Smart Testing and Selective Quarantine for the Control of Epidemics

TL;DR

This paper proposes a stochastic dynamic system-based policy for smart testing and quarantine during epidemics, aiming to efficiently identify and isolate cases to control disease spread with minimal confinement.

Contribution

It introduces an optimal sensor selection approach for epidemic testing, adapting to evolving infection probabilities to improve containment strategies.

Findings

Reduces disease spread compared to contact tracing.

Limits the number of individuals confined.

Effective in a simulated community of 10,000 people.

Abstract

This paper is based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures. This decision problem is closely related to the problem of optimal sensor selection, which is a very active research subject in control engineering. The goal of this paper is to propose a policy to smartly select the individuals to be tested. The main idea is to model the epidemics as a stochastic dynamic system and to select the individual to be tested accordingly to some optimality criteria, e.g. to minimize the probability of undetected asymptomatic cases. Every day, the probability of infection of the different individuals is updated making use of the stochastic model of the phenomenon and of the information collected in the previous days. Simulations for a closed community of 10000 individuals show that the proposed technique, coupled with a selective confinement policy, can reduce the spread of the disease while limiting the number of individuals confined if compared to the simple contact tracing of positive and to an off-line test selection strategy based on the number of contacts.