Author: Paul F Rodriguez
Title: Predicting Whom to Test is More Important Than More Tests - Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation Document date: 2020_4_6
ID: 06vc2y9y_1
Snippet: As the COVID-19 virus spreads there are wide calls for increased testing to identify cases and slow the spread. Colburn (2020) has pointed out that we need to model the effects of testing, contact tracking, isolation, and related interventions in order to better use resources. Currently, (March 28 th , 2020), South Korea seems to have successfully contained the spread using a variety of strategies, including wide-spread testing and tracking. Howe.....
Document: As the COVID-19 virus spreads there are wide calls for increased testing to identify cases and slow the spread. Colburn (2020) has pointed out that we need to model the effects of testing, contact tracking, isolation, and related interventions in order to better use resources. Currently, (March 28 th , 2020), South Korea seems to have successfully contained the spread using a variety of strategies, including wide-spread testing and tracking. However, unless the number of tests approaches the size of the population, or the size of all possibly infected persons, there will be some criteria for choosing whom to test. Choosing whom to test is a decision problem for which predicting who is pre-symptomatic would be most beneficial. How beneficial depends on the true positive rate (i.e. ratio of true positives to sum of true positives and false negatives). But the difficulty in modeling the true positive (TP) rate of testing and related strategies is that the number of false negatives (misses) are unknown.
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