Sexually transmitted infections (STI) remain highly endemic in the southeast United States. Unlike most of the rest of the US, STIs in the southeast are prevalent in both urban and rural areas. Controlling STIs requires a combination of ongoing sustained efforts and rapid intervention during outbreaks. Previously, spatial representations of STI have provided useful information for surveillance and targeted intervention, while simultaneously providing important clues regarding STI epidemiology. For example, geographical core areas, a phenomenon described over 20 years ago, remains an important construct today. Despite these important contributions of spatial representations of STIs, most applications of spatial statistics in STI epidemiology have been relatively rudimentary and based solely in urban areas. We propose to use modern geostatistical methods to develop improved methods of STI surveillance and investigate core areas of transmission. Specifically, our aims are 1 )To develop and evaluate a spatiotemporal model to monitor reported STIs and identify outbreaks at an early stage, using syphilis and gonorrhea as examples; 2) To characterize the spatial pattern of syphilis incidence rates and case densities during a known outbreak extending across two rural counties and to relate this pattern to a sexual network analysis; and 3) To evaluate the concept of geographical core, or risk space, for gonorrhea in rural and urban areas and at multiple spatial scales. To address these aims, we will use existing and routinely collected surveillance data for syphilis and gonorrhea. Our primary strategy will use Bayesian Maximum Entropy models, incorporating both spatial and temporal data. We will also assess other methods to determine the most efficient strategies for evaluating outbreaks and representing network structure spatially. We introduce and evaluate a new spatial measure, termed the """"""""transmission potential"""""""", which combines aspects of incidence rate and case density. Our development and application of these spatial methods will improve surveillance procedures and provide a modeling procedure generalizable throughout the U.S. Use of these procedures will facilitate targeted interventions to reduce the burden of these important diseases. Furthermore, we will obtain critical, new information regarding STI epidemiology in southeastern U.S., a region with disproportionate burden of these infections. ? ? ? ?
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