ABSTRACT
A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID-19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimising cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analysing this with a single diagnostic test) has been suggested. In this paper, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This paper then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Only for less than 50% of infections being symptomatic does pooled testing outperform symptomatic testing in terms of metrics such as total infections and length of epidemic. Additionally, we present the novel feature for testing of non-compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population (>2%) needs to not comply with the testing procedure.