Group Testing under Superspreading Dynamics

TL;DR

This paper introduces a contact tracing-informed group testing method tailored for superspreading COVID-19 dynamics, significantly reducing tests needed compared to traditional Dorfman's method, especially in overdispersed infection scenarios.

Contribution

The paper develops a dynamic programming-based group testing approach that incorporates contact tracing data, improving efficiency over existing methods under superspreading conditions.

Findings

Significantly fewer tests required with the new method compared to Dorfman's.

Greater benefits observed in highly overdispersed secondary infection scenarios.

Method effective across various reproduction numbers and dispersion levels.

Abstract

Testing is recommended for all close contacts of confirmed COVID-19 patients. However, existing group testing methods are oblivious to the circumstances of contagion provided by contact tracing. Here, we build upon a well-known semi-adaptive pool testing method, Dorfman's method with imperfect tests, and derive a simple group testing method based on dynamic programming that is specifically designed to use the information provided by contact tracing. Experiments using a variety of reproduction numbers and dispersion levels, including those estimated in the context of the COVID-19 pandemic, show that the pools found using our method result in a significantly lower number of tests than those found using standard Dorfman's method, especially when the number of contacts of an infected individual is small. Moreover, our results show that our method can be more beneficial when the secondary infections are highly overdispersed.