Optimising the pool test method for COVID-19 using evolutionary algorithms

Abstract

The spread of the COVID-19 pandemic, quickly became a public health crisis which acted on many levels. The most challenging one of these was the sudden unavailability of protective gear and a complete lack of testing capacity. Although availability of masks and protective equipment has improved in the last few months, the testing capacity still remains a limited resource for most countries. One mitigation strategy for addressing the scarcity of tests is to pool biological samples in a single test, as demonstrated by the Frankfurt Goethe University. In this paper we add to the body of knowledge on the problem of optimizing the pooled testing strategy by optimizing a multistage adaptive testing scenario using an evolutionary algorithm. We also propose a generic framework by which optimisations can be advanced even further and will help to massively increase the testing capacity for stopping the current pandemic.