Abstract
The Highly Pathogenic Avian Influenza H5N1 (HPAI) virus is now considered endemic in several Asian countries. In Cambodia, the virus has been circulating in the poultry population since 2004, with a dramatic effect on farmers’ livelihoods and public health. In Thailand, surveillance and control are still important to prevent any new H5N1 incursion. Risk mapping can contribute effectively to disease surveillance and control systems, but is a very challenging task in the absence of reliable disease data. In this work, we used spatial multicriteria decision analysis (MCDA) to produce risk maps for HPAI H5N1 in poultry. We aimed to i) evaluate the performance of the MCDA approach to predict areas suitable for H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for H5N1 in poultry in Cambodia. Our results showed that the expert-based model had a very high predictive capacity in Thailand (AUC = 0.97). Applied in Cambodia, MCDA mapping made it possible to identify hotspots suitable for HPAI H5N1 in the Tonlé Sap watershed, around the cities of Battambang and Kampong Cham, and along the Vietnamese border.
Highlights
The Highly Pathogenic Avian Influenza (HPAI) H5N1 virus is considered endemic in several Southeast Asian countries
Three anthropogenic risk factors which have been previously identified with greater HPAI H5N1 risk[17,18,22,25,26] were included. They are surrogates for poultry markets and areas of intensive poultry trading activities, which may increase HPAI transmission through flows of contaminated poultry or fomites. It cannot be considered as a causal factor, altitude was introduced in the model as it has been repeatedly found associated with HPAI in previous research[19], and it is a correlated with several variables involved in the transmission of HPAI
The suitability for occurrence of HPAI H5N1 in domestic poultry was displayed on a continuous scale ranging from 0 to 1 (Fig. 1)
Summary
The Highly Pathogenic Avian Influenza (HPAI) H5N1 virus is considered endemic in several Southeast Asian countries. In countries with weak primary health care systems such as Cambodia, their production can be hampered by a lack of reliable disease data In such situations, knowledge-driven modeling methods, including spatial multicriteria decision analysis (MCDA), have been identified as an alternative to classical statistical approaches[8]. We aimed to i) evaluate the performance of an MCDA approach to predict suitable areas for HPAI H5N1 based on a dataset from Thailand, comparing the predictive capacities of two sources of a priori knowledge (literature and experts), and ii) apply the best method to produce a risk map for HPAI H5N1 in poultry in Cambodia
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