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

Authors

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

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 (Pearson's 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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 11 Jul 2026.

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