CovidFranceV1, Main, Exploration, bibRecord, 000A20

On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation.

Identifieur interne : 000A20 ( Main/Exploration ); précédent : 000A19; suivant : 000A21

On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation.

Auteurs : Eitan Altman [France] ; Izza Mounir [France] ; Fatim-Zahra Najid [France] ; Samir M. Perlaza [France]

Source :

International journal of environmental research and public health [ 1660-4601 ] ; 2020.

RBID : pubmed:32722110

Descripteurs français

KwdFr :
- Betacoronavirus (isolement et purification), Humains (MeSH), Infections à coronavirus (virologie), Infections à coronavirus (épidémiologie), Pandémies (MeSH), Pneumopathie virale (virologie), Pneumopathie virale (épidémiologie), Probabilité (MeSH), Prévalence (MeSH), Sensibilité et spécificité (MeSH).
MESH :
- isolement et purification : Betacoronavirus.
- virologie : Infections à coronavirus, Pneumopathie virale.
- épidémiologie : Infections à coronavirus, Pneumopathie virale.
- Humains, Pandémies, Probabilité, Prévalence, Sensibilité et spécificité.

English descriptors

KwdEn :
- Betacoronavirus (isolation & purification), Coronavirus Infections (epidemiology), Coronavirus Infections (virology), Humans (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (virology), Prevalence (MeSH), Probability (MeSH), Sensitivity and Specificity (MeSH).
MESH :
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- isolation & purification : Betacoronavirus.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Humans, Pandemics, Prevalence, Probability, Sensitivity and Specificity.

Abstract

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.

DOI: 10.3390/ijerph17155328
PubMed: 32722110
PubMed Central: PMC7432803

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Main, to step Corpus: 000815
to stream Main, to step Curation: 000815

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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<Abstract><AbstractText>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.</AbstractText>
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On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation.

On the True Number of COVID-19 Infections: Effect of Sensitivity, Specificity and Number of Tests on Prevalence Ratio Estimation.

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