Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study

In 2008–2011 Forsyth County, North Carolina (NC) experienced a four-fold increase in syphilis rising to over 35 cases per 100,000 mirroring the 2021 state syphilis rate. Our methodology extends current models with: 1) donut geomasking to enhance resolution while protecting patient privacy; 2) a moving window uniform grid to control the modifiable areal unit problem, edge effect and remove kriging islands; and 3) mitigating the “small number problem” with Uniform Model Bayesian Maximum Entropy (UMBME). Data is 2008–2011 early syphilis cases reported to the NC Department of Health and Human Services for Forsyth County. Results were assessed using latent rate theory cross validation. We show combining a moving window and a UMBME analysis with geomasked data effectively predicted the true or latent syphilis rate 5% to 26% more accurate than the traditional, geopolitical boundary method. It removed kriging islands, reduced background incidence rate to 0, relocated nine outbreak hotspots to more realistic locations, and elucidated hotspot connectivity producing more realistic geographical patterns for targeted insights. Using the Forsyth outbreak as a case study showed how the outbreak emerged from endemic areas spreading through sexual core transmitters and contextualizing the outbreak to current and past outbreaks. As the dynamics of sexually transmitted infections spread have changed to online partnership selection and demographically to include more women, partnership selection continues to remain highly localized. Furthermore, it is important to present methods to increase interpretability and accuracy of visual representations of data.Author summary: From 2008 to 2011, Forsyth County, North Carolina saw a dramatic increase in syphilis cases reaching over 35 cases per 100,000, aligning with the state’s highly elevated 2021 rate. Our study addresses the challenges of mapping such outbreaks by introducing innovative methodologies that enhance spatial resolution while preserving patient privacy. Analyzing syphilis surveillance data from the North Carolina Department of Health and Human Services, our work finds the combination of these techniques resulted in more accurate predictions of true syphilis rates, improving accuracy by 5% to 26% over traditional geopolitical mapping methods. The results also identified localized hotspots more effectively, revealing a complex network of transmission emerging from urban endemic areas appearing to spread through sexual core transmitters.