Possibility and Probability of Untested Positive Cases - A Reliability Check on The Efficacy of the Quality and Quantity of COVID-19 Testing.

Raghuram K.S.¹ , Sathya Krishna K.R.²

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.3 , pp.23-27, Jun-2020

Online published on Jun 30, 2020

Copyright © Raghuram K.S., Sathya Krishna K.R. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
With an exponential scaling up of COVID-19 testing, healthcare systems and lawmakers globally are overwhelmed with the unprecedented COVID-19 positive cases. Innovative strategies of testing like random testing and drive-thru testing are being implemented to identify the suspected cases, isolate, quarantine and treat them in isolation until an antidote or vaccine is developed for large scale administration. This paper dwells into the repercussions of using low-reliability testing kits and the possible damage caused by undetected positive cases due to the low accuracy of this testing. Bayes law is used to develop country-specific logarithmic functions to fit a model that identifies the probability of infected but undetected cases in any given population. Sixty-eight percent of total confirmed cases globally and the ?tests per million? data from thirteen countries are considered to develop a model. This predictive model identifies the probability of infected but undetected cases in any given population.

Key-Words / Index Term :
COVID-19, Bayes law, False Positives, Sensitivity, Specificity, Covid-19 testing, Coronavirus, Wuhan, Testing Kit

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