Abstract
Amplifying the testing capacity and making better use of testing resources is a crucial measure when fighting any pandemic. A pooled testing strategy for SARS-CoV-2 has theoretically been shown to increase the testing capacity of a country, especially when applied in low prevalence settings. Experimental studies have shown that the sensitivity of reverse transcription-polymerase chain reaction is not affected when implemented in small groups. Previous models estimated the optimum group size as a function of the historical prevalence; however, this implies a homogeneous distribution of the disease within the population. This study aimed to explore whether separating individuals by age groups when pooling samples results in any further savings on test kits or affects the optimum group size estimation compared to Dorfman's pooling, based on historical prevalence. For this evaluation, age groups of interest were defined as 0-19 years, 20-59 years and over 60 years old. Generalisation of Dorfman's pooling was performed by adding statistical weight to the age groups based on the number of confirmed cases and tests performed in the segment. The findings showed that when the pooling samples are based on age groups, there is a decrease in the number of tests per subject needed to diagnose one subject. Although this decrease is minuscule, it might account for considerable savings when applied on a large scale. In addition, the savings are considerably higher in settings where there is a high standard deviation among the positivity rate of the age segments of the general population.
Highlights
Testing for early recognition of infection sources and cutting off transmission forms the cornerstone of any public health response to emerging outbreaks [1]
The main findings provide a relationship for the number of tests per subject needed to diagnose one subject (z) using a pool testing strategy, which is denoted by z = z(n, x), where n stands for the optimum group size and x stands for the historical prevalence of positive tests in a particular context
A lower value of z means greater test savings, since fewer tests are needed to diagnose one subject, and a greater portion of the population can be covered. This inverse relationship between heterogeneity and z implies that the limits of the usefulness of the strategy can potentially be stretched to work on an even higher prevalence of positives in a context where the heterogeneity of the population is high and pooling of the samples is separated by age groups
Summary
Testing for early recognition of infection sources and cutting off transmission forms the cornerstone of any public health response to emerging outbreaks [1]. The foundational work for this strategy dates back to 1943, based on Dorfman’s pooling, who first introduced the concept of pooling clinical specimens to save on testing resources [5] This strategy could potentially increase worldwide testing capacity many times over [6], if used correctly, in the right segment of the population and under the specific historical prevalence of positive results [7, 8] and test sensitivity [9]. The WHO has recently established that group testing for SARS-CoV-2 is a feasible strategy that can increase testing capacity and can be applied in low prevalence settings It does not recommend routine pooling in laboratories or using pooled samples for contact tracing purposes [13]
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