Zero-inflated models for identifying disease risk factors when case detection is imperfect: Application to highly pathogenic avian influenza H5N1 in Thailand

Timothée Vergne (), Mathilde Paul (), Wanida Chaengprachak, Benoit Durand (), Marius Gilbert, Barbara Dufour, François Roger (), Suwicha Kasemsuwan and Vladimir Grosbois ()
Additional contact information
Timothée Vergne: UPR AGIRs - Animal et gestion intégrée des risques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Laboratoire de santé animale, sites de Maisons-Alfort et de Normandie - ANSES - Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail, Royal Veterinary College [London] - University of London [London], Veterinary Epidemiology, Economics and Public Health Group - Royal Veterinary College
Mathilde Paul: UPR AGIRs - Animal et gestion intégrée des risques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, IHAP - Interactions hôtes-agents pathogènes [Toulouse] - INRA - Institut National de la Recherche Agronomique - ENVT - Ecole Nationale Vétérinaire de Toulouse - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse
Wanida Chaengprachak: DLD - Department of Livestock Development
Benoit Durand: Laboratoire de santé animale, sites de Maisons-Alfort et de Normandie - ANSES - Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail
Marius Gilbert: ULB - Université libre de Bruxelles, FNRS - Fonds National de la Recherche Scientifique [Bruxelles]
Barbara Dufour: ENVA - École nationale vétérinaire d'Alfort
François Roger: UPR AGIRs - Animal et gestion intégrée des risques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Cirad-ES - Département Environnements et Sociétés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Suwicha Kasemsuwan: Faculty of Veterinary Medicine [Kasetsart University, Thaïlande] - KU - Kasetsart University [Bangkok, Thailand] - Partenaires IRSTEA - IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture
Vladimir Grosbois: UPR AGIRs - Animal et gestion intégrée des risques - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Cirad-ES - Département Environnements et Sociétés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement

Post-Print from HAL

Abstract: Logistic regression models integrating disease presence/absence data are widely used to identify risk factors for a given disease. However, when data arise from imperfect surveillance systems, the interpretation of results is confusing since explanatory variables can be related either to the occurrence of the disease or to the efficiency of the surveillance system. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP) regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1 outbreaks that were reported at subdistrict level in Thailand during the second epidemic wave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effectively than its non-spatial version. This model clarified the role of the different variables: for example, results suggested that human population density was not associated with the disease occurrence but was rather associated with the number of reported outbreaks given disease occurrence. In addition, these models allowed estimating that 902 (95% CI 881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only 779 were reported to veterinary authorities, leading to a general surveillance sensitivity of 86.4% (95% Cl 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spatial distribution of the probability that a subdistrict could have been a false negative. The methodology presented here can easily be adapted to other animal health contexts.

Keywords: Zero-inflation; Under-detection; Risk factors; Evaluation; Avian influenza H5N1; Bias; Count; Spatial; Conditional autoregressive model; Capture-recapture; Surveillance (search for similar items in EconPapers)
Date: 2014-04-01
Note: View the original document on HAL open archive server: https://hal.inrae.fr/hal-02630698v1
References: View complete reference list from CitEc
Citations:

Published in Preventive Veterinary Medicine, 2014, 114 (1), pp.28-36. ⟨10.1016/j.prevetmed.2014.01.011⟩

Downloads: (external link)
https://hal.inrae.fr/hal-02630698v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02630698

DOI: 10.1016/j.prevetmed.2014.01.011

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().