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Title: A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors
We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.  more » « less
Award ID(s):
1814090
PAR ID:
10342817
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume:
380
Issue:
2214
ISSN:
1364-503X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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