A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.
Philos Trans A Math Phys Eng Sci
; 380(2214): 20210121, 2022 Jan 10.
Article
in English
| MEDLINE | ID: covidwho-2250742
ABSTRACT
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'.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19 Testing
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Systematic review/Meta Analysis
Limits:
Humans
Language:
English
Journal:
Philos Trans A Math Phys Eng Sci
Journal subject:
Biophysics
/
Biomedical Engineering
Year:
2022
Document Type:
Article
Affiliation country:
Rsta.2021.0121
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