Emerging frontiers in infectious disease modelling: reassessing the data-driven feedback loop between human behaviour and disease dynamics
Mallory J. Harris, Alyssa H. Sinclair, Giulia Pullano, Stephen J. Beckett, Leah LeJeune, Folashade B. Agusto, Chris T. Bauch, Cynthia Baur, Henri Berestycki, Jonathan Dushoff, Quentin Griette, Simon A. Levin, Jorge X. Velasco-Hernández, Jianhong Wu, Joshua S. WeitzAbstract
Mathematical models have produced important insights into the relationships between disease transmission, risk perception, and human behaviour. However, the increase in scale and complexity of health and behavioural data accelerated by the COVID-19 pandemic demands a critical evaluation of the realism and readiness of mathematical models of infectious disease dynamics to inform preparation and response to (re)emerging public health threats. In this scoping review, we identify five thematic frontiers in extending coupled models of disease dynamics and human behaviour, spanning (1) information quality and scale, (2) data-driven versus perceived risk, (3) social influence, (4) barriers related to fatigue and access, and (5) disruptive events. For each frontier, we summarize key modelling approaches and results, identify knowledge gaps and provide recommendations on future directions with a focus on integrating big data into epidemiological models, incorporating social behavioural theory and accounting for heterogeneity within populations. In turn, each of these frontiers involves challenges that call for new analytical and technical developments. We close by providing general recommendations for modelers with a focus on interdisciplinary collaboration with social and behavioural scientists and establishing best practices for data collection and management to improve the public health relevance of infectious disease models.