Group Testing under Superspreading Dynamics
This work addresses the need for more efficient testing strategies in pandemic response, particularly for contact tracing scenarios, though it is incremental as it builds on existing methods.
The paper tackled the problem of inefficient group testing for COVID-19 contacts by incorporating contact tracing information into a dynamic programming method, resulting in significantly fewer tests than standard Dorfman's method, especially with small contact numbers and high dispersion.
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.