Strategic Disease Control via Complex Systems Modeling: Computational Methods and Empirical Validation of Population-Level Interventions and Individual Decision-Making

In recent years, the threat of severe infectious diseases has highlighted the challenges in disease prevention and control and the critical need for effective healthcare resource allocation strategies. To address this issue, in this paper, we propose a complex systems modeling approach to characterize the spatiotemporal transmission risks of infectious diseases. Based on the inferred transmission risks, we develop a risk-guided strategy to allocate limited healthcare resources, particularly testing capacities, to areas with the greatest need to control the spread of infectious diseases. We assess the effectiveness of our proposed strategy at both the population and individual levels. At the population level, we measure the strategy's impact on reducing the peak number of daily infections. At the individual level, we evaluate how well the strategy influences individuals' decisions to undergo disease detection tests. Experimental results on simulation data with various parameter settings demonstrate that our strategy significantly mitigates disease spread by reducing peak daily infections and effectively guides individuals to comply with testing policies.