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We study the problem of containing epidemic spreading processes in temporalnetworks. We specifically focus on the problem of finding a resource allocationto suppress epidemic infection, provided that an empirical time-series data ofconnectivities between nodes is available. Although this problem is ofpractical relevance, it has not been clear how an empirical time-series datacan inform our strategy of resource allocations, due to the computationalcomplexity of the problem. In this direction, we present a computationallyefficient framework for finding a resource allocation that satisfies a givenbudget constraint and achieves a given control performance. The framework isbased on convex programming and, moreover, allows the performance measure to bedescribed by a wide class of functionals called posynomials with nonnegativeexponents. We illustrate our theoretical results using a data of temporalinteraction networks within a primary school.

Resource Allocation for Containing Epidemics from Temporal Network Data