Covid-19 Testing Reaches New Heights with Pooled Testing Procedures

Image by HFCM Communicate. [CC-BY-SA 4.0] By Shanelle Jayawickreme

Have you been tested for Covid-19? As I anxiously ual swab samples into one big pool before testing for the viawaited to take my test, deep down I wondered if I rus. In the case that the test comes back negative, the scienwas being exposed to the virus at that very minute, tists save a large quantity of testing materials by only using standing in a long line. Sitting in my chair, part of me knew one set for the entire cohort. If instead the pooled sample only a thin layer of disinfectant spray served as the barrier yields a positive result, then each individual sample within between my germs and those of the people who preceded the pool is retested in order to identify those responsible for me. Luckily, I ended up testing negative. This is not the case the positive result. This process can also involve an intermefor the group of people who test positive, or the even larg- diary stage, in which the total group assay is split into smaller population affected by limited access to resources and er and smaller groups until the positive tests are located. In funding for testing and treatment this manner, testing resources are conserved while still alsites in their area. lowing the scientists to observe the results of each sample. The COVID-19 outbreak has dominated the lives of U.S. citizens well into 2020, leaving many struggling to find stability in a period of overwhelming uncertainty. Although the coronavirus pandemic has been present for over seven Dr. Daniel Westreich months, the public constantly craves more information on how to better handle this situation. With increasing demand and a limited supply of testing resources, it is imperative that new solutions and strategies are publicized for the greater good. Figure 1. Pooled Testing Procedures. Image courtesy of UNC-Chapel Hill researchers Daniel Westreich and Michael Jean-Etienne Minh-Duy Poirrier [CC BY-SA 2.0] G. Hudgens from the department of Epidemiology and the department of Biostatistics, respectively, in collaboration Westreich began by producing a model that illuswith Christopher D. Pilcher from the University of Califor- trated the distribution of the window for SARS-CoV-2 denia San Francisco, have proposed a solution for upscaling tection, which predicts changes in viral concentration from COVID testing. “Pooled testing of a virus is an efficient way the first day to the last day of infection. This proved difficult to move the testing agenda forward,” allowing for higher to consolidate into a mean projection due to the fluctuatproductivity and proper utilization of resources when com- ing nature of the virus.2 Westreich explains, “there is a gap pared to individual testing methods already in place.1 between when you’re infected with the virus, and when Pooled testing is a procedure that has been used for your body starts to produce measurable responses to the many decades by scientists and involves combining individ- virus.”1 In that gap, one can be very infectious to others. Vi-

Carolina Scientific medicine & health ral loads, the measured quantities of the virus, increase and that, for pooled testing methods in particular, efficiency decrease significantly throughout the duration of an infec- is high where the virus prevalence is low. In other words, tion. For example, someone may pick up a large viral load in a population likely to have a high density of negative that diminishes before it is able to duplicate. In order to cre- cases, pooled testing methods allow for minimal retesting, ate a viral detection model, Westreich, Pilcher, and Hudgens since there are fewer positive cases. Low risk areas such as set the default detection window at 14 days and generated healthcare facilities and large clinical studies can benefit an equation for estimating peak viral load, rate of viral in- from such a strategy. Smaller sample pools, on the other crease, and the slope of viral decay. hand, prove better suited for high-risk populations.2 WestFigure 2. Model for Window of Detection. Image courtesy of “Group Testing for Severe Acute Respiratory Syndrome–

Coronavirus 2 to Enable Rapid Scale-up of Testing and Real-

Time Surveillance of Incidence” Using their model, Westreich, Pilcher, and Hudgens predicted a detection window for group testing and compared it with the detection window for widely used individual testing. In particular, they wanted to determine how well the sensitivity of group testing compares to individual testing, and if group testing results in a significant dilution of each sample.2 The methods in which this model was meant to be used are similar to those once utilized in group testing software for HIV. Both take into account diagnostic sensitivity, or a measure of how accurately the test diagnoses a patient, and analytic sensitivity, or the test’s accuracy in identifying positive results. Westreich, Pilcher, and Hudgens identified a tradeoff: large pool size allowed high output but lowered analytic sensitivity, while smaller pool sizes exhibited high analytic sensitivity at a slower rate.2 Their model illustrated that pool sizes greater than 25 people tend to reduce analytic sensitivity, so they set the upper limit at 25 samples. Each pooling strategy presents unique benefits and shortcomings, but such methods generally allow for 2-20 times the number of specimens to be processed using the same number of tests as are currently used. Their study investigated both two-stage and three-stage pooling. Both versions include the large-scale pool test and individual test (when necessary). The three-stage also includes an intermediary test. Such methods improved average time to results, sensitivity, expected number of screenings, and Positive Predictive Westreich and Hudgens ultimately detailed a wide range of methods to improve COVID-19 testing. They found reich wants these findings to be communicated transparently so that people can “make the tradeoffs between gains in efficiency and losses in detection due to the dilution of samples.”1 To top it off, Westreich, Pilcher, and Hudgens also created a free online calculator to help labs make decisions

Values (PPV, the proportion of test results that are positive).2 regarding how to conduct their pooled testing.2

Figure 3. Efficiency for Various Pool Sizes. Image courtesy of “Group Testing for Severe Acute Respiratory Syndrome– Coronavirus 2 to Enable Rapid Scale-up of Testing and Real-Time Surveillance of Incidence”

Although SARS-Cov-2 represents a good candidate for pooled testing methods, a degree of analytic sensitivity can and will likely be sacrificed due to a lack of proper resources. However, the effects can be lessened with the implementation of pooled testing, as it requires less resources per assay. Looking ahead, serological antibody tests seem to optimize the diagnostic sensitivity of pool testing. Westreich, Pilcher and Hudgens recommend that large-scale laboratories begin implementing group testing methods in order to maximize use of the physical tests.2 Westreich hopes that his pool testing models will “help people with the rapid and massive scale up of testing.”1 Furthermore, he shares that the state of North Carolina is currently funding the expansion of their web calculator, and Westreich and Hudgens have launched a number of ongoing projects to ultimately make COVID-19 testing methods even more efficient.1

References

1. Interview with Daniel Westreich, Ph.D. September 7th, 2020. 2. Pilcher, Christopher D.; Westreich, Daniel; Hudgens, Michael G.; The Journal of Infectious Diseases 2020, 222, 903–909.