Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review

Abstract

BackgroundDuring the COVID-19 pandemic there was a plethora of dynamical forecasting models created, but their ability to effectively describe future trajectories of disease was mixed. A major challenge in evaluating future case trends was forecasting the behavior of individuals. When behavior was incorporated into models, it was primarily incorporated exogenously (e.g., fitting to cellphone mobility data). Fewer models incorporated behavior endogenously (e.g., dynamically changing a model parameter throughout the simulation). MethodsThis review aimed to qualitatively characterize models that included an adaptive (endogenous) behavioral element in the context of COVID-19 transmission. We categorized studies into three approaches: 1) feedback loops, 2) game theory/utility theory, and 3) information/opinion spread. FindingsOf the 92 included studies, 72% employed a feedback loop, 27% used game/utility theory, and 9% used a model if information/opinion spread. Among all studies, 89% used a compartmental model alone or in combination with other model types. Similarly, 15% used a network model, 11% used an agent-based model, 7% used a system dynamics model, and 1% used a Markov chain model. Descriptors of behavior change included mask-wearing, social distancing, vaccination, and others. Sixty-eight percent of studies calibrated their model to observed data and 25% compared simulated forecasts to observed data. Forty-one percent of studies compared their model to a version without endogenous behavior. Models with endogenous behavior tended to show a smaller and delayed initial peak with subsequent periodic waves. InterpretationWhile many COVID-19 models incorporated behavior exogenously, these approaches may fail to capture future adaptations in human behavior, resulting in under- and overestimates of disease burden. By incorporating behavior endogenously, the next generation of infectious disease models could more effectively predict outcomes so that decision makers can better prepare for and respond to epidemics. FundingThis study was funded in-part by Centers for Disease Control and Prevention (CDC) MInD-Healthcare Program (1U01CK000536), the National Science Foundation (NSF) Modeling Dynamic Disease-Behavior Feedbacks for Improved Epidemic Prediction and Response grant (2229996), and the NSF PIPP Phase I: Evaluating the Effectiveness of Messaging and Modeling during Pandemics grant (2200256).