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Transbound Emerg Dis
. 2024 Sep 13:2024:6474182.
doi: 10.1155/2024/6474182. eCollection 2024. Geospatial and Temporal Analysis of Avian Influenza Risk in Thailand: A GIS-Based Multi-Criteria Decision Analysis Approach for Enhanced Surveillance and Control
Waratida Sangrat 1 2 , Weerapong Thanapongtharm 1 , Suwicha Kasemsuwan 2 , Visanu Boonyawiwat 2 , Somchai Sajapitak 2 , Chaithep Poolkhet 3
Affiliations
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.
Keywords: Thailand; highly pathogenic avian influenza; risk; spatial; temporal.
. 2024 Sep 13:2024:6474182.
doi: 10.1155/2024/6474182. eCollection 2024. Geospatial and Temporal Analysis of Avian Influenza Risk in Thailand: A GIS-Based Multi-Criteria Decision Analysis Approach for Enhanced Surveillance and Control
Waratida Sangrat 1 2 , Weerapong Thanapongtharm 1 , Suwicha Kasemsuwan 2 , Visanu Boonyawiwat 2 , Somchai Sajapitak 2 , Chaithep Poolkhet 3
Affiliations
- PMID: 40303130
- PMCID: PMC12017017
- DOI: 10.1155/2024/6474182
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.
Keywords: Thailand; highly pathogenic avian influenza; risk; spatial; temporal.