Comparative evaluation of behavioral epidemic models using COVID-19 data

Modeling the interplay between human behavior and infectious disease transmission remains one of the key challenges in Epidemiology. In this study, we evaluate the performance of three mechanistic behavioral epidemic models designed to address this issue. We compare data-driven and analytical approaches across the first COVID-19 wave, spanning nine diverse locations and two modeling tasks. While the optimal model may vary depending on factors such as data availability and geography, our findings show that approaches explicitly modeling behavioral feedback mechanisms often outperform data-driven approaches, even when considering data quality and the increased numbers of free parameters of these models.

https://www.pnas.org/doi/10.1073/pnas.2421993122