tetano
Editor, Senior Moderator
Sci Rep
. 2021 Mar 9;11(1):5491.
doi: 10.1038/s41598-021-84173-1.
Predictive values, uncertainty, and interpretation of serology tests for the novel coronavirus
Naomi C Brownstein[SUP] 1 [/SUP], Yian Ann Chen[SUP] 2 [/SUP]
Affiliations
Abstract
Antibodies testing in the coronavirus era is frequently promoted, but the underlying statistics behind their validation has come under more scrutiny in recent weeks. We provide calculations, interpretations, and plots of positive and negative predictive values under a variety of scenarios. Prevalence, sensitivity, and specificity are estimated within ranges of values from researchers and antibodies manufacturers. Illustrative examples are highlighted, and interactive plots are provided in the Supplementary Information. Implications are discussed for society overall and across diverse locations with different levels of disease burden. Specifically, the proportion of positive serology tests that are false can differ drastically from up to 3%-88% for people from different places with different proportions of infected people in the populations while the false negative rate is typically under 10%.
. 2021 Mar 9;11(1):5491.
doi: 10.1038/s41598-021-84173-1.
Predictive values, uncertainty, and interpretation of serology tests for the novel coronavirus
Naomi C Brownstein[SUP] 1 [/SUP], Yian Ann Chen[SUP] 2 [/SUP]
Affiliations
- PMID: 33750810
- DOI: 10.1038/s41598-021-84173-1
Abstract
Antibodies testing in the coronavirus era is frequently promoted, but the underlying statistics behind their validation has come under more scrutiny in recent weeks. We provide calculations, interpretations, and plots of positive and negative predictive values under a variety of scenarios. Prevalence, sensitivity, and specificity are estimated within ranges of values from researchers and antibodies manufacturers. Illustrative examples are highlighted, and interactive plots are provided in the Supplementary Information. Implications are discussed for society overall and across diverse locations with different levels of disease burden. Specifically, the proportion of positive serology tests that are false can differ drastically from up to 3%-88% for people from different places with different proportions of infected people in the populations while the false negative rate is typically under 10%.