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Physical and Environmental Geography Commons™
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Articles 1 - 2 of 2
Full-Text Articles in Physical and Environmental Geography
Integrating Malaria Surveillance With Climate Data For Outbreak Detection And Forecasting: The Epidemia System, Christopher L. Merkord, Yi Liu, Abere Mihretie, Teklehaymanot Gebrehiwot, Worku Awoke, Estifanos Bayabil, Geoffrey M. Henebry, Gebeyaw T. Kassa, Mastewal Lake, Michael C. Wimberly
Integrating Malaria Surveillance With Climate Data For Outbreak Detection And Forecasting: The Epidemia System, Christopher L. Merkord, Yi Liu, Abere Mihretie, Teklehaymanot Gebrehiwot, Worku Awoke, Estifanos Bayabil, Geoffrey M. Henebry, Gebeyaw T. Kassa, Mastewal Lake, Michael C. Wimberly
GSCE Faculty Publications
Background: Early indication of an emerging malaria epidemic can provide an opportunity for proactive interventions. Challenges to the identification of nascent malaria epidemics include obtaining recent epidemiological surveillance data, spatially and temporally harmonizing this information with timely data on environmental precursors, applying models for early detection and early warning, and communicating results to public health officials. Automated web-based informatics systems can provide a solution to these problems, but their implementation in real-world settings has been limited.
Methods: The Epidemic Prognosis Incorporating Disease and Environmental Monitoring for Integrated Assessment (EPIDEMIA) computer system was designed and implemented to integrate disease surveillance with …
Multi-Year Modis Active Fire Type Classification Over The Brazilian Tropical Moist Forest Biome, David P. Roy, S. S. Kumar
Multi-Year Modis Active Fire Type Classification Over The Brazilian Tropical Moist Forest Biome, David P. Roy, S. S. Kumar
GSCE Faculty Publications
The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity …