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USGS has a wealth of GIS-based models for the distribution of a wide range of climate and environmental variables that Griffin’s team is now combining with the results of polymer chain reaction–based assays for particular microorganisms, including anthrax, Bacillus species, and Naegleria fowleri, a highly lethal amoeba that consumes brain tissue. For the latter organism, low levels of copper and high levels of zinc appear to correlate to where cases of infection with the amoeba have been reported. These models were scheduled to be available on the USGS website for public use in late 2016.
In response to a question, Griffin said it is important to combine knowledge about microbial biology with the information gleaned from the model. For example, Griffin’s analysis identified strontium as an element present in soils where anthrax was found, and when an anthrax researcher questioned him about this, he was able to remind the researcher that strontium is critical to anthrax spore formation.
GIS AND VECTOR-BORNE DISEASES
By combining published information on a variety of climate and geographical data with outbreaks of various infectious disease and known locations of the vectors that transmit the infectious organism and by using a tool called similarity search, Attaway has been able to generate maps that relate environmental and climate conditions to the likelihood of future outbreaks (see Figure 4-4). A similarity search, which relies on a statistical application known as cosine similarity, makes it possible to identify the features or candidates that are most similar or dissimilar to specific features or attributes in much the same way that a consumer application such as Yelp makes recommendations based on a customer criteria.
ArcGIS is another tool that ESRI has developed for predictive analysis, Attaway said. This free tool provides the ability to look at suitable locations for outbreaks and offers other applications as well, such as threat detection, drug use, and urban planning, based on historical data. ArcGIS uses a process called pattern-of-life analysis that enables hypothesis testing and retesting over multiple iterations and produces predictive maps. For example, Attaway and his colleagues used ArcGIS to analyze temperature, precipitation, elevation, land cover, population density, and other variables available from public sources to identify locations suitable for year-round Aedes mosquito activity (see Figure 4-5). In response to a question, he acknowledged that while the analysis itself can be done in minutes, it depends on the availability of data collected over months and even years. ArcGIS’s strength, he said, is its ability to pull data together from a variety of sources, analyze it, and produce actionable insights.
MODELING THE SPREAD OF DISEASE AT SCALE
The challenge that Sadilek is attempting to address involves using artificial intelligence or machine learning in combination with online data to enable the