GIScience Workshop Description

Challenges in Modeling the Spatial and Temporal Dimensions of the Ecology of Infectious Diseases

We organized a workshop, entitled "Challenges in Modeling the Spatial and Temporal Dimensions of the Ecology of Infectious Diseases", which aimed to link researchers in the fields of disease ecology and geographic information science, and geography. The workshop highlighted issues prevalent in modeling disease dynamics, including scale and uncertainty in spatial and temporal data. The NSF-funded workshop was held immediately preceding the GIScience 2012 conference (http://www.giscience.org) on September 18, 2012 in Columbus, OH.

Workshop Description

The purpose of the workshop was to engage researchers from the GIScience and geography communities with NSF EEID program. GIScience (geographic information science) is broad field of study that encompasses disciplines where geographic information is used in models for social and environmental phenomena. It is our understanding that the majority of the researchers in GIScience and geography have not yet been involved in much of the EEID work, but their expertise will be valuable to make significant contributions. Through an informal survey among a number of active researchers in the areas of GIScience and medical geography, we came to believe that many in these communities are still unaware of the EEID program. Our preliminary survey also indicated a strong interest in these communities to attend a workshop that would introduce the EEID program and provide opportunity to learn about current EEID projects.
 
We set the overall theme of this workshop, challenges in modeling the spatial and temporal dimension of the ecology of infectious diseases, around questions that are commonly addressed by researchers from both fields of EEID and GIScience. Space and time are fundamental components in almost all models for understanding patterns of infectious disease transmission and underlying drivers in different environments. Generally, researchers face many challenges when they model infectious disease transmission. In the disease modeling literature (see Anderson and May 1991, Keeling and Rohani 2008), a variety of modeling strategies have been developed (e.g., partial differential equation-based approaches such as various versions of SIR models, agent-based models, and network models); these strategies typically require a specific representation of space and time that may lead to different results. It is also well known that issues such as scale, study area extent, and uncertainty in spatial and temporal data are critical role in disease modeling (Pitzer et al. 2009, Clark and Bjørnstad 2004, Riley 2007).
 
While these challenges are common in perhaps all models, they are closely related to the core research areas of many GIScience and geography researchers who would also attend the GIScience 2012 conference held immediately after the day of the workshop. Public health and infectious diseases have become a recent research focus in GIScience and geography. An example is the successful Symposium on Space-Time Integration in Geography and GIScience that has attracted a large number of authors and participants at the 2011 Annual Meeting of the Association of American Geographers in Seattle, WA, with many of the participants contributing papers on topics in public health and spatial epidemiology. In the literature, significant advances have been made by geographers and GIScience researchers in areas of representing space and time and modeling infectious diseases, touching critical issues such as disease mapping and analysis (Cromley and McLafferty 2011), modeling individual movements (Hornsby and Egenhofer 2000), spatial-temporal pattern of infectious diseases (Carrel et al. 2009), impact of neighborhood on infectious diseases (Root, forthcoming), spatial databases (Linard and Tatem 2012). The workshop provided an intellectual and stimulating environment for GIScience researchers to learn about the state of the art in disease modeling and to discuss their own contributions to EEID projects.
 

References cited

Anderson, R.M., & May, R.M. 1991. Infectious Diseases of Humans: Dynamics and Control. Oxford, UK: Oxford University Press.
 
Carrel, M., Emch, M., Streatfield, P.K., & Yunus, M. (2009) Spatio-temporal Clustering ofCholera: The Impact of Flood Control in Matlab, Bangladesh, 1983-2003. Health & Place 15(3): 741-752.
 
Clark, J.S. and Bjornstad, O.N. 2004. Population time series - Process variability, observation errors, missing values, lags, and hidden states. Ecology 85: 3140-3150.
 
Cromley, EK and McLafferty, SL. 2011. GIS and Public Health. New York: The Guilford Press.
 
Hornsby, K. and Egenhofer, M.J.. 2000. Identity-based change: a foundation for spatio-temporalknowledge representation. International Journal of Geographical Information Science 14(3):207-224
 
Keeling, M.J. and Rohani, P. 2008. Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.
 
Linard, C. and Tatem, A.J., 2012, Large-scale spatial population databases in infectious disease research, International Journal of Health Geographics, 11, 7.
 
Pitzer, V.E, Viboud, C., Simonsen, L., Steiner, C., Panozzo, C.A., Alonso, W.J., Miller, M.A., Glass, R.I., Glasser, J.W., Parashar, U.D., and Grenfell, B.T. 2009. Demographic variability, vaccination, and the spatiotemporal dynamics of rotavirus epidemics. Science. 325 (5938), 290-294. 
 
Riley, S. 2007. Large-scale spatial-transmission models of infectious disease. Science 316: 1298-1301.
 
Root, E.D. (Forthcoming) Moving Neighborhood and Health Research Forward: Using Geographic Methods to Examine how Spatial Scale and Mobility Affect Neighborhood Effects on Health. Annals of the Association of American Geographers.
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