Modelling the Utility of Group Testing for Public Health Surveillance

TL;DR

This paper extends group testing theory for public health by incorporating error costs and resource constraints, proposing optimal testing strategies to minimize expected costs during epidemics.

Contribution

It introduces a new theoretical framework that accounts for error costs in group testing, optimizing testing strategies under resource limitations.

Findings

Lower bounds on expected testing costs derived using information theory

Simple strategies shown to outperform existing methods

Illustrative example demonstrates practical application of the theory

Abstract

In epidemic or pandemic situations, resources for testing the infection status of individuals may be scarce. Although group testing can help to significantly increase testing capabilities, the (repeated) testing of entire populations can exceed the resources of any country. We thus propose an extension of the theory of group testing that takes into account the fact that definitely specifying the infection status of each individual is impossible. Our theory builds on assigning to each individual an infection status (healthy/infected), as well as an associated cost function for erroneous assignments. This cost function is versatile, e.g., it could take into account that false negative assignments are worse than false positive assignments and that false assignments in critical areas, such as health care workers, are more severe than in the general population. Based on this model, we study the optimal use of a limited number of tests to minimize the expected cost. More specifically, we utilize information-theoretic methods to give a lower bound on the expected cost and describe simple strategies that can significantly reduce the expected cost over currently known strategies. A detailed example is provided to illustrate our theory.