A simple correction for COVID-19 sampling bias.
J Theor Biol
; 512: 110556, 2021 03 07.
Article
in English
| MEDLINE | ID: covidwho-1002842
ABSTRACT
COVID-19 testing has become a standard approach for estimating prevalence which then assist in public health decision making to contain and mitigate the spread of the disease. The sampling designs used are often biased in that they do not reflect the true underlying populations. For instance, individuals with strong symptoms are more likely to be tested than those with no symptoms. This results in biased estimates of prevalence (too high). Typical post-sampling corrections are not always possible. Here we present a simple bias correction methodology derived and adapted from a correction for publication bias in meta analysis studies. The methodology is general enough to allow a wide variety of customization making it more useful in practice. Implementation is easily done using already collected information. Via a simulation and two real datasets, we show that the bias corrections can provide dramatic reductions in estimation error.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Computer Simulation
/
SARS-CoV-2
/
COVID-19
/
Models, Biological
Type of study:
Observational study
/
Reviews
Limits:
Humans
Language:
English
Journal:
J Theor Biol
Year:
2021
Document Type:
Article
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