Title: Resource Allocation for Containing Epidemics from Temporal Network Data,
Abstract: We study the problem of containing epidemic spreading processes in temporal
networks. We specifically focus on the problem of finding a resource allocation
to suppress epidemic infection, provided that an empirical time-series data of
connectivities between nodes is available. Although this problem is of
practical relevance, it has not been clear how an empirical time-series data
can inform our strategy of resource allocations, due to the computational
complexity of the problem. In this direction, we present a computationally
efficient framework for finding a resource allocation that satisfies a given
budget constraint and achieves a given control performance. The framework is
based on convex programming and, moreover, allows the performance measure to be
described by a wide class of functionals called posynomials with nonnegative
exponents. We illustrate our theoretical results using a data of temporal
interaction networks within a primary school.