Raghavan, Ram K.2019-04-152019-04-152019-05-01http://hdl.handle.net/2097/39499Recent advances in disease mapping allow for the simultaneous evaluation of space-time dynamics of diseases and the drivers of such dynamics, which are useful for designing public health campaigns and surveillance systems. This study determined the space-time patterns of spotted fever group rickettsioses (SFR), a group of tick-borne Rickettsial diseases widely prevalent in the U.S, and further evaluated the associations of socio-economic and environmental (land cover, climate) factors with SFR. County-level SFR cases reported to the Kansas Department of Health and Environment between years 2013 -- 2018 and publicly available covariate data were used in a Bayesian hierarchical modeling framework to quantify trends and associations. The results show a steady increase in space-time trend for SFR in Kansas, the spread of SFR to newer counties over the study period, and two clusters of high-risk areas in the southeast and northeastern parts of Kansas. The space-time pattern of SFR is influenced by poverty status, the number of older homes in a county, and higher relative humidity conditions. The relevance of these findings is discussed in the context of public health and climate change implications on health.en-USThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).RickettsiosisSpotted feverTick-borne diseaseBayesianSpatiotemporalPublic healthThesis