Systematic Review Examines COVID-19 Models Incorporating Human Behavior
A systematic review published on arXiv assessed SARS-CoV-2 transmission models that incorporated human behavioral responses to epidemic dynamics, finding mixed results. While the pandemic spurred expanded use of empirical data in so-called epi-behavioural modelling, researchers identified persistent gaps including limited behavioral data use, lack of structural model innovation, and insufficient collaboration across disciplines and with policymakers. The findings carry implications for pandemic preparedness, suggesting that better data infrastructure, AI integration, and interdisciplinary approaches are critical priorities.
A preprint systematic review submitted to arXiv in June 2026 examined the state of COVID-19 epidemic models that endogenously incorporate human behavior—meaning models where behavioral changes are driven by epidemic dynamics rather than imposed externally. The authors found that the pandemic represented a significant opportunity to advance this area of epidemiological modelling, and some progress was made in leveraging empirical data. However, the review also identified notable shortcomings: behavioral empirical data remained underutilized, model structures showed limited innovation, and engagement with other disciplines and decision-makers was insufficient. The authors argue that these gaps must be addressed to improve future pandemic preparedness. Key recommended strategies include establishing robust behavioral data collection infrastructure, identifying clear priorities in model design, harnessing advances in artificial intelligence, and fostering greater interdisciplinarity. The review spans 16 pages, 5 figures, and 1 table, and covers the intersection of physics, social science, and epidemiology.
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A systematic review of COVID-19 epidemic models with endogenous human behaviour. What's next?
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