Scenario Projections of COVID-19 in the U.S.
FLU FORECASTINGCOVID-19 FORECASTINGFLU SCENARIO MODELINGThe COVID-19 Scenario Modeling Hub convened several modeling teams to provide long-term, 6-month projections in the US. The Hub has produced so far 17 rounds of projections based on scenarios aimed at enveloping the future drivers of the COVID-19 trajectory in the US (Vaccine delivery/administration, SARS-CoV-2 variants prevalence, relaxation of non-pharmaceuticals interventions (NPIs), etc.). The Hub aims at providing results based on ensembling the results of the different modeling teams.
Here we report the specific results of our specific modeling approach. The projections in this study are intended to bound plausible outbreak trajectories and should not be considered as forecasts of the most likely outcome. Considerable uncertainty is inherent when modeling the trajectory of COVID-19 over long timeframes because of deviations that may or may not be captured by the different scenarios (e.g., vaccine hesitancy, change in the pace of NPIs relaxation, etc.). The results for Round 4 appear in the MMWR report according to scenarios and data defined in late March 2021.
Update 7/29/21: Scenarios 4 through 6 do not include explicitly the modeling of the Delta variant, according to the scenario hub definitions, and we have discontinued their update.
Update 11/24/21: Scenarios 7 through 9 do not include explicitly the modeling of the Omicron variant, according to the scenario hub definitions, and we have discontinued their update.
Update 6/13/22: Scenarios 13 and 13.1 do not explicitly include the modeling of the Omicron sub-variants: BA.2, BA.2.12.1, BA.4, and BA.5.
Update 6/30/22: Scenario 14 does not explicitly include the modeling of the Omicron sub-variants: BA.4 and BA.5
To study the spatiotemporal COVID-19 spread, we use the Global Epidemic and Mobility Model (GLEAM), an individual-based, stochastic, and spatial epidemic model [1, 2, 3, 4, 5, 6]. GLEAM uses real-world data to perform in-silico simulations of the spatial spread of infectious diseases at the global level. We use the model to analyze the spatiotemporal spread and magnitude of the COVID-19 epidemic in the USA at the county level. The data are aggregated to provide the estimates at the State level. The model generates an ensemble of possible epidemic projections described by the number of newly generated infections, hospitalizations, and deaths. The model is calibrated on weekly deaths data from the Johns Hopkins Centers for Civic Impact by using an information theoretical approach.
Disclaimer: There are large uncertainties around the transmission of COVID-19, the effectiveness of different policies and the extent to which the population is compliant to social distancing measures. The presented material is based on modeling scenario assumptions informed by current knowledge of the disease and subject to change as more data become available.
Acknowledgements: We acknowledge support from grant HHS/CDC 6U01IP001137, HHS/CDC 5U01IP0001137 and the Cooperative Agreement no. NU38OT000297 from the Council of State and Territorial Epidemiologists (CSTE). The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the funding agencies, the National Institutes of Health, or the U.S. Department of Health and Human Services.
Northeastern University/MOBS Lab
• Matteo Chinazzi
• Jessica T. Davis
• Kunpeng Mu
• Ana Pastore y Piontti
• Xinyue Xiong
• Alessandro Vespignani
