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 movi...
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| Language: | English |
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Public Library of Science (PLoS)
2024-10-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012464&type=printable |
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| author | Lani Fox William C Miller Dionne Gesink Irene Doherty Marc Serre |
| author_facet | Lani Fox William C Miller Dionne Gesink Irene Doherty Marc Serre |
| author_sort | Lani Fox |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0be1ea883864a99be701a919f3c1335 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e0be1ea883864a99be701a919f3c13352025-02-05T05:30:42ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-10-012010e101246410.1371/journal.pcbi.1012464Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study.Lani FoxWilliam C MillerDionne GesinkIrene DohertyMarc SerreIn 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012464&type=printable |
| spellingShingle | Lani Fox William C Miller Dionne Gesink Irene Doherty Marc Serre Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. PLoS Computational Biology |
| title | Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. |
| title_full | Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. |
| title_fullStr | Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. |
| title_full_unstemmed | Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. |
| title_short | Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. |
| title_sort | enhancing insights in sexually transmitted infection mapping syphilis in forsyth county north carolina a case study |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012464&type=printable |
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