Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. In this presentation, I will discuss high-performance computation techniques for the rapid detection of space-time patterns of vector-borne diseases in urban areas, with an application to Cali, Colombia. Three-dimensional visualization techniques will be presented to gain insight in the shape of these space-time patterns.
Eric M. Delmelle, is an Associate Professor of Geography at the University of North Carolina at Charlotte with experience in the development of new, robust geocomputational approaches to deepen the understanding of the dynamics of infectious and non-infectious diseases in space, time and at different scales. His current research includes (1) modeling the co-occurrence of vector-borne diseases (Dengue, Zika, Chikungunya) in developing countries; (2) evaluating the impact of residential mobility on health care access in Florida and (3) space-time variation in the concentration of contamination from private wells in rural North Carolina. His research is funded by the Centers for Disease Control and Prevention, and the North Carolina Water Resources Research Institute.
Run into any problems with the new member website? Please contact the ASPRS office at (301) 493-0290 or by email at firstname.lastname@example.org to let us know!