Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks
Abstract
The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MinINF problem for optimal social distancing involves minimizing the expected number of infections, when we are allowed to break at most edges; similarly the MinINFNode problem involves removing at most vertices. These are fundamental problems in epidemiology and network science. While a number of heuristics have been considered, the complexity of these problems remains generally open. In this paper, we present two bicriteria approximation algorithms for MinINF, which give the first non-trivial approximations for this problem. The first is based on the cut sparsification result of Karger \cite{karger:mathor99}, and works when the transmission probabilities are not too small. The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model. We also extend some of our results to tackle the MinINFNode problem.
Cite
@article{arxiv.2202.08296,
title = {Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks},
author = {Amy Babay and Michael Dinitz and Aravind Srinivasan and Leonidas Tsepenekas and Anil Vullikanti},
journal= {arXiv preprint arXiv:2202.08296},
year = {2022}
}
Comments
To appear at AISTATS 2022