The Flu Scenario Modeling Hub is an extension of the COVID-19 Scenario Modeling Hub. Currently, the Flu-SMH has produced 3 rounds that provide long-term, at least 6-months, projections of the current flu season in the United States. The scenario projections include estimates of the peak time, peak magnitude, and the weekly number of hospital admissions. The scenarios assume varying levels of vaccine efficacy and population immunity. Similar to the COVID-19 SMH, the Flu-SMH provides results based on ensembling the models from different teams.
Here we report the specific results of our 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 human behavior).
To study the spatiotemporal spread of the flu, 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. The data are aggregated to provide the estimates at the US state level. GLEAM generates an ensemble of possible epidemic projections described by the number of newly generated infections, hospitalizations, and deaths. The model is calibrated on weekly hospital admissions data from the US Department of Health and Human Services using an information theoretical approach.
Disclaimer: The presented material is based on modeling scenario assumptions informed by knowledge of the transmission of the influenza virus and subject to change as more data become available.
Acknowledgements: We acknowledge support from grant HHS/CDC 6U01IP001137, HHS/CDC 5U01IP0001137.
Northeastern University/MOBS Lab
• Matteo Chinazzi
• Jessica T. Davis
• Kunpeng Mu
• Ana Pastore y Piontti
• Xinyue Xiong
• Alessandro Vespignani