To determine whether anthrax events occurred more frequently in certain geographic location, we used data from 8 counties that reported the highest number of anthrax events, aggregated the events by sub-county, and expressed this as a percent of the total number of the events in the county. We used percentages instead of raw count of anthrax events in order to have a metric for standardization across sub-counties and counties. Each county consisted of 4–12 sub-counties. Four intervals of anthrax events occurrence were used to classify sub-counties in each county; no occurrence (0%), low (1–15%), medium (16–30%), and high (> 30%) occurrences. Using this classification, choropleth maps for each county were developed in QGIS version 3.4.4 software (, using different color codes to denote no risk (white), low (beige), medium (brown), and high (maroon) risk of anthrax occurrence [18].

To describe spatial clustering by county, we used 86 anthrax events that had GPS coordinates by assigning each to AEZ and fitting a univariate generalized linear mixed-effects model with the Poisson distribution and log link function. Administratively, Kenya has 47 counties while agriculturally it is divided into seven AEZs based on soil types, landforms, and climatic condition that determine agricultural potential. For ease of analysis, the AEZs were reclassified into 5; (i) agro-alpine, (ii) high and medium potential, (iii) semi arid, (iv) arid, and (v) very arid and desert conditions. For the Poisson mixed effects regression, the AEZ, analyzed as a categorical variable was used as fixed effects and subcounty as random effect outcome variable focusing on the expected number of anthrax events in each AEZ as our outcome variable [19]. For AEZs, we used log transformation (ln) of the livestock population in each AEZ as an offset to account for differences in population sizes and then used STATA software (version 14) to fit a random intercept model accounting for spatial dependency.

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