No abstract

Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world's population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease's geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs.

Heat-related death is considered the number one weather-related cause of mortality throughout the world. There is growing concern that, heat waves, the primary meteorological phenomena responsible, will become more intense and numerous in the near future. Provided with this growing hazard the responsibility for mitigation, early detection and warning rests with emergency response agencies as well as academic researchers. Numerous tools exist in the present time to model very complex relationships that truly define vulnerability to such impending disasters. However, compared to other disasters (i.e. flooding, hurricanes, tornadoes, earthquakes, etc.) heatrelated effects have not been thoroughly investigated in a geospatial framework. It seems likely that such approaches will provide significant benefit to the vulnerable communities and to policy makers responsible for planning. These approaches involve the usage of multiple sensor data (multi-sensor data fusion) coupled with socioeconomic characteristics to truly capture the fabric of social vulnerability. Evidence is growing that these approaches are beginning to have an impact in forecasting and planning for heat-related health disasters.

Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.

Mortality from extreme heat is a leading cause of weather-related fatality, which is expected to increase in frequency with future climate scenarios. This study examines the spatiotemporal variations in heat-related health risk in three Midwestern cities in the United States between the years 1990 to 2010; cities include Chicago, Illinois, Indianapolis, IN, and Dayton, OH. In order to examine these variations we utilize the recently developed Extreme Heat Vulnerability Index (EHVI) that uses a principal components solution to vulnerability. The EHVI incorporates data from the U.S. Decadal Census and remotely sensed variables to determine heat-related vulnerability at an intra-urban level (census block group). The results demonstrate significant spatiotemporal variations in heat-health risk within the cities involved.