from baseline to the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps constant at baseline levels maintained at baseline levels indefinitely 95% after two doses, 50% after one dose, doses 3.5 weeks apart Actually administered doses in December and January, vaccine administration rate observed so far in January persists through the end of the month, 25 million courses distibuted per month thereafter (NOTE: administration refers to actual receipt by an individual, distribution to the doses being sent to states) variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model) NA
Fatigue and Hesitancy Scenario, No Variant Strain C-2021-01-22 fatigue_no_var current elevated state orders with regards to NPIs continue for stated length or three weeks after the NPI is started if length is unstated; thereafter interventions step down from baseline to an additional 5% below the lowest levels seen since May 2020 in a particular jurisdiction over two one-month steps constant at baseline levels adherence to these measures steps down from baseline to an additional 5% below the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps 95% after two doses, 50% after one dose, doses 3.5 weeks apart. Actually administered doses in December and January, vaccine administration rate observed to date in January persists indefinitely until the proportion vaccinated reaches the hesitancy threshold no variant strain no more than 50% of any priority group accepts the vaccine
Fatigue and Hesitancy Scenario, Variant Strain D-2021-01-22 fatigue_var current elevated state orders with regards to NPIs continue for stated length or three weeks after the NPI is started if length is unstated; thereafter interventions step down from baseline to an additional 5% below the lowest levels seen since May 2020 in a particular jurisdiction over two one-month steps constant at baseline levels adherence to these measures steps down from baseline to an additional 5% below the lowest levels seen since September 2020 in a particular jurisdiction over two one-month steps 95% after two doses, 50% after one dose, doses 3.5 weeks apart. Actually administered doses in December and January, vaccine administration rate observed to date in January persists indefinitely until the proportion vaccinated reaches the hesitancy threshold variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model) no more than 50% of any priority group accepts the vaccine



Common Assumptions


- Submission date: January 29, 2021 (soft deadline)

- Baseline date: January 23, 2021 - date of baseline intervention levels

- End date for fitting data: January 23, 2021 - no fitting should be done to data from after this date

- Start date for first-round scenarios: January 24, 2021 (week ending January 30) - first date of simulated outcomes

- Simulation end date: at least through week ending April 24, 2021 (13-week horizon); preferably July 24, 2021 (26-week horizon)

- Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.

- Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects

- Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team

- Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team

- Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it

- Vaccine uptake: It is unlikely vaccine uptake will be 100% within prioritized groups, however sufficient data are not available to specify this; we will leave this to team discretion, but we ask that they include these assumptions in their meta-data file

- Vaccine rollout: rollout to follow ACIP recommendations unless known to be contradicted by state recommendations

 - Phase 1a: health care workers, long-term care facilities

 - Phase 1b: frontline essential workers, adults 75+

 - Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74

- NPI assumptions: phased reductions of NPIs in 2021 that align with patterns observed at different times in the course of the epidemic in 2020 (see scenario-specific guidance); teams have some liberty on how to implement these reductions within the guidelines

- Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 - Coronavirus Government Response Tracker | Blavatnik School of Government

 - Coronavirus State Actions - National Governors Association

- Geographic scope: state-level and national projections

- Results: some subset of the following

 - Weekly incident deaths

 - Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident cases

 - Weekly cumulative cases since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident hospitalizations

 - Weekly cumulative hospitalizations since simulation start

 - Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week

- “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.

- Metadata: We will require a brief meta-data form, TBD, from all teams.

- Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles

- Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles


Scenario Definition


Scenario Fullname Scenario Id Scenario Name Social Distancing Measures Testing-Trace-Isolate Masking Vaccine Efficacy (2-Dose Vaccines) Vaccine Availability B.1.1.7 Variant Strain Vaccine Coverage
High Vaccination, Moderate NPI A-2021-03-05 highVac_modNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change. Declines gradually over a period of 5 months starting at the beginning of March and ending in August at 50% of the effectiveness of control observed for February 2021. Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns), but should occur over the full period. The effectiveness of control in February 2021 should be based on the last two weeks of the month. Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Feb 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize that there is uncertainty about what the effects would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. First dose: 90% against disease, 14 days after 1st dose Second dose: 95% against disease, 14 days after 2nd dose Transmission impact at teams’ discretion and should be clearly documented in team’s metadata. Doses 3.5 weeks apart December, January, and February: based on data on administered doses (second doses should be taken into account) March-August: 35 million administered first doses/month, with the intention of protocols being followed (70M doses/mo) If the maximum level of vaccination specified (e.g., 90% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) The specified scenarios do not include the Johnson and Johnson one-dose vaccine, so it should not be modeled. Next round may include the explicit introduction of J&J vaccine. Teams can model the B.1.1.7 variant as appropriate to their model. Any assumptions should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences in severity, mortality, or VE are assumed in default. No more than 90%_ of any population group receives the vaccine
High Vaccination, Low NPI B-2021-03-05 highVac_lowNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change. Declines gradually over a period of 5 months starting at the beginning of March and ending in August at 20% of the effectiveness of control (i.e., an 80% reduction in effectiveness) observed for February 2021. Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns), but should occur over the full period. The effectiveness of control in February 2021 should be based on the last two weeks of the month. Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Feb 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place but would still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. First dose: 90% against disease, 14 days after 1st dose Second dose: 95% against disease, 14 days after 2nd dose Transmission impact at teams’ discretion and should be clearly documented in team’s metadata. Doses 3.5 weeks apart December, January, and February: Administered doses (second doses should take into account) March-August: 35 million administered first doses/month, with the intention of protocols being followed (70M doses/mo) If the maximum level of vaccination specified (e.g., 90% in this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) The specified scenarios do not include the Johnson and Johnson one-dose vaccine, so it should not be modeled. Next round may include the explicit introduction of J&J vaccine. Teams can model the B.1.1.7 variant as appropriate to their model. Any assumptions should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences in severity, mortality, or VE are assumed in default. No more than 90%_ of any population group receives the vaccine
Low Vaccination, Moderate NPI C-2021-03-05 lowVac_modNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change. Declines gradually over a period of 5 months starting at the beginning of March and ending in August at 50% of the effectiveness of control observed for February 2021. Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns), but should occur over the full period. The effectiveness of control in February 2021 should be based on the last two weeks of the month. Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Feb 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place but would still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. First dose: 50% against disease, 14 days after 1st dose Second dose: 75% against disease, 14 days after 2nd dose Transmission impact at teams’ discretion and should be clearly documented in team’s metadata. Doses 3.5 weeks apart December, January, and February: based on data on administered doses (second doses should take into account) March-August: 20 million administered first doses/month, with the intention of protocols being followed (40M doses/mo) If the maximum level of vaccination specified (e.g., 50% in this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) The specified scenarios do not include the Johnson and Johnson one-dose vaccine, so it should not be modeled. Next round may include the explicit introduction of J&J vaccine. Teams can model the B.1.1.7 variant as appropriate to their model. Any assumptions should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences in severity, mortality, or VE are assumed in default. No more than 50%_ of any population group receives the vaccine
Low Vaccination & Low NPI D-2021-03-05 lowVac_lowNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change. Declines gradually over a period of 5 months starting at the beginning of March and ending in August at 20% of the effectiveness of control (i.e., an 80% reduction in effectiveness) observed for February 2021. Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns), but should occur over the full period. The effectiveness of control in February 2021 should be based on the last two weeks of the month. Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Feb 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place but would still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. First dose: 50% against disease, 14 days after 1st dose Second dose: 75% against disease, 14 days after 2nd dose Transmission impact at teams’ discretion and should be clearly documented in team’s metadata. Doses 3.5 weeks apart December, January, and February: based on data on administered doses (second doses should take into account) March-August: 20 million administered first doses/month, with the intention of protocols being followed (40M doses/mo) If the maximum level of vaccination specified (e.g., 50% in this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) The specified scenarios do not include the Johnson and Johnson one-dose vaccine, so it should not be modeled. Next round may include the explicit introduction of J&J vaccine. Teams can model the B.1.1.7 variant as appropriate to their model. Any assumptions should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of cases infected by a single case over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences in severity, mortality, or VE are assumed in default. No more than 50%_ of any population group receives the vaccine



Common Assumptions


- Submission date: March 9, 2021

- Baseline date: See specific details below

- End date for fitting data: March 6, 2021 - no fitting should be done to data from after this date

- Start date for third-round scenarios: March 7, 2021 (week ending March 12) - first date of simulated outcomes

- Simulation end date: at least through week ending June 5, 2021 (13-week horizon); preferably Sept 4, 2021 (26-week horizon)

- Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.

- Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects

- Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team

- Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team

- Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it

- Vaccine uptake: See specific details below.

- Vaccine rollout: rollout to follow ACIP recommendations unless known to be contradicted by state recommendations

 - Phase 1a: health care workers, long-term care facilities

 - Phase 1b: frontline essential workers, adults 75+

 - Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74

- NPI assumptions: phased reductions of NPIs in 2021 that align with patterns observed at different times in the course of the epidemic in 2020 (see scenario-specific guidance); teams have some liberty on how to implement these reductions within the guidelines

- Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 - Coronavirus Government Response Tracker | Blavatnik School of Government

 - Coronavirus State Actions - National Governors Association

- Geographic scope: state-level and national projections

- Results: some subset of the following

 - Weekly incident deaths

 - Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident reported cases

 - Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident hospitalizations

 - Weekly cumulative hospitalizations since simulation start

 - Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week

- “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.

- Metadata: We will require a brief meta-data form, TBD, from all teams.

- Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles

- Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles


Scenario Definition


Scenario Fullname Scenario Id Scenario Name Social Distancing Measures Testing-Trace-Isolate Masking Vaccination - Pfizer / Moderna Vaccination - Johnson & Johnson Vaccination Coverage B.1.1.7 Variant Strain
High Vaccination, Moderate NPI A-2021-03-28 highVac_modNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.Declines over a period of 6 months starting in April 2021 and ending in September 2021 at 50% of the effectiveness of control observed for March 2021.Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns).Decline can be gradual or sudden, and can differ in speed between states.The effectiveness of control in March 2021 should be based on the last two weeks of the month.Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Mar 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. Vaccine efficacy (2-dose vaccines): First dose: 75% against disease, 14 days after 1st dose Second dose: 95% against disease, 14 days after 2nd dose Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.Doses 3.5 weeks apart. Vaccine availability: December, January, February, and March: based on data on administered doses (second doses should be taken into account) April-September: 50 million administered first doses/month, with the intention of protocols being followed (70M doses/mo) If the maximum level of vaccination specified (e.g., 90% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) Vaccine efficacy (1-dose vaccine): Single dose: 70% against symptoms, 14 days after doseEffectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.. Vaccine availability: March: based on data on administered doses, with continuing at rate current on date of projection for remainder of monthApril-September: 10M administered in April, 15M in May, 20M June, 20M July, 20M August, 20M September administered doses/month. No more than 90% of any population group receives the vaccine. If the maximum level of vaccination specified (e.g., 90% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new dose administration beyond this amount). Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.
High Vaccination, Low NPI B-2021-03-28 highVac_lowNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.Declines over a period of 6 months starting in April 2021 and ending in September 2021 at 20% of the effectiveness of control (i.e., an 80% reduction in effectiveness) of control observed for March 2021.Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns).Decline can be gradual or sudden, and can differ in speed between states.The effectiveness of control in March 2021 should be based on the last two weeks of the month.Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Mar 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. Vaccine efficacy (2-dose vaccines): First dose: 75% against disease, 14 days after 1st dose Second dose: 95% against disease, 14 days after 2nd dose Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.Doses 3.5 weeks apart. Vaccine availability: December, January, February, and March: based on data on administered doses (second doses should be taken into account) April-September: 50 million administered first doses/month, with the intention of protocols being followed (70M doses/mo) If the maximum level of vaccination specified (e.g., 90% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) Vaccine efficacy (1-dose vaccine): Single dose: 70% against symptoms, 14 days after doseEffectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.. Vaccine availability: March: based on data on administered doses, with continuing at rate current on date of projection for remainder of monthApril-September: 10M administered in April, 15M in May, 20M June, 20M July, 20M August, 20M September administered doses/month. No more than 90% of any population group receives the vaccine. If the maximum level of vaccination specified (e.g., 90% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new dose administration beyond this amount). Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.
Low Vaccination, Moderate NPI C-2021-03-28 lowVac_modNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.Declines over a period of 6 months starting in April 2021 and ending in September 2021 at 50% of the effectiveness of control observed for March 2021.Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns).Decline can be gradual or sudden, and can differ in speed between states.The effectiveness of control in March 2021 should be based on the last two weeks of the month.Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Mar 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. Vaccine efficacy (2-dose vaccines): First dose: 50% against disease, 14 days after 1st dose Second dose: 85% against disease, 14 days after 2nd dose Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.Doses 3.5 weeks apart. Vaccine availability: December, January, February, and March: based on data on administered doses (second doses should be taken into account) April-September: 45 million administered first doses/month, with the intention of protocols being followed (90M doses/mo) If the maximum level of vaccination specified (e.g., 75% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) Vaccine efficacy (1-dose vaccine): Single dose: 60% against symptoms, 14 days after doseEffectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.. Vaccine availability: March: based on data on administered doses, with continuing at rate current on date of projection for remainder of monthApril-September: 5M administered doses/month. No more than 75% of any population group receives the vaccine. If the maximum level of vaccination specified (e.g., 75% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new dose administration beyond this amount). Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.
Low Vaccination, Low NPI D-2021-03-28 lowVac_lowNPI Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.Declines over a period of 6 months starting in April 2021 and ending in September 2021 at 20% of the effectiveness of control (i.e., an 80% reduction in effectiveness) observed for March 2021.Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns).Decline can be gradual or sudden, and can differ in speed between states.The effectiveness of control in March 2021 should be based on the last two weeks of the month.Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of Mar 2021 effectiveness) would approximate an epidemic without NPIs (e.g. no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize that there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections. constant at baseline levels Included as part of “Social Distancing Measures” above. Vaccine efficacy (2-dose vaccines): First dose: 50% against disease, 14 days after 1st dose Second dose: 85% against disease, 14 days after 2nd dose Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.Doses 3.5 weeks apart. Vaccine availability: December, January, February, and March: based on data on administered doses (second doses should be taken into account) April-September: 45 million administered first doses/month, with the intention of protocols being followed (90M doses/mo) If the maximum level of vaccination specified (e.g., 75% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new doses beyond this amount) Vaccine efficacy (1-dose vaccine): Single dose: 60% against symptoms, 14 days after doseEffectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.. Vaccine availability: March: based on data on administered doses, with continuing at rate current on date of projection for remainder of monthApril-September: 5M administered doses/month. No more than 75% of any population group receives the vaccine. If the maximum level of vaccination specified (e.g., 75% for this scenario) is reached in all population groups, assume that no more vaccination occurs (i.e., do not model new dose administration beyond this amount). Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata.The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.



Common Assumptions


- Submission date: March 30, 2021

- Baseline date: See specific details below

- End date for fitting data: March 27, 2021 - no fitting should be done to data from after this date

- Start date for third-round scenarios: March 28, 2021 (week ending April 3) - first date of simulated outcomes

- Simulation end date: at least through week ending June 26, 2021 (13-week horizon); preferably Sept 25, 2021 (26-week horizon)

- Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.

- Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects

- Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team

- Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team

- Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it

- Vaccine uptake: See specific details below.

- Vaccine rollout: rollout to follow ACIP recommendations unless known to be contradicted by state recommendations

 - Phase 1a: health care workers, long-term care facilities

 - Phase 1b: frontline essential workers, adults 75+

 - Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74

- NPI assumptions: phased reductions of NPIs in 2021 that align with patterns observed at different times in the course of the epidemic in 2020-21 (see scenario-specific guidance); teams have some liberty on how to implement these reductions within the guidelines

- Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 - Coronavirus Government Response Tracker | Blavatnik School of Government

 - Coronavirus State Actions - National Governors Association

- Geographic scope: state-level and national projections

- Results: some subset of the following

 - Weekly incident deaths

 - Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident reported cases

 - Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)

 - Weekly incident hospitalizations

 - Weekly cumulative hospitalizations since simulation start

 - Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week

- “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.

- Metadata: We will require a brief meta-data form, TBD, from all teams.

- Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles

- Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

Round 5 Scenarios

Scenario Differences

* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 16 years or older until June 1, 2021. On June 1, the eligible population is presumed to extend to individuals aged 12 years and older. * Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 83% saturation is defined using the current estimates from the Delphi group (link) from March 13, 2021 data. The low saturation estimate of 68% is the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey from March 15, 2021 data (link). Both of these saturation levels are assumed to be among the population eligible for vaccination, not the full population.

Common Specifications

Vaccination

  • Doses available:
    • 50M Moderna/Pfizer 1st doses, 15M J&J doses available monthly
    • Supply has likely eclipsed demand at this stage. Number of doses are for reference and as a reminder to account for different VE by manufacturer, but no longer indicate number of doses administered. Distribution of doses by manufacturer and associated vaccine efficacy should fit within these dose bounds.
  • VE:
    • 75% and 95% against symptoms (Moderna/Pfizer 1st and 2nd dose)
    • 70% against symptoms (J&J)
    • VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death). Data on VE studies of infection and symptomatic disease are included below.

Vaccination Hesitancy

Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are illustrative National reference points to guide defining hesitancy. The high vaccination scenario (low hesitancy) saturates at approximately 83% vaccination coverage nationally among the eligible population, as defined by current estimates from the Delphi group (link) from March 13, 2021 data (red line in figure). The low vaccination scenario (high hesitancy) saturates at approximately 68% vaccination coverage nationally among the eligible population, defined by the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey (link) from March 15, 2021 data. The speed of vaccination saturation should be defined by the modeling team, and can be defined as a logistic function (red and blue lines in figure below) or at different speeds (green line below). State or smaller geospatial unit and/or age group hesitancy limits should be defined by the modeling team using their best judgement. Overall national hesitancy should be similar to the illustrative levels defined in the scenarios, though is not expected to be exact. The eligible population for vaccination is presumed to be individuals aged 16 years or older until June 1, 2021. On June 1, the eligible population is presumed to extend to individuals aged 12 years and older.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. High Vaccination, Moderate NPI highVac_modNPI A-2021-05-02
Scenario B. High Vaccination, Low NPI highVac_lowNPI B-2021-05-02
Scenario C. Low Vaccination, Moderate NPI lowVac_modNPI C-2021-05-02
Scenario D. Low Vaccination & Low NPI lowVac_lowNPI D-2021-05-02
  • Submission date: May 4, 2021
  • End date for fitting data: May 1, 2021 - no fitting should be done to data from after this date
  • Start date for fifth-round scenarios: May 2, 2021 (week ending May 8) - first date of simulated outcomes
  • Simulation end date: October 30, 2021 (26-week horizon)

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Declines over a period of 6 months starting at the start of May 2021 and ending in October 2021 at designated level of control effectiveness observed for April 2021.
    • Decline can be implemented at teams’ discretion (e.g., daily or monthly stepdowns).
    • Decline can be gradual or sudden, and can differ in speed between states.
    • The effectiveness of control in April 2021 should be based on the last two weeks of the month.
    • Reduction should be implemented based on each team’s best judgment, but should be done in such a way that a 100% reduction (0% of April 2021 effectiveness) would approximate an epidemic without NPIs (e.g., no masks, no social distancing) in place, but still including immunity, vaccination, etc. We recognize there is uncertainty about what transmission would be without NPIs; this uncertainty should be incorporated into the scenario projections.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • First dose: 75% against symptoms, 14 days after 1st dose
        • Second dose: 95% against symptoms, 14 days after 2nd dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • December-April: based on data on administered doses
        • May-October: 50 million available first doses/month, with the intention of protocols being followed (100M doses/mo)
    • Johnson & Johnson
      • Vaccine efficacy (1-dose vaccine):
        • Single dose: 70% against symptoms, 14 days after dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
      • Vaccine availability:
        • March-April: based on data on administered doses, with continuing at rate current on date of projection for remainder of month.
        • April-September: 15M doses/month available May-October 2021
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. National vaccination saturation levels designated for each scenario serve as illustrative reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • B.1.1.7 Variant strain: Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata. The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects
  • Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it
  • Vaccine uptake: See specific details.
  • Vaccine rollout: rollout to follow ACIP recommendations unless known to be contradicted by state recommendations
    • Phase 1a: health care workers, long-term care facilities
    • Phase 1b: frontline essential workers, adults 75+
    • Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74
  • NPI assumptions: phased reductions of NPIs in 2021 that align with patterns observed at different times in the course of the epidemic in 2020-21 (see scenario-specific guidance); teams have some liberty on how to implement these reductions within the guidelines
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

Round 6 Scenarios

Scenario Differences

* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 16 years or older until May 12, 2021. On May 12, the eligible population is extended to individuals aged 12 years and older, through the end of the projection period. * Vaccine hesitancy expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 86% saturation is defined using the current estimates from the Delphi group (link, updated from Round 5). The low saturation estimate of 75% is the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey from Apr 14-26, 2021 data (link), which is updated from Round 5.

Common Specifications

NPI: In contrast to past scenarios, we do not specify different levels of non-pharmaceutical interventions (NPI) use here. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata.

Vaccination

  • Doses available:
    • 50M Moderna/Pfizer 1st doses available monthly, June-November 2021
    • J&J no longer available (after May 2021)
    • Supply has likely eclipsed demand at this stage. Number of doses are for reference and as a reminder to account for different VE by manufacturer, but no longer indicate number of doses administered. Distribution of doses by manufacturer and associated vaccine efficacy should fit within these dose bounds.
  • VE:
    • 50% and 90% against symptoms (Moderna/Pfizer 1st and 2nd dose; vs. B.1.1.7/B.1.617 and other variants circulating in the projection period). This is based on reports from the UK and Israel indicating decreased protection against new variants such as B117 and B1617 after 1st dose, and no substantial decrease after 2nd dose. These estimates should be used against current and future circulating strains during June-Nov; however higher estimates of VE can be used to account for prior circulating strains.
    • VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death). Data on VE studies of infection and symptomatic disease are included below.

B.1.617+ variant strain with increased transmissibility: The scenarios define the B.1.617-like variants as 20% and 60% more transmissible than B.1.1.7 and other strains circulating in the US and is at 5% national prevalence on May 29, 2021. This 5% proportion on May 29th is a national estimate; teams can use state-specific data if they wish to. Timeframe of the increase in variant prevalence is up to each team, but it should be assumed the variant(s) become dominant due to increased transmissibility. The variant is more transmissible but it is not an immune escape variant; further, no decline of immunity from vaccination (other than VE) or natural infection should be modeled for B.1.617+ or other circulating variants. Other assumptions are at the discretion of each team, but should be documented in metadata. More info on next page.

Vaccination Hesitancy

Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are illustrative National reference points to guide defining hesitancy. The high vaccination scenario (low hesitancy) saturates at 86% vaccination coverage nationally among the vaccine-eligible population (updated from 83% in Round 5), as defined by current estimates from the Delphi group (link) (red line in figure, borrowed from round 5, but the same spirit applies to round 6). The low vaccination scenario (high hesitancy) saturates at 75% vaccination coverage nationally among the vaccine-eligible population, defined by the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey (link) from April 24, 2021 data. The speed of vaccination saturation should be defined by the modeling teams, and can be defined as a logistic function (red and blue lines in figure below) or at different speeds (green line below). State or smaller geospatial unit hesitancy limits should be defined by the modeling team using their best judgment. Overall national hesitancy should be similar to the illustrative levels defined in the scenarios, though is not expected to be exact. The eligible population for vaccination is presumed to be individuals aged 12 years and older.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. High Vaccination, Low Variant Transmissibility Increase highVac_lowVar A-2021-06-08
Scenario B. High Vaccination, High Variant Transmissibility Increase highVac_highVar B-2021-06-08
Scenario C. Low Vaccination, Low Variant Transmissibility Increase lowVac_lowVar C-2021-06-08
Scenario D. Low Vaccination, High Variant Transmissibility Increase lowVac_highVar D-2021-06-08
  • Due date: June 8, 2021
  • End date for fitting data: May 29, 2021 (no fitting should be done to data from after this date)
  • Start date for scenarios: May 30, 2021 (first date of simulated transmission/outcomes)
  • Simulation end date: November 27, 2021 (26-week horizon)

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Current and future levels of social distancing are to be defined by the teams based on their understanding of current and planned control and behavior and expectations. Teams should consider that most jurisdictions are opening fairly quickly. No reactive interventions should be planned.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7, B.1.617+, and currently circulating variants in the US
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • December-May: based on data on administered doses
        • June-November: 50 million available first doses/month, with the intention of protocols being followed (100M doses/mo)
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617+
      • Vaccine availability:
        • March-May: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June-November: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. National vaccination saturation levels designated for each scenario serve as illustrative reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • B.1.1.7 Variant strain: Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata. The default assumptions are that the variant is 1.5x more transmissible than current strains and reaches 50% dominance by March 15 and 100% dominance by May 1 (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and current strains in severity, mortality, or VE are assumed in default.
  • B.1.617+ Variant strain: Transmission advantage of B.1.617+ should follow the scenario guidelines; additional features are at the discretion of the team. Any additional assumptions (e.g., differences in severity/mortality, VE) should be clearly defined in the metadata. The scenarios define the B.1.617-like variants as 20% or 60% more transmissible than B.1.1.7 and other strains circulating in the US and is at 5% national prevalence on May 29, 2021. The 5% proportion on May 29th is a national estimate; teams can use state-specific data if they wish to. Here a 20%/60% increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 20/60% increase in R0 in a classic epidemic model). Timeframe of the increase in variant prevalence is up to each team, but it should be assumed the variant(s) become dominant due to increased transmissibility. No immune escape feature for B.1.617+.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects
  • Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it
  • Vaccine uptake: See specific details.
  • Vaccine roll-out: roll-out to follow ACIP recommendations unless known to be contradicted by state recommendations
    • Phase 1a: health care workers, long-term care facilities
    • Phase 1b: frontline essential workers, adults 75+
    • Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

Round 7 Scenarios

Round 7 is an update of Round 6 with updated data and understanding of both the Delta variant and Vaccination hesitancy.

Scenario Differences

* Vaccine-eligible population. The eligible population for vaccination is presumed to be individuals aged 12 years and older through the end of the projection period. ** Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are National reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling team. The high vaccination 80% saturation is defined crudely as using the current estimates from the Delphi group, adjusted for potential bias in respondents, who tend to be more highly vaccinated that the general US population (link, updated from Round 6). The low saturation estimate of 70% is based on an adjustment of the Pulse Survey overall estimate, adjusted for survey participant vaccination coverage. This number also mirrors the lowest county-level estimate (73.3%) from the U.S. Census Bureau’s Pulse Survey from May 26-June 7, 2021 (link), which is updated from Round 6.

Common Specifications

NPI: In contrast to past scenarios, we don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools intend to return to in-person education in the fall. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata.

Vaccination

  • Doses available:
    • 50M Moderna/Pfizer 1st doses available monthly, June 2021-January 2022
    • J&J no longer available (after May 2021)
    • Supply has likely eclipsed demand at this stage. Number of doses are for reference and as a reminder to account for different VE by manufacturer, but no longer indicate number of doses administered. Distribution of doses by manufacturer and associated vaccine efficacy should fit within these dose bounds.
  • VE:
    • Optimistic (50% and 90% against symptoms (Moderna/Pfizer 1st and 2nd dose) vs. Delta) is based on reports from the UK and the manufacturers indicating decreased protection against new variants such as Alpha and Delta after 1st dose, and no substantial decrease after 2nd dose.
    • Pessimistic (35% and 85% against symptoms (Moderna/Pfizer 1st and 2nd dose) vs. Delta) is based on reports from the UK and Israel indicating further decreased protection against the Delta variant after 1st dose, and notable decrease after 2nd dose.
    • VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death).

Delta variant strain with increased transmissibility: The scenarios define the Delta (B.1.617.2) variant as 40% and 60% more transmissible than Alpha (B.1.1.7.) Initial prevalence should be estimated or defined by the teams based on sequencing and other relevant data, preferably at the state level. Timeframe of the increase in variant prevalence is up to each team, but it should be assumed the variant(s) become dominant due to increased transmissibility. The variant is more transmissible but it is not an immune escape variant; further, no decline of immunity from vaccination (other than VE) or natural infection should be modeled for Delta or other circulating variants. Other assumptions are at the discretion of each team, but should be documented in metadata. More info on next page.

Vaccination Hesitancy

Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The saturation levels provided in these scenarios are illustrative National reference points to guide defining hesitancy. The high vaccination scenario (low hesitancy) saturates at 86% vaccination coverage nationally among the vaccine-eligible population (updated from 83% in Round 5), as defined by current estimates from the Delphi group (link) (red line in figure, borrowed from round 5, but the same spirit applies to round 6). The low vaccination scenario (high hesitancy) saturates at 75% vaccination coverage nationally among the vaccine-eligible population, defined by the lowest county-level estimate from the U.S. Census Bureau’s Pulse Survey (link) from April 24, 2021 data. The speed of vaccination saturation should be defined by the modeling teams, and can be defined as a logistic function (red and blue lines in figure below) or at different speeds (green line below). State or smaller geospatial unit hesitancy limits should be defined by the modeling team using their best judgment. Overall national hesitancy should be similar to the illustrative levels defined in the scenarios, though is not expected to be exact. The eligible population for vaccination is presumed to be individuals aged 12 years and older.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. High Vaccination, Low Variant Transmissibility Increase highVac_lowVar A-2021-07-13
Scenario B. High Vaccination, High Variant Transmissibility Increase highVac_highVar B-2021-07-13
Scenario C. Low Vaccination, Low Variant Transmissibility Increase lowVac_lowVar C-2021-07-13
Scenario D. Low Vaccination, High Variant Transmissibility Increase lowVac_highVar D-2021-07-13
  • Due date: July 13, 2021
  • End date for fitting data: July 03, 2021 (no fitting should be done to data from after this date)
  • Start date for scenarios: July 04, 2021 (first date of simulated transmission/outcomes)
  • Simulation end date: January 01, 2022 (26-week horizon)

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Current and future levels of social distancing are to be defined by the teams based on their understanding of current and planned control and behavior and expectations. Teams should consider that most jurisdictions are opening fairly quickly, and most schools intend to return to in-person education in the fall. No reactive interventions should be planned.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Immune waning and Immune escape: As defined by the modeling team.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% vs 50% against symptoms, 14 days after 1st dose
          • Second dose: 85% vs 90% against symptoms, 14 days after 2nd dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • December-June: based on data on administered doses
        • July-January: 50 million available first doses/month, with the intention of protocols being followed (100M doses/mo)
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June-January: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. National vaccination saturation levels designated for each scenario serve as illustrative reference points to guide defining hesitancy, though the speed of that saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • Alpha (B.1.1.7) variant strain: Teams should model the B.1.1.7 variant as appropriate to their model. Any assumptions (e.g., differences in severity/mortality, VE, or natural immunity) should be clearly defined in the metadata. The default assumptions are that the variant is 1.5x more transmissible than wild-type strains and followed the trajectory outlined here (see MMWR report); here a 1.5x increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 1.5x increase in R0 in a classic epidemic model). No differences between B.1.1.7 and wild strains in severity, mortality, or VE are assumed in default.
  • Delta (B.1.617.2) variant strain: Transmission advantage of Delta should follow the scenario guidelines; additional features are at the discretion of the team. Any additional assumptions (e.g., differences in severity/mortality, VE) should be clearly defined in the metadata. The scenarios define the Delta variant as 40% or 60% more transmissible than Alpha and other strains circulating in the US. Prevalence is not pre-specified – teams are expected to define this on their own. Here a 40%/60% increase in transmissibility is defined as the increase in the expected number of infections by a single infected individual over their entire course of infection when there are no interventions or immunity in the population (e.g., a 40/60% increase in R0 in a classic epidemic model). Timeframe of the increase in variant prevalence is up to each team, but it should be assumed the variant(s) become dominant due to increased transmissibility. No immune escape feature for Delta variant.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects
  • Vaccine effectiveness: level of effectiveness and available doses are specified for each scenario; assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it
  • Vaccine uptake: See specific details.
  • Vaccine roll-out: roll-out to follow ACIP recommendations unless known to be contradicted by state recommendations
    • Phase 1a: health care workers, long-term care facilities
    • Phase 1b: frontline essential workers, adults 75+
    • Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

 

Round 8 focuses on waning immunity.

Scenario Differences

Interpretation: These scenarios illustrate a gradual decay of immune protection with time, rather than the impact of an immune escape variant.

Model structure: Teams are encouraged to model waning immunity as a partial loss of immune protection, where individuals go back to a partially immune state after a period prescribed in the scenarios (mean of 1 or 3 yrs depending on the scenario). Individuals who have reached a partially immune state have reduced probabilities of reinfection and severe disease compared to naive individuals. In scenarios B-D, the distribution of immune decay should follow an exponential distribution. Scenario A has no waning. The same parameters should be used for waning immunity from natural infection and vaccination. Teams are encouraged to model these compartments separately however, in preparation for future scenarios focused on vaccine boosters.

Model parameters defined in scenarios: Parameters in these scenarios are based on observational studies of reinfection (natural immunity), vaccine breakthroughs, and models of decay of antibodies and VE over time. To illustrate the meaning of the scenario parameters, in scenario B for example, we have a protection from infection of 70% for individuals <65yrs in the partially immune state. This means that, for older individuals, the transition out of the partially immune state and into infection is 0.3*force of infection applied to naive individuals of the same age. If we apply this waning parameter to vaccinated people, this corresponds to a VE of 70% against infection. Further, in this scenario, protection against hospitalization and death is 90%. This estimate is similar to VE against hospitalization and death, so it is not a conditional probability. This means that if we follow two individuals over time, one with partial immunity and one completely naive, the probability that the partially immune individual will be hospitalized (die) from COVID19 is 0.1 times the probability that a naive individual will be hospitalized (die). Hence this probability combines protection against infection and protection against hospitalization/death given infection. If we apply this parameter to vaccinated individuals for whom immunity has partially waned, their VE against hospitalization and death becomes 90%.

Unconstrained model parameters: Teams should use their own judgments to parametrize protection against symptoms in the partially immune state, and any reduction in transmission that partially immune individuals may have. Teams can choose to treat individuals who have been infected and vaccinated differently from individuals who had a single exposure to the pathogen/antigen. We do not specify any waning for J&J (for which the starting point VE is much lower): teams can choose to ignore J&J, which represents a small fraction of all vaccinated in the US, or apply a different waning for J&J. We do not specify any waning for those who only get a 1st dose of Pfizer or Moderna and hence never acquire full vaccine immunity. We believe this represents a small fraction of all vaccinated. Teams can choose to apply a different waning to these individuals, or ignore them. All of these assumptions should be documented in meta-data.

Common Specifications

Vaccination

  • Doses available:
    • 50M Moderna/Pfizer 1st doses available monthly, June 2021-January 2022
    • J&J no longer available (after May 2021)
    • Supply has likely eclipsed demand at this stage. Number of doses are for reference and as a reminder to account for different VE by manufacturer, but no longer indicate number of doses administered. Distribution of doses by manufacturer and associated vaccine efficacy should fit within these dose bounds.
  • Coverage:
    • Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage.
  • VE:
    • We recommend that teams use a VE of 35% for 1st dose and 85% for second dose against symptoms for Moderna and Pfizer versus the Delta variant. These estimates reflect VE before any waning takes place.
    • VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death).

Variant progression and transmissibility: Teams should use their own judgment to project the continued progress and transmissibility of the Delta variant across US states. In this scenario, no new variant would arrive in the US between now and the end of the projections. Initial prevalence should be estimated or defined by the teams based on sequencing and other relevant data, preferably at the state level. The variant is more transmissible but it is not an immune escape variant. Teams can set an increased severity of the Delta variant, but this should be documented in meta-data.

NPI: In contrast to past scenarios, we don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools intend to return to in-person education in the fall. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021.The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. No Waning noWan A-2021-08-17
Scenario B. High Protection, Fast Waning highPro_fastWan B-2021-08-17
Scenario C. Low Protection, Slow Waning lowPro_slowWan C-2021-08-17
Scenario D. Low Protection, Fast Waning lowPro_fastWan D-2021-08-17
  • Due date: August 17, 2021
  • End date for fitting data: August 7-14, 2021 (cut-off date at the discretion of individual teams; we’d prefer data through August 7 at least be used; no fitting should be done to data after August 14)
  • Start date for scenarios: August 15, 2021 (first date of simulated transmission/outcomes)
  • Simulation end date: February 12, 2022 (26-week horizon)

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Current and future levels of social distancing are to be defined by the teams based on their understanding of current and planned control and behavior and expectations. Teams should consider that most jurisdictions are opening fairly quickly, and most schools intend to return to in-person education in the fall. No reactive interventions should be planned.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Immune waning and Immune escape: As defined by the scenarios.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% against symptoms, 14 days after 1st dose
          • Second dose: 85% against symptoms, 14 days after 2nd dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • December-August 13: based on data on administered doses
        • August 14-February 2022: 50 million available first doses/month, with the intention of protocols being followed (100M doses/mo)
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June-January: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May at team's discretion.
  • Vaccine Hesitancy: At teams' discretion.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. No immune escape feature for Delta variant. Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no prescribed R0, interventions, etc.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it
  • Vaccine uptake: See specific details.
  • Vaccine roll-out: roll-out to follow ACIP recommendations unless known to be contradicted by state recommendations
    • Phase 1a: health care workers, long-term care facilities
    • Phase 1b: frontline essential workers, adults 75+
    • Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

 

To assist with upcoming ACIP recommendations on childhood vaccination (ages 5-11), Round 9 of SMH will concentrate on evaluating the impact of childhood vaccination on COVID-19 dynamics. Results are expected to be needed by mid-September 2021.

Scenario Differences

Interpretation: These scenarios are intended to demonstrate the impact of vaccination among children ages 5 to 11. We additionally include a stress test axis which illustrates the potential impact of the emergence of a new more transmissible variant.

Model parameters defined in scenarios: With regards to the childhood vaccination axis, the data childhood vaccination begins and the state-level uptake trajectory is defined in the scenario. State-level uptake should reflect the percentage coverage increases observed in the 12 to 17-year-old age group observed since distribution to this group began on May 13, 2021. Baseline state-level age-specific vaccination data can be found here. Teams should specify in their metadata file if they use an alternative source for vaccination uptake. All assumptions about saturation over the course of the projection period should be specified in the metadata. Vaccine uptake among individuals age 12 and over should be the same in all four scenarios. Uptake in these age groups can be extrapolated from past vaccine coverage curves and vaccine hesitancy surveys (Pulse, CovidCast) with the methodology specified in the metadata. With regards to the new variant axis, the date of emergence, starting prevalence, and transmissibility increase compared to the Delta variant is specified by the scenarios.

Unconstrained model parameters: The following parameters are left to the disrection of the teams and should be noted in the metadata

  • VE (infection, symptoms, severe outcomes) in all age groups
    • Suggested values: Data from the REACT study suggests 60% overall VE against infection with Delta. In a study of US healthcare workers during the period of Delta variant circulation, VE was 66% against infection. Data from the UK suggests an overall VE against symptoms of 88% for Delta. VE against hospitalization ranges between 90-96% in US and UK studies against the Delta variant.
    • Teams can choose different VE values for different age groups. However, chosen values should be reported in the metadata.
  • Transmissibility for vaccinated and unvaccinated children, and vaccinated adults.
  • Waning immunity (teams can choose to ignore waning immunity)
  • Susceptibility by age
  • NPIs; note that multiple jurisdictions have reinstated indoor masking and a number of schools will require masking in the fall

Outputs: In addition to the usual outputs, it would be helpful (but not required) for teams to plan to extract incident and cumulative cases, hospitalizations, and deaths for under 12 years AND 12+ years (ideal). Alternative age-specific projections will also be helpful (e.g., 0-17, 5-17). Please plan to submit quantiles for the complement of the younger age group submitted as it is not possible to extract quantiles for the older age-group by subtracting from quantiles submitted for the total population. This will allow us to provide some information on indirect effects of vaccinating children 5 to 11 years of age. Additionally, please provide population data relevant to the age groups used so appropriate rates can be calculated.

Common Specifications

Vaccination

  • Doses available:
    • 50M Moderna/Pfizer 1st doses available monthly, June 2021-January 2022
    • J&J no longer available (after May 2021)
    • Supply has likely eclipsed demand at this stage. Number of doses are for reference and as a reminder to account for different VE by manufacturer, but no longer indicate number of doses administered. Distribution of doses by manufacturer and associated vaccine efficacy should fit within these dose bounds.
  • Coverage:
    • Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage.
  • VE:
    • We recommend that teams use a VE of 35% for 1st dose and 85% for second dose against symptoms for Moderna and Pfizer versus the Delta variant. These estimates reflect VE before any waning takes place.
    • VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death).

Variant progression and transmissibility: Teams should use their own judgment to project the continued progress and transmissibility of the Delta variant across US states. Initial prevalence should be estimated or defined by the teams based on sequencing and other relevant data, preferably at the state level. Teams can set an increased severity of the Delta variant, but this should be documented in metadata.

NPI: In contrast to past scenarios, we don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools intend to return to in-person education in the fall. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021.The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. Childhood Vaccination, No Variant ChildVax_noVar A-2021-09-14
Scenario B. No Childhood Vaccination, No Variant noChildVax_noVar B-2021-09-14
Scenario C. Childhood Vaccination, New Variant ChildVax_Var C-2021-09-14
Scenario D. No Childhood Vaccination, New Variant noChildVax_Var D-2021-09-14
  • Due date: September 14, 2021
  • End date for fitting data: September 4 - September 11, 2021 (cut-off date at the discretion of individual teams; we’d prefer data through September 4 at least be used; no fitting should be done to data after September 11)
  • Start date for scenarios: September 12, 2021 (first date of simulated transmission/outcomes)
  • Simulation end date: March 12, 2022 (26-week horizon)

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Current and future levels of social distancing are to be defined by the teams based on their understanding of current and planned control and behavior and expectations. Teams should consider that most jurisdictions are opening fairly quickly, and most schools intend to return to in-person education in the fall. No reactive interventions should be planned.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Immune waning and Immune escape: As defined by the scenarios.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% against symptoms, 14 days after 1st dose
          • Second dose: 85% against symptoms, 14 days after 2nd dose
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • December-August 13: based on data on administered doses
        • August 14-February 2022: 50 million available first doses/month, with the intention of protocols being followed (100M doses/mo)
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June-January: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May at team's discretion.
  • Vaccine Hesitancy: At teams' discretion.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no prescribed R0, interventions, etc.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects unless specified by the scenarios
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine allocation: between-state allocation is based on population per the CDC/NAS guidelines (proportional allocation); within-state allocation and the impact of vaccine hesitancy are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it
  • Vaccine uptake: See specific details.
  • Vaccine roll-out: roll-out to follow ACIP recommendations unless known to be contradicted by state recommendations
    • Phase 1a: health care workers, long-term care facilities
    • Phase 1b: frontline essential workers, adults 75+
    • Phase 1c: other essential workers, adults with high-risk conditions, adults 65-74
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99 quantiles
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

 

Round 10 of SMH will concentrate on evaluating the impact of boosters and waning immunity on COVID-19 dynamics. We have designed a 2*2 scenario structure where booster coverage is represented in one axis and the characteristics of waning immunity are on the other axis.

Scenario Differences

Interpretation and structure of waning:

Interpretation: These scenarios are intended to illustrate a gradual decay of immune protection with time, rather than the impact of an immune escape variant.

Model structure: Teams are encouraged to model waning immunity as a partial loss of immune protection, where individuals go back to a partially immune state after a period prescribed in the scenarios (mean of 6 month or 1 year depending on the scenario). Individuals who have reached a partially immune state have reduced probabilities of reinfection and severe disease compared to naive individuals.

The same parameters should be used for waning immunity from natural infection and vaccination.

Model parameters defined in scenarios:
Interpretation of waning parameters is similar to that of round 8.
Specifically, in the optimistic waning scenario, protection from infection is 60% for individuals < 65yrs in the partially immune state. This means that, for these individuals, the transition out of the partially immune state and into infection is 0.4*force of infection applied to naive individuals of the same age. If we apply this waning parameter to vaccinated people, this corresponds to a VE of 60% against infection.
Further, in this scenario, protection against hospitalization is 90% for those under 65 yrs. This estimate is similar to VE against hospitalization and death, so it is not a conditional probability. This means that if we follow two individuals over time, one with partial immunity and one completely naive, the probability that the partially immune individual will be hospitalized from COVID19 is 0.1 times the probability that a naive individual will be hospitalized. Hence this probability combines protection against infection and protection against hospitalization given infection. If we apply this parameter to vaccinated individuals for whom immunity has partially waned, their VE against hospitalization becomes 90%.

Unconstrained model parameters:
Teams can choose different distributions of waning immunity (exponential, gamma); we only prescribe the mean.
Teams should use their own judgments to parametrize protection against symptoms in the partially immune state, and any reduction in transmission that partially immune individuals may have.
Teams can choose to treat individuals who have immunity from natural infection and vaccination differently from individuals who had a single exposure to the pathogen/antigen.
We do not specify any waning for J&J (for which the starting point VE is much lower): teams can choose to ignore J&J, which represents a small fraction of all vaccinated in the US, or apply a different waning for J&J.
We do not specify any waning for those who only get a 1st dose of Pfizer or Moderna and hence never acquire full vaccine immunity. We believe this represents a small fraction of all vaccinated. Teams can choose to apply a different waning to these individuals, or ignore them.
All of these assumptions (especially the distribution of waning times) should be documented in meta-data.

Initial VE (before waning) and boosters:

Initial VE (before waning): We suggest that teams use a VE of 80% against symptomatic COVID-19 in those over 65 yrs, and VE of 90% in those under 65 years, while protection against infection and severe outcomes remains at teams’ discretion. This is based on data from US, UK, CDC, NY and CDC MMWR.

Impact of boosters on VE: Boosters should be implemented in a way that individuals who have received a booster shot will revert to the same level of protection that they had before any waning occurred.

Booster coverage:

  • Past booster data until start of simulations should be based on state-specific booster uptakes for the period up to present. Data on vaccine boosters coverage is available from CDC (13% coverage in 65+, 5% in 18+ on 10/14/2021).
  • In high booster scenarios, we recommend a saturation level of 70% for booster coverage, which is 70% of adults who have already received a full vaccine course. The timing and pace of getting to saturation is left at teams discretion; note that a 6-month interval between the initial vaccine course and boosters is recommended. We recommend that 70% be applied to the state-specific coverage of 2nd dose in adults. 70% is based on the upper bound of a September survey of Kaiser Permanente that monitors propensity to get a booster shot among those who have already been vaccinated.
  • In low booster scenarios, we recommend a saturation level of 40% for booster coverage, which is 40% of adults who have already received a full vaccine course. The timing and pace of getting to saturation is left at teams discretion. We recommend that 40% be applied to the state-specific coverage of 2nd dose in adults. 40% is based on the lower bound of the Kaiser Permanente survey (full range of survey responses, 40-70%, across various socio-demographic groups and political affiliations).
  • We do not specify different parameters for different combinations of vaccines available (eg, initial vaccination with Pfizer followed by Moderna booster, etc).

Booster timing:

  • A recommendation for boosters targeted at older and high-risk adults was issued on September 24, 2021. These recommendations are very inclusive and include multiple groups prone to high-risk exposures, representing a large fraction of all US adults. Accordingly, we do not consider a formal extension of ACIP recommendations to all adults. Instead we consider two saturation levels for boosters.

Common Specifications


Vaccination

Coverage of initial vaccine courses (pre-boosters): Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents (link) and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage (link). Vaccine-eligible population. The eligible population for 1st/2nd dose vaccination is presumed to be individuals aged 12 years and older until November 15, and 5 years and older from November 15 through the end of the projection period.

For vaccine coverage in the 5-11 yo, starting on November 15, 2021, we recommend the same strategy as in round 9. Specifically, state-specific vaccine coverage in 12-17 yrs from May 2021 onwards should be applied to the 5-11 yo.

  • VE:
    • We recommend that teams use the following for VE against symptoms: VE=35% (first dose), VE=80% (2nd dose, > 65 yrs), VE= 90% (2nd dose, < 65 yrs) for Moderna/Pfizer, against Delta. This is the initial VE, before any waning. VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death).

  • Dose Available:
    • J&J no longer available (after May 2021)
    • Supply has eclipsed demand at this stage.
    • We do not anticipate any constraint in booster supply

Variant progression and transmissibility:
Teams should use their own judgment to project the continued progress and transmissibility of the Delta variant, and related lineages, across US states. In this round, there is no new variant that arrives in the US between now and the end of the projections. \ Teams can implement increases in transmissibility or severity of the Delta variant, but these should fit within the scenario specifications and should be fully documented in meta-data.

NPI:
We don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools have returned to in-person education in fall 2021 and high level health officials have noted that “people should feel safe to mingle at Thanksgiving and Christmas”. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. Optimistic waning, widespread boosters optWan_highBoo A-2021-11-09
Scenario B. Optimistic waning, restricted boosters optWan_lowBoo B-2021-11-09
Scenario C. Pessimistic waning, widespread boosters pessWan_highBoo C-2021-11-09
Scenario D. Pessimistic waning, restricted boosters pessWan_lowBoo D-2021-11-09
  • Due date: December 3, 2021 (desired); December 6, 2021 (hard deadline)
  • End date for fitting data: No earlier than Nov 13, 2021 and no later than Nov 20, 2021 (cut-off date at the discretion of individual teams; no fitting should be done to data after Nov 20)
  • Start date for scenarios: Nov 14, 2021 (first date of simulated transmission/outcomes). The week ending Nov 20th is week 1 of projection (week from 2021-11-14 to 2021-11-20). Note that if you used data until Nov 20th for calibration, your first week of projections (Nov 14- Nov 20) will be your model-fitted incidences for 1 wk ahead and the first target_end_date will be Nov 20, 2021.
  • Simulation end date: Nov 12, 2022 (52-week horizon); Projections with horizon between 26 week and 52 week are also accepted.

Scenario and Simulation Details

  • Social Distancing Measures:
    • Includes combined effectiveness/impact of all non-pharmaceutical interventions and behavior change.
    • Current and future levels of social distancing are to be defined by the teams based on their understanding of current and planned control and behavior and expectations. Teams should consider that most jurisdictions are opening fairly quickly, and most schools intend to return to in-person education in the fall. No reactive interventions should be planned.
  • Testing-Trace-Isolate: constant at baseline levels
  • Masking: Included as part of “Social Distancing Measures” above.
  • Immune waning and Immune escape: Immune waning as described above; immune escape as defined by the modeling team.
  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • VE against symptoms: see above
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability:
        • No constraint in supply.
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May 2021: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June 2021-Nov 2022: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May 2021 at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. Speed and level of saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. No immune escape feature for Delta variant.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects.
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it.
  • Vaccine uptake: See specific details.
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:
  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • “Ground Truth”: The same data sources as the forecast hub will be used to represent “true” cases, deaths and hospitalizations. Specifically, JHU CSSE data for cases and deaths and HHS data for hospitalization.
  • Metadata: We will require a brief meta-data form, TBD, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0 (min), 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99, 1 (max) quantiles.
  • Ensemble Inclusion: at present time, in order to be included in the ensemble models need to provide a full set of quantiles

 

Round 11 of the COVID-19 Scenario Modeling Hub will concentrate on evaluating the impact of Omicron on COVID-19 dynamics. We have designed a 2*2 scenario structure where Omicron transmissibility and immune escape are represented in one axis and severity of Omicron are on the other axis. We will consider a 3-month horizon.

Assumption synopsis and model requirements:

  • The effect of boosters and waning do not need to be explicitly incorporated in the model as long as reasonable assumptions about the proportion of fully susceptible and immune individuals (with recommended breakdown by partial and fully immune status) can be made at the start of simulations
  • Booster coverage (for teams incorporating explicitly): At teams’ discretion, suggested between 40-70% of those previously vaccinated
  • Waning (for teams incorporating explicitly): At teams’ discretion, recommended timescale 6-12month. We provide recommendations for age-specific protection parameters below.
  • Child vaccination:
    • 5-11yr: continue as previous rounds, with rates and saturation at teams’ discretion.
    • 6m-4yr: no vaccination
  • Updated vaccines: Manufacturers are working on updated vaccines formulated for Omicron, though the timeframe and rollout of these are unknown. For R11 we will not include these.
  • Initial conditions: Prevalence of Omicron at the start of the projection period (December 19, 2021) is at the discretion of the teams based on their interpretation/analysis of the available data and estimates at the the time of projection.). Variation in initial prevalence between states is left at teams’ discretion.
  • NPIs, control, behavior change: Reduction in transmission resulting from non-susceptibility or virus characteristics is left to each group’s discretion. However, R11 should not include responsive changes in NPIs or behavior (i.e., increased control due to Omicron concerns). We may explore these impacts in the follow-up round, however, for now there remains too much uncertainty about the potential transmission and this is the focus.

Interpretation and structure of immune escape:

Immune escape represents an increase in risk of infection among those with immunity from prior exposure to SARS-CoV-2 (of any kind, vaccination or natural infection), due to changes in the genetic makeup of Omicron. As an illustration, an immune escape of 60% indicates that among those with prior immunity to past variants, 60% will be susceptible to Omicron infection, and 40% will be protected against Omicron infection. Among those infected with Omicron who had previous immunity due to vaccination or prior infection, a reduction in the probability of severe disease may occur. This is specified in the severity axis of the scenarios.

Interpretation and structure of transmissibility:

We provide both absolute R0 for Omicron and a fold increase over Delta. Assumptions are based on a ratio of Rt_Omicron to Rt_Delta of 2.8. Here Rt=S(t)*R0*alpha(t), where alpha represents the impact of NPI and seasonal forcing on transmission. We can assume that NPI and seasonal forcing is the same for both variants, so the ratio of 2.8 can be explained as differences in S(t) (immune differences, e.g., link) and R0 (intrinsic transmissibility differences). The parameters chosen for these scenarios cover a possible range of immunity and transmissibility differences between variants that would contribute to an observed Rt ratio of 2.8. We have used intermediate estimates based on results from the MOBS and Bedford labs.

Interpretation and structure of waning:

The presence, duration, and extent of waning is left to the team’s discretion. For teams including waning explicitly, we recommend the following:

  • Speed: Average transition time to partially immune state between 6-12 months
  • Residual protection among waned individuals:
    • Less than 65 years of age: Protection from infection: 60%, hospitalization: 90%, death: 95%
    • 65 years and older: Protection from infection: 40%, hospitalization: 80%, death: 90%

Model structure: For teams explicitly modeling waning, teams are encouraged to consider immunity as a partial loss of immune protection, where individuals go back to a partially immune state after a period of time which is left to the teams’ discretion (suggested 6 months to 1 year). Individuals who have reached a partially immune state have reduced probabilities of reinfection and severe disease compared to naive individuals. The same parameters can be used for waning immunity from natural infection and vaccination.

Suggested waning parameters: Interpretation of waning parameters is similar to that of Round 8. Specifically, protection from infection is 60% for individuals <65yrs in the partially immune state. This means that, for these individuals, the transition out of the partially immune state and into infection is 0.4*force of infection applied to naive individuals of the same age. If we apply this waning parameter to vaccinated people, this corresponds to a VE of 60% against infection. Further, suggested protection against hospitalization is 90% for those under 65 yrs. This estimate is similar to VE against hospitalization and death, so it is not a conditional probability. This means that if we follow two individuals over time, one with partial immunity and one completely naive, the probability that the partially immune individual will be hospitalized from COVID-19 is 0.1 times the probability that a naive individual will be hospitalized. Hence this probability combines protection against infection and protection against hospitalization given infection. If we apply this parameter to vaccinated individuals for whom immunity has partially waned, their VE against hospitalization becomes 90%.

Unconstrained model parameters:

  • Teams can choose different distributions of waning immunity (exponential, gamma)
  • Teams should use their own judgments to parametrize protection against symptoms in the partially immune state, and any reduction in transmission that partially immune individuals may have.
  • Teams can choose to treat individuals who have immunity from natural infection and vaccination differently from individuals who had a single exposure to the pathogen/antigen.
  • We do not provide any suggested waning parameters for J&J (for which the starting point VE is much lower): teams can choose to ignore J&J, which represents a small fraction of all vaccinated in the US, or apply a different waning for J&J.
  • We do not provide any suggested waning parameters for those who only get a 1st dose of Pfizer or Moderna and hence never acquire full vaccine immunity. We believe this represents a small fraction of all vaccinated. Teams can choose to apply a different waning to these individuals, or ignore them. All of these assumptions (especially the distribution of waning times) should be documented in meta-data.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. Optimistic severity, High immune escape/Low transmissibility increase optSev_highIE A-2021-12-21
Scenario B. Optimistic severity, Low immune escape/High transmissibility increase optSev_lowIE B-2021-12-21
Scenario C. Pessimistic severity, High immune escape/Low transmissibility increase pessSev_highIE C-2021-12-21
Scenario D. Pessimistic severity, Low immune escape/High transmissibility increase pessSev_lowIE D-2021-12-21
  • Due date: December 21, 2021
  • End date for fitting data: Dec 18, 2021
  • Start date for scenarios: Dec 19, 2021 (first date of simulated transmission/outcomes)
  • Simulation end date: Mar 12, 2022 (12-week horizon)

Other submission requirements

  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • Metadata: We will require a brief meta-data form, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99,. Teams are also encouraged to submit 0 (min value) and 1 (max) quantiles if possible. At present time, inclusion in ensemble models requires a full set of quantiles from 0.01 to 0.99.

Common Specifications

Vaccination

Vaccine coverage: Coverage of initial vaccine courses (pre-boosters): Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents (link) and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage (link).

Vaccine-eligible population: The eligible population for 1st/2nd dose vaccination is presumed to be individuals aged 5 years and older through the end of the projection period.

Vaccine coverage in the 5-11yo: At team’s discretion.

Vaccine effectiveness: We recommend that teams use the following for VE against symptoms: VE=35% (first dose), VE=80% (2nd dose, > 65 yrs), VE= 90% (2nd dose, < 65 yrs) for Moderna/Pfizer, against Delta. This is the initial VE, before any waning or Omicron. VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death)

Impact of boosters on VE against Omicron: Boosters should be implemented in a way that individuals who have received a booster shot will revert to the same level of protection that they had before any waning occurred. Early data suggests that boosters of mRNA vaccine revert neutralization titers to Omicron to their base levels (the expectation would be that protection against all outcomes would revert to the levels seen with Delta, although there is considerable uncertainty) https://www.pfizer.com/news/press-release/press-release-detail/pfizer-and-biontech-provide-update-omicron-variant

Booster doses:

  • Booster coverage: With the emergence of the Omicron variant, we expect boosters to reach the higher end of coverage previously expected. However, multiple factors could complicate this, including loss of trust in the vaccine with immune escape from it. We will leave it to the teams.
    • Past booster data until start of simulations should be based on state-specific booster uptakes for the period up to present. Data on vaccine boosters coverage is available at: https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total
    • We recommend a saturation level of 40-70% for booster coverage, which is 40-70% of individuals who have already received a full vaccine course. The timing and pace of getting to saturation is left at teams discretion; note that a 6-month interval between the initial vaccine course and boosters is recommended. We recommend that 40-70% be applied to the state-specific coverage of 2nd dose in adults. 40% and 70% are based on the lower and upper bounds of a September survey of Kaiser Permanente that monitors propensity to get a booster shot among those who have already been vaccinated. https://www.kff.org/coronavirus-covid-19/dashboard/kff-covid-19-vaccine-monitor-dashboard/
    • We do not specify different parameters for different combinations of vaccines available (eg, initial vaccination with Pfizer followed by Moderna booster, etc).

Booster timing: Current booster eligibility is 6 months after an individual’s 2nd dose.

Control Measures

We don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools have returned to in-person education in fall 2021 and high level health officials have noted that “people should feel safe to mingle at Thanksgiving and Christmas”. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021. Additional scenario and simulation details

Additional scenario and simulation details

  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% against symptoms, 14 days after 1st dose
          • Second dose: 80/90% against symptoms, 14 days after 2nd dose, >< 65 yrs
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability: No constraint in supply.
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May 2021: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June 2021-Nov 2022: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May 2021 at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. Speed and level of saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. No immune escape feature for Delta variant.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects.
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it.
  • Vaccine uptake: See specific details.
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 

Round 12 of the COVID-19 Scenario Modeling Hub (SMH) will serve as an update of Round 11 to evaluate the impact of the Omicron wave, with updated data and epidemiological understanding. We have designed a 2*2 scenario structure where Omicron transmissibility and immune escape are represented in one axis and severity of Omicron are on the other axis. We will consider a 3-month horizon.

Assumption synopsis and model requirements:

  • VACCINE EFFECTIVENESS: Teams should take special care to make sure vaccine effectiveness assumptions incorporated into their models match the immune escape and severity reduction specifications.
  • The effect of boosters and waning do not need to be explicitly incorporated in the model as long as reasonable assumptions about the proportion of fully susceptible and immune individuals (with recommended breakdown by partial and fully immune status) can be made at the start of simulations
  • Booster coverage (for teams incorporating explicitly): At teams’ discretion, suggested between 40-70% of those previously vaccinated
  • Waning (for teams incorporating explicitly): At teams’ discretion, recommended timescale 6-12month. We provide recommendations for age-specific protection parameters below.
  • Child vaccination:
    • 5-11yr: continue as previous rounds, with rates and saturation at teams’ discretion.
    • 6m-4yr: no vaccination
  • Updated vaccines: Manufacturers are working on updated vaccines formulated for Omicron, though the timeframe and rollout of these are unknown. For R12 we will not include these.
  • Initial conditions: Prevalence of Omicron at the start of the projection period (January 9, 2022) is at the discretion of the teams based on their interpretation/analysis of the available data and estimates at the the time of projection.). Variation in initial prevalence between states is left at teams’ discretion.
  • NPIs, control, behavior change: Reduction in transmission resulting from non-susceptibility or virus characteristics is left to each group’s discretion. However, R12 should not include responsive changes in NPIs or behavior (i.e., increased control due to Omicron concerns). We may explore these impacts in the follow-up round, however, for now there remains too much uncertainty about the potential transmission and this is the focus.

Interpretation and structure of immune escape:

Immune escape represents an increase in risk of infection among those with immunity from prior exposure to SARS-CoV-2 (of any kind, vaccination or natural infection), due to changes in the genetic makeup of Omicron. As an illustration, an immune escape of 80% indicates that among those with prior immunity to past variants, 80% will be susceptible to Omicron infection (or 80% more likely to be infected in a leaky immunity model), and 20% will be protected against Omicron infection. Among those infected with Omicron who had previous immunity due to vaccination or prior infection, a reduction in the probability of severe disease may occur. This is specified in the severity axis of the scenarios. Since boosters seem to restore the protection lost by Omicron’s immune escape, teams can choose to reduce the impact of immune escape on boosted individuals. Alternatively, teams can apply the booster effect as a reduced probability of symptoms, hospitalization and death given infection.

Interpretation and structure of transmissibility:

We do not provide guidance on transmissibility, only on immune escape. Teams can use the growth curve of Omicron in the US or elsewhere, or other datasets, to set this parameter.

Interpretation and structure of waning:

The presence, duration, and extent of waning is left to the team’s discretion.

Model structure: For teams explicitly modeling waning, teams are encouraged to consider immunity as a partial loss of immune protection, where individuals go back to a partially immune state after a period of time which is left to the teams’ discretion (suggested 6 months to 1 year). Individuals who have reached a partially immune state have reduced probabilities of reinfection and severe disease compared to naive individuals. The same parameters can be used for waning immunity from natural infection and vaccination.

Suggested waning parameters: Interpretation of waning parameters is similar to that of Round 8. Specifically, protection from infection is 60% for individuals <65yrs in the partially immune state. This means that, for these individuals, the transition out of the partially immune state and into infection is 0.4*force of infection applied to naive individuals of the same age. If we apply this waning parameter to vaccinated people, this corresponds to a VE of 60% against infection. Further, suggested protection against hospitalization is 90% for those under 65 yrs. This estimate is similar to VE against hospitalization and death, so it is not a conditional probability. This means that if we follow two individuals over time, one with partial immunity and one completely naive, the probability that the partially immune individual will be hospitalized from COVID-19 is 0.1 times the probability that a naive individual will be hospitalized. Hence this probability combines protection against infection and protection against hospitalization given infection. If we apply this parameter to vaccinated individuals for whom immunity has partially waned, their VE against hospitalization becomes 90%.

Unconstrained model parameters:

  • Teams can choose different distributions of waning immunity (exponential, gamma)
  • Teams should use their own judgments to parametrize protection against symptoms in the partially immune state, and any reduction in transmission that partially immune individuals may have.
  • Teams can choose to treat individuals who have immunity from natural infection and vaccination differently from individuals who had a single exposure to the pathogen/antigen.
  • We do not provide any suggested waning parameters for J&J (for which the starting point VE is much lower): teams can choose to ignore J&J, which represents a small fraction of all vaccinated in the US, or apply a different waning for J&J.
  • We do not provide any suggested waning parameters for those who only get a 1st dose of Pfizer or Moderna and hence never acquire full vaccine immunity. We believe this represents a small fraction of all vaccinated. Teams can choose to apply a different waning to these individuals, or ignore them. All of these assumptions (especially the distribution of waning times) should be documented in meta-data.

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. Optimistic severity, High immune escape optSev_highIE A-2022-01-09
Scenario B. Optimistic severity, Low immune escape optSev_lowIE B-2022-01-09
Scenario C. Pessimistic severity, High immune escape pessSev_highIE C-2022-01-09
Scenario D. Pessimistic severity, Low immune escape pessSev_lowIE D-2022-01-09
  • Due date: January 14, 2022
  • End date for fitting data: January 8, 2022
  • Start date for scenarios: January 9, 2022 (first date of simulated transmission/outcomes)
  • Simulation end date: April 2, 2022 (12-week horizon)

Other submission requirements

  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • Metadata: We will require a brief meta-data form, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99,. Teams are also encouraged to submit 0 (min value) and 1 (max) quantiles if possible. At present time, inclusion in ensemble models requires a full set of quantiles from 0.01 to 0.99.

Common Specifications

Vaccination

Vaccine coverage: Coverage of initial vaccine courses (pre-boosters): Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents (link) and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage (link).

Vaccine-eligible population: The eligible population for 1st/2nd dose vaccination is presumed to be individuals aged 5 years and older through the end of the projection period.

Vaccine coverage in the 5-11yo: At team’s discretion.

Vaccine effectiveness: We recommend that teams use the following for VE against symptoms: VE=35% (first dose), VE=80% (2nd dose, > 65 yrs), VE= 90% (2nd dose, < 65 yrs) for Moderna/Pfizer, against Delta. This is the initial VE, before any waning or Omicron. VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death)

Impact of boosters on VE against Omicron: Boosters should be implemented in a way that individuals who have received a booster shot will revert to the same level of protection that they had before any waning occurred. Early data suggests that boosters of mRNA vaccine revert neutralization titers to Omicron to their base levels (the expectation would be that protection against all outcomes would revert to the levels seen with Delta, although there is considerable uncertainty) https://www.pfizer.com/news/press-release/press-release-detail/pfizer-and-biontech-provide-update-omicron-variant

Booster doses:

  • Booster coverage: With the emergence of the Omicron variant, we expect boosters to reach the higher end of coverage previously expected. However, multiple factors could complicate this, including loss of trust in the vaccine with immune escape from it. We will leave it to the teams.
    • Past booster data until start of simulations should be based on state-specific booster uptakes for the period up to present. Data on vaccine boosters coverage is available at: https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total
    • We recommend a saturation level of 40-70% for booster coverage, which is 40-70% of individuals who have already received a full vaccine course. The timing and pace of getting to saturation is left at teams discretion; note that a 6-month interval between the initial vaccine course and boosters is recommended. We recommend that 40-70% be applied to the state-specific coverage of 2nd dose in adults. 40% and 70% are based on the lower and upper bounds of a September survey of Kaiser Permanente that monitors propensity to get a booster shot among those who have already been vaccinated. https://www.kff.org/coronavirus-covid-19/dashboard/kff-covid-19-vaccine-monitor-dashboard/
    • We do not specify different parameters for different combinations of vaccines available (eg, initial vaccination with Pfizer followed by Moderna booster, etc).

Booster timing: Current booster eligibility is 6 months after an individual’s 2nd dose.

Control Measures

We don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools have returned to in-person education in fall 2021 and high level health officials have noted that “people should feel safe to mingle at Thanksgiving and Christmas”. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021.

Additional scenario and simulation details

  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% against symptoms, 14 days after 1st dose
          • Second dose: 80/90% against symptoms, 14 days after 2nd dose, >< 65 yrs
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability: No constraint in supply.
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May 2021: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June 2021-Nov 2022: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May 2021 at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. Speed and level of saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. No immune escape feature for Delta variant.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects.
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it.
  • Vaccine uptake: See specific details.
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point:

 

Round 13 of the COVID-19 Scenario Modeling Hub (SMH) considers the interaction of waning immunity against infection (first dimension) and the emergence of a new variant (2nd dimension) over a 52-week period. We follow the usual 2x2 table structure.

Risk of severe disease, conditional on infection, does not wane with time and does not change with variant X (see protection against severe disease section below).

Waning Immunity

Protection against infection: Waning is defined as a transition to a partially immune state, where individuals retain a long-lasting yet partial level of protection against (re)infection. This can be considered an asymptotic plateau for immunity, where the trajectory of antibodies and other immune components stabilizes on a timescale of 4 or 10 months, depending on the scenario.

We prescribe the relative reduction in protection against infection after the waning period, where comparison is to the levels observed immediately after natural infection or vaccination. For example in the optimistic waning scenarios, a 40% reduction from baseline levels corresponds to the case where protection from infection is 60% of the baseline levels reported immediately after exposure (vaccination or infection). In the pessimistic scenarios, 60% reduction corresponds to the case where protection from infection is 40% of the baseline levels reported immediately after exposure.

We leave the baseline levels of protection at teams’ discretion (eg, VE immediately after a 3rd vaccine dose), and only prescribe the relative reduction that applies after the waning period.

We assume that the timescale of waning does not depend on the number and type of exposures. For example, in scenario A, transition to a partially immune state would occur after a median of 10 mo after 2 vaccine doses, and so would the transition after 3 vaccine doses, or the transition after infection or re-infection.

Teams can choose to bump individuals to a higher level of protection after repeat exposures (where exposure is vaccination or infection), but waning would still occur on a 4 to 10 mo timescale after each new exposure. If repeat exposures raise immunity to a high level, then after 4 or 10 mo of waning, an individual could reach an asymptotic plateau that is higher than where the individual would be 4 or 10mo after a single exposure.

Natural immunity can be treated differently from vaccine-induced immunity, although the characteristics of decay of protection against infection should follow the parameters prescribed in the scenarios.

Teams can choose different distributions of waning immunity (exponential, gamma) as long as the median is as specified in the scenarios.

*For scenarios B and D that consider new variant X, the risks of infection will need to be increased by the immune escape parameter provided in the second dimension of the table. * Examples: For instance, let’s assume that VE against Omicron infection is 50% immediately after a booster shot in an individual <65 yrs. Then, per scenario A, protection should decline to 60% of the initial value after 10 mo of waning (40% reduction, cf table), so that protection should be 0.50*0.60=30% against Omicron infection 10 mo after boosting. This means VE is 30% against Omicron infection after 10 mo of waning for a boosted individual, or equivalently that their infection risk is 0.7*risk of infection of an unvaccinated individual.

The second example illustrates how a repeat exposure could bump individuals to a higher protection level. Let’s consider the same person from before, who was in a plateau of 30% protection against infection after 10 mo, relative to an unvaccinated individual. Let’s assume that this individual gets infected, immunity is boosted, resulting in a protection of 70% immediately after this new infection. After 10 mo, per scenario A, the residual protection against infection would be 0.7*0.6= 42% in this individual, relative to an unvaccinated individual.

References for VE by variant, number of doses, and time since vaccination, can be found here:

Protection against severe disease: We expect that at this stage of the pandemic, close to 100% of the US population has been naturally infected, vaccinated, or both, so that the entire population has long-lasting protection against severe disease upon (re)infection. The probabilities of hospitalization and death given (re)infection are left at teams’ discretion, with the understanding that this parameter can be calibrated against data during the Omicron wave, or defined based on (recent) literature (see below). It is assumed that the probabilities of hospitalization and death given (re)infection do not wane over the timescale of the projections and apply to all circulating variants, including new variant X. The probability of severe disease given (re)infection can vary by age and/or risk factors however. In other words, these conditional probabilities do not vary with time nor variants, but they can vary based on clinical and demographic host factors.

References on probability of hospitalization, conditional on (re)infection:

Immune escape. In scenarios B and D, we model the emergence of a new variant X, with moderate immune escape characteristics, taken to be 30%. Let’s consider an individual who is currently in a state of immunity to infection, gained from past exposure to SARS-CoV-2 antigens circulating before March 2022 (ie, infection with the wild type, Alpha, Delta, Omicron…) or vaccination (any number of doses). This individual, upon exposure to variant X, will have a 30% probability of infection with X, or a 30% increased probability of infection in a leaky model.

Immune escape only applies to risk of infection with X. Risk of severe disease given infection with variant X is a constant and is the same as that observed with Omicron, per the previous section.

Transmissibility, severity. The intrinsic transmissibility of the new variant should be the same as that of Omicron (same R0 as Omicron, with the R0 value of Omicron left at teams discretion). Similarly, the intrinsic severity of X should be the same as Omicron.

Introduction and ramp up. Variant X is to be seeded in the first week of May 2022 (May 1-7, 2022), with 50 active infections of variant X to be introduced during this week in the US (illustrating incoming variants from abroad). There will be a continuous influx of 50 weekly infections of variant X for the next 16 weeks (weeks starting May 1, 2022 and ending August 20, 2022). Geographic dispersion of these infections is left at teams discretion. The ramp up of the new variant due to local transmission is also left at the teams’ discretion.

Immunity after infection with variant X. Infection with variant X provides immunity to previously observed variants (e.g., Delta, Omicron). After infection with variant X, immune waning should progress as specified by the scenarios.

Case projections and testing propensity:

Scenarios in the 2*2 table specify the risks of infection, while the risks of hospitalization and death conditional on (re)infection are left at teams discretion but remain constant. We do not address case projections in the scenarios, and do not make particular assumptions on case ascertainment. At this point of the pandemic, reported cases include a mix of symptomatic infections reported to local authorities via active and passive surveillance testing, along with an unknown amount of asymptomatic infections. At home testing is not captured in case observations. We assume that over the 1 year projection period, testing propensity will remain constant at the level estimated at the start of the projection period. In other words, the infection to case ratio should be calibrated to observations in the weeks leading to the start of the projection period and be kept constant for the following year.

Vaccine policy:

We assume that vaccine policy is set at the start of the projection period in March 2022 and remains constant for the duration of simulations. As of Feb 2022, vaccination is recommended for all individuals over 5 yrs, (one round of) boosters are recommended for all individuals over 12 yrs, there is no vaccine for children under 5yrs, there is no Omicron-specific vaccine, and two rounds of boosters (4 doses of mRNA vaccines) are not recommended for the general population. If new measures were to be announced before the start of round 13, we would include these measures in the scenarios.

Unconstrained model parameters:

  • VACCINE EFFECTIVENESS: VE is left at teams’ discretion (recent refs below)
  • The effect of boosters do not need to be explicitly incorporated in the model as long as reasonable assumptions about VE, averaged over different number of doses, can be made
  • Booster coverage recommendations (for teams incorporating explicitly):
    • Past booster data until start of simulations should be based on state-specific booster uptakes for the period up to present. Data on vaccine boosters coverage is available on CDC Covid Data Tracker.
    • We recommend a saturation level of 40-70% for booster coverage, which is 40-70% of individuals who have already received a full vaccine course. The timing and pace of getting to saturation is left at teams discretion; note that a 6-month interval between the initial vaccine course and boosters is recommended. We recommend that 40-70% be applied to the state-specific coverage of 2nd dose in adults. 40% and 70% are based on the lower and upper bounds of a September 2021 survey of Kaiser Permanente that monitors propensity to get a booster shot among those who have already been vaccinated.
    • We do not specify different parameters for different combinations of vaccines available (eg, initial vaccination with Pfizer followed by Moderna booster, etc).
    • Booster timing: Current booster eligibility is 6 months after an individual’s 2nd dose.
  • Child vaccine coverage:
    • 5-11yr: continue as previous rounds, with rates and saturation at teams’ discretion.
    • 6m-4yr: no vaccination.
  • Updated vaccines: Manufacturers are working on updated vaccines formulated for Omicron, though the timeframe and rollout of these are unknown. For R13 we will not include these.
  • Initial Conditions: Prevalence of Omicron at the start of the projection period (March 13, 2022) is at the discretion of the teams based on their interpretation/analysis of the available data and estimates at the the time of projection. Variation in initial prevalence between states is left at teams’ discretion.
  • NPIs, control, behavior change: Seasonal changes in transmission are left to each group’s discretion. However, R13 should NOT include responsive changes in NPIs or behavior (i.e., increased control due to Omicron or variant X concerns).
  • Teams should use their own judgments to parametrize protection against symptoms in the partially immune state, and any reduction in transmission that partially immune individuals may have.
  • Teams can choose to treat individuals who have immunity from natural infection and vaccination differently from individuals who had a single exposure to the pathogen/antigen.
  • We do not provide any suggested waning parameters for J&J (for which the starting point VE is much lower): teams can choose to ignore J&J, which represents a small fraction of all vaccinated in the US, or apply a different waning for J&J.
  • We do not provide any suggested waning parameters for those who only get a 1st dose of Pfizer or Moderna and hence never acquire full vaccine immunity. We believe this represents a small fraction of all vaccinated. Teams can choose to apply a different waning to these individuals, or ignore them. All of these assumptions should be documented in meta-data and abstract.

Projection time horizon: We consider a 52-week projection period

Submission Information

Scenario Scenario name for submission file Scenario ID for submission file
Scenario A. Optimistic waning, No immune escape variant optWan_noVar A-2022-02-25
Scenario B. Optimistic waning, New immune escape variant optWan_Var B-2022-02-25
Scenario C. Pessimistic waning, No immune escape variant pessWan_noVar C-2022-02-25
Scenario D. Pessimistic waning, New immune escape variant pessWan_Var D-2022-02-25
  • Due date: March 15, 2022
  • End date for fitting data: March 12, 2022 (no later than March 12, no earlier than March 5)
  • Start date for scenarios: March 13, 2022 (first date of simulated transmission/outcomes)
  • Simulation end date: March 11, 2023 (52-week horizon)

Other submission requirements

  • Geographic scope: state-level and national projections
  • Results: some subset of the following
    • Weekly incident deaths
    • Weekly cumulative deaths since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident reported cases
    • Weekly cumulative reported cases since start of pandemic (use JHU CSSE for baseline)
    • Weekly incident hospitalizations
    • Weekly cumulative hospitalizations since simulation start
    • Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
  • Metadata: We will require a brief meta-data form, from all teams.
  • Uncertainty: aligned with the Forecasting Hub we ask for 0.01, 0.025, 0.05, every 5% to 0.95, 0.975, and 0.99,. Teams are also encouraged to submit 0 (min value) and 1 (max) quantiles if possible. At present time, inclusion in ensemble models requires a full set of quantiles from 0.01 to 0.99.

Common Specifications

Vaccination

Vaccine coverage: Coverage of initial vaccine courses (pre-boosters): Vaccine hesitancy is expected to cause vaccination coverage to slow and eventually saturate at some level below 100%. The coverage saturation, the speed of that saturation, and heterogeneity between states (or other geospatial scales) and/or age groups are at the discretion of the modeling teams. We suggest that the teams use estimates from the Delphi group, adjusted for potential bias in respondents (link) and the Pulse Survey overall estimates, adjusted for survey participant vaccination coverage (link).

Vaccine-eligible population: The eligible population for 1st/2nd dose vaccination is presumed to be individuals aged 5 years and older through the end of the projection period.

Vaccine coverage in the 5-11yo: At team’s discretion.

Vaccine effectiveness: We recommend that teams use the following for VE against symptoms: VE=35% (first dose), VE=80% (2nd dose, > 65 yrs), VE= 90% (2nd dose, < 65 yrs) for Moderna/Pfizer, against Delta. This is the initial VE, before any waning or Omicron. VE is defined here as vaccine effectiveness against symptomatic disease. Teams should make their own informed assumptions about effectiveness and impacts on other outcomes (e.g., infection, hospitalization, death)

Impact of boosters on VE against Omicron: Boosters should be implemented in a way that individuals who have received a booster shot will revert to the same level of protection that they had before any waning occurred. Early data suggests that boosters of mRNA vaccine revert neutralization titers to Omicron to their base levels (the expectation would be that protection against all outcomes would revert to the levels seen with Delta, although there is considerable uncertainty) https://www.pfizer.com/news/press-release/press-release-detail/pfizer-and-biontech-provide-update-omicron-variant

Control Measures

We don’t specify different levels of non-pharmaceutical interventions (NPI) use; however, teams should consider that most schools have returned to in-person education in fall 2021 and high level health officials have noted that “people should feel safe to mingle at Thanksgiving and Christmas”. The future level of NPIs are left at the discretion of the modeling teams and should be specified in the teams’ metadata. Teams should also note the change in CDC mask recommendations for vaccinated people in high-transmission areas on 07/27/2021.

Additional scenario and simulation details

  • Vaccination:
    • Pfizer / Moderna
      • Vaccine efficacy (2-dose vaccines):
        • B.1.1.7
          • First dose: 50% against symptoms, 14 days after 1st dose
          • Second dose: 90% against symptoms, 14 days after 2nd dose
        • B.1.617.2
          • First dose: 35% against symptoms, 14 days after 1st dose
          • Second dose: 80/90% against symptoms, 14 days after 2nd dose, >< 65 yrs
        • Effectiveness and impact on infection and other outcomes (hospitalizations, deaths) is at team’s discretion and should be clearly documented in team’s metadata.
        • Doses 3.5 weeks apart
      • Vaccine availability: No constraint in supply.
    • Johnson & Johnson
      • Vaccine efficacy (1-dose):
        • 70% VE against previous strains; 60% VE against B.1.1.7/B.1.617.2
      • Vaccine availability:
        • March-May 2021: based on data on administered doses, with continuing at rate current on date of projection for remainder of month (~10M total administered).
        • June 2021-Nov 2022: No longer available; only 10M of 20M doses administered, supply, safety, and demand issues.
        • Manner for accounting for protection provided in the 10M vaccinated during March-May 2021 at team's discretion.
  • Vaccine Hesitancy: Vaccine hesitancy expected to cause vaccination coverage to slow and saturate below 100%. Speed and level of saturation and heterogeneity between states (or other geospatial scale) and/or age groups are at the discretion of the team.
  • Delta (B.1.617.2) variant strain: At teams’ discretion. No immune escape feature for Delta variant.
  • Transmission assumptions: models fit to US state-specific dynamic up until End date for fitting data specified above – no proscribed R0, interventions, etc.
  • Pathogenicity assumptions: no exogenous fluctuations in pathogenicity/transmissibility beyond seasonality effects.
  • Vaccine effectiveness: see recommendations (same VE in all scenarios); assumptions regarding time required to develop immunity, age-related variation in effectiveness, duration of immunity, and additional effects of the vaccine on transmission are left to the discretion of each team
  • Vaccine immunity delay: There is approximately a 14 day delay according to the Pfizer data; because we suspect the post first dose and post second dose delays may be of similar length, we do not believe there is any need to explicitly model a delay, instead groups can delay vaccine receipt by 14 days to account for it.
  • Vaccine uptake: See specific details.
  • NPI assumptions: NPI estimates should be based on current trends and reported planned changes.
  • Database tracking of NPIs: teams may use their own data if desired, otherwise we recommend the following sources as a common starting point: