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Article Abstract
Avian influenza (AI) is a viral infection that profoundly affects global poultry production. This study aimed to identify the spatial and temporal factors associated with AI in Thailand, using a geographic information system (GIS)-based multi-criteria decision analysis (MCDA) approach. We discovered that high-risk areas for AI were primarily concentrated in the central and lower northern regions of the country, with fewer occurrences in the northeastern and southern regions. Model validation using historical outbreak data showed moderate agreement (AUC = 0.60, 95% CI = 0.58-0.61). This study provides valuable insights for planning national AI surveillance programs and aiding in disease prevention and control efforts. The efficiency and effectiveness of disease surveillance at the national level can be improved using this GIS-based MCDA, in conjunction with temporal risk factor analysis.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017017 | PMC |
| http://dx.doi.org/10.1155/2024/6474182 | DOI Listing |
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