Hess, G.R. et al. (2001).

"Spatial aspects of disease dynamics"
Ch 6 from The Ecology of Wildlife Diseases (ed. Hudson P.J.)

Reviewed Oct 07, 2011

Disease risk at the local or global scale can be modeled in a variety of different ways. Analyses range from historical looking approaches that attempt to recreate using models an observed dynamic in space and time to forward looking approaches that hope to identify key areas for disease prevention action. Metapopulation theory has been of interest to ecologists for a variety of different reasons for several decades now. The basic model describing a series of patches in space that are all equidistant from one another and between which their is some level of immigration and recolonization by members of one patch into another. This dynamical process is believed to prevent large scale, or regional, extinction even if a local population dies out due to stochastic effects.
There are some problems with the classic metapopulation theory that have been addressed by other researchers in recent years, attempting to add in a large dominant patch, where extinction is not possible, or adding environmental variation into the patch quality structure. Work has also been done looking at the application of this theory to epidemiological systems, either modeling hosts themselves as a patch, or modeling a population of hosts as a patch. many of the variables used as inputs into metapopulation models have direct analogues with those for disease systems e.g. an empty patch equates to a susceptible host, extinction of a colony is similar to recovered host or one that died.
Some modifications to the metapopulation model have shown that a metapopulation designed like a wheel and spoke (a central, hub colony connected to outer, spoke colonies - which themselves are not interconnected) have a more consistent reduction in metapopulation extinction than some other spatial orientations including island and stepping-stone. This could have important ramifications for conservationists designing corridors hoping to save host populations without allowing the pathogen levels to also rise.
Many scientists also hope to link this particular spatial framework with landscape epidemiology. Landscape epidemiology has the potential to map disease risk using GIS layers that connect abiotic and biotic inputs to observed presence of pathogen, vector, or hosts. The method for connection of the variables to the disease system can either use statistics to rank the importance of collected factors on disease prevalence or utilize a more mechanistic approach that could link the biology of a pattern with changing conditions. Landscape epidemiology has a lot of important applications but there are some major issues with resolution of the spatial and temporal data due to pixel size from remotely sensed sources or limits to data archiving on those same sources. A balance between spatial accuracy and long-term analyses must be achieved if landscape epidemiology is to have any hope of being relevant for study at more regional or local scales.