Dynamic maps: a visual-analytic methodology for exploring spatio-temporal disease patterns

Abstract

BackgroundEpidemiologic studies are often confounded by the human and environmental interactions that are complex and dynamic spatio-temporal processes. Hence, it is difficult to discover nuances in the data and generate pertinent hypotheses. Dynamic mapping, a method to simultaneously visualize temporal and spatial information, was introduced to elucidate such complexities. A conceptual framework for dynamic mapping regarding principles and implementation methods was proposed.MethodsThe spatio-temporal dynamics of Salmonella infections for 2002 in the U.S. elderly were depicted via dynamic mapping. Hospitalization records were obtained from the Centers of Medicare and Medicaid Services. To visualize the spatial relationship, hospitalization rates were computed and superimposed onto maps of environmental exposure factors including livestock densities and ambient temperatures. To visualize the temporal relationship, the resultant maps were composed into a movie.ResultsThe dynamic maps revealed that the Salmonella infections peaked at specific spatio-temporal loci: more clusters were observed in the summer months and higher density of such clusters in the South. The peaks were reached when the average temperatures were greater than 83.4°F (28.6°C). Although the relationship of salmonellosis rates and occurrence of temperature anomalies was non-uniform, a strong synchronization was found between high broiler chicken sales and dense clusters of cases in the summer.ConclusionsDynamic mapping is a practical visual-analytic technique for public health practitioners and has an outstanding potential in providing insights into spatio-temporal processes such as revealing outbreak origins, percolation and travelling waves of the diseases, peak timing of seasonal outbreaks, and persistence of disease clusters.

Visualizing the Diffusion of Digital Mammography in New York State

“…We follow in a long tradition of spatiotemporal mapping that has been used to depict the spread of ideas, technology, and disease (27,28). The process has become much more technically straightforward in recent years, as our maps required only a single short R program rather than expertise in multiple commercial graphical software programs, as was the case for a previous cartographic animation of the spread of Salmonella cases in the United States (29).…”

A Survey of Visual Analytics for Public Health

“…For example, they may be misleading in case of smaller regions with a low absolute number of diseases. In such cases, Castronova et al [CCN09] suggest to re-aggregate the data, i.e. to merge adjacent districts until a significant number of cases is achieved.…”

Patterns of Protozoan Infections: Spatiotemporal Associations with Cattle Density

“…To facilitate temporal and spatial analysis, for each county we determined counts of each disease, estimated annual rates, and created weekly time-series of counts. To minimize spurious high rates caused by extremely low denominators, a spatial aggregation scheme was applied to incorporate counties with low elderly population into the adjacent counties until the total number of elderly exceeded 1,000, resulting in a total of 2755 counties for analysis (for details related to aggregation rules see Castronovo et al, 2009).…”