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

作者：Alisa Hamilton, Fardad Haghpanah, Alexander Tulchinsky, Nodar Kipshidze, Suprena Poleon, Gary Lin, Hongru Du, Lauren Gardner, Eili Klein · 发表于：Dialogues in Health · 年份：2024 · DOI：10.1016/j.dialog.2024.100179 · 被引用次数：25 · 研究领域：COVID-19 epidemiological studies、Opinion Dynamics and Social Influence、COVID-19 Digital Contact Tracing

Background: During 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). Methods: This 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. Findings: Of 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 versions of their model wit...