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
Year: 2020
Type: preprint
Abstract: I estimate plausible true positive (TP) rates for the number of COVID-19 tests per day, most relevant when the number of test is on the same order of magnitude as number of infected persons. I then modify a standard SEIR model to model current growth patterns and detection rates in South Korea and New York state. Although reducing transmission rates have the largest impact, increasing TP rates by ∼10% in New York can have an impact equal to adding tens of thousands of new tests per day. Increasing both TP rates and tests per day together can have significant impacts and likely be more easily sustained than social distancing restrictions. Systematic and standardized data collection, even beyond contact tracking, should be ongoing and quickly made available for research teams to maximize the efficacy of testing.
Source: medRxiv
Author: Paul Rodriguez
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3014543735