Ecology-Based Risk Assessment and Early Warning for HPAI in Asia

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DESCRIPTION (provided by applicant): Since late 2003 several East and Southeast Asia countries have experienced large outbreaks of Highly Pathogenic Avian Influenza (HPAI, subtype H5N1), or bird flu. By April 6, 2006, HPAI outbreaks occurred in 45 countries, 3 continents, with a total of 192 human cases (109 deaths). Widespread circulation of the avian flu virus also increases the chances of mutation into a form that could pass from human to human, which could result in a new human flu pandemic of unknown magnitude. At present, the efforts in risk assessment early warning of HPAI are to large degree hampered by (i) the lack of comprehensive understanding of the ecology of avian influenza, particularly in relation to agricultural systems, poultry, wild birds, biophysical and biochemical environments, and (ii) the facts that geospatial datasets used for risk assessment and early warning are often out-of-date and at coarse spatial (e.g., national or provincial) and temporal (e.g., at annual) resolutions. This interdisciplinary and international project combines epidemiology, ornithology, agriculture and environmental remote sensing. Its overall goal is threefold: (a) to better understand the ecology of HPAI in Asia; (b) to develop a data-model integration system that could identify "hot spots" (location-varying risk) and "hot times" (time-varying risk) of HPAI in Asia, and (c) to provide near-real-time ecology-based risk assessment and early warning of HPAI in 2007-2009 for Asia. Specifically, it will (1) provide updated and improved geospatial datasets of HPAI-relevant ecological factors, including migration flyways and timing of wild waterfowls, agricultural systems (cropping intensity, crop calendar, and irrigation), seasonality of wetlands, and biophysical variables (e.g., land surface temperature) at moderate spatial resolution (500-m to 1-km); (2) quantify the relationships between HPAI and ecological variables; (3) develop a GIS-based epidemiological model; and (4) develop an internet-based geospatial web system (primarily through linking GoogleEarth with research-oriented websites, e.g., http://remotesensing.unh.edu') for distributed mapping and timely distribution of HPAI-relevant information. This project employs remote sensing and GIS/GPS, in-situ observations, epidemiological models, and internet technology. This interdisciplinary project will make significant contribution to the ecology of infectious diseases and public health. The resultant dynamic maps of "hot spots" and "hot times" of HPAI risk regions, when distributed in a timely fashion, will aid the public, researchers, business, and decision makers in preparedness of potential HPAI pandemic crisis. This project will also provide interdisciplinary education for next generation scientists (both graduate and undergraduate students) in epidemiology, ornithology, public health and Earth system science.