In their study of H1N1 cases within Cameron County, Texas, Wilson et. al. sought to analyze the outbreak of April 2009 on “a fine spatial scale within a community”. Researchers drew on data from three different methods of testing for H1N1 – the presence of flu-like symptoms recorded by a health professional (ILI), a rapid test that could be done on site with quick results (RIDT), and a laboratory-confirmed test that is highly accurate but takes weeks to process (S-OIV). After obtaining these datasets from health centers, Wilson et. al. geocoded all cases into a GIS and grouped them by testing method. Researchers also categorized the data by age and estimated time of outbreak. The tool SaTScan was used to identify clusters and analyze the spatial distribution of cases in and around Brownsville, the urban center of Cameron County.
Wilson et. al.’s findings showed that all three measures used to identify cases of H1N1 were spatially correlated – indicating that cheaper, faster methods of testing (such as RIDT) could be used just as effectively as slower methods to identify cases, reducing the time needed to locate patients sick with H1N1. The findings also suggested that real-time analysis of ILI and RIDT tests, at the peak of an initial outbreak, could be used to identify the “highest geographical risk areas” early.
By mapping the areas with greatest risk of infection in real-time, medical professionals could have enough information to act quickly and target resources at the areas that most need them in the event of an outbreak. Wilson et. al. suggest that these measures could “[minimize] the impact of future outbreaks on local communities”, potentially saving money and lives.
Wilson, J. G., Ballou, J., Yan, C., Fisher-Hoch, S. P., Reininger, B., Gay, J., ... & Lopez, L. (2010). Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention.Health & place, 16(6), 1230-1239.
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