COVID-19 prevalence estimation by random sampling in population - optimal sample pooling under varying assumptions about true prevalence.

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 tot

SUBMITTER: Brynildsrud O 

PROVIDER: S-EPMC7376319 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature