A decision support system for optimizing the cost of social distancing in order to stop the spread of COVID-19

TL;DR

This paper introduces an optimization-based decision support system to help governments determine effective social distancing measures that balance virus containment with economic impact, using models, algorithms, and theoretical analysis.

Contribution

It presents new combinatorial optimization models, theoretical complexity results, an algorithm for complex problems, and an integer linear programming formulation for social distancing decisions.

Findings

Algorithm effectively reduces virus spread while minimizing economic impact.

Theoretical results clarify the computational complexity of the problem.

Implementation demonstrates practical applicability of the proposed methods.

Abstract

Currently there are many attempts around the world to use computers, smartphones, tablets and other electronic devices in order to stop the spread of COVID-19. Most of these attempts focus on collecting information about infected people, in order to help healthy people avoid contact with them. However, social distancing decisions are still taken by the governments empirically. That is, the authorities do not have an automated tool to recommend which decisions to make in order to maximize social distancing and to minimize the impact for the economy. In this paper we address the aforementioned problem and we design an algorithm that provides social distancing methods (i.e., what schools, shops, factories, etc. to close) that are efficient (i.e., that help reduce the spread of the virus) and have low impact on the economy. On short: a) we propose several models (i.e., combinatorial optimization problems); b) we show some theoretical results regarding the computational complexity of the formulated problems; c) we give an algorithm for the most complex of the previously formulated problems; d) we implement and test our algorithm; and e) we show an integer linear program formulation for our problem.