Zero-inflated models for identifying disease risk factors whencase detection is imperfect: Application to highly pathogenicavian influenza H5N1 in Thailand

Vergne Timothée, Paul Mathilde, Chaengprachak Wanida, Durand Benoit, Gilbert Marius, Dufour Barbara, Roger François, Kasemsuwan Suwicha, Grosbois Vladimir. 2014. Zero-inflated models for identifying disease risk factors whencase detection is imperfect: Application to highly pathogenicavian influenza H5N1 in Thailand. Preventive Veterinary Medicine, 114 (1) : pp. 28-36.

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Quartile : Q1, Sujet : VETERINARY SCIENCES

Abstract : Logistic regression models integrating disease presence/absence data are widely used toidentify risk factors for a given disease. However, when data arise from imperfect surveil-lance systems, the interpretation of results is confusing since explanatory variables canbe related either to the occurrence of the disease or to the efficiency of the surveillancesystem. As an alternative, we present spatial and non-spatial zero-inflated Poisson (ZIP)regressions for modelling the number of highly pathogenic avian influenza (HPAI) H5N1outbreaks that were reported at subdistrict level in Thailand during the second epidemicwave (July 3rd 2004 to May 5th 2005). The spatial ZIP model fitted the data more effec-tively 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 withthe disease occurrence but was rather associated with the number of reported outbreaksgiven disease occurrence. In addition, these models allowed estimating that 902 (95% CI881-922) subdistricts suffered at least one HPAI H5N1 outbreak in Thailand although only779 were reported to veterinary authorities, leading to a general surveillance sensitivityof 86.4% (95% CI 84.5-88.4). Finally, the outputs of the spatial ZIP model revealed the spa-tial distribution of the probability that a subdistrict could have been a false negative. Themethodology presented here can easily be adapted to other animal health contexts. (Résumé d'auteur)

Mots-clés Agrovoc : Influenzavirus aviaire, Surveillance épidémiologique, Modèle de simulation, Modèle mathématique, Méthode statistique, Évaluation du risque, Facteur de risque, Distribution spatiale, Grippe aviaire

Mots-clés géographiques Agrovoc : Thaïlande

Classification Agris : L73 - Animal diseases
U10 - Computer science, mathematics and statistics

Champ stratégique Cirad : Axe 4 (2014-2018) - Santé des animaux et des plantes

Auteurs et affiliations

  • Vergne Timothée, Agence de Sécurité Sanitaire (FRA)
  • Paul Mathilde, Université de Toulouse (FRA)
  • Chaengprachak Wanida, Department of Livestock Development (THA)
  • Durand Benoit, ANSES (FRA)
  • Gilbert Marius, ULB (BEL)
  • Dufour Barbara, ENVA (FRA)
  • Roger François, CIRAD-ES-UPR AGIRs (FRA) ORCID: 0000-0002-1573-6833
  • Kasemsuwan Suwicha, Kasetsart University (THA)
  • Grosbois Vladimir, CIRAD-ES-UPR AGIRs (FRA)

Source : Cirad - Agritrop (

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