Abstract
There is a rapidly emerging risk that essential supply chains could collapse during the COVID-19 pandemic. Therefore, strategies to protect essential workers regardless of whether they have symptoms should be implemented by governments. We propose a “stochastic filtrate” of such workers, based on periodic real-time-RT-PCR-testing to reduce risky physical interactions with workers infected with SARS-CoV-2. Such a focused strategy, when combined with other preventive measures, could be successfully replicated in many countries to reactivate the world’s economy safely. We present a mathematical framework, conceptual model, and simulation of this stochastic filtrate process to support its viability.
Highlights
Real-time reverse transcription-polymerase chain reaction (RT-PCR) testing is a method commonly employed in molecular biology laboratories
The highly contagious SARS-CoV-2 has resulted in the COVID-19 pandemic, which is affecting the economy in an unprecedented way, despite extensive efforts to use real-timeRT-PCR testing of SARS-CoV-2 to mitigate virus propagation [1]
A possible reason for this failure is that many countries are only administering these tests to patients presenting COVID19-like symptoms, or to those who were in close contact with such patients, disregarding the asymptomatic SARS-CoV-2 infected population [2]
Summary
Real-time reverse transcription-polymerase chain reaction (RT-PCR) testing is a method commonly employed in molecular biology laboratories. It is one of the most widely used laboratory techniques for detecting the severe acute respiratory syndrome-related coronavirus 2 (SARSCoV-2). We propose an approach, termed “stochastic filtrate” with a filtrating agent, which could be employed to timely identify and isolate, under quarantine, essential workers infected with SARS-CoV-2. We propose focused real-timeRT-PCR-testing as an optimal filtrating agent (though other future tests for early detection of SARS-CoV-2 could be employed). Our method takes advantage of the random nature of stochastic processes, as in randomized controlled trials [3] Such random sampling has been found to be effective in providing a “quick count” in elections, in which a random sample of polling places is used to identify the “winner” without having to count all the votes.
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