Guidance on strategic response options to the COVID-19 pandemic has been greatly influenced in the U.S. and elsewhere by predictive epidemiological models and similar individual- or agent-based models (ABMs).
Response options for non-pharmaceutical interventions (NPIs; such as social distancing) unfortunately are based on an abundance of very large uncertainties around psychological and behavioral responses to NPIs. People in different regions and subgroups with different individual mindsets and capabilities respond differently to NPIs, and those responses change over time (e.g., “shelter-in-place fatigue”).
As the pandemic progresses, decision-makers must move from a blanket approach to more precise, sustainable restrictions tailored to geographical regions, social groups, households, and individuals. Consequently, there is a need for more precise and accurate models, and especially models that make predictions about new kinds of behavior change (e.g., to wear masks to restaurants or places of worship). To make progress on these challenges, we propose to research novel computational models of human responses to epidemics and NPIs and integrate them into a state-of-the-art epidemiological model. Uncertainty in models can arise from low-fidelity computational modeling of specific local conditions and the social-psychological reactions of individuals in their population. For decision-makers, a useful model of the efficacy of implementation of NPIs (such as social distancing) will depend not only on traditional epidemiology, but on a host of factors including locale, social norms, attitudes, intentions, etc. Most importantly, those individual response parameters change dynamically with the evolution of the situation and cannot simply be estimated from past data.
Figures
