Conference Abstract | Volume 9 (ConfPro8): Abstract 116 | Published: 14 Oct 2026
Felix Mbiba1,&, Vincent Uwumboriyhie Gmayinaam1,2, Hubert Amu3
1Department of Epidemiology and Biostatistics, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Ho, 2Institute of Health Research, University of Health and Allied Sciences, Ho, 3Department of Population and Behavioural Sciences, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Ho
&Corresponding author: Felix Mbiba, Department of Epidemiology and Biostatistics, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Ho, Ghana, Email: mbibafelix16@gmail.com, ORCID: https://orcid.org/0009-0004-0850-7866
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Received: 11 Mar 2026, Accepted: 30 Aug 2026, Published: 14 Oct 2026
Domain: Infectious Disease Epidemiology
Keywords: HIV, spatio-temporal analysis, Bayesian hierarchical modelling, spatial clustering, Ghana
©Felix Mbiba 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: Felix Mbiba et al ., Spatio-temporal modelling of variations in HIV risk in Ghana from 2018–2024. Journal of Interventional Epidemiology and Public Health. 2026;9(ConfPro8):116. https://doi.org/10.37432/JIEPH-CONFPRO8-00116
We extracted annual laboratory-confirmed HIV cases and district populations for 2018–2024 from the District Health Information Management System II and linked them to boundary files for 260 districts. Missing values were multiply imputed. Standardised Incidence Ratio (SIR) compared observed cases with expected cases. Smoothed Relative Risk (RRs) were estimated with a Bayesian hierarchical spatio-temporal Poisson model with spatial and temporal random effects, fitted by integrated nested Laplace approximation. Local indicators of spatial association identified clusters. Estimates were displayed on an interactive web map.
A total of 76,686 HIV cases were reported. Ayawaso East Municipal had consistently high SIRs, rising from 10.79 in 2018 to 17.98 in 2024, whereas SIRs declined in Techiman Municipal (11.67 to 8.15) and Yilo Krobo (12.34 to 4.25). The overall predicted RR was 0.20 (95% credible interval [CrI]: 0.04–0.50). Annual mean RR increased from 0.09 (95% CrI: 0.03–0.26) in 2018 to 0.20 (95% CrI: 0.03–0.92) in 2024, although intervals overlapped. Nandom, Ada West, Afadzato South, Nkwanta South, and Ashaiman had relatively high RR in most years. In 2024, the highest RRs were in Adansi Akrofuom (0.77), Karaga (0.67), and Asene Akroso Manso (0.65). West Akim formed a high–high cluster throughout, while Kumbungu and Bawku West were persistent high–low outliers.
HIV risk in Ghana was geographically uneven and shifted over time, with persistent and emerging higher-risk districts and distinct spatial clusters. Because estimates derive from routinely reported cases without explanatory covariates, they should guide regularly reviewed geographic prioritisation and investigation of local drivers, supporting Sustainable Development Goal target 3.3.
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