As Covid-19 takes the world by storm, the need for diagnostic capacities is a big challenge for healthcare systems. Medical teams and laboratories are plagued with the task of diagnosing an unprecedented number of samples but they face shortages of time, equipment and personnel.
Tools called multiplex barcoding and RNA-Seq could scale up diagnoses but the expertise needed means they are not readily available.
However, a simpler approach can be found in sample pooling, a practical and cost-effective way of undertaking large-scale studies. Examples of the gains obtained through sample pooling is shown below.
Pooling of biological specimens has become a frequent practice among doctors and researchers and is commonly used to cut the cost of screening a large number of individuals for infectious diseases.
In its simplest form, individual specimens are mixed into a common pool and tested together with subsequent individual tests carried out only if the pool tests positive.
This procedure has been perfected over the years and has been used in cases ranging from disease screening of American Red Cross blood donations to West Nile virus surveillance in mosquitoes.
Reports from around the world show that one of the main bottlenecks in Covid-19 testing is a reaction known as RT-qPCR. As the number of PCR reactions performed globally increases, demand rises for chemical reagents which are increasingly in short supply.
Hence, many laboratories have begun to show that viral RNA from pooled samples can be detected in RT-qPCR despite dilution. Pooled testing has significantly increased testing capacity, especially to those who may be unknowingly spreading the virus, so much that Professor William Ampfo, Head of Virology in Ghana says pooled testing has increased capacity to 10,000 samples a day compared with 1,200.
Health officials say infected people who are not showing symptoms are largely responsible for the rising number of cases. Dr Anthony Fauci, America’s top infectious-disease expert, says the method of pooling samples presents is a really good tool in schools and at community level.
The benefit is that if a laboratory receives 100 samples and uses a one-by-one approach, then 100 tests will be performed. If the samples are pooled in batches of 10 then only ten tests will need to be performed. A negative result obtained by a single RT-qPCR reaction, concludes that 10 individuals are negative and samples that yielded a positive result are either retested individually or in smaller batches.
A study by the Nebraska Public Health Laboratory showed that when the incidence of Covid-19 is 10% or less, group testing will result in the saving of reagents and personnel time with an overall increase in testing capability of at least 10%.
Experts generally recommend the technique when fewer than 10% of people are expected to test positive. For example, pooling would not be cost-effective in Arizona, where a surge has pushed positive test results to well over 10%. But the approach could make sense in areas with a lower rate of positive results.
However, when deciding whether to pool specimens one needs to consider factors that may limit its success. These include disease prevalence rate, test-kit sensitivity, pool size, pooling strategy and sample dilution.
The rate of occurrence of the virus in a given population is just one of many factors that influence the use of group testing. Further considerations include the sample size and the downstream effects on sample dilution and assay sensitivity.
Researchers at the University of Saarland University Medical Centre in Germany started pooling samples from medical staff without symptoms of Covid-19. They tested a range of pool sizes, from four to 30 samples.
Due to the sensitivity of the assay the team could combine samples from 30 people in one test tube, massively increasing the capacity for testing. Professor Smola, director of the Institute of Virology, said, “If the rate of infection is low and if many of the pools are negative, this can save significant numbers of test kits and increase the test capacity of the existing infrastructure.”
A pooling strategy depends on the community prevalence of the virus, and pool size will need to be adjusted accordingly. The Centre for Disease Control and Prevention (CDC) recommends that laboratories should determine prevalence based on a rolling average of the positivity rate of their own Covid-19 testing over the previous 7–10 days.
Before employing pooling, the sensitivity of the testing technique as well as the prevalence rate of the disease needs to be characterized. Some qRT-PCR kits are known to provide accurate results with smaller viral load, whereas some are known to require a large viral load to detect the virus. Sensitivity characteristics need to be understood before the formulation of plans for pooled testing. This knowledge would then help to decide the optimal pool size and number of replications required.
Incorporating risk factors in pooled testing populations and assessing the impact of different pooling and extraction methods on the recovery of RNA need also be evaluated. Therefore, laboratories must perform their own validation pool studies for kits used for each RNA extraction and amplification based on the prevalence rate of Covid-19 in their own region.
A rapidly changing epidemic requires testing strategies that can adapt to increases in the positive test rate. In a pandemic, strategies must be employed to closely monitor the use of pooling as the positive rate of test specimens increases so as to not miss low positive samples from less sensitive assays.
Pooling can be effectively utilised to ramp up the number of tests performed in a health outbreak diagnosis and it is especially useful for routine community surveys and for the monitoring of cohesive groups. Diagnosis of even a single positive person typically results in the quarantine of an entire group to prevent further spread in the community.
With pooling, more routine monitoring can be established, especially among asymptomatic carriers, thereby reducing transmission and easing the strain on health services.