Allocation of COVID-19 Testing Budget on a Commute Network of Counties

TL;DR

This paper presents a network-based optimization approach for allocating COVID-19 testing resources across counties in a commute network, improving efficiency by considering traffic-based connections.

Contribution

It introduces a novel network model and an optimization strategy for testing allocation that leverages commute data, outperforming traditional methods that ignore network structure.

Findings

The method improves testing efficiency over non-network approaches.

Application to Massachusetts and Hubei networks demonstrates effectiveness.

The approach can be extended to vaccine allocation strategies.

Abstract

The screening testing is an effective tool to control the early spread of an infectious disease such as COVID-19. When the total testing capacity is limited, we aim to optimally allocate testing resources among n counties. We build a (weighted) commute network on counties, with the weight between two counties a decreasing function of their traffic distance. We introduce a network-based disease model, in which the number of newly confirmed cases of each county depends on the numbers of hidden cases of all counties on the network. Our proposed testing allocation strategy first uses historical data to learn model parameters and then decides the testing rates for all counties by solving an optimization problem. We apply the method on the commute networks of Massachusetts, USA and Hubei, China and observe its advantages over testing allocation strategies that ignore the network structure. Our approach can also be extended to study the vaccine allocation problem.