Designing network based intervention strategies for epidemics of infectious diseases from edge based infection probability.

Epidemics underscore the critical role of human contact networks in shaping the spread of infectious diseases. Transmission varies depending on a range of factors, including virus characteristics, the type and duration of contact, and whether it occurs indoors or outdoors. However, not only does the probability of transmission differ, but the impact of each transmission event depends on the ability of a single event to spread the virus to new, previously unaffected, socially segmented groups in society. Effective policymaking should be guided by a nuanced understanding of how infections spread, ensuring that interventions are proportional to the risks they aim to address. In this study, we conducted a series of theoretical experiments on generated networks that are structurally similar to real social contact networks. Using models that distinguish between regular, repeated contacts and occasional, random, or transient contacts, we simulated fictitious epidemics on different sample graphs with varying contact restrictions and then compared their trajectories. Based on the observed differences, we identified the contact types whose restriction can effectively curb the epidemic. We find that it is particularly important to focus on relationships that form a bridge between clusters or communities and on contacts with particularly high transmission probability. By doing so, public health efforts can more effectively balance the dual goals of minimizing transmission and maintaining social and economic stability.