My research focuses on answering fundamental epidemiological questions where spatial and spatio-temporal methodology are critical. I (along with students and other colleagues) am interested in developing new, robust geocomputational methodologies that deepen our understanding on the dynamics of infectious and non-infectious diseases. I am concentrating efforts in the development of new visualization techniques to detect space-time patterns at different scales (e.g. clusters), and leverage on state-of-the art computational techniques (e.g. cyberGIS, parallel computing) for unusually large datasets. I am interested in students with strong computational, visualization skills and interest in health geography.
1. Mapping Dengue Fever Outbreaks in Space and Time
We conduct the Space-Time Kernel Density Estimation (STKDE) in a parallel computational framework to map space-time clusters of dengue fever in an urban environment of Colombia. We extract intricate disease dynamics in an interactive manner, using a powerful 3D environment. Findings from our work can help better understand the dynamics of a quickly spreading disease.
2. Parallel Spatial Computing Solution for Space-Time Algorithms
Spatial and Spatio-temporal algorithms are computationally intensive. For instance, to evaluate uncertainty in clustering tests, one must conduct a large number of Monte-Carlo simulations. We are interested in developing robust computational methods to facilitate these simulations.
3. Impact of space-time uncertainties on the detection of dengue fever outbreaks
We evaluate the impact of positional and temporal inaccuracies on the mapping and detection of potential outbreaks of dengue fever. To test the robustness of disease intensities in space and time when accounting for the potential space-time error, each dengue case is perturbed using Monte-Carlo simulations. A space-time kernel density estimation (STKDE) is conducted on both perturbed and observed sets of dengue cases to extract, map and compare the intensity of disease outbreaks
4. Prediction of Dengue Fever Outbreaks in Urban Environments of Colombia
We develop predictive models of dengue incidence rate, based on relevant local weather and regional climate parameters. We analyze time series of epidemiological and meteorological data for the dynamic urban environment of Cali, Colombia from January 2000 through December 2011. We develop environment-based multivariate auto-regressive forecast models that allow dengue outbreaks to be anticipated from 2 weeks to 6 months in advance. Our results have the potential to enhance existing dengue early warning systems, which can ultimately be used to support public health decisions regarding the timing and scale of vector control efforts.
5. On-line Tools for the Monitoring of Dengue Fever Outbreaks
One important component of research within the Center for Applied GIS is the development of tools and techniques available for different communities. This is critical to disseminate findings among different communities, and foster knowledge discovery. We have developed an interactive online GIS application with the objective to monitor dengue fever outbreaks at different scales. Our on-line toolkit allows to 1) deploy and share epidemiological data information at the individual point level, 2) conduct temporal and spatial queries on dengue fever events, 3) generate spatial distribution maps in a raster format across a region within a reasonable computational time to better determine the occurrence of hot spots and 4) visualize space-time connection among disease events locally.
RECENTLY COMPLETED PROJECTS
Modeling Travel Impedance to Medical Care for Children with Birth Defects Using Geographic Information Systems
Children with birth defects face significant geographic barriers accessing medical care and specialized services. Using a Geographic Information Systems (GIS)-based approach, we estimate travel time and distance to access medical care for children born with birth defects in Florida (e.g spina bifida). Infants with birth defects living in rural areas in Florida experienced greater travel times compared with those living in urban areas. GIS methods are important in evaluating accessibility and geographic barriers to care and could be used among children with special health care needs.
Assessing socioeconomic vulnerability to dengue fever: statistical vs. expert-based modeling
We develop a methodology for the spatial assessment of current socioeconomic vulnerabilities to dengue fever in Cali, Colombia. We develop a spatial approach for both expert-based and purely statistical-based modeling of current vulnerability levels across neighborhoods of the city using a GIS. The results of both approaches are comparatively evaluated by means of spatial statistics. A web-based approach is proposed to facilitate the dissemination of the output vulnerability index to the community.
I also maintain an active agenda in the field of spatial optimization. My research tries to answer two critical questions, namely (1) where to locate additional public facilities (e.g. schools, hospitals) to a set of existing facilities, and (2) which existing facilities should be removed to minimize overlapping service redundancy such as transit bus stops for instance. Common to these research questions is the challenge to find ways of effectively using existing information on facility locations to guide the addition/deletion of facilities with the underlying motivation of creating more efficiency in location decisions. Solving these complex problems involves the development of new solution techniques integrated with GIS; forming a direct contribution to the field of spatial optimization. Some unique contributions to the field of geography and GIScience which appeared in top tier journals include:
1. Exploiting the strength of GIS and constructing innovative visualization approaches to support 3D location modeling
2. Optimal space-time location of public schools while considering closest assignments, capacities and budget constraints.