Geographic information systems applied to the international surveillance and control of transboundary animal diseases, a focus on highly pathogenic avian influenza
- PMID: 20422520
Geographic information systems applied to the international surveillance and control of transboundary animal diseases, a focus on highly pathogenic avian influenza
Abstract
To respond to the lack of early warning in dealing with livestock diseases, the Food and Agriculture Organization (FAO) developed and launched the Emergency Prevention System for Transboundary Animal and Plant Pests and Diseases (EMPRES) programme in 1994. Emphasis was placed on the prevention of emergencies due to transboundary epidemic diseases of livestock of significant economic, trade and/or food security importance. EMPRES early warning activities, mainly based on disease surveillance, reporting and epidemiological analysis are supported by the EMPRES-i information system which enables integration, analysis and sharing of animal health data, combined with relevant layers of information, such as socio-economic, production and climatic data. Indeed, data integration, analysis and mapping represent a key step towards a better understanding of the distribution and behaviour, source and evolution of a disease (or infection) for the definition of appropriate cost-effective disease control strategies. With the emergence of highly pathogenic avian influenza (HPAI) H5N1 in South-East Asia and its rapid spread beyond its known original distribution range, through its EMPRES programme the FAO has invested time and resources in the implementation of several studies to reveal HPAI epidemiological features in specific ecosystems of Asia and advise member countries accordingly on the best disease control options. Some of the key findings are presented in this paper and illustrate the incredible potential of using geographic information systems as part of international early warning systems and their multiple applications in the surveillance and control of infectious diseases, such as HPAI.
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