PandemieGrippaleV1, PascalFrancis, Corpus, bibRecord, 000012

Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach

Identifieur interne : 000012 ( PascalFrancis/Corpus ); précédent : 000011; suivant : 000013

Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach

Auteurs : Camille Pelat ; Neil M. Ferguson ; Peter J. White ; Carrie Reed ; Lyn Finelli ; Simon Cauchemez ; Christophe Fraser

Source :

American journal of epidemiology [ 0002-9262 ] ; 2014.

RBID : Pascal:15-0008688

Descripteurs français

Pascal (Inist)
- Maladie émergente, Infection, Méthodologie, Surveillance sanitaire, Homme, Taux de létalité, Estimation, Grippe, Santé publique, Monde, Statistique, Planification, Epidémiologie, Pandémie.

English descriptors

KwdEn :
- Case fatality rate, Emerging disease, Epidemiology, Estimation, Human, Infection, Influenza, Methodology, Planning, Public health, Sanitary surveillance, Statistics, World.

Abstract

In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms-the symptomatic case fatality ratio (sCFR)-is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 15-0008688 INIST
ET :	Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach
AU :	PELAT (Camille); FERGUSON (Neil M.); WHITE (Peter J.); REED (Carrie); FINELLI (Lyn); CAUCHEMEZ (Simon); FRASER (Christophe)
AF :	MRC Centre for Outbreak Analysis and Modelling and National Institute for Health Research Health Protection Research Unit in Modelling Methodology at Imperial College London, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London/London/Royaume-Uni (1 aut., 2 aut., 3 aut., 6 aut., 7 aut.); Modelling and Economics Unit, Public Health England/London/Royaume-Uni (3 aut.); Influenza Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention/Atlanta, Georgia/Etats-Unis (4 aut., 5 aut.); Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur/Paris/France (6 aut.)
DT :	Publication en série; Niveau analytique
SO :	American journal of epidemiology; ISSN 0002-9262; Coden AJEPAS; Etats-Unis; Da. 2014; Vol. 180; No. 10; Pp. 1036-1046; Bibl. 32 ref.
LA :	Anglais
EA :	In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms-the symptomatic case fatality ratio (sCFR)-is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.
CC :	002B30A01A1; 002B05C02C
FD :	Maladie émergente; Infection; Méthodologie; Surveillance sanitaire; Homme; Taux de létalité; Estimation; Grippe; Santé publique; Monde; Statistique; Planification; Epidémiologie; Pandémie
FG :	Virose
ED :	Emerging disease; Infection; Methodology; Sanitary surveillance; Human; Case fatality rate; Estimation; Influenza; Public health; World; Statistics; Planning; Epidemiology
EG :	Viral disease
SD :	Enfermedad emergente; Infección; Metodología; Vigilancia sanitaria; Hombre; Tasa de letalidad; Estimación; Gripe; Salud pública; Mundo; Estadística; Planificación; Epidemiología
LO :	INIST-663.354000502527180090
ID :	15-0008688

Links to Exploration step

Pascal:15-0008688

Le document en format XML

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<front><div type="abstract" xml:lang="en">In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms-the symptomatic case fatality ratio (sCFR)-is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.</div>
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<ET>Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach</ET>
<AU>PELAT (Camille); FERGUSON (Neil M.); WHITE (Peter J.); REED (Carrie); FINELLI (Lyn); CAUCHEMEZ (Simon); FRASER (Christophe)</AU>
<AF>MRC Centre for Outbreak Analysis and Modelling and National Institute for Health Research Health Protection Research Unit in Modelling Methodology at Imperial College London, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London/London/Royaume-Uni (1 aut., 2 aut., 3 aut., 6 aut., 7 aut.); Modelling and Economics Unit, Public Health England/London/Royaume-Uni (3 aut.); Influenza Division, National Center for Immunization and Respiratory Diseases, US Centers for Disease Control and Prevention/Atlanta, Georgia/Etats-Unis (4 aut., 5 aut.); Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur/Paris/France (6 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
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<EA>In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the probability of death after developing symptoms-the symptomatic case fatality ratio (sCFR)-is essential. Estimation of the sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally, we review the equally important issue of accuracy.</EA>
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<ED>Emerging disease; Infection; Methodology; Sanitary surveillance; Human; Case fatality rate; Estimation; Influenza; Public health; World; Statistics; Planning; Epidemiology</ED>
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Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach

Optimizing the Precision of Case Fatality Ratio Estimates Under the Surveillance Pyramid Approach

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