Cargando…
On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation
In this paper, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432803/ https://www.ncbi.nlm.nih.gov/pubmed/32722110 http://dx.doi.org/10.3390/ijerph17155328 |
_version_ | 1783571878530514944 |
---|---|
author | Altman, Eitan Mounir, Izza Najid, Fatim-Zahra Perlaza, Samir M. |
author_facet | Altman, Eitan Mounir, Izza Najid, Fatim-Zahra Perlaza, Samir M. |
author_sort | Altman, Eitan |
collection | PubMed |
description | In this paper, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio between the number of positive tests and the total number of tested individuals leads to dramatically high estimation errors, and thus, unadapted public health policies. The relevance of estimating the prevalence ratio using the formula presented in this work is that precision increases with the number of tests. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved with a finite number of tests, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different than one. Second, the key parameter for reducing the estimation error is the number of tests. For a large number of tests, as long as the sum of the sensitivity and specificity is different than one, the exact values of these parameters have very little impact on the estimation error. |
format | Online Article Text |
id | pubmed-7432803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74328032020-08-27 On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation Altman, Eitan Mounir, Izza Najid, Fatim-Zahra Perlaza, Samir M. Int J Environ Res Public Health Article In this paper, a formula for estimating the prevalence ratio of a disease in a population that is tested with imperfect tests is given. The formula is in terms of the fraction of positive test results and test parameters, i.e., probability of true positives (sensitivity) and the probability of true negatives (specificity). The motivation of this work arises in the context of the COVID-19 pandemic in which estimating the number of infected individuals depends on the sensitivity and specificity of the tests. In this context, it is shown that approximating the prevalence ratio by the ratio between the number of positive tests and the total number of tested individuals leads to dramatically high estimation errors, and thus, unadapted public health policies. The relevance of estimating the prevalence ratio using the formula presented in this work is that precision increases with the number of tests. Two conclusions are drawn from this work. First, in order to ensure that a reliable estimation is achieved with a finite number of tests, testing campaigns must be implemented with tests for which the sum of the sensitivity and the specificity is sufficiently different than one. Second, the key parameter for reducing the estimation error is the number of tests. For a large number of tests, as long as the sum of the sensitivity and specificity is different than one, the exact values of these parameters have very little impact on the estimation error. MDPI 2020-07-24 2020-08 /pmc/articles/PMC7432803/ /pubmed/32722110 http://dx.doi.org/10.3390/ijerph17155328 Text en © 2020 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Altman, Eitan Mounir, Izza Najid, Fatim-Zahra Perlaza, Samir M. On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title | On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title_full | On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title_fullStr | On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title_full_unstemmed | On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title_short | On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation |
title_sort | on the true number of covid-19 infections: effect of sensitivity, specificity and number of tests on prevalence ratio estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432803/ https://www.ncbi.nlm.nih.gov/pubmed/32722110 http://dx.doi.org/10.3390/ijerph17155328 |
work_keys_str_mv | AT altmaneitan onthetruenumberofcovid19infectionseffectofsensitivityspecificityandnumberoftestsonprevalenceratioestimation AT mounirizza onthetruenumberofcovid19infectionseffectofsensitivityspecificityandnumberoftestsonprevalenceratioestimation AT najidfatimzahra onthetruenumberofcovid19infectionseffectofsensitivityspecificityandnumberoftestsonprevalenceratioestimation AT perlazasamirm onthetruenumberofcovid19infectionseffectofsensitivityspecificityandnumberoftestsonprevalenceratioestimation |