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. 2022 Nov 24;10(12):2000. doi: 10.3390/vaccines10122000

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© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Consequences of correcting seroprevalence estimates using biased estimates of sensitivity (Sn.) and specificity (Sp.): simulation-based analysis. Serological assay with (a) high: a true Sn. at 95.0% and a true Sp. at 99.0%, (b) good: a true Sn. at 90.0% and a true Sp. at 97.0%, (c) and moderate performance: a true Sn. at 80.0% and a true Sp. at 87.0%. Note: The dot–dash lines provide an interval which indicates the seroprevalence adjusted for the mis-specified assay performance at a given error level was still within ±5% deviation of the true seroprevalence. The prevalence adjustment was performed using the formula by Sempos and Tian [20]. Notice that an assay with underestimated Sn. and Sp. is unable to provide prevalence estimates after adjustment at a low prevalence setting: (a) 5.3% and (b) 5.6%. An assay with overestimated Sn. and Sp. tends to inflate seroprevalence after adjustment when the true prevalence is low: (b) 3.0%.