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COVID-19

Evaluating the real-world false-negative rate of SARS-CoV-2 PCR tests

Goal
To develop an accurate method for evaluating the real-world false-negative rate (FNR) and map the parameters affecting it
Impact
Quantifying the FNR of SARS-CoV-2 tests will allow a more accurate testing strategy and guide containment efforts of the pandemic
KI team
Collaborator(s)
Ben-Gurion University of the Negev, Maccabi Institute for Research and Innovation

Widespread and accurate testing of the severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) is essential for managing the epidemic and mitigating its clinical and financial impact. Unreliable and inaccurate testing may undermine any effort to contain the pandemic, specifically, a high rate of false-negatives may mislabel infectious individuals as safe and continue the spread of the disease.

 

We are developing a mathematical model of false-negatives derived from real-world data of SARS-CoV-2 tested individuals from the Maccabi Healthcare Services dataset. Patients are defined as either S (sick) or H (healthy) at any given time, and the window between the first positive test and the second-to-last positive test is being studied, marking negative test results within this streak as false negatives.

 

Continuous efforts are directed to evaluate test accuracy and parameters affecting false-negative results in the population to support more accurate detection of SARS-CoV-2.

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