Computational analysis of the COVID-19 test suggests that the amount of test is more important than the sensitivity of the test used to minimize the number of infections in the population. Philip Cherian and Gautam Menon of Ashoka University in Sonipat, India, and Sandeep Krishna of the National Center for Biological Sciences TIFR in Bangalore, India have published their findings in an open access journal. PLOS Computational Biology..
Different states in India use different combinations of the two major tests for COVID-19. It is a very sensitive reverse transcriptase-polymerase chain reaction (RT-PCR). test Insensitive rapid antigen test. Traditional thinking is that all RT-PCR approaches will ultimately reduce overall infection. The RT-PCR test is more sensitive than the rapid antigen test, but it is expensive and does not give immediate results. Therefore, the exact combination of tests needed to optimize the results while taking cost constraints into account was unknown.
Cherian et al. Used computational models to simulate how COVID-19 spreads across populations, given the various combinations of tests and the economic trade-offs between them. Considering the movement of people between different locations, they calculated the total number of infections that would occur by the end of the pandemic under each scenario.
Analysis shows that using only the rapid antigen test may give similar results in terms of total infections, as long as the number of test subjects is large enough, as with the RT-PCR test alone. there is.This is because the government is at the bottom Middle income country Optimal results may be achieved by focusing on enhancing the test with less sensitive tests rather than prioritizing RT-PCR.
The authors also note that the government needs to continue to investigate different combinations of tests that result in the greatest reductions. number Of the case. Given the lower cost of testing, you can also readjust this mix on a regular basis to monitor what is most economically meaningful.
“Tests are continually improving, and even with low sensitivity, trade-offs favor rapid testing,” says Menon. “Modeling the impact of using different combinations of tests with relative costs in mind can suggest specific policy changes that have a substantive impact on epidemic trajectory changes.”
Cherian P, Krishna S, Menon GI (2021) Optimization of COVID-19 testing in India. PLoS Comput Biol 17 (7): e1009126. doi.org/10.1371/journal.pcbi.1009126
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