COVID-19: Should We Test Everyone?
Grace Yi, Wenqing He, Dennis Kon-Jin Lin, Chun-Ming Yu

TL;DR
This paper analyzes COVID-19 testing strategies considering resource limitations and test inaccuracies, providing evidence-based guidance for optimal testing decisions from a statistical perspective.
Contribution
It offers a statistical evaluation of COVID-19 testing policies, addressing resource constraints and test uncertainties to inform better decision-making.
Findings
Highlights the impact of test accuracy on detection effectiveness.
Provides recommendations for resource-efficient testing strategies.
Analyzes the trade-offs between testing coverage and accuracy.
Abstract
Since the beginning of 2020, the coronavirus disease 2019 (COVID-19) has spread rapidly in the city of Wuhan, P.R. China, and subsequently, across the world. The swift spread of the virus is largely attributed to its stealth transmissions in which infected patients may be asymptomatic. Undetected transmissions present a remarkable challenge for the containment of the virus and pose an appalling threat to the public. An urgent question that has been asked by the public is "Should I be tested for COVID-19 if I am sick?". While different regions established their own criteria for screening infected cases, the screening criteria have been modified based on new evidence and understanding of the virus as well as the availability of resources. The shortage of test kits and medical personnel has considerably limited our ability to do as many tests as possible. Public health officials and…
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Taxonomy
TopicsCOVID-19 diagnosis using AI · COVID-19 Clinical Research Studies · COVID-19 epidemiological studies
