Infection risk assessment for socially structured population using stochastic microexposure model

Predicting infection outbreak dynamics within local microenvironments is a challenging task. Some methods assume smaller population pools and often lack the statistical power of inferences. Other methods are designed for larger population pools and cannot be downscaled to accommodate the details of microenvironments, such as a gym or cafeteria. Moreover, typically, individuals have a relatively small circle of friends, family, and co-workers with whom most contacts are taking place, while the external contacts occur sporadically, rendering the population clustered. Practicable infection risk assessment models should account for population size, geometry and occupancy of public places, behavioral and professional patterns that define daily routines, and societal structure.

We describe a novel methodology and investigate effects of the population social structure, along with other local constraints, on infection outbreak dynamics.

The study is based on the recently developed stochastic microexposure model (S-MEM). The model has been generalized to describe clustered populations. The methodology is demonstrated for a generic community of several thousand students living on campus. The student population possesses a natural social structure of being clustered into classes.

The results indicate that the social structure has the first order effect on the spread of the infection. Depending on the number, size, and degree of inner- and outer-cluster connections, the outbreak exhibits distinct durations, power, and multiple peaks of infection. Moreover, the contribution of different microenvironments to infection risk evolves during the course of the outbreak.

Social structure plays a major role in infection spread, and therefore should be accounted for in risk prediction tools. Furthermore, the contribution of different microenvironments to infection risk changes with time during the course of an outbreak. An optimal infection spread mitigation policy should, correspondingly, change in time depending on the evolving risk factors; the policy should not be static. The stochastic microexposure model accounts for the social structure of a population at multiple scales, and can predict the dynamic contributions of different microenvironments to infection spread risks.