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.
- Pub Date:
- January 2018
- Computer Science - Social and Information Networks;
- Physics - Physics and Society
- M. Ogura and J. Harada, Resource allocation for containing epidemics from temporal network data, in 23rd International Symposium on Mathematical Theory of Networks and Systems, 2018, pp. 537--542