Conference Abstract | Volume 8, Abstract NACNDC/19JASH022 (Oral) | Published: 20 Nov 2025
Diana Cherotin1, Clark Joshua Brianwong1, Richard Jjuuko1, Eddy Okwir1, Frehd Nghania1, Ssekiswa Lwanga Zimwanguyiza1, Alexander Mugume1, Dithan Kiragga1
1Baylor Foundation, Kampala, Uganda
&Corresponding author: Diana Cherotin, Baylor Foundation, Kampala, Uganda. Email: dianacherotin@gmail.com, ORCID: https://orcid.org/0009-0004-3095-8794
Received: 12 Sept 2025, Accepted: 20 Oct 2025, Published: 20 Nov 2025
Domain: Infectious Disease Epidemiology
Keywords: GIS, HIV testing, geospatial analysis, hotspot mapping, Uganda
©Diana Cherotin et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Diana Cherotin et al., GIS-informed targeting to optimise HIV case finding in Eastern Uganda. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc6):00022. https://doi.org/10.37432/JIEPH-CONFPRO6-00022
Uganda continues to report nearly 1,000 new HIV infections every week despite sustained investments in prevention and treatment. The 2020 Uganda Population-based HIV Impact Assessment (UPHIA) estimated adult HIV prevalence in Eastern Uganda at 4.2%, contributing approximately 250 new infections per week. With constrained testing resources, the Ministry of Health recommends targeted testing in high-burden geographic areas; however, identifying undiagnosed individuals remains difficult. To strengthen precision public health efforts, Geographic Information Systems (GIS) were applied to guide micro-targeted HIV testing in Eastern Uganda.
Between January 2023 and December 2024, thematic GIS mapping was implemented across 16 districts in Eastern Uganda. Hotspots were defined as sub-counties reporting ≥27 new HIV diagnoses, ≥20 treatment interruptions, and ≥28 cases of high viremia (≥1,000 copies/mL), guided by context-specific thresholds derived from national pilot data. Routine HMIS data were geocoded at village level and analyzed using QGIS to visualize burden distribution. Hotspot maps were reviewed monthly with district teams to update priority areas for targeted community HIV testing.
By June 2024, ART coverage in Eastern Uganda was 91%, below the national target of 95%. Tororo District had the highest HIV burden, with Eastern Division emerging as the most affected sub-county. From June to December 2024, GIS-guided testing at Tororo Hospital increased HIV positivity yield from 1.2% (18/1,441) to 3.2% (21/664), representing a three-fold improvement despite fewer tests conducted. GIS-based targeting improved case identification efficiency, reduced test-kit wastage, and enhanced community mobilization. Health workers reported improved focus on high-risk areas compared to traditional outreach strategies.
GIS-informed targeting improved efficiency and precision of HIV testing in high-burden districts. Integrating new diagnoses, treatment interruptions, and viremia strengthened hotspot identification, while routine data cleaning-maintained mapping accuracy. The approach is being expanded to additional districts and adapted for tuberculosis hotspot mapping, with future opportunities to integrate socio-economic and mobility layers using Artificial Intelligence.
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