Resource Allocation for Containing Epidemics from Temporal Network Data

TL;DR

This paper develops a convex programming framework to optimize resource allocation for epidemic containment using empirical temporal network data, addressing computational challenges and demonstrating effectiveness on real-world school interaction data.

Contribution

It introduces a computationally efficient convex optimization approach for resource allocation in epidemic control based on empirical temporal network data.

Findings

Framework effectively utilizes real-world temporal data

Achieves desired control performance within budget constraints

Demonstrated on primary school interaction 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.