Dynamic Infection Spread Model Based Group Testing

TL;DR

This paper introduces dynamic algorithms for controlling infection spread over time using limited testing capacity, extending classical group testing to a time-evolving infection model and analyzing their effectiveness through simulations.

Contribution

It proposes two novel dynamic algorithms for infection control with limited tests, incorporating group testing and randomized methods in a time-dependent infection spread context.

Findings

Algorithms effectively reduce infection spread over time.

Simulation results validate theoretical performance bounds.

Group testing approach improves detection efficiency.

Abstract

We study a dynamic infection spread model, inspired by the discrete time SIR model, where infections are spread via non-isolated infected individuals. While infection keeps spreading over time, a limited capacity testing is performed at each time instance as well. In contrast to the classical, static, group testing problem, the objective in our setup is not to find the minimum number of required tests to identify the infection status of every individual in the population, but to control the infection spread by detecting and isolating the infections over time by using the given, limited number of tests. In order to analyze the performance of the proposed algorithms, we focus on the mean-sense analysis of the number of individuals that remain non-infected throughout the process of controlling the infection. We propose two dynamic algorithms that both use given limited number of tests to identify and isolate the infections over time, while the infection spreads. While the first algorithm is a dynamic randomized individual testing algorithm, in the second algorithm we employ the group testing approach similar to the original work of Dorfman. By considering weak versions of our algorithms, we obtain lower bounds for the performance of our algorithms. Finally, we implement our algorithms and run simulations to gather numerical results and compare our algorithms and theoretical approximation results under different sets of system parameters.