COVID-19 Scenario Modeling Hub

Last updated: 12-13-2021 for Round 11 Scenarios.

Previous Round Scenarios and Results:

https://covid19scenariomodelinghub.org/viz.html

Round 10: Scenario Descriptions and Model Details

Rationale

Even the best models of emerging infections struggle to give accurate forecasts at time scales greater than 3-4 weeks due to unpredictable drivers such as a changing policy environment, behavior change, the development of new control measures, and stochastic events. However, policy decisions around the course of emerging infections often require projections in the time frame of months. The goal of long-term projections is to compare outbreak trajectories under different scenarios, as opposed to offering a specific, unconditional estimate of what “will” happen. As such, long-term projections can guide longer-term decision-making while short-term forecasts are more useful for situational awareness and guiding immediate response. The need for long-term epidemic projections is particularly acute in a severe pandemic, such as COVID-19, that has a large impact on the economy; for instance, economic and budget projections require estimates of outbreak trajectories in the 3-6 month time scale.

From weather to infectious diseases, it has been shown that synergizing results from multiple models gives more reliable projections than any one model alone. In the COVID-19 pandemic this approach has been exemplified by the COVID-19 Forecast Hub, which combines the results of over 30 models (see a report on the first wave of the pandemic). Further, a comparison of the impact of interventions across 17 models has illustrated how any individual model can grossly underestimate uncertainty, while ensemble projections can offer robust projections of COVID-19 the course of the epidemic under different scenarios at a 6-month time scale.

The COVID-19 Forecasting Hub provides useful and accurate short-term forecasts, but there remains a lack of publicly available model projections at 3-6 month time scale. Some single models are available online (e.g., IHME, or Imperial College), but a decade of infectious disease forecasts has demonstrated that projections from a single model are particularly risky. Single model projections are particularly problematic for emerging infections where there is much uncertainty about basic epidemiological parameters (such as the waning of immunity), the transmission process, future policies, the impact of interventions, and how the population may react to the outbreak and associated interventions. There is a need for generating long-term COVID-19 projections combining insights from different models and making them available to decision-makers, public health experts, and the general public. We plan to fill this gap by building a public COVID-19 Scenario Hub to harmonize scenario projections in the United States.

We have specified a set of scenarios and target outcomes to allow alignment of model projections for collective insights. Scenarios have been designed in consultation with academic modeling teams and government agencies (e.g., CDC).

How to participate

The COVID-19 Scenario Modeling Hub is be open to any team willing to provide projections at the right temporal and spatial scales, with minimal gatekeeping. We only require that participating teams share point estimates and uncertainty bounds, along with a short model description and answers to a list of key questions about design. A major output of the projection hub would be ensemble estimates of epidemic outcomes (e.g., cases, hospitalization and/or deaths), for different time points, intervention scenarios, and US jurisdictions.

Those interested to participate should register here. Registration does not commit participants to submitting model contributions.

Model projections should be submitted via pull request to the data-processed folder of this GitHub repository. Technical instructions for submission and required file formats can be found here.

Round 11 Scenarios

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:
- Coronavirus Government Response Tracker | Blavatnik School of Government (ox.ac.uk)
- Coronavirus State Actions - National Governors Association (nga.org)

Previous Rounds' Scenarios

Submitting model projections

Groups interested in participating can submit model projections for each scenario in a CSV file formatted according to our specifications, and a metadata file with a description of model information. See here for technical submission requirements. Groups can submit their contributions as often as they want; the date of when a model projection was made (projection date) is recorded in the model submission file.

Model projection dates

Model projections will have an associated model_projection_date that corresponds to the day the projection was made.

For week-ahead model projections with model_projection_date of Sunday or Monday of EW12, a 1 week ahead projection corresponds to EW12 and should have target_end_date of the Saturday of EW12. For week-ahead projections with model_projection_date of Tuesday through Saturday of EW12, a 1 week ahead projection corresponds to EW13 and should have target_end_date of the Saturday of EW13. A week-ahead projection should represent the total number of incident deaths or hospitalizations within a given epiweek (from Sunday through Saturday, inclusive) or the cumulative number of deaths reported on the Saturday of a given epiweek. We have created a csv file describing projection collection dates and dates to which projections refer to can be found. Model projection dates in the COVID-19 Scenario Modeling Hub are equivelent to the forecast dates in the COVID-19 Forecast Hub.

Gold standard data

We will use the daily reports containing COVID-19 cases and deaths data from the JHU CSSE group as the gold standard reference data for cases and deaths in the US. We will use the distribution of the JHU data as provided by the COVIDcast Epidata API maintained by the Delphi Research Group at Carnegie Mellon University.

For COVID-19 hospitalizations, we will use the same truth data as the COVID-19 Forecast Hub, i.e., the HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries. These data are released weekly although, sometimes, are updated more frequently.

A supplemental data source with daily counts that should be updated more frequently (typically daily) but does not include the full time-series is HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State.

Work is in progress to distribute these hospitalization data through the Covidcast Epidata API. For more information about hospitalization data, see the data section on the COVID-19 Forecast Hub.

Locations

Model projections may be submitted for any state in the US and the US at the national level.

Probabilistic model projections

Model projections will be represented using quantiles of predictive distributions. Similar to the COVID-19 Forecast hub, we encourage all groups to make available the following 25 quantiles for each distribution: c(0, 0.01, 0.025, seq(0.05, 0.95, by = 0.05), 0.975, 0.99, 1). One goal of this effort is to create probabilistic ensemble scenarios, and having high-resolution component distributions will provide data to create better ensembles.

Ensemble model

We aim to combine model projections into an ensemble. Methods and further information will be shared when the first round of model projections have been received.

Data license and reuse

We are grateful to the teams who have generated these scenarios. The groups have made their public data available under different terms and licenses. You will find the licenses (when provided) within the model-specific folders in the data-processed directory. Please consult these licenses before using these data to ensure that you follow the terms under which these data were released.

All source code that is specific to the overall project is available under an open-source MIT license. We note that this license does NOT cover model code from the various teams or model scenario data (available under specified licenses as described above).

Computational power

Those teams interested in accessing additional computational power should contact Katriona Shea at [email protected].

Shared Code Resources

Teams are encouraged to share code they think will be useful to other teams via the github repo. This directory can be found in code_resources. It currently contains code to:

Pull age-specific, state-specific, time-series data on vaccination in the US from the CDC API. get_cdc_stateagevacc.R

Teams and models

Johns Hopkins ID Dynamics COVID-19 Working Group — COVID Scenario Pipeline
- Joseph C. Lemaitre (EPFL), Juan Dent Hulse (Johns Hopkins Infectious Disease Dynamics), Kyra H. Grantz (Johns Hopkins Infectious Disease Dynamics), Joshua Kaminsky (Johns Hopkins Infectious Disease Dynamics), Stephen A. Lauer (Johns Hopkins Infectious Disease Dynamics), Elizabeth C. Lee (Johns Hopkins Infectious Disease Dynamics), Justin Lessler (UNC), Hannah R. Meredith (Johns Hopkins Infectious Disease Dynamics), Javier Perez-Saez (Johns Hopkins Infectious Disease Dynamics), Shaun A. Truelove (Johns Hopkins Infectious Disease Dynamics), Claire P. Smith (Johns Hopkins Infectious Disease Dynamics), Allison Hill (Johns Hopkins Infectious Disease Dynamics), Lindsay T. Keegan (University of Utah), Kathryn Kaminsky, Sam Shah, Josh Wills, Pierre-Yves Aquilanti (Amazon Web Service), Karthik Raman (Amazon Web Services), Arun Subramaniyan (Amazon Web Services), Greg Thursam (Amazon Web Services), Anh Tran (Amazon Web Services)
Johns Hopkins University Applied Physics Lab — Bucky
- Matt Kinsey (JHU/APL), Kate Tallaksen (JHU/APL), R.F. Obrecht (JHU/APL), Laura Asher (JHU/APL), Cash Costello (JHU/APL), Michael Kelbaugh (JHU/APL), Shelby Wilson (JHU/APL), Lauren Shin (JHU/APL), Molly Gallagher (JHU/APL), Luke Mullany (JHU/APL), Kaitlin Lovett (JHU/APL)
Karlen Working Group — pypm
- Dean Karlen (University of Victoria and TRIUMF)
Northeastern University MOBS Lab — GLEAM COVID
- Matteo Chinazzi (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA), Jessica T. Davis (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA), Kunpeng Mu (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA), Xinyue Xiong (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA), Ana Pastore y Piontti (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA), Alessandro Vespignani (Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA)
University of Southern California — SI kJalpha
- Ajitesh Srivastava (University of Southern California)
University of Virginia — adaptive
- Przemyslaw Porebski (UVA), Srini Venkatramanan (UVA), Anniruddha Adiga (UVA), Bryan Lewis (UVA), Brian Klahn (UVA), Joseph Outten (UVA), James Schlitt (UVA), Patrick Corbett (UVA), Pyrros Alexander Telionis (UVA), Lijing Wang (UVA), Akhil Sai Peddireddy (UVA), Benjamin Hurt (UVA), Jiangzhuo Chen (UVA), Anil Vullikanti (UVA), Madhav Marathe (UVA)
Columbia University - Age-Stratified Model
- Marta Galanti (CU), Teresa Yamana (CU), Sen Pei (CU), Jeffrey Shaman (CU)
University of North Carolina at Charlotte - hierbin
- Shi Chen (UNC Charlotte Department of Public Health Sciences & School of Data Science), Rajib Paul (UNC Charlotte Department of Public Health Sciences and School of Data Science), Daniel Janies (UNC Charlotte Department of Bioinformatics and Genomics), Jean-Claude Thill (UNC Charlotte Department of Geography and Earth Sciences and School of Data Science)
Institute for Health Metrics and Evaluation – IHME COVID model deaths unscaled
- Robert C Reiner, Joanne Amlag, Ryan M. Barber, James K. Collins, Peng Zheng, James Albright, Catherine M. Antony, Aleksandr Y. Aravkin, Steven D. Bachmeier, Marlena S. Bannick, Sabina Bloom, Austin Carter, Emma Castro, Kate Causey, Suman Chakrabarti, Fiona J. Charlson, Rebecca M. Cogen, Emily Combs, Xiaochen Dai, William James Dangel, Lucas Earl, Samuel B. Ewald, Maha Ezalarab, Alize J. Ferrari, Abraham Flaxman, Joseph Jon Frostad, Nancy Fullman, Emmanuela Gakidou, John Gallagher, Scott D. Glenn, Erik A. Goosmann, Jiawei He, Nathaniel J. Henry, Erin N. Hulland, Benjamin Hurst, Casey Johanns, Parkes J. Kendrick, Samantha Leigh Larson, Alice Lazzar-Atwood, Kate E. LeGrand, Haley Lescinsky, Emily Linebarger, Rafael Lozano, Rui Ma, Johan Månsson, Ana M. Mantilla Herrera, Laurie B. Marczak, Molly K. Miller-Petrie, Ali H. Mokdad, Julia Deryn Morgan, Paulami Naik, Christopher M. Odell, James K. O’Halloran, Aaron E. Osgood-Zimmerman, Samuel M. Ostroff, Maja Pasovic, Louise Penberthy, Geoffrey Phipps, David M. Pigott, Ian Pollock, Rebecca E. Ramshaw, Sofia Boston Redford, Sam Rolfe, Damian Francesco Santomauro, John R. Shackleton, David H. Shaw, Brittney S. Sheena, Aleksei Sholokhov, Reed J. D. Sorensen, Gianna Sparks, Emma Elizabeth Spurlock, Michelle L. Subart, Ruri Syailendrawati, Anna E. Torre, Christopher E. Troeger, Theo Vos, Alexandrea Watson, Stefanie Watson, Kirsten E. Wiens, Lauren Woyczynski, Liming Xu, Jize Zhang, Simon I. Hay, Stephen S. Lim & Christopher J. L. Murray
University of Virginia - EpiHiper
- Jiangzhuo Chen (UVA), Stefan Hoops (UVA), Parantapa Bhattacharya (UVA), Dustin Machi (UVA), Bryan Lewis (UVA), Madhav Marathe (UVA)
University of Notre Dame - FRED
- Guido Espana, Sean Cavany, Sean Moore, Alex Perkins

The COVID-19 Scenario Modeling Hub Coordination Team

Justin Lessler, University of North Carolina
Katriona Shea, Penn State University
Cécile Viboud, NIH Fogarty
Shaun Truelove, Johns Hopkins University
Rebecca Borchering, Penn State University
Claire Smith, Johns Hopkins University
Emily Howerton, Penn State University
Nick Reich, University of Massachussetts at Amherst
Wilbert Van Panhuis, University of Pittsburgh
Harry Hochheiser, University of Pittsburgh
Michael Runge, USGS
Lucie Contamin, University of Pittsburgh
John Levander, University of Pittsburgh
Jessica Kerr, University of Pittsburgh
J Espino, University of Pittsburgh
Luke Mullany, Johns Hopkins University
Kaitlin Lovett, John Hopkins University
Michelle Qin, Harvard University

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