Quantitative assessment of a spatial multicriteria model for highly pathogenic avian influenza H5N1 in Thailand, and application in Cambodia

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.

Influenza A H5N1 and H7N9 in China: A spatial risk analysis

“…6. Limitations inherent in this risk assessment methods are similar to those described for MCDA [32,82], which include (i) overweighing variables from correlations between different environmental variables, and correlations between environmental and population variables, and (ii) subjectivity in the selection of variables, formulation of risk equation and selection of fuzzy membership functions and parameters.…”

Risk Mapping of Influenza D Virus Occurrence in Ruminants and Swine in Togo Using a Spatial Multicriteria Decision Analysis Approach

“…The validation of knowledge-driven models like MCDA is very challenging because of the absence of complete epidemiological data, which in itself is often a driving factor to why the MCDA approach is used in the first place. Despite this challenge, spatial MCDA has been validated in several studies in different countries and on different diseases (for example avian influenza in Asia and African swine fever and Rift valley fever in Africa), underlining the benefit of using this method for risk-based surveillance [22,23,[34][35][36]. In our study, the visual comparison of risk maps with serological results from the field showed that most of the sampled areas with negative results were at low risk of IDV occurrence whereas the positive samples were in high and low risk areas of the maps.…”

Spatial Multicriteria Evaluation for Mapping the Risk of Occurrence of Peste des Petits Ruminants in Eastern Africa and the Union of the Comoros

“…In conclusion, the knowledge-driven approach proposed in this work to map the areas suitable for PPR occurrence and spread in Eastern Africa and Union of the Comoros provide valuable tools for several purposes: (i) to integrate the available knowledge about the disease; (ii) to provide suitability maps at regional scale using free geographic data. Applied in different geographical and epidemiological contexts (33,34,36,59,60), such an approach allows straightforward and easy updating of maps for users by including more precise geographic data, newly described risk factors, by modifying the weights of each factor, or by comparing scenarios of transmission. Another perspective of this work deals with the combination of the produced PPR suitability maps with modeling approaches accounting for temporal variability and transmission processes (28), in order to provide recommendations for vaccination strategies.…”