It is important for healthcare staff to receive COVID-19 test results quickly. Researchers have now devised a COVID-19 research technique that maximizes the proportion of unfavorable outcomes after a single round of testing, allowing for timely results notification. In addition, since fewer additional tests are needed, the method eliminates the need for increasingly scarce test reagents. Elsevier’s Journal of Molecular Diagnostics wrote an article on their approach.
COVID-19 delivery must be stopped as soon as possible in hospitals and nursing centers, and proper staffing must be maintained. To improve capacity, group research techniques with pooled samples have been proposed; however, the currently used strategies are sluggish.
Traditional Dorfman sequential (DS) pooling blends several samples and tests all of the constituent samples if the pool result is positive. This suggests that even those who test negative for COVID-19 would have to separate in a healthcare environment. The researchers devised a nonadaptive combinatorial (NAC) pooling method that measures the same sample in several pools at the same time. Initially, the algorithm assumes that each sample is positive.
It then tries to disprove this hypothesis by locating a well in which the sample was put that had tested negative. Then, from the list of remaining potentially positive samples, another algorithm is used to locate positive wells that hold a single sample. Samples that are inconclusive are retested.
At a low prevalence rate, all NAC matrices performed well, with a total of 585 tests saved per assay in the 700 sample matrix. All of the NAC matrices allowed fewer retests than the DS research scheme in models of low-to-medium prevalence ranges (0.1 percent – 3 percent), which is the prevalence predicted in an asymptomatic healthcare worker population. The output of each matrix, however, decreased as the population prevalence increased.
“Over the last year, a number of high-throughput testing schemes for SARS-CoV-2 detection have been created. We show how adaptable automatic, advanced mathematical methods can be used to increase COVID-19 diagnostic capabilities while remaining healthy. Such a solution may affect a much larger number of people, save lives, and be implemented in a long-term manner. Prof. Black and Dr. McDermott agreed, “Undoubtedly, this has great significance to other potential population-based screening methods.”