The lay abstract featured today (for How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study by Bethany Heath, Sofía S. Villar and David S. Robertson) is from Statistics in Medicine with the full Open Access article now available to read here.

Heath B, Villar SS, Robertson DS. How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study. Statistics in Medicine. 2024; 1–24. doi: 10.1002/sim.10062 

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 testing 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, the researchers present results from an extensive and realistic simulation study comparing three different testing policies, based on:

1) Symptomatic testing: individually testing subject with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic),
2) Random testing: Individually testing subjects at random, or
3) Pooled testing: pooling subjects at random and testing the combined pool.

To compare these testing policies, a representative small town in the UK is modelled by simulating the connections between children, adults and elderly people in the population. Cases of COVID-19 are introduced into the population to assess how each of the testing policies would perform against a range of objectives. The realistic situation of a limited testing capacity is used.

The study also looks at how the proportion of infected people with symptoms affects the performance of these testing strategies. Symptomatic testing performs better than pooled testing except when only a small percentage of infected people show symptoms.

The study then also incorporates a novel feature of non-compliance, considering the impact when individuals do not follow the rules of the testing policy in place. When more than a small proportion of the population (over 10%) does not follow the rules correctly, pooled testing outperforms testing symptomatic people individually. Therefore, policymakers should carefully consider the expected level of non-compliance in the population as well as characteristics of the epidemic (such as proportion of cases that are symptomatic) when choosing a testing policy.