Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa

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Castelán-Sánchez, Akua K. Yalley, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7906379/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Reconstructing the origins and transmission of the HIV epidemic in Ghana has, since the first diagnosis in 1986, yet to be reported. This study described the transmission clusters of HIV in the Ghanaian setting using a maximum-likelihood tree via the IQTree approach. Using 71 newly described full-length Ghanaian HIV-1 sequences, we performed molecular phylodynamic analysis to determine major drivers of HIV transmission in Ghana, a West African population where the HIV-1 CRF02_AG recombinant is prevalent. However, to reconstruct the origin of the most predominant subtype CRF02_AG, we combined 48 CRF02_AG sequences in our dataset with 140 full-length CRF02_AG sequences downloaded from the Los Alamos National Laboratory HIV database and utilized the ancestral trait reconstruction model in BEAST v.1.10.5 to reconstruct an MCC tree, summarized in TreeAnnotator and visualized with treeio package in R. Phylogeographic reconstruction to estimate the earliest introduction of HIV-1 showed that Cameroon and Nigeria were the sources of nine major introductions, with a time to most recent common ancestor (tMRCA) of 1964.2. Most intra-country transmission occurred from Greater Accra to other major regions. This is the first study to combine full-length HIV-1 genomic sequences with patient metadata to estimate the population dynamics and reconstruct the introduction of the predominant HIV-1 CRF02_AG in Ghana. This study illuminates our understanding of HIV transmission dynamics in Ghana and underscores the utility of combining demographic and molecular data in prospectively tracking HIV transmission to inform targeted public health interventions. HIV phylogenetic reconstruction phylodynamics molecular epidemiology transmission dynamics comparative genomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Manuscript’s importance Understanding the major dynamics of HIV transmission remains crucial. This study performed molecular phylodynamics – an advanced genetic analysis, to understand HIV molecular epidemiology and transmission dynamics of HIV in the Ghanaian setting within the West African sub-region. The analysis involved full genomic sequences of HIV from infected individuals in Ghana. The genetic relationships of the Ghanaian sequences were compared with global samples. The analysis showed that the most common type of HIV in Ghana, which is the CRF02_AG, likely came from Cameroon and Nigeria. The virus spread mostly from the Greater Accra region to other parts of the country. This study is the first to trace how HIV spread in Ghana since it was first identified in 1986. The findings highlight the importance of using both virus and host patient data to track HIV spread. Accumulation of such data can help health officials plan effective strategies to control the spread of the virus and protect vulnerable communities. INTRODUCTION In Ghana, the first case of HIV was diagnosed in 1986, and soon after, the Ghana Health Service (GHS) introduced HIV sentinel surveillance across antenatal care (ANC) and sexually transmitted infections (STI) clinics [ 1 , 2 ]. several programs have been rolled out since then. These include the ‘treat-all’ intervention initiated in September 2016 that makes available antiretroviral drugs to all persons diagnosed with HIV, irrespective of the CD4 + status [ 3 ]. For about two decades now, the HIV epidemic in Ghana has been predominantly due to the CRF02_AG subtype [ 4 ]. This finding has been reported mostly from cross-sectional studies by individual researchers using representative cohorts in selected areas. Despite advances in research and national preventive efforts, reports by the National AIDS/STI Control Programme indicate that has reported that, since the surveillance commenced, there have been about 334,095 cases with an estimated 17,774 new infections in 2023 [ 5 ]. The continued increase in the number of cases necessitates a multifaceted approach to tracking transmission in real time to help control the further spread of the virus. Knowledge of spread and transmission network sources would prove useful in our control endeavours. Like most other pathogens, HIV transmission dynamics studies offer useful models that help to clarify some essential relations between epidemiological factors underlying an overall pattern of the HIV epidemic. For example, it is useful to estimate essential parameters such as the number of secondary infections produced by a primary infectious case; that is, effective reproductive number ( Re ), and describe the ancestral traits and demographic contributors of the HIV pandemic in a given population. They are also helpful in identifying the kinds of epidemiological data needed to make predictions about future trends. However, HIV molecular epidemiology studies in Ghana have been somewhat limited. Since the epidemic began, there has yet to be any data on HIV transmission dynamics in Ghana. Until quite recently (2022), there were only 31 full-genome HIV sequences from Ghana available in the global sequence database, all of which were acquired in 2003 or earlier. In 2020–2022, we conducted the first-ever comprehensive HIV molecular epidemiology studies that employed both Sanger and next-generation sequencing techniques to produce 71 whole genome HIV sequences for the analysis of HIV subtypes, drug resistance and coreceptor usage [ 6 ]. The present study builds on the 2020–2022 study by utilizing full-length HIV-1 genomic sequences with patient metadata to estimate the Re and reconstruct HIV-1 transmission in Ghana. We do this by employing relevant phylogenetic tools and models that effectively estimate molecular epidemiology and transmission parameters across time and space [ 7 , 8 ] and predict possible future outbreaks [ 9 ]. To the best of our knowledge and according to documented literature, this represents the first of its kind to be conducted in Ghana, West Africa. MATERIALS AND METHODS Ethics statement The protocol and ethics of this study were approved by the Scientific and Technical Committee (STC)/Institutional Review Board (IRB) of the Korle Bu Teaching Hospital, Ghana (KBTH-STC/IRB/00075/2020). Persons included in the study voluntarily gave written informed consent to participate, as well as permission for drawing blood. Consent was documented on a Consent Document Form, which was part of the protocol approved for the study. The procedures for participant assent/consent and blood sampling were done in accordance with the tenets of the Declaration of Helsinki. Sample collection and sequence characteristics Sequences analyzed in this study were obtained from blood samples collected from HIV-1 infected antiretroviral naïve Ghanaians accessing routine care at the Korle-Bu Teaching Hospital, Ghana between 2020 to 2022. This generated 71 Illumina MiSeq-acquired HIV whole genome consensus sequences from the previous study and deposited with the GenBank under accession numbers within OQ121842 – OQ121917. The samples were mostly (48, 68%) of the CRF02_AG subtype, the predominant subtype in the West African sub-region. The sociodemographic characteristics of the study subjects thus remain as previously described [ 6 ]. Maximum-likelihood tree inference and transmission cluster analyses We selected 140 publicly available HIV-1 full-length genomic sequences from the Los Alamos National Laboratory HIV database (LANL) ( https://www.hiv.lanl.gov ) for which sampling location and year were available at the time of analysis. We aligned these sequences with the 71 Ghanaian full-length sequences using Maximum Alignment using Fast Fourier Transform (MAFFT) [ 10 ]. We selected the best nucleotide substitution model via ModelFinder [ 11 ] and a maximum likelihood phylogenetic tree was estimated directly from the chosen model, GTR + G4 in IQTree after 1000 iterations [ 12 ]. The ClusterPicker tool [ 13 ] was used to investigate putative transmission clusters in the dataset. Statistical support for clusters was defined by a non-parametric Shimodaira-Hasegawa (SH)-like test with node support of ≥ 99% and genetic distance of 0.05. Estimation of the number of introductions of the CRF02_AG subtype in Ghana and phylogeographic reconstruction The phylogeographic reconstruction analysis was performed to identify and quantify the introduction events of the 48 CRF02_AG subtype of the HIV lineage in Ghana. For this analysis, we used a time-scaled tree created with TreeTime v0.7.4 [ 14 ]. A time-scaled phylogenetic tree was constructed and evaluated for a temporal signal with TempEst [ 15 ] and outliers were removed. The Time to the Most Recent Common Ancestor (tMRCA) was estimated using simple least-squares regression in TreeTime v0.7.4 [ 14 ]. This time-scaled phylogeny was treated as a fixed empirical tree, considering two potential ancestral locations: "Ghana" and "other location." A discrete diffusion model was applied using the BEAST v1.10.4 software [ 16 ]. The Bayesian analysis via Markov chain Monte Carlo (MCMC) was run for 100 million steps. MCMC convergence and mixing properties were assessed using Tracer v1.72 [ 17 ], achieving effective sample sizes greater than 200 for all parameters. Visualization of the introductions of the CRF02_AG subtype was performed using a modification of the script described by Dellicour and colleagues utilizing the Seraphim package [ 18 ] to extract spatiotemporal information from the data and visualize the phylogeographic reconstructions. Additionally, to count the number of introductions of the CRF02_AG subtype, we conducted a formal discrete phylogeography analysis to understand the dispersion of this subtype in Ghana. This analysis was performed using BEAST v1.10.5 [ 16 ] and the BEAGLE 3 library to improve computational performance [ 19 ]. The method employed a GTR + Γ parametrization, a relaxed clock model with rates drawn from an underlying lognormal distribution, and a chain length of 100 million steps, with log parameters recorded every 10,000 steps. The maximum clade credibility (MCC) tree was inferred using TreeAnnotator [ 20 ] and visualized using the treeio package in R [ 21 ]. Estimation of the temporal dynamics of the effective reproductive number Re , and geographic dispersal of HIV in Ghana We performed phylodynamic analyses using the Bayesian Skygrid coalescent tree prior implemented in BEAST v1.10.5 [ 16 ]. This approach allows us to estimate the Re over time, which gives us insights into the dynamics of HIV transmission within the population. The Bayesian Skygrid model, which accounts for fluctuations in population size and transmission rates, is particularly useful for understanding how the epidemic evolves and for predicting future trends. For the phylogeographic analysis, SPREAD (Spatial Phylogenetic Reconstruction of Evolutionary Dynamics) software was used to track the spatial spread of HIV subtypes in different regions. The results were visualized using Google Earth to provide a clear, geographical representation of the movement and spread of the disease. RESULTS Study sequence characteristics The distribution of HIV subtypes (A, B, CRF02_AG, CRF06_cpx, G) over time in samples from Ghana is shown in Fig. 1 A. It is noteworthy that the number of these subtypes is generally decreasing, except subtype B, which shows a rising peak in 2021. Figure 1 B shows the distribution of marital status in the study population over the years. The data shows a relatively stable distribution, with the categories 'Married' and 'Single' consistently being the most common. Figure 1 C shows that the distribution of educational level remains largely stable, the most common categories being 'Primary education' and 'Secondary education'. Figure 1 D shows the distribution of risk factors for the disease studied. The 'heterosexual' category consistently has the highest number, while 'homosexual" and 'needle prick' have significantly lower values. This distribution indicates that heterosexual transmission is the predominant mode of transmission in this population group. The distribution of HIV subtypes within the Ghanaian population varies across different tribes, as depicted in Fig. 2 A. The Akan tribe has the highest number of HIV patients and exhibits the greatest diversity of subtypes. CRF02_AG is the most prevalent subtype in this tribe, followed by CRF02_cpx, subtype A, and subtype G, with subtype B being the least common. In the Northern tribe, CRF02_AG, CRF02_cpx, and subtype A are present. CRF02_AG and CRF02_cpx subtypes are present in other tribes as well. Figure 2 B illustrates the distribution of subtypes according to sex, with men exhibiting the most diversity in subtypes. Figure 3 C presents the distribution of subtypes across regions. The figure shows that all described subtypes are present in the Greater Accra region. Maximum-likelihood and transmission cluster analyses The best tree was selected after 700 iterations using the Generalized Time Reversible (GTR) model and gamma distribution across sites with four categories (G4) using IQTree. In the presence of global data, two putative transmission clusters of two sequences each were found for the Ghanaian data at ≥ 99% bootstrap and 0.05 genetic distance when ClusterPicker was used. The inclusion of the global data was to help determine any spurious clustering between Ghanaian and global data. However, no spurious clustering was found (Fig. 3 ). Phylogeographic reconstruction and introductions of subtype CRF02_AG to Ghana Times for the most recent common ancestor (tMRCA) were calculated using TreeTime. The results indicated an approximate date of 1964.2 (Fig. 4 A). The root-to-tip regression analysis revealed a strong, positive correlation between the genetic distance of HIV-1 sequences and their sampling dates, confirming the presence of a molecular clock. The estimated substitution rate was 1.78e-03 substitutions per site per year. Focusing on the HIV subtype CRF02_AG, which is more prevalent among the studied population, we determined the number of introductions of this subtype into Ghana. The analysis identified a minimum of 9 introduction events for this recombinant subtype (95% HPD interval = [ 7 – 10 ]), based on the phylogenetic analysis of 48 of the samples studied. Figure 4 B shows the phylogenetic tree, in which the number of introductions is marked with red triangles, and branches in green correspond to samples from Ghana. Eight of the nine introduction events were independent, while one was responsible for the dissemination within the transmission cluster. The most probable origin of the CRF02_AG subtype in Ghana is Cameroon (CM) (Fig. 4 C), as the most recent common ancestor of all viruses of this subtype traces back to this location, from which the subtype began its dissemination. The largest clade of samples from Ghana also shares a common ancestor with samples from CM. However, not all Ghanaian samples have the same ancestral origin, indicating that introductions came from different locations. Another significant importation to Ghana likely originated from Nigeria (NG). CM and NG are the primary countries from which the CRF02_AG subtype began spreading to other regions. Estimation of the temporal dynamics of the effective Re , and geographic dispersal of HIV in Ghana To explore the changes in population dynamics we analyzed the distribution of the general trend in population growth from 1986 to 2022. The analysis showed a significant increase in population size until around 2010, after which growth either plateaued or declined slightly. The vertical dotted lines in the graph represent the estimated time of disease emergence, and the epidemiological threshold, marked by a Re of 1, indicates the point at which the disease can sustain itself in the population (Fig. 5 A). To understand the domestic spread of HIV subtypes in Ghana, SPREAD analysis was performed and the results were visualized with Google Earth programs (Fig. 5 B). The results as displayed in the map point to the origin of the epidemic in the Greater Accra Region and its subsequent spread to other parts of Ghana. The black lines on the map illustrate the direction and possible transmission routes of the virus. DISCUSSION With regards to controlling the HIV epidemic in Ghana, the available record shows that by the end of 2020, 63% of HIV-infected individuals knew their infection status, 95% of diagnosed individuals were on antiretroviral therapy (ART), and 73% of those on ART achieved viral suppression [ 23 , 24 ]. These indicators somewhat place Ghana far from realizing the United Nations Joint Programme on HIV/AIDS (UNAIDS) ambitious targets set in December 2020 for ending AIDS – the 95-95-95 targets, which aim for 95% of people living with HIV to know their status, 95% of diagnosed individuals to be on ART, and 95% of those on ART to achieve viral suppression by 2025 [ 25 ]. Achieving these targets certainly requires intensified efforts and multifaceted approaches to tackling the epidemic. Understanding of the major drivers of HIV transmission remains crucial. This underscores the importance of molecular epidemiology studies [ 26 ]. The stable distribution of risk factors, with 'heterosexual' being the most common, aligns with previous findings suggesting that heterosexual transmission remains a significant concern. The relatively lower prevalence of 'homosexual' and 'needle prick' transmission underscores the need for targeted prevention and education efforts, especially within high-risk groups that are less frequently represented in the data. The diversity of HIV subtypes observed among different tribes, particularly the predominance of CRF02_AG in the Akan tribe, suggests varying regional transmission patterns. This diversity could be reflective of historical migration patterns, cultural practices, or different levels of healthcare access. Additionally, using the Skygrid model in BEAST v1.10.4, we estimated past population dynamics of the Ghanaian dataset from 1986 to 2022 and the Re after 2005 to 2022. Generally, the population size was relatively higher before 2000 and after 2010. The reproductive number was also less than the epidemiological threshold ( 1). This shift in the growth pattern can be attributed to various factors like improvements in healthcare, economic development or changes in birth and death rates. Improved access to healthcare could reduce mortality rates, while economic progress could influence birth rates and migration patterns. Furthermore, the introduction of mass education and other prevention strategies such as condom use was communicated and well promoted among the public and in the mainstream media after the year 2000 [ 2 ]. This coincided with the introduction of ART. Notwithstanding the reduced population size seen between the years 2000–2010, the Re value increased substantially until it dropped again after 2020, perhaps due to other interventions like pre-exposure prophylaxis use and potent ARTs that enhance reduced and prolonged viral load suppression. Moreover, our estimates of the Re are indicative of efforts to increase contact tracing and subsequent genotyping of cases. Thus, cases which are not analyzed here are likely to consist of undiagnosed infections. Phylodynamic reconstruction to estimate the earliest introduction of the CRF02_AG using our dataset showed that Nigeria was the source of two major introductions. It is worth noting that the predominant HIV-1 subtypes in Nigeria have mostly been subtypes A, G and CRF02_AG as reported by many studies [ 27 – 29 ]. Of note, in Ghana, the transmission of the CRF02_AG subtype occurred from the country’s capital, Accra in the Greater Accra region into other major regions of the country. Factors such as transportation networks, population density and social interactions are likely to have influenced the observed spread of the disease. Even though there could be blind spots with no sequence data due to financial constraints to routinely sequence HIV-1 in clinical settings and the general population, the socio-demographic characteristics, public health service delivery and population mobility patterns may lend credence to the observations made in this study. The Nigeria-Ghana relationship goes as far back as the 1980s – specifically during the 1983 famine when many Ghanaians left for Nigeria for survival, and their subsequent expulsion back to Ghana in the mid to late 1980s, a period described as “Ghana must go” [ 30 , 31 ]. This period coincided with the period in which HIV was first detected in Ghana [ 1 ]. Moreover, the Greater Accra region represents the economic hub of the country and where the majority of infection and testing are likely to take place; hence the larger number of sequences obtained from this region. In this study, newly acquired HIV-1 full-length sequences were subjected to phylodynamic analysis to unravel the reproductive numbers and transmission pattern of the HIV-1 epidemic in Ghana. Although this study used data that represented only a small percentage of the country’s reported HIV-1 cases, it is the first study ever, since HIV was first diagnosed in 1986 [ 1 ], to use full-length HIV-1 sequences in a statistically rigorous Bayesian phylogenetic approach to better understand and reconstruct the HIV epidemic in Ghana. Overall, the findings of this study provide insights into the population dynamics and epidemiology of HIV disease in Ghana and bring to the fore growth trends and the geographical distribution pattern of HIV outbreaks. CONCLUSION This study described the transmission dynamics of HIV using full-genome sequences recently obtained in Ghana. Though our dataset was relatively modest in size, the sequences analyzed were nevertheless representative of the epidemiology of HIV in Ghana. To the best of our knowledge, this study represents the first to perform a Bayesian phylogenetic analysis of full-length HIV-1 sequences to estimate and reconstruct the HIV epidemic in Ghana. Although public health efforts have likely decreased the rate of transmission over the last 5 years, declines are not uniform across key populations. Invariably, this study found there is an increasing emergence of other less prevalent HIV-1 subtypes and thus demonstrates the importance of combining demographic data with molecular data for analysis to prospectively inform real-time targeted public health interventions. Abbreviations AIDS Acquired Immunodeficiency Syndrome ART Antiretroviral Therapy BEAST Bayesian Evolutionary Analysis Sampling Trees CD4+ Cluster of Differentiation 4 ESS Effective Sampling Size GHS Ghana Health Service GTR Generalized Time Reversible HIV Human Immunodeficiency Virus HPD Highest Posterior Density LANL Los Alamos National Laboratory MAFFT Multiple Alignment using Fast Fourier Transform MCC Maximum Clade Credibility MCMC Markov Chain Monte Carlo PR Protease Re Effective Reproductive Number RT Reverse Transcriptase SPREAD Spatial Phylogenetic Reconstruction of Evolutionary Dynamics STI Sexually Transmitted Infection UNAIDS Joint United Nations Programme on HIV/AIDS Declarations Acknowledgements We gratefully acknowledge people living with HIV and all the individuals whose samples were used for this study. Authors’ contributions Conceptualization: Nicholas I. Nii-Trebi, Billal M. Obeng Billal M. Obeng, Hugo G. Castelán-Sánchez Formal Analysis: Billal M. Obeng, Hugo G. Castelán-Sánchez Methodology: Billal M. Obeng, Hugo G. Castelán-Sánchez, Nicholas I. Nii-Trebi Data curation: Akua K. Yalley, Makafui Seshie Resources: Nicholas I. Nii-Trebi, Kwamena W. C. Sagoe Supervision: Nicholas I. Nii-Trebi Writing – original draft: Billal M. Obeng, Nicholas I. Nii-Trebi, Akua K. Yalley Writing – review and editing: Nicholas I. Nii-Trebi, Billal M. Obeng, Hugo G. Castelán-Sánchez All authors reviewed and approved the final version of the manuscript. Funding The study reported here received no external funding. Funding declaration is not applicable. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Availability of data and materials This study did not generate a new dataset; hence data sharing does not apply to this article. Sequence datasets analyzed in this study are available in public databases. These have been duly referenced in the text of the article. ORCID Billal Musah Obeng https://orcid.org/0000-0002-1158-7922 Hugo G. Castelán-Sánchez https://orcid.org/0000-0002-4763-0267 Nicholas Israel Nii-Trebi https://orcid.org/0000-0001-5012-9564 Akua Koaso Yalley https://orcid.org/0000-0002-7429-1707 References Amofah GK. AIDS in Ghana: profile, strategies and challenges. AIDS Anal Afr. 1992;2(5):5. GAC, Ghana, HIV/AIDS strategic framework II. : 2006–2010. 2005.Available from: https://extranet.who.int/countryplanningcycles/sites/default/files/country_docs/Ghana/hiv_plan_ghana.pdf ; [Accessed December 28, 2023]. WHO. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. World Health Organization; 2015. Obeng BM, Bonney EY, Asamoah-Akuoko L, Nii-Trebi NI, Mawuli G, Abana CZ-Y, et al. Transmitted drug resistance mutations and subtype diversity amongst HIV-1 sero-positive voluntary blood donors in Accra, Ghana. Virol J. 2020;17(1):1–8. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7906379","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538787836,"identity":"64c3714d-13f6-494a-8695-226d4a869cb6","order_by":0,"name":"Billal Musah Obeng","email":"","orcid":"","institution":"University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Billal","middleName":"Musah","lastName":"Obeng","suffix":""},{"id":538787837,"identity":"57e38467-8652-49f1-b000-a3bd2b043784","order_by":1,"name":"Hugo G. 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Nii-Trebi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACZgaGAwkghgRj4wOwyAGwIEEtBiAtzQbEaYEAkBYGNgmitJiz8x488IDhTz6/dHNbNc8vBjm+GwnMrwvwaLFs5ksAOcxy5pyDbbd5+xiMJW8ksFnPwOeiwzwGBxL/GRgY3EgEaulhSNwA1GLMQ0gL0BYDe6CWYqCWeuK1GEgktjHz/GBIMAD65TERWowNJO4cbJac2yBhOPPMQ6BefFrOnzH++INBzoB/dvvDD2/+2MjzHU8+/BmfFlTA2CYBJ4kFf8Ak8wcStIyCUTAKRsHwBwD6zUyDqi99UAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ghana","correspondingAuthor":true,"prefix":"","firstName":"Nicholas","middleName":"I.","lastName":"Nii-Trebi","suffix":""}],"badges":[],"createdAt":"2025-10-20 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06:38:48","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":78865,"visible":true,"origin":"","legend":"","description":"","filename":"1dbe351d86d840daa69fc05257a19eaf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/38312703b39b5033582f8cf6.xml"},{"id":95172421,"identity":"e8df4b95-55e4-4347-a3a5-54e4be764410","added_by":"auto","created_at":"2025-11-05 06:38:47","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":87967,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/335125dc991f25cc353a2951.html"},{"id":95172376,"identity":"5a53b2cb-649d-4f3e-8c97-1e39b872a11e","added_by":"auto","created_at":"2025-11-05 06:38:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":712021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of the samples\u003c/strong\u003e. A) Predominant HIV subtypes in Ghana. B) Marital status of the patients. C) Educational level of the patients. D) Risk factors for infection\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/0a12cc961cc23c1151056d06.png"},{"id":95172438,"identity":"a17207b0-1312-479e-b1e7-d02bfbd67b7d","added_by":"auto","created_at":"2025-11-05 06:38:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":539907,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMosaic plot of HIV subtypes in our samples.\u003c/strong\u003eA) Tribes of Ghana, highlighting the higher abundance of the CRF02_AG subtype across all tribes. B) Subtypes by sex, showing the same proportions overall, but subtype B appearing exclusively in females. C) Distribution of HIV subtypes across regions of Ghana.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/0020c151f26d06dc39f620d9.png"},{"id":95172422,"identity":"38c666f0-a6b2-41a3-8cc9-609740e2843d","added_by":"auto","created_at":"2025-11-05 06:38:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":445838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic clustering of study sequences.\u003c/strong\u003eFigure shows phylogenetic tree generated using IQTree and putative transmission cluster determined with ClusterPicker at ≥99% bootstrap evaluations and 0.05 genetic distance. Analysis found 12 clusters among the global sequences (red branches) and 2 clusters from the Ghanaian sequences (blue branches).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/d49f53fa534a2da00790fde6.png"},{"id":95172428,"identity":"cd7ee38e-547a-4a55-b843-c3edde550c06","added_by":"auto","created_at":"2025-11-05 06:38:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":876993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRF02_AG subtype phylogeography analysis\u003c/strong\u003e. (A) tMRCA of CRF02_AG subtype in Ghana. (B) The analyses on the temporal scale show the 9 introduction events in Ghana (red triangles) while the branches in green correspond to the samples from Ghana. (C) Maximum clade credibility (MCC) tree of CRF02_AG subtype HIV, the dots in colors correspond to different countries, Angola (AO), Belgium (BE), Ivory Coast (CI), Cameroon (CM), Germany (DE), Spain (ES), France (FR), United Kingdom (GB), Ghana (GH), Guinea-Bissau (GW), South Korea (KR), Nigeria (NG), Pakistan (PK), Russia (RU), Sweden (SE), Senegal (SN) and United States of America (US).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/dc92728c5f58a33bc46229be.png"},{"id":95172435,"identity":"f96a9a4e-6d55-4654-8a09-1dd2be2da179","added_by":"auto","created_at":"2025-11-05 06:38:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":492449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimation of past population dynamics from 1986 to 2022.\u003c/strong\u003e Fig 5A illustrates the epidemiological threshold of \u003cem\u003eRe\u003c/em\u003e is set at 1. Fig 5B illustrates the transmission within Ghana, the infection spreads from Greater Accra into other regions of the country as illustrated by the black arrowed lines inferred within SPREAD software and visualized with Google Earth [22].\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/b60f55d9938886b41da3f2eb.png"},{"id":95312157,"identity":"8735825f-1898-498b-a7bf-7fb113724d05","added_by":"auto","created_at":"2025-11-06 15:47:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3907963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7906379/v1/00d4f5cc-f39d-426f-9b1a-61c8ad068222.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa","fulltext":[{"header":"Manuscript’s importance","content":"\u003cp\u003eUnderstanding the major dynamics of HIV transmission remains crucial. This study performed molecular phylodynamics \u0026ndash; an advanced genetic analysis, to understand HIV molecular epidemiology and transmission dynamics of HIV in the Ghanaian setting within the West African sub-region. The analysis involved full genomic sequences of HIV from infected individuals in Ghana. The genetic relationships of the Ghanaian sequences were compared with global samples. The analysis showed that the most common type of HIV in Ghana, which is the CRF02_AG, likely came from Cameroon and Nigeria. The virus spread mostly from the Greater Accra region to other parts of the country. This study is the first to trace how HIV spread in Ghana since it was first identified in 1986. The findings highlight the importance of using both virus and host patient data to track HIV spread. Accumulation of such data can help health officials plan effective strategies to control the spread of the virus and protect vulnerable communities.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eIn Ghana, the first case of HIV was diagnosed in 1986, and soon after, the Ghana Health Service (GHS) introduced HIV sentinel surveillance across antenatal care (ANC) and sexually transmitted infections (STI) clinics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. several programs have been rolled out since then. These include the \u0026lsquo;treat-all\u0026rsquo; intervention initiated in September 2016 that makes available antiretroviral drugs to all persons diagnosed with HIV, irrespective of the CD4\u003csup\u003e+\u003c/sup\u003e status [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For about two decades now, the HIV epidemic in Ghana has been predominantly due to the CRF02_AG subtype [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This finding has been reported mostly from cross-sectional studies by individual researchers using representative cohorts in selected areas. Despite advances in research and national preventive efforts, reports by the National AIDS/STI Control Programme indicate that has reported that, since the surveillance commenced, there have been about 334,095 cases with an estimated 17,774 new infections in 2023 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The continued increase in the number of cases necessitates a multifaceted approach to tracking transmission in real time to help control the further spread of the virus. Knowledge of spread and transmission network sources would prove useful in our control endeavours.\u003c/p\u003e\u003cp\u003eLike most other pathogens, HIV transmission dynamics studies offer useful models that help to clarify some essential relations between epidemiological factors underlying an overall pattern of the HIV epidemic. For example, it is useful to estimate essential parameters such as the number of secondary infections produced by a primary infectious case; that is, effective reproductive number (\u003cem\u003eRe\u003c/em\u003e), and describe the ancestral traits and demographic contributors of the HIV pandemic in a given population. They are also helpful in identifying the kinds of epidemiological data needed to make predictions about future trends. However, HIV molecular epidemiology studies in Ghana have been somewhat limited. Since the epidemic began, there has yet to be any data on HIV transmission dynamics in Ghana.\u003c/p\u003e\u003cp\u003eUntil quite recently (2022), there were only 31 full-genome HIV sequences from Ghana available in the global sequence database, all of which were acquired in 2003 or earlier. In 2020\u0026ndash;2022, we conducted the first-ever comprehensive HIV molecular epidemiology studies that employed both Sanger and next-generation sequencing techniques to produce 71 whole genome HIV sequences for the analysis of HIV subtypes, drug resistance and coreceptor usage [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The present study builds on the 2020\u0026ndash;2022 study by utilizing full-length HIV-1 genomic sequences with patient metadata to estimate the \u003cem\u003eRe\u003c/em\u003e and reconstruct HIV-1 transmission in Ghana. We do this by employing relevant phylogenetic tools and models that effectively estimate molecular epidemiology and transmission parameters across time and space [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and predict possible future outbreaks [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. To the best of our knowledge and according to documented literature, this represents the first of its kind to be conducted in Ghana, West Africa.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthics statement\u003c/h2\u003e\u003cp\u003eThe protocol and ethics of this study were approved by the Scientific and Technical Committee (STC)/Institutional Review Board (IRB) of the Korle Bu Teaching Hospital, Ghana (KBTH-STC/IRB/00075/2020). Persons included in the study voluntarily gave written informed consent to participate, as well as permission for drawing blood. Consent was documented on a Consent Document Form, which was part of the protocol approved for the study. The procedures for participant assent/consent and blood sampling were done in accordance with the tenets of the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSample collection and sequence characteristics\u003c/h3\u003e\n\u003cp\u003eSequences analyzed in this study were obtained from blood samples collected from HIV-1 infected antiretroviral na\u0026iuml;ve Ghanaians accessing routine care at the Korle-Bu Teaching Hospital, Ghana between 2020 to 2022. This generated 71 Illumina MiSeq-acquired HIV whole genome consensus sequences from the previous study and deposited with the GenBank under accession numbers within OQ121842 \u0026ndash; OQ121917. The samples were mostly (48, 68%) of the CRF02_AG subtype, the predominant subtype in the West African sub-region. The sociodemographic characteristics of the study subjects thus remain as previously described [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMaximum-likelihood tree inference and transmission cluster analyses\u003c/h3\u003e\n\u003cp\u003eWe selected 140 publicly available HIV-1 full-length genomic sequences from the Los Alamos National Laboratory HIV database (LANL) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hiv.lanl.gov\u003c/span\u003e\u003cspan address=\"https://www.hiv.lanl.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for which sampling location and year were available at the time of analysis. We aligned these sequences with the 71 Ghanaian full-length sequences using Maximum Alignment using Fast Fourier Transform (MAFFT) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. We selected the best nucleotide substitution model via ModelFinder [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and a maximum likelihood phylogenetic tree was estimated directly from the chosen model, GTR\u0026thinsp;+\u0026thinsp;G4 in IQTree after 1000 iterations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The ClusterPicker tool [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] was used to investigate putative transmission clusters in the dataset. Statistical support for clusters was defined by a non-parametric Shimodaira-Hasegawa (SH)-like test with node support of \u0026ge;\u0026thinsp;99% and genetic distance of 0.05.\u003c/p\u003e\n\u003ch3\u003eEstimation of the number of introductions of the CRF02_AG subtype in Ghana and phylogeographic reconstruction\u003c/h3\u003e\n\u003cp\u003eThe phylogeographic reconstruction analysis was performed to identify and quantify the introduction events of the 48 CRF02_AG subtype of the HIV lineage in Ghana. For this analysis, we used a time-scaled tree created with TreeTime v0.7.4 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A time-scaled phylogenetic tree was constructed and evaluated for a temporal signal with TempEst [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and outliers were removed. The Time to the Most Recent Common Ancestor (tMRCA) was estimated using simple least-squares regression in TreeTime v0.7.4 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis time-scaled phylogeny was treated as a fixed empirical tree, considering two potential ancestral locations: \"Ghana\" and \"other location.\" A discrete diffusion model was applied using the BEAST v1.10.4 software [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Bayesian analysis via Markov chain Monte Carlo (MCMC) was run for 100\u0026nbsp;million steps. MCMC convergence and mixing properties were assessed using Tracer v1.72 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], achieving effective sample sizes greater than 200 for all parameters. Visualization of the introductions of the CRF02_AG subtype was performed using a modification of the script described by Dellicour and colleagues utilizing the Seraphim package [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to extract spatiotemporal information from the data and visualize the phylogeographic reconstructions.\u003c/p\u003e\u003cp\u003eAdditionally, to count the number of introductions of the CRF02_AG subtype, we conducted a formal discrete phylogeography analysis to understand the dispersion of this subtype in Ghana. This analysis was performed using BEAST v1.10.5 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and the BEAGLE 3 library to improve computational performance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The method employed a GTR\u0026thinsp;+\u0026thinsp;Γ parametrization, a relaxed clock model with rates drawn from an underlying lognormal distribution, and a chain length of 100\u0026nbsp;million steps, with log parameters recorded every 10,000 steps. The maximum clade credibility (MCC) tree was inferred using TreeAnnotator [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and visualized using the treeio package in R [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of the temporal dynamics of the effective reproductive number\u003c/b\u003e \u003cb\u003eRe\u003c/b\u003e, \u003cb\u003eand geographic dispersal of HIV in Ghana\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed phylodynamic analyses using the Bayesian Skygrid coalescent tree prior implemented in BEAST v1.10.5 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This approach allows us to estimate the \u003cem\u003eRe\u003c/em\u003e over time, which gives us insights into the dynamics of HIV transmission within the population. The Bayesian Skygrid model, which accounts for fluctuations in population size and transmission rates, is particularly useful for understanding how the epidemic evolves and for predicting future trends. For the phylogeographic analysis, SPREAD (Spatial Phylogenetic Reconstruction of Evolutionary Dynamics) software was used to track the spatial spread of HIV subtypes in different regions. The results were visualized using Google Earth to provide a clear, geographical representation of the movement and spread of the disease.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStudy sequence characteristics\u003c/h2\u003e\u003cp\u003eThe distribution of HIV subtypes (A, B, CRF02_AG, CRF06_cpx, G) over time in samples from Ghana is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. It is noteworthy that the number of these subtypes is generally decreasing, except subtype B, which shows a rising peak in 2021. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB shows the distribution of marital status in the study population over the years. The data shows a relatively stable distribution, with the categories 'Married' and 'Single' consistently being the most common. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC shows that the distribution of educational level remains largely stable, the most common categories being 'Primary education' and 'Secondary education'. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD shows the distribution of risk factors for the disease studied. The 'heterosexual' category consistently has the highest number, while 'homosexual\" and 'needle prick' have significantly lower values. This distribution indicates that heterosexual transmission is the predominant mode of transmission in this population group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe distribution of HIV subtypes within the Ghanaian population varies across different tribes, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The Akan tribe has the highest number of HIV patients and exhibits the greatest diversity of subtypes. CRF02_AG is the most prevalent subtype in this tribe, followed by CRF02_cpx, subtype A, and subtype G, with subtype B being the least common. In the Northern tribe, CRF02_AG, CRF02_cpx, and subtype A are present. CRF02_AG and CRF02_cpx subtypes are present in other tribes as well. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB illustrates the distribution of subtypes according to sex, with men exhibiting the most diversity in subtypes. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC presents the distribution of subtypes across regions. The figure shows that all described subtypes are present in the Greater Accra region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMaximum-likelihood and transmission cluster analyses\u003c/h3\u003e\n\u003cp\u003eThe best tree was selected after 700 iterations using the Generalized Time Reversible (GTR) model and gamma distribution across sites with four categories (G4) using IQTree. In the presence of global data, two putative transmission clusters of two sequences each were found for the Ghanaian data at \u0026ge;\u0026thinsp;99% bootstrap and 0.05 genetic distance when ClusterPicker was used. The inclusion of the global data was to help determine any spurious clustering between Ghanaian and global data. However, no spurious clustering was found (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePhylogeographic reconstruction and introductions of subtype CRF02_AG to Ghana\u003c/h3\u003e\n\u003cp\u003eTimes for the most recent common ancestor (tMRCA) were calculated using TreeTime. The results indicated an approximate date of 1964.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The root-to-tip regression analysis revealed a strong, positive correlation between the genetic distance of HIV-1 sequences and their sampling dates, confirming the presence of a molecular clock. The estimated substitution rate was 1.78e-03 substitutions per site per year.\u003c/p\u003e\u003cp\u003eFocusing on the HIV subtype CRF02_AG, which is more prevalent among the studied population, we determined the number of introductions of this subtype into Ghana. The analysis identified a minimum of 9 introduction events for this recombinant subtype (95% HPD interval = [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]), based on the phylogenetic analysis of 48 of the samples studied.\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows the phylogenetic tree, in which the number of introductions is marked with red triangles, and branches in green correspond to samples from Ghana. Eight of the nine introduction events were independent, while one was responsible for the dissemination within the transmission cluster.\u003c/p\u003e\u003cp\u003eThe most probable origin of the CRF02_AG subtype in Ghana is Cameroon (CM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), as the most recent common ancestor of all viruses of this subtype traces back to this location, from which the subtype began its dissemination. The largest clade of samples from Ghana also shares a common ancestor with samples from CM. However, not all Ghanaian samples have the same ancestral origin, indicating that introductions came from different locations. Another significant importation to Ghana likely originated from Nigeria (NG). CM and NG are the primary countries from which the CRF02_AG subtype began spreading to other regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEstimation of the temporal dynamics of the effective\u003c/b\u003e \u003cb\u003eRe\u003c/b\u003e, \u003cb\u003eand geographic dispersal of HIV in Ghana\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the changes in population dynamics we analyzed the distribution of the general trend in population growth from 1986 to 2022. The analysis showed a significant increase in population size until around 2010, after which growth either plateaued or declined slightly. The vertical dotted lines in the graph represent the estimated time of disease emergence, and the epidemiological threshold, marked by a \u003cem\u003eRe\u003c/em\u003e of 1, indicates the point at which the disease can sustain itself in the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eTo understand the domestic spread of HIV subtypes in Ghana, SPREAD analysis was performed and the results were visualized with Google Earth programs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The results as displayed in the map point to the origin of the epidemic in the Greater Accra Region and its subsequent spread to other parts of Ghana. The black lines on the map illustrate the direction and possible transmission routes of the virus.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWith regards to controlling the HIV epidemic in Ghana, the available record shows that by the end of 2020, 63% of HIV-infected individuals knew their infection status, 95% of diagnosed individuals were on antiretroviral therapy (ART), and 73% of those on ART achieved viral suppression [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These indicators somewhat place Ghana far from realizing the United Nations Joint Programme on HIV/AIDS (UNAIDS) ambitious targets set in December 2020 for ending AIDS \u0026ndash; the 95-95-95 targets, which aim for 95% of people living with HIV to know their status, 95% of diagnosed individuals to be on ART, and 95% of those on ART to achieve viral suppression by 2025 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Achieving these targets certainly requires intensified efforts and multifaceted approaches to tackling the epidemic. Understanding of the major drivers of HIV transmission remains crucial. This underscores the importance of molecular epidemiology studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe stable distribution of risk factors, with 'heterosexual' being the most common, aligns with previous findings suggesting that heterosexual transmission remains a significant concern. The relatively lower prevalence of 'homosexual' and 'needle prick' transmission underscores the need for targeted prevention and education efforts, especially within high-risk groups that are less frequently represented in the data. The diversity of HIV subtypes observed among different tribes, particularly the predominance of CRF02_AG in the Akan tribe, suggests varying regional transmission patterns. This diversity could be reflective of historical migration patterns, cultural practices, or different levels of healthcare access.\u003c/p\u003e\u003cp\u003eAdditionally, using the Skygrid model in BEAST v1.10.4, we estimated past population dynamics of the Ghanaian dataset from 1986 to 2022 and the \u003cem\u003eRe\u003c/em\u003e after 2005 to 2022. Generally, the population size was relatively higher before 2000 and after 2010. The reproductive number was also less than the epidemiological threshold (\u0026lt;\u0026thinsp;1) until 2015 when it began to rise above the threshold (\u0026gt;\u0026thinsp;1). This shift in the growth pattern can be attributed to various factors like improvements in healthcare, economic development or changes in birth and death rates. Improved access to healthcare could reduce mortality rates, while economic progress could influence birth rates and migration patterns. Furthermore, the introduction of mass education and other prevention strategies such as condom use was communicated and well promoted among the public and in the mainstream media after the year 2000 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This coincided with the introduction of ART.\u003c/p\u003e\u003cp\u003eNotwithstanding the reduced population size seen between the years 2000\u0026ndash;2010, the \u003cem\u003eRe\u003c/em\u003e value increased substantially until it dropped again after 2020, perhaps due to other interventions like pre-exposure prophylaxis use and potent ARTs that enhance reduced and prolonged viral load suppression. Moreover, our estimates of the \u003cem\u003eRe\u003c/em\u003e are indicative of efforts to increase contact tracing and subsequent genotyping of cases. Thus, cases which are not analyzed here are likely to consist of undiagnosed infections.\u003c/p\u003e\u003cp\u003ePhylodynamic reconstruction to estimate the earliest introduction of the CRF02_AG using our dataset showed that Nigeria was the source of two major introductions. It is worth noting that the predominant HIV-1 subtypes in Nigeria have mostly been subtypes A, G and CRF02_AG as reported by many studies [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Of note, in Ghana, the transmission of the CRF02_AG subtype occurred from the country\u0026rsquo;s capital, Accra in the Greater Accra region into other major regions of the country. Factors such as transportation networks, population density and social interactions are likely to have influenced the observed spread of the disease.\u003c/p\u003e\u003cp\u003eEven though there could be blind spots with no sequence data due to financial constraints to routinely sequence HIV-1 in clinical settings and the general population, the socio-demographic characteristics, public health service delivery and population mobility patterns may lend credence to the observations made in this study. The Nigeria-Ghana relationship goes as far back as the 1980s \u0026ndash; specifically during the 1983 famine when many Ghanaians left for Nigeria for survival, and their subsequent expulsion back to Ghana in the mid to late 1980s, a period described as \u0026ldquo;Ghana must go\u0026rdquo; [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This period coincided with the period in which HIV was first detected in Ghana [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Moreover, the Greater Accra region represents the economic hub of the country and where the majority of infection and testing are likely to take place; hence the larger number of sequences obtained from this region.\u003c/p\u003e\u003cp\u003eIn this study, newly acquired HIV-1 full-length sequences were subjected to phylodynamic analysis to unravel the reproductive numbers and transmission pattern of the HIV-1 epidemic in Ghana. Although this study used data that represented only a small percentage of the country\u0026rsquo;s reported HIV-1 cases, it is the first study ever, since HIV was first diagnosed in 1986 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], to use full-length HIV-1 sequences in a statistically rigorous Bayesian phylogenetic approach to better understand and reconstruct the HIV epidemic in Ghana. Overall, the findings of this study provide insights into the population dynamics and epidemiology of HIV disease in Ghana and bring to the fore growth trends and the geographical distribution pattern of HIV outbreaks.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study described the transmission dynamics of HIV using full-genome sequences recently obtained in Ghana. Though our dataset was relatively modest in size, the sequences analyzed were nevertheless representative of the epidemiology of HIV in Ghana. To the best of our knowledge, this study represents the first to perform a Bayesian phylogenetic analysis of full-length HIV-1 sequences to estimate and reconstruct the HIV epidemic in Ghana. Although public health efforts have likely decreased the rate of transmission over the last 5 years, declines are not uniform across key populations. Invariably, this study found there is an increasing emergence of other less prevalent HIV-1 subtypes and thus demonstrates the importance of combining demographic data with molecular data for analysis to prospectively inform real-time targeted public health interventions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcquired Immunodeficiency Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eART\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAntiretroviral Therapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBEAST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian Evolutionary Analysis Sampling Trees\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCD4+\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCluster of Differentiation 4\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eESS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEffective Sampling Size\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGhana Health Service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGTR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGeneralized Time Reversible\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHPD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHighest Posterior Density\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLANL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLos Alamos National Laboratory\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAFFT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultiple Alignment using Fast Fourier Transform\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMaximum Clade Credibility\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMarkov Chain Monte Carlo\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProtease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cem\u003eRe\u003c/em\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEffective Reproductive Number\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReverse Transcriptase\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSPREAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpatial Phylogenetic Reconstruction of Evolutionary Dynamics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSexually Transmitted Infection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUNAIDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJoint United Nations Programme on HIV/AIDS\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge people living with HIV and all the individuals whose samples were used for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Nicholas I. Nii-Trebi, Billal M. Obeng Billal M. Obeng, Hugo G. Castel\u0026aacute;n-S\u0026aacute;nchez\u003c/p\u003e\n\u003cp\u003eFormal Analysis: Billal M. Obeng, Hugo G. Castel\u0026aacute;n-S\u0026aacute;nchez\u003c/p\u003e\n\u003cp\u003eMethodology: Billal M. Obeng, Hugo G. Castel\u0026aacute;n-S\u0026aacute;nchez, Nicholas I. Nii-Trebi\u003c/p\u003e\n\u003cp\u003eData curation: Akua K. Yalley, Makafui Seshie\u003c/p\u003e\n\u003cp\u003eResources: Nicholas I. Nii-Trebi, Kwamena W. C. Sagoe\u003c/p\u003e\n\u003cp\u003eSupervision: Nicholas I. Nii-Trebi\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Billal M. Obeng, Nicholas I. Nii-Trebi, Akua K. Yalley\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review and editing: Nicholas I. Nii-Trebi, Billal M. Obeng, Hugo G. Castel\u0026aacute;n-S\u0026aacute;nchez\u003c/p\u003e\n\u003cp\u003eAll authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study reported here received no external funding. Funding declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not generate a new dataset; hence data sharing does not apply to this article. Sequence datasets analyzed in this study are available in public databases. These have been duly referenced in the text of the article. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBillal Musah Obeng https://orcid.org/0000-0002-1158-7922\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHugo G. Castel\u0026aacute;n-S\u0026aacute;nchez \u0026nbsp; https://orcid.org/0000-0002-4763-0267 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNicholas Israel Nii-Trebi https://orcid.org/0000-0001-5012-9564\u003c/p\u003e\n\u003cp\u003eAkua Koaso Yalley \u0026nbsp;https://orcid.org/0000-0002-7429-1707\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmofah GK. AIDS in Ghana: profile, strategies and challenges. 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GHANA MUST GO: The ugly history of Africa\u0026rsquo;s most famous bag. 2019.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aids-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arty","sideBox":"Learn more about [AIDS Research and Therapy](http://aidsrestherapy.biomedcentral.com/)","snPcode":"12981","submissionUrl":"https://submission.nature.com/new-submission/12981/3","title":"AIDS Research and Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, phylogenetic reconstruction, phylodynamics, molecular epidemiology, transmission dynamics, comparative genomics","lastPublishedDoi":"10.21203/rs.3.rs-7906379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7906379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReconstructing the origins and transmission of the HIV epidemic in Ghana has, since the first diagnosis in 1986, yet to be reported. This study described the transmission clusters of HIV in the Ghanaian setting using a maximum-likelihood tree via the IQTree approach. Using 71 newly described full-length Ghanaian HIV-1 sequences, we performed molecular phylodynamic analysis to determine major drivers of HIV transmission in Ghana, a West African population where the HIV-1 CRF02_AG recombinant is prevalent. However, to reconstruct the origin of the most predominant subtype CRF02_AG, we combined 48 CRF02_AG sequences in our dataset with 140 full-length CRF02_AG sequences downloaded from the Los Alamos National Laboratory HIV database and utilized the ancestral trait reconstruction model in BEAST v.1.10.5 to reconstruct an MCC tree, summarized in TreeAnnotator and visualized with treeio package in R. Phylogeographic reconstruction to estimate the earliest introduction of HIV-1 showed that Cameroon and Nigeria were the sources of nine major introductions, with a time to most recent common ancestor (tMRCA) of 1964.2. Most intra-country transmission occurred from Greater Accra to other major regions. This is the first study to combine full-length HIV-1 genomic sequences with patient metadata to estimate the population dynamics and reconstruct the introduction of the predominant HIV-1 CRF02_AG in Ghana. This study illuminates our understanding of HIV transmission dynamics in Ghana and underscores the utility of combining demographic and molecular data in prospectively tracking HIV transmission to inform targeted public health interventions.\u003c/p\u003e","manuscriptTitle":"Molecular epidemiology, evolution and transmission dynamics of HIV-1 in Ghana, West Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 06:38:37","doi":"10.21203/rs.3.rs-7906379/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-24T14:57:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-24T14:22:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T07:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116530342070329666416044877766126482172","date":"2025-11-08T12:49:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-06T19:20:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305080587345662970017256732911465306323","date":"2025-11-06T19:05:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78284073577296930086174214162039509292","date":"2025-10-29T09:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T18:01:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T02:05:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-22T09:09:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"AIDS Research and Therapy","date":"2025-10-20T13:48:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aids-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arty","sideBox":"Learn more about [AIDS Research and Therapy](http://aidsrestherapy.biomedcentral.com/)","snPcode":"12981","submissionUrl":"https://submission.nature.com/new-submission/12981/3","title":"AIDS Research and Therapy","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"baa6bc6a-8640-4383-94c7-cadd8c6feedd","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T15:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 06:38:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7906379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7906379","identity":"rs-7906379","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}