Project Details
Council of State and Territorial Epidemiologists (CSTE)
Timely forecasts and expert-guided scenario projections for infectious diseases are key to evidence-based decision-making and risk communication. The United States Centers for Disease Control and Prevention (CDC) has coordinated epidemic forecasting exercises through its Epidemic Prediction Initiative (EPI) since 2013-14. Such collaborative efforts have been critical for the COVID-19 response in the United States (US) and Europe.
In this project, we aim to combine our existing frameworks for generating infectious disease forecasts and scenario-based projections. Next, we will apply them specifically to seasonal influenza during the 2022-23 season. The forecasting pipeline incorporates various statistical and machine learning methods and is being combined through a phase-informed ensemble. Scenario-based projections are generated through mechanistic metapopulation models using age-stratified and spatially explicit models. The project will also leverage our work developing meaningful indicators of disease activity which will provide useful insights for policymakers.
We find that phase-informed ensembles are more robust across the different epochs of the COVID-19 pandemic than a traditionally trained ensemble. We also find that using auxiliary sources, such as search activity and outpatient syndromic surveillance leads to more robust ensembles. These findings were reported in a series of papers at the IAAI 2022 and IEEE BigData 2022 conferences. The latter was recognized as the Best Paper submitted.