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.
