GIS Modeling for Avian Influenza Risk Areas

  • Mongkolsawat, C. and Kamchai, T T


Given the first outbreak of highly pathogenic avian influenza (HPAI) in Thailand in 2004, the zoning of HPAI disease is needed for effective prevention and control and also to reduce the socio-economic impact of the outbreak. The purpose of this study is therefore to establish a model for predicting the areas at risk of the avian flu using the integrated variables or themes involved. The risk areas can be used for surveillance of avian flu outbreaks and eradication. The methodology included an analysis of affected theme layers, the overlay processing of the themes and the assessment of the result. The theme layers are: a distance from the former outbreak communities (C), density of poultry population (D), a distance from the poultry farms (F), land use type (L), and a distance from the market or the slaughterhouse or the cockpit (P). Khon Kaen province was selected as the study area, which covers an area of about 10,886 and is located in the Northeastern part of Thailand. Each of the above theme layers mentioned with its associated attribute data were digitally performed in GIS database to eventually create five thematic layers. Simultaneous overlay operation on these layers with the defined model produces a resultant polygonal layer, each of which is a mapping unit with the risk area class. The process involved the formulation and tested the model followed by the iteration of the model to the geo-referenced information. We used the model (Risk Area = CDFLP) with 5-factor ratings and selected the best choice of the ratings for each variable and overall value. These are classified into 4 classes of high, moderate, low and very low risk areas. The study indicates that the high, moderate, low and very low risk areas cover an area of about 1.60, 26.31, 71.88 and 0.21 % of the entire province area, respectively. The reliability of the result was also tested to validate the defined model. The established information has been stored in GIS and can be rapidly used for future analysis.