Fear, Behaviour, and the COVID-19 Pandemic: A City-Scale Agent-Based Model Using Socio-Demographic and Spatial Map Data

Abstract

Modeling infectious diseases has been shown to be of great importance and utility during the ongoing COVID-19 pandemic. From today's globalized information landscape, however, a plethora of new factors arise that have not been covered in previous models. In this paper, we present an agent-based model that reflects the complex interplay between the spread of a pathogen and individual protective behaviors under the influence of media messaging. We use the Rescorla-Wagner model of associative learning for the growth and extinction of fear, a factor that has been proposed as a major contributor in the determination of protective behavior. The model space, as well as heterogeneous social structures among the agents, are created from empirical data. We incorporate factors like age, gender, wealth, and attitudes towards public health institutions. The model is able to reproduce the empirical trends of fear and protective behavior in Germany but struggles to simulate the accurate scale of disease spread. The decline of fear seems to promote a second wave of disease and the model suggests that individual protective behavior has a significant impact on the outcome of the epidemic. The influence of media in the form of messages promoting protective behavior is negligible in the model. Further research regarding factors influencing long-term protective behavior is recommended to improve communication and mitigation strategies.