COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence.
BMC Med Res Methodol
; 20(1): 196, 2020 07 23.
Article
in English
| MEDLINE | ID: covidwho-685422
ABSTRACT
BACKGROUND:
The number of confirmed COVID-19 cases divided by population size is used as a coarse measurement for the burden of disease in a population. However, this fraction depends heavily on the sampling intensity and the various test criteria used in different jurisdictions, and many sources indicate that a large fraction of cases tend to go undetected.METHODS:
Estimates of the true prevalence of COVID-19 in a population can be made by random sampling and pooling of RT-PCR tests. Here I use simulations to explore how experiment sample size and degrees of sample pooling impact precision of prevalence estimates and potential for minimizing the total number of tests required to get individual-level diagnostic results.RESULTS:
Sample pooling can greatly reduce the total number of tests required for prevalence estimation. In low-prevalence populations, it is theoretically possible to pool hundreds of samples with only marginal loss of precision. Even when the true prevalence is as high as 10% it can be appropriate to pool up to 15 samples. Sample pooling can be particularly beneficial when the test has imperfect specificity by providing more accurate estimates of the prevalence than an equal number of individual-level tests.CONCLUSION:
Sample pooling should be considered in COVID-19 prevalence estimation efforts.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Specimen Handling
/
Population Surveillance
/
Coronavirus Infections
/
Diagnostic Tests, Routine
/
Betacoronavirus
Type of study:
Diagnostic study
/
Observational study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
BMC Med Res Methodol
Journal subject:
Medicine
Year:
2020
Document Type:
Article
Affiliation country:
S12874-020-01081-0
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