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The recent pandemic emphasized the need to consider the role of human behavior in shaping epidemic dynamics. In particular, it is necessary to extend beyond the classical epidemiological structures to fully capture the interplay between the spread of disease and how people respond. Here, we focus on the challenge of incorporating change in human behavior in the form of “risk response” into compartmental epidemiological models, where humans adapt their actions in response to their perceived risk of becoming infected. The review examines 37 papers containing over 40 compartmental models, categorizing them into two fundamentally distinct classes: exogenous and endogenous approaches to modeling risk response. While in exogenous approaches, human behavior is often included using different fixed parameter values for certain time periods, endogenous approaches seek for a mechanism internal to the model to explain changes in human behavior as a function of the state of disease. We further discuss two different formulations within endogenous models as implicit versus explicit representation of information diffusion. This analysis provides insights for modelers in selecting an appropriate framework for epidemic modeling.
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This content will become publicly available on April 1, 2027
Faster Uptake, Slower Let‐Down: Asymmetric Community Responses to Changing Risks During a Pandemic
ABSTRACT From how public opinion responds to economic outcomes to how risk perception shapes social interactions during a pandemic, many important social processes involve the assimilation of information to form opinions and perceptions, which in turn guide individual and societal actions. While such perception delays are well recognized, empirically identifying them is nontrivial. We study this problem in the context of human responses to disease dynamics, mediated by public risk perception. Public risk perception changes through an information diffusion process with significant delays, where perception adjustment delay may differ depending on whether the risk is increasing or decreasing. Despite these complexities, most models assume either fixed‐delay structures or exponential structures with symmetric delay periods. First, using synthetic data (where ground truth is known), we show that incorrect delay structures and the assumption of symmetric delay periods can lead to biased and misleading estimates. We then explore alternative approaches to identify more appropriate delay structures that can overcome these challenges. Second, we apply these asymmetric delay structures to state‐level US COVID‐19 disease and mobility data to demonstrate how estimates of public sensitivity to mortality depend on the assumed delay structure. Our results provide evidence that during the pandemic, human risk perception adjusted with asymmetric delays to changing risk: people perceived rising risks more quickly than they perceived declining risks.
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- Award ID(s):
- 2229819
- PAR ID:
- 10675291
- Publisher / Repository:
- Risk Analysis
- Date Published:
- Journal Name:
- Risk Analysis
- Volume:
- 46
- Issue:
- 4
- ISSN:
- 0272-4332
- Subject(s) / Keyword(s):
- epidemics human response mobility risk perception system dynamics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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