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We provide an optimization-based framework that identifies social separation policies to mitigate the spread of diseases in social networks. The study considers subject-specific risk information, social structure, and the negative economic impact of imposing restrictions. We first analyze a simplified variation of the problem consisting of a single period and a specific social structure to establish key structural properties and construct a tailored globally-convergent solution scheme. We extend this solution scheme to heuristically solve the more general model with multiple time periods and any social structure. We use real COVID-19 data to illustrate the benefits of proposed framework. Our results reveal that the optimized policies substantially reduce the spread of the disease when compared with existing benchmark algorithms and policies that are based on a single risk factor. In addition, we utilize the considered framework to identify important subject attributes when distributing Personal Protective Equipment. Moreover, results reveal that the optimized policies continue to outperform under a more realistic setting. Our results underscore the importance of considering subject-specific information when designing policies and provide high-level data-driven observations to policy-makers that are tailored to the specific risk profile of the population that is being served.

An optimization-based framework to minimize the spread of diseases in social networks with heterogeneous nodes