by Lauren Glenn Manfuso
Place matters—that’s epidemiology 101.
Until recently, gathering data and charting it on maps meant going door to door to survey families and then plotting the findings on paper. It was a tedious process at best and mostly two-dimensional. Data mapping might show, for instance, which households within a community had been affected by a certain disease or problem and where the condition was most prominent, but would most likely reveal nothing about relevant climate or social demographics in that particular area.
Today’s tools for epidemiological mapping offer much more, not only in detail but also in efficiency and ease. As a result, geographic information systems (GIS) are changing the way epidemiologists approach the entire concept of place as a component of public health. In some cases, GIS mapping requires little more than basic computer skills and a global positioning system (GPS)-enabled cell phone. More complicated applications require extensive training but also offer multidimensional analyses, including environmental factors like elevation and climate. Either way, for scientists looking to demonstrate the importance of place in public health, the options have far outstripped the tools of 20 years ago.
“Some of the biggest advances made have been in supporting technologies, like remote sensing of the environment using satellites and finding out, for instance, the soil moisture in Zambia today versus two days ago. Those are things you certainly couldn’t have gotten in the past,” says epidemiologist Greg Glass, PhD, a professor in the Department of Molecular Microbiology and Immunology.
“One of the limitations we often cite is that we're unclear about the effects of the environment on individuals and communities. GIS has brought a population-based approach that looks at the relationship between people and place.”—Debra Furr-Holden
Such environmental factors often correlate directly with epidemiological concerns. Take, for instance, the relationship between rainfall and the prevalence of malaria-carrying mosquitoes. Knowing which areas are more vulnerable to malaria outbreaks, says Glass, allows aid workers to predict where assistance—such as bed net distribution—is most needed.
In earlier work, Glass used geographic mapping to predict an increased risk of hantavirus in certain areas, based on increased precipitation and vegetation. More vegetation means a booming population of the rodents that carry hantavirus.
The resulting images vary depending on circumstances, sometimes appearing as heat maps—brightly colored maps resembling those used by meteorologists, with the highest risk areas showing a bright red. A map of a predicted malaria outbreak, on the other hand, might illustrate which areas of Bangladesh are both highly populated and highly vulnerable to malaria, encasing those areas in concentric circles.
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