Last updated: 10-20-2022 for Round 16 Scenarios.
https://covid19scenariomodelinghub.org/viz.html
Round 15: Scenario Descriptions and Model Details
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).
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 email scenariohub@midasnetwork.us .
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 16 focuses on the impact of bivalent booster uptake (first dimension) with the epidemiology of the variant “swarms” projected to dominate in the coming months (2nd dimension) over a 26-week period. We follow the usual 2X2 table structure.
The VE of reformulated (i.e., bivalent) boosters currently administered to those five and older should be considered to have an effectiveness of 80% against symptomatic disease to BA.5.2 and all other Omicron variants not modeled in the immune escape swarms scenarios. For variants included in levels 5, 6 and 7, VE should be reduced based on the estimated immune escape factor compared to BA.5.2. If updated data on VE becomes available prior to submission, teams are free to use this data, but it should be noted in the abstract. Variant swarms in levels 5 to 7 partially escape immunity against infection, where immunity is conferred by all available vaccines and prior infection with Omicron BA1 through BA5 and pre-Omicron lineages.
Variants by level of escape (from presentation by Cornelius Roemer, based on RBD mutations from BA.2):
- Level 0: Stock BA.2
- Level 1: BA.2.12.1, and others with S:L452R/Q/M
- Level 2: BA.2.74, BH.1
- Level 3: Stock BA.⅘, BA.2.75, BA.2.77
- Level 4: BA.4.6, BF.7, BA.5.9, BA.2.75.5, BL.1, BL.2
- Level 5: BQ.1, BU.1, BS.1, BW.1, BA.2.75.2, BM.1.1, BA.2.3.20, BJ.1
- Level 6: BQ.1.1, BN.1, BM.1.1.1
- Level 7: XBB (BJ.1 x BM.1.1.1), CJ.1 (BM.1.1.1 with S:486P)
- Level 8: None designated yet
- Waning of immunity against infection
Models should include waning against infection.
The median waning time of protection against infection should range between 3-8 months. Teams can sample this range, or use any value within this range as a point estimate. Teams can consider differences in waning of natural and vaccine-induced immunity, or in waning after Omicron infection vs waning from other types of SARS-CoV-2 exposures; however the median waning time should remain within the 3-8 month range.
We recommend that in the waned classes, teams consider a reduction from baseline levels of protection ranging between 40 and 60%, corresponding to x0.60 and x0.40 of the baseline levels reported immediately after exposure (vaccination or infection). This follows the same scheme as in round 13-15. Teams can sample the recommended range of protection reductions, which is 40-60%, or use any value within this range as a point estimate.
These guidelines should not preclude teams from considering longer waning times, especially if they would like to integrate detailed waning data. A recent study suggests that vaccine-induced immunity wanes on long time scales and has not stabilized at 9 months. Accordingly, teams can choose to model longer time scales of waning, with a lower set point than prescribed above. If they do so, teams should ensure that in their formulation, 50% of their population have a 40-60% reduced protection 3-8 months after (re-)exposure, aligned with the above guidelines.
- Waning of immunity against severe disease
The extent and speed of the waning of protection against severity, conditional on infection, are at the discretion of the teams. Our assumptions are that protection against severity, conditional on infection, wanes on a slower time scale than waning against infection, but may wane eventually. Assumptions regarding waning of protection against severity, conditional on infection, should be provided in the abstract. For reference, several publications have estimates: NEJM, MMWR.
We model the emergence of new variants with different immune escape characteristics by level of escape (based on RBD mutations compared to BA.2). With little data on new emerging variants, a specific variant is not explicitly considered in the scenarios. Instead, new variants are grouped into levels based on their immune escape characteristics. Therefore, each group of variants with a particular level of immune escape can be modeled as a single variant with the specified immune escape characteristics. Levels 5, 6 and 7 variants are taken into account in Round 16, and the detailed characteristics of variants by level are defined in the scenarios as follows:
Current variants classified into levels 5 , 6, and 7:
- Level 5: BQ.1, BU.1, BS.1, BW.1, BA.2.75.2, BM.1.1, BA.2.3.20, BJ.1
- Level 6: BQ.1.1, BN.1, BM.1.1.1
- Level 7: XBB (BJ.1 x BM.1.1.1), CJ.1 (BM.1.1.1 with S:486P)
Immune escape characteristics:
- Scenarios A & C: 25% immune escape from BA.5.2 applies to variants in level 5
- Scenarios B & D: 50% immune escape from BA.5.2 applies to variants in levels 6 and 7
For example, in scenarios A and C, individuals who are previously immunized via either infection with BA.5.2 or vaccination with the reformulated vaccines will have a 25% reduction in the assumed level of protection conferred by that infection/vaccination against infection with Level 5 variants. For individuals who were most recently immunized by a less recent variant (i.e., BA.1) or vaccine (booster 1), protection against infection with Level 5 variants will be reduced by 25% on top of additional immune escape from that variant or vaccine by BA.5.2.
The relationship between immune escape against infection and against symptomatic disease is at the discretion of the teams.
Emerging variants not specified in the scenarios should be treated as not having an epidemiologically significant impact. For example, in scenarios A and C (the level 5 variant scenarios) level 6 and 7 variants should be treated the same as level 0-4 variants. In addition, level 0-4 variants should be considered as low or no immune escape compared to BA.5.
Severe disease with new variants, given infection: The risks of severe disease for both Level 5 & 6/7 variants, conditional on infection and an individual’s immune class, are identical with Omicron (including BA.5.x). This is also true for other currently circulating variants. Accordingly, the risk for hospitalization and death, conditional on infection, is equivalent to Omicron.
Transmissibility: The intrinsic transmissibility of the new variant should be the same as that of the strains circulating at the start of the projection period (i.e. the same R0 as Omicron variants and sub-variants = same effective transmissibility in a fully naive population, with the R0 value of Omicron left at teams’ discretion).
Initial variant prevalence: The initial prevalence of the Level 5 & 6/7 variants should be based on observed combined prevalence of all variants included in the given level at the start of the projection period in the US. Teams are free to use available data to inform the prevalence of new variants. Teams are free to model importations as they see fit based on their analysis of the local and global epidemiological situation. 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.
In June 2022, FDA recommended that vaccines be reformulated and include two components, an original Wuhan-like strain and an Omicron BA.4/BA.5 strain. Reformulated bivalent BA4/5 boosters are currently being administered, and are available to people five years and older. We assume that reformulated bivalent boosters will provide a moderately improved protection above existing boosters; yet the exact VE will depend on circulating strains this fall. Teams should set the VE of reformulated vaccines at 80% against symptomatic disease from BA.5 and all variants not captured in the immune escape scenarios. For immune escape variants, a reduction of VE against (infection and) symptomatic disease should be implemented based on the denoted extent of immune escape.
Relevant references for VE of non reformulated vaccines include:
- Effect of 4th dose/2nd booster on infection and severe disease in Isreal https://www.nejm.org/doi/full/10.1056/NEJMoa2201570
- UK data on VE against symptomatic disease, hospitalization and deaths, by dose, time since vaccination and sublineage of Omicron (including BA.4/BA.5) https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1109618/vaccine-surveillance-report-week-40.pdf
- US data on waning of VE against ED and hospitalization with Delta and Omicron, after 2nd and 3rd doses https://www.cdc.gov/mmwr/volumes/71/wr/mm7107e2.htm
- South Africa data on waning of VE against hospitalization with Omicron (BA.1-BA.2/BA.4-BA.5), after 2nd and 3rd doses https://www.nejm.org/doi/full/10.1056/NEJMc2210093
- Portugal data on VE against hospitalization and death with BA.5, after 2nd and 3rd doses https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.37.2200697
- Neutralizing antibody response of a reformulated vaccine (original/BA.1 strain) against Omicron https://www.nejm.org/doi/full/10.1056/NEJMoa2208343
- Long term vaccine strategy for COVID
The bivalent booster is authorized for use in individuals ages 5+. The rate of booster uptake and final coverage levels are defined in the scenarios as follows.
Accelerating uptake (scenarios A & B): Booster uptake rates accelerate in the coming months and saturate by February 1st, 2023 at 90% of the state-specific flu coverage reported for the 2020-2021 fall-winter season among ages 5+ (provided here). Teams are free to use available data and information from previous rollouts as they see fit to define rates.
Current uptake (scenarios C & D): Booster uptake rates stay at rates implied by current data and saturate by the end of the projection period at the level of the uptake of the booster 1 coverage. The plateau date should be based on current rates and is flexible as long as it occurs before the end of the projection period. Teams can adjust rates up if needed to achieve adequate coverage (based on booster 1) by target date. Teams are free to use available data and information from current and previous rollouts as they see fit to define rates.
The distribution of who gets a booster among those for whom it is the 1st, 2nd or 3rd booster, age differences in coverage, and heterogeneity in coverage between states, is at the teams’ discretion.
Dose spacing: Accounting for dose spacing is not required.
Teams should include their best estimate of COVID-19 seasonality in their model but we do not prescribe a specific level of seasonal forcing.
Round 16 should NOT include reactive changes in NPIs imposed by health authorities to curb transmission, e.g., reinstatement of mask mandates, or closure of schools and businesses. However, teams can incorporate inherent changes in population behavior in response to increasing or decreasing incidences (eg, changes in contacts or masking), if these changes were inferred from earlier phases of the pandemic and are already part of the model.
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)
The mix of circulating strains at the start of the projection period (October 30, 2022) is at the discretion of the teams based on their interpretation/analysis of the available data and estimates of the level 5 and 6/7 variants at the the time of projection. Variation in initial prevalence between states is left at teams’ discretion.
Ascertainment of cases, hospitalizations and deaths will proceed at the same level as they were at the start of the projection period. We will continue to collect the same targets (cases, hospitalizations, deaths) but note that VRBPAC and ACIP are talking about a focus on severe disease moving forward.
All of the teams’ specific assumptions should be documented in meta-data and abstract.
Projection time horizon: We consider a 26-week projection period.
| Scenario | Scenario name for submission file | Scenario ID for submission file |
|---|---|---|
| Scenario A. High boosters, Moderate immune escape variant | highBoo_modVar | A-2022-10-29 |
| Scenario B. High boosters, High immune escape variant | highBoo_highVar | B-2022-10-29 |
| Scenario C. Low boosters, Moderate immune escape variant | lowBoo_modVar | C-2022-10-29 |
| Scenario D. Low boosters, High immune escape variant | lowBoo_highVar | D-2022-10-29 |
- Due date: Nov 2, 2022
- End date for fitting data: October 29, 2022 (no later than October 29, no earlier than October 22)
- Start date for scenarios: October 30, 2022 (first date of simulated transmission/outcomes)
- Simulation end date: April 29, 2023 (26-week horizon)
- Desire to release results by first or second week of November
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
- Individual simulations nationally and by state (weekly incident cases, hospitalizations, and deaths; no cumulative estimates needed; 100 randomly selected simulations per location)
- Weeks will follow epi-weeks (Sun-Sat) dated by the last day of the week
- Abstract: We will require a brief abstract describing model assumptions and results, from all teams.
- 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. At present time, inclusion in ensemble models requires a full set of quantiles from 0.01 to 0.99.
- Round 2 Scenarios
- Round 3 Scenarios
- Round 4 Scenarios
- Round 5 Scenarios
- Round 6 Scenarios
- Round 7 Scenarios
- Round 8 Scenarios
- Round 9 Scenarios
- Round 10 Scenarios
- Round 11 Scenarios
- Round 12 Scenarios
- Round 13 Scenarios
- Round 14 Scenarios
- Round 15 Scenarios
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.
The "model_projection_date" and date in the filename should correspond to the start date for scenarios (first date of simulated transmission/outcomes).
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. Model projection dates in the COVID-19 Scenario Modeling Hub are equivelent to the forecast dates in the COVID-19 Forecast Hub.
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 used 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.
Starting Round 13 (W12-2022), for COVID-19 hospitalizations, we will use the same truth data as the COVID-19 Forecast Hub, i.e., the hospitalization data from the HHS for example the HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State Timeseries and HealthData.gov COVID-19 Reported Patient Impact and Hospital Capacity by State. We will use the distribution of the HHS data as provided by the COVIDcast Epidata API maintained by the Delphi Research Group at Carnegie Mellon University.
Model projections may be submitted for any state in the US and the US at the national level.
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.
We aim to combine model projections into an ensemble.
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).
Those teams interested in accessing additional computational power should contact Katriona Shea at k-shea@psu.edu.
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
- 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
- University of Florida - ABM
- Thomas Hladish (University of Florida), Alexander Pillai (University of Florida), Kok Ben Toh (Northwestern University), Ira Longini Jr. (University of Florida)
- North Carolina State University - COVSIM
- Erik Rosenstrom (North Carolina State University), Jessica Mele (North Carolina State University), Julie Swann (North Carolina State University), Julie Ivy (North Carolina State University), Maria Mayorga (North Carolina State University)
- University of Texas at Austin - ImmunoSEIRS
- Anass Bouchnita (Lead modeler, University of Texas at Austin), Spencer Fox (University of Texas at Austin), Michael Lachmann (University of Texas at Austin), Lauren Ancel Meyers (Senior author, University of Texas at Austin), and the UT COVID-19 Modeling Consortium
- 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