Chimeric Forecasting: Blending Human Judgment and Computational Methods for Improved, Real-time Forecasts of Influenza Hospitalizations

Abstract

Infectious disease forecasts can reduce mortality and morbidity by supporting evidence-based public health decision making. Most epidemic models train on surveillance and structured data (e.g. weather, mobility, media), missing contextual information about the epidemic. Human judgment forecasts are novel data, asking humans to generate forecasts based on surveillance data and contextual information. Our primary hypothesis is that an epidemic model trained on surveillance plus human judgment forecasts (a chimeric model) can produce more accurate long-term forecasts of incident hospitalizations compared to a control model trained only on surveillance. Humans have a finite amount of cognitive energy to forecast, limiting them to forecast a small number of states. Our secondary hypothesis is that a model can map human judgment forecasts from a small number of states to all states with similar performance. For the 2023/24 season, we collected weekly incident influenza hospitalizations for all US states, and 696 human judgment forecasts of peak epidemic week and the maximum number of hospitalizations (peak intensity) for ten of the most populous states. We found a chimeric model outperformed a control model on long-term forecasts. Compared to human judgment, a chimeric model produced forecasts of peak epidemic week and peak intensity with similar or improved performance. Forecasts of peak epidemic week and peak intensity for the ten states where humans input forecasts vs a model that extended these forecasts to all states showed similar performance to one another. Our results suggest human judgment forecasts are a viable data source that can improve infectious disease forecasts and support public health decisions.