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Purpose: The effectiveness of passive animal health surveillance system depends on its capacity to gather sanitary information from the animal production sector. We analyzed the flow of sanitary information related to Highly Pathogenic Avian Influenza (HPAI) suspicions in poultry. Methods: Two study areas with two different production sectors were targeted: commercial broiler chicken production in Northern Vietnam and backyard native chicken production in Northern Thailand. Poultry producers and other actors of the poultry sector were randomly selected in each study area. Data related to their ways of sharing HPAI suspicion information were collected in focus groups and individual interviews. These data were used to build a network of information-sharing between these actors. Results: First, matrix logistic regression was used to assess the influence of several link attributes on the probability of information transmission. In both study areas, actors living in the same village were more likely to share information. Secondly, the influence of several poultry farming attributes on a measure of actors' centrality - Bonacich's alpha centrality - was assessed using linear regressions with permutation tests. According to the production sector, specific types of actors had better access to HPAI suspicion information. In Vietnam commercial broiler sector feed sellers, who provide veterinary support to their customers had the highest centrality. In Thailand native chicken farming sector, cock fighting practitioners were the most central in the information network. Conclusions: The results show that the structure of the network of information sharing vary according to the production sector. Relevance: This result has strong implication on the types of actors who should be targeted to enhance passive surveillance systems to improve data collection and early warning. (Texte intégral)

International audience; 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.

The implementation of surveillance programs has to face many obstacles regarding low participation of the livestock breeders' communities and the stakes associated with livestock's health. In Thailand, fighting cocks were perceived as "high risk" for disease spreading and have specifically been targeted by HPAI H5N1 surveillance and control measures. However, the implementation of HPAI surveillance programs in the cockfighting community encountered many difficulties, due to specific socioeconomic and cultural issues which diverged from other poultry productions. We provide in this communication a deeper understanding of these issues and stakes, and highlight the gaps between logics and rationales underlying surveillance and those associated with cockfighting activitities.

International audience

Resources for research, surveillance, control and others public health activities are limited and it is difficult to compare the importance of diseases, which vary in terms of occurrence, impacts, etc. So, in this context of scarce resources and multiple competing priorities, it is necessary to allocate rationally human and financial resources on relevant health priorities. Prioritization is an objective tool to make the best use of limited human and financial resources for funders of research and for organizations in charge of diseases' surveillance and implementation of disease control. In order to develop an efficient method for prioritization of zoonotic diseases in South East Asia, we performed a literature review on the different methods already developed to rank diseases. Several priority setting procedures have been used and described by various organizations (national, regional or international) and technical institutions with different models and goals. Mainly, qualitative and semi-quantitative approaches are used, in which experts are asked to score some criteria against which diseases are prioritized. Few initiatives for quantitative models have been undergone yet, mainly in the field of the food-borne diseases. Whatever the approach used to perform the prioritization exercise, some limitations to the current developed models arise from the analysis of scientific articles and organization reports. Following the identification of weak points in the methods already applied, we discuss about the potential means that can be used to improve current models or to develop innovative tools for prioritization of zoonoses in the specific context of South East Asia.