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bioRxiv · 10.64898/2026.07.09.737414

Chronic Disease Emergence in Coevolving Social Networks via Behavioral Feedback and Demographic Turnover

Lee, H.-W.·Lo, J.-Y.·O'Flaherty, M.
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Abstract

Chronic diseases emerge within social systems where individuals reorganize their ties, adapt their behaviors, and turn over through demographic processes; yet, how these coupled dynamics shape long-run disease trajectories remains poorly understood. We developed an agent-based model of how the network structure, behavioral adaptation, and population renewal jointly generate cardiovascular disease incidence in an evolving population. The model integrated age-dependent intrinsic risk from the Framingham Risk Score framework, behavioral transmission through social ties, homophily-driven network formation, and demographic turnover across simulated populations of up to 420 agents and horizons of 18-240 months. Highly connected agents showed lower simulated incidence than weakly connected agents (adjusted hazard ratio 0.43; 95% confidence interval 0.33, 0.57; top-tertile degree indicator) after adjustment for age, sex, smoking, blood pressure, body-mass index, physical activity, and diet. Individual disease probability rose with ego-network disease prevalence (Pearsons r = 0.703, p < 0.001). Global sensitivity analysis identified baseline incidence, activity level, and behavioral transmission as dominant drivers (78% of variance). Bayesian calibration concentrated within the empirical acceptance region, with posterior standard deviation of 12% of prior. These results point toward a systems-level understanding of non-communicable disease emergence.

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Lee, H.-W., Lo, J.-Y., O'Flaherty, M.. 2026-07-10. Chronic Disease Emergence in Coevolving Social Networks via Behavioral Feedback and Demographic Turnover. https://doi.org/10.64898/2026.07.09.737414

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