Adaptive human behavior in epidemiological models

Abstract

The science and management of infectious disease are entering a new stage. Increasingly public policy to manage epidemics focuses on motivating people, through social distancing policies, to alter their behavior to reduce contacts and reduce public disease risk. Person-to-person contacts drive human disease dynamics. People value such contacts and are willing to accept some disease risk to gain contact-related benefits. The cost-benefit trade-offs that shape contact behavior, and hence the course of epidemics, are often only implicitly incorporated in epidemiological models. This approach creates difficulty in parsing out the effects of adaptive behavior. We use an epidemiological-economic model of disease dynamics to explicitly model the trade-offs that drive person-to-person contact decisions. Results indicate that including adaptive human behavior significantly changes the predicted course of epidemics and that this inclusion has implications for parameter estimation and interpretation and for the development of social distancing policies. Acknowledging adaptive behavior requires a shift in thinking about epidemiological processes and parameters.

Fig. 1.

(A and B) The disease courses for the adaptive epi-economic model (solid line), the classic ex ante model using the baseline parameters (dotted line), and the classic ex post model, based on the apparent R₀ (dashed line).

Fig. 2.

The minimum number of contacts made by susceptible individuals over the course of the epidemic and the peak prevalence of the epidemic for various susceptible individual planning horizons.

Fig. 3.

Disease course with interventions. The shaded line is a no intervention case with baseline parameters. The solid line presents the optimal 2-wk decrease in b^h (7%) starting at day 35. The dashed line presents an alternative intervention with greater case reductions, but at a net cost (b^h reduced by 30%).