Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review

Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review View ORCID Profile Martin Zang Pam , View ORCID Profile Alex Odoom , View ORCID Profile Marian Serbeh doi: https://doi.org/10.1101/2025.09.20.25336235 Martin Zang Pam 1 Department of Epidemiology and Disease Control, School of Public Health, University of Ghana , Legon, Accra Ghana 2 Department of Animal Health, Federal College of Animal Health and Production Technology, National Veterinary Research Institute , Vom, Plateau state, Nigeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Zang Pam For correspondence: pamartinz80{at}gmail.com Alex Odoom 3 Department of Medical Microbiology, University of Ghana Medical School , Korle Bu, Accra, Ghana Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alex Odoom Marian Serbeh 4 Department of Social and behavioral science, School of Public Health, University of Ghana , Legon, Accra Ghana Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marian Serbeh Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract This scoping review explores how predictive modelling can strengthen pre-exposure prophylaxis (PrEP) uptake among high-risk populations in Africa, where HIV prevalence remains disproportionately high. Although PrEP is highly effective (40–90%), its uptake and adherence remain suboptimal in LMICs. Predictive modelling provides a promising solution by identifying individuals at elevated risk, enabling targeted, evidence-based interventions. Using Arksey and O’Malley’s framework and PRISMA-ScR strategy, PubMed, Cochrane Library, ProQuest, and Google Scholar were searched for Africa-based studies from 2015–2025. Eligible studies focused on high-risk groups, including men who have sex with men, sex workers, persons who inject drugs, adolescents, and serodiscordant couples, and applied machine learning, regression models, deep learning, and neural networks. Out of 209 records screened, 10 studies met inclusion criteria. Conducted between 2019–2025, they demonstrated how predictive tools can stratify HIV risk, enhance adherence monitoring, and improve resource allocation. Sixty percent relied on demographic and behavioural data and achieved strong predictive performance, particularly for HIV status prediction (70%). However, stigma, weak health systems, poor integration, and limited data quality still hinder implementation. The review underscores predictive modelling’s transformative potential to scale PrEP services across Africa. Integrating machine learning, behavioural modelling, and community-based approaches can improve programmatic efficiency, equity, and targeting. Yet substantial gaps persist in translating predictive outputs into actionable interventions, addressing ethical issues, and validating models in diverse, resource-limited settings. Strengthening collaborations between data scientists, healthcare workers, and policymakers are essential to deliver cost-effective, context-specific PrEP services and accelerate HIV prevention efforts across the continent. KEY MESSAGES - What is already known: The use of Predictive modelling for identifying high-risk individuals to improve PrEP targeting, holds substantial promise for reducing HIV incidence among vulnerable groups, yet its integration into African health systems remains constrained by structural, data, and equity barriers. - What this study adds : This scoping review demonstrates, for the first time, how diverse predictive modelling approaches like machine learning, deep learning, and clustering applied to epidemiological and behavioural data can enhance PrEP uptake and adherence among high-risk groups in LMIC African settings. - How this study could affect research, practice or policy: The review findings highlight priority areas for integrating predictive tools with youth-friendly, community-based, and health system–strengthening strategies to scale cost-effective PrEP delivery, improve adherence, and guide evidence-based HIV prevention policy in Africa. Introduction Since its discovery in the early 1980s, the human immunodeficiency virus (HIV) has remained one of the world’s foremost public health challenges, affecting approximately 38.4 million people globally. Despite significant advances in diagnosis, treatment and prevention, above 2.5 million new cases occur each year, mostly among people aged 15–49 years, with an estimated 19% unaware of their status. 1 2 Although global strategies aim to end the epidemic by 2030, persistent new infections, ongoing HIV-related comorbidities such as Tuberculosis, Oesophageal Candidiasis and Pneumocystis pneumonia—and health system limitations continue to hinder progress. 3 These challenges are most pronounced in low- and middle-income countries (LMICs), especially across Africa, which carries the largest burden of HIV due to fragile healthcare systems, poverty, limited access to services, and overburdened infrastructures. Social stigma, harmful cultural norms, low education and widespread inequality further exacerbate the epidemic, complicating prevention and long-term treatment outcomes. 4 – 6 Pre-exposure prophylaxis (PrEP) has recently come up as a cornerstone HIV-preventive strategy when taken consistently and as prescribed. It is particularly vital for high-risk populations (HRPs) such as men who engage in sex with men (MSM), persons who inject drugs (PWID), female sex workers (FSWs) and persons in serodiscordant relationships. 7 – 10 Recognised by the World Health Organization as an essential intervention, PrEP’s success depends heavily on adherence, access to quality health services and public awareness. A comprehensive approach encompassing risk identification, monitoring, management of potential complications and adherence support is necessary to maximise PrEP’s impact. 11 Scaling up PrEP across sub-Saharan Africa, which accounts for about 70% of the global HIV burden, could dramatically reduce new infections and bolster progress towards achieving the UNAIDS “Fast-track” 95–95– 95 targets. 12 13 These targets form the foundation for accelerating reductions in HIV incidence and moving towards the ultimate goal of ending the epidemic by 2030. Predictive modelling has become an increasingly valuable tool in the control of infectious diseases, particularly in LMICs. By analysing disease trends, population movements and environmental factors, predictive models support early detection, outbreak tracking and timely interventions. 14 In the context of HIV prevention, predictive modelling enhances PrEP uptake by identifying individuals most at risk, predicting adherence patterns and enabling more efficient outreach, personalised care and resource allocation. 15 These tools are especially critical in resource-constrained settings where efficiency, timely action and minimising missed prevention opportunities are paramount. 14 16 Accurate estimation of HIV risk is a critical step in guiding testing campaigns, initiating PrEP and deploying targeted prevention strategies. While traditional models have been useful in identifying population-level risk, growing interest now centres on predictive modelling approaches that can refine individual- and community-level outcomes. Models such as Susceptible–Exposed–Infected–Recovered (SEIR) or Susceptible–Infected–Refractory– Susceptible (SIRS), as well as geospatial frameworks, have proven instrumental in epidemic forecasting and mapping high-prevalence areas, particularly in resource-inadequate locations. Agent-based models (ABMs) simulate behaviour-driven transmission dynamics, while machine learning (ML) and Markov models provide personalised risk prediction and cost-effectiveness analysis. Artificial intelligence (AI) is increasingly integrated to enable real-time surveillance and dynamic risk profiling. This review therefore seeks to map and critically evaluate the application of predictive modelling tools in improving PrEP uptake and HIV prevention approaches among high-risk groups across the African continent. By synthesising existing evidence, it aims to identify knowledge gaps, highlight best practices and offer insights for more targeted, cost-effective and equitable PrEP delivery in low-resource settings. Review question How can predictive modelling approaches be applied to identify high-risk populations and improve PrEP uptake and adherence among HIV-vulnerable groups in Africa? Review Objectives To examine how predictive modelling identifies high-risk HIV groups and guides targeted PrEP strategies in Africa, and to assess the effectiveness of commonly used predictive models in improving PrEP uptake and adherence among vulnerable populations. Methods The scoping review used the proposed methodology of Arksey and O’Malle (2005); 17 addition to the scoping review types. 18 It was put down in consonance with the Preferred Reporting Items in Systematic Reviews and Meta-analyses (PRISMA) Sources of data and search strategies Data for this integrative scoping review were obtained from a comprehensive search of peer-revised and grey works, comprising primary research studies, systematic and narrative reviews, theoretical articles, editorials and relevant policy documents. Medical subject headings (MeSH) with relevant keywords were employed to design the investigation strategy derived from the study’s objectives and research questions. Core terms included: HIV , Pre-exposure Prophylaxis (PrEP) , Predictive Modelling , High-Risk Populations , and Low-resource settings (Africa) . Boolean operators (OR, AND), truncation, and phrase searching were used to refine and combine the search terms. Literature checks were performed across four major free-access electronic databases, including PubMed, Cochrane Library, ProQuest and direct Google Scholar search, covering articles published between 2015 and 2025 ( Table A.1 Appendix). All retrieved studies were first scrutinised from a downloaded CSV file while checking their title and abstract’s relatedness to the work, with full-text reviews performed for those meeting the study’s eligibility criteria. This rigorous approach ensured a broad yet focused synthesis of the existing knowledge on the topic. Study Selection This review focused on reviewed materials such as article publications, book chapters, screened thesis works and policy documents from the last 10 years (2015–2025) that employed predictive modelling for HIV PrEP uptake across the African continent. Eligible studies included both quantitative and qualitative research involving adolescent/adult high-risk populations aged 18 and above—specifically female sex workers (FSWs), men engaging in sex with men (MSM), people who inject drugs (PWID), adolescents–young adults and serodiscordant cohorts. Only English-language studies that applied predictive modelling to improve PrEP uptake or adherence among these groups in the African setting were included. Studies published before 2015, conducted outside LMICs (Africa), or involving other key populations not specified above were excluded. Also excluded were articles lacking a predictive modelling component or those unrelated to HIV prevention. To enhance the breadth of the review, reference list mining and citation tracking were also conducted using Google Scholar to identify additional relevant studies. Two independent reviewers screened the titles and abstracts of the identified articles, and any inconsistencies were resolved via consultations with a third evaluator. View this table: View inline View popup Table 1. Characteristics of the selected materials used in the review following the Population, Intervention Comparison and Outcome (PICO) format Data extraction The data extraction began with a download of a CSV file and was refined using a Microsoft Excel (365) spreadsheet. Pooled studies were outlined on a pre-planned data retrieval pattern with features like study author(s), title, abstract, year study was conducted, key study take away/findings and country where the work was conducted in Africa recorded and summarized in a pre-formed table. Data synthesis approach/Analysis This scoping review applied the Arksey and O’Malley outline, complemented by Joanna Briggs Institute (JBI) methodology, to systematically identify and synthesize evidence on predictive modelling for improving PrEP uptake among high-risk HIV groups in Africa. Data were thematically organized by target populations, study context and PrEP outcomes, publication year, predictive model types, and study locations. Quantitative findings like initiation and adherence rates, PrEP efficacy measures, and model performance were summarized descriptively in tables and figures. Qualitative data on implementation barriers, facilitators, and contextual factors underwent thematic analysis to identify patterns, gaps, and opportunities for scaling predictive approaches. This integrative process enabled a comparative understanding of how predictive tools operate across diverse African settings, their implications for equity and scalability, and the contextual conditions needed for effective implementation. By combining quantitative and qualitative evidence, the review highlights benefits, limitations, and key research gaps to inform future applications in LMICs. Consent and ethical considerations The study did not directly involve participants, stakeholders or communities, hence consent or ethical clearance was not applicable. Results Search result A total of 209 records were recovered from PubMed, Cochrane Library, ProQuest, and Google Scholar, covering studies from 2015 to 2025. After excluding 135 irrelevant records and removing 4 duplicates, 70 articles remained. Fifty-five (55) studies were screened out for not using predictive modelling, leaving 15 that met the study’s partial eligibility. After the full text review, 5 studies were removed because they were not done in an LMIC (African setting), did not include high-risk populations, or misaligned with the review objectives, leaving 10 eligible works that fully met the studies objectives and inclusion criteria ( Figure 1 . below). Download figure Open in new tab Figure 1. PRISMA Flow chart of the screened studies used in the scoping review Characteristics of the included studies Table 2 summarises the 10 studies that met the inclusion criteria for this scoping review, all conducted between 2019 and 2025 in various African countries. These studies collectively investigated the application of predictive modelling tools to improve the identification of individuals at elevated risk of contracting HIV, support PrEP uptake, and enhance adherence across diverse vulnerable populations. The populations targeted in these studies were female sex workers (FSWs), men who have sex with men (MSM), people who inject drugs (PWID), HIV-serodiscordant couples, and in some cases, general high-risk adolescents and youth. The geographical coverage of the studies spans countries such as Zimbabwe, South Africa, Ethiopia, Uganda, Rwanda, and Kenya and included multi-country analyses across Eastern and Southern Africa, reflecting regional diversity in data, risk profiles, and healthcare system challenges. View this table: View inline View popup Table 2. Summary of the study characteristics A wide array of predictive modelling approaches was employed across the studies. These included machine learning models (e.g., XGBoost, random forest, decision trees), deep learning models (such as recurrent neural networks, RNNs), regression-based models, and unsupervised learning techniques such as agglomerative hierarchical clustering. These tools were applied to analyse large-scale datasets derived from national demographic and health surveys to detect patterns in socio-behavioural, demographic, and clinical variables that are predictive of HIV status or future risk. The study designs varied widely, encompassing retrospective cross-sectional studies, quantitative data analyses, systematic reviews, meta-analyses, and doctoral research projects. Although some studies focused on model comparison to assess predictive accuracy, others explored implementation strategies, cost-effectiveness, and population-level impact, offering a comprehensive picture of how predictive models can guide targeted PrEP interventions in resource-limited settings ( Table 2 ). Thematic Categorisation of included studies Thematic and key findings from the included studies were grouped into categories (model types, geographic relevance, target populations, predictor variables, and outcome relevance). View this table: View inline View popup Table 3: Thematic Categorisation and Frequency of Key Findings Across 10 Studies on Predictive Modelling and PrEP-Related HIV Risk in African LMICs Pictoral themes from the 10 included study findings The frequency of thematic findings across the 10 studies on predictive modelling for HIV prevention in African LMICs ( Figure 2 ) revealed the dominance of AI/ML applications, key populations focus, socio-behavioural data use, and implications for PrEP delivery and health policy Download figure Open in new tab Figure 2. Chart showing the thematic frequency of findings across the 10 studies on predictive modelling for HIV prevention in African LMICs Discussion Predictive models improve the identification of high-risk individuals and can increase pre-exposure prophylaxis (PrEP) uptake, but successful implementation in Africa and other LMICs requires addressing contextual barriers, fostering key facilitators aimed at increasing PrEP implementation outcomes and ensuring effective risk communication. Predictive modelling has emerged as a valuable tool for improving PrEP uptake among high-risk HIV groups in Africa by enabling more precise identification of individuals at greatest risk and informing targeted interventions. This scoping review examined 10 studies 19 – 28 conducted across some African countries between 2019 and 2025. These studies applied various predictive modelling techniques such as machine learning (ML), logistic regression, decision trees, deep learning, and neural networks to enhance PrEP acceptance among high-risk HIV individuals. The models assessed demographic, behavioural, clinical, and geographic data to identify individuals at elevated HIV risk from the key populations studied. Some of the predictive accuracies reported from these studies were in line with those seen from similar works reported by Rosenberg et al., and Romero et al., 29 30 who described same especially when incorporating variables like sexual partner behaviours, HIV viremia, and local prevalence rates. For example, integrating predictive tools into clinical workflows in South Africa and Uganda significantly increased PrEP eligibility identification and prescriptions. Grant (2020) 21 emphasised in her work the role such predictive data-driven tools play in enhancing cost-effective PrEP scale-up among high-risk populations, while Hunidzarira et al. 19 found that using sexually transmitted infection incidence alone was insufficient— highlighting the need for multifactorial models. Models observed using national health survey data in Ethiopia and supervised machine learning in Zimbabwe have showed promising results. 22 – 24 These models successfully pinpointed predictors such as lifetime sexual partners, cohabitation duration, and socio-behavioural factors emphasising the value of tailoring models to local epidemiological and social contexts. Despite technical success, many studies acknowledged limited real-world impact due to structural and behavioural barriers. Challenges such as stigma, low risk perception, fear of side effects, and socio-economic constraints continue to impede PrEP uptake in. 31 – 33 These findings suggest that predictive modelling must be paired with culturally sensitive, community-driven approaches, especially youth-friendly services, to translate risk identification into effective intervention. While predictive modelling offers powerful tools for targeting HIV prevention efforts, its success depends on aligning technological innovation with health system readiness and community engagement strategies. Implications for Implementation and Challenges in Policy, Practice, and Research This review reveals critical implications for applying predictive modelling in real-world PrEP delivery across Africa and other LMICs. First, predictive analytics present a cost-effective strategy for prioritising limited HIV prevention resources, particularly in targeting interventions towards high-risk populations. This approach aligns with the WHO and UNAIDS recommendations that emphasise scaling up PrEP services in regions with high HIV incidence and low uptake, especially among adolescent girls and young women (AGYW), MSM and other main populations. 34 35 Secondly, integrating predictive tools into national HIV information systems and electronic health records (EHRs) could enhance real-time decision-making and enable data-driven resource allocation. However, implementation remains limited in LMICs due to infrastructural gaps, financial constraints, and a shortage of personnel trained in data science and artificial intelligence (AI). Addressing these barriers will require government investment in workforce development and strategic public-private partnerships to advance digital health ecosystems. In addition, ethical and regulatory frameworks must guide the predictive model deployment. These include ensuring a transparent model design, safeguarding patient data privacy, and preventing algorithmic bias that may intensify health inequities among already marginalised groups. Community engagement is vital for building trust, especially in settings with historical mistrust in healthcare systems. From a research standpoint, future studies in the fields of implementation science should assess the level of predictive modelling usage and accuracy to examine the practical translation of such model outputs in improving PrEP initiation, retention, and adherence. Mixed-methods research that integrates implementation research can provide insights into the contextual effectiveness of predictive modelling whilst looking at gaps from the health systems that can impede the precipitation of better PrEP outcomes. Cost-effectiveness analyses and evaluations of model scalability and cross-border adaptability will also be critical for shaping sustainable, region-wide strategies. Despite the demonstrated predictive accuracy, most models remain underutilised in routine clinical practice. Siyamayambo et al. 20 noted the limited integration of Fourth Industrial Revolution (4IR) technologies such as machine learning and AI into African health systems, citing infrastructural and capacity challenges. Furthermore, many models rely on retrospective secondary data and underrepresent socio-behavioural factors such as stigma, disclosure fears, and perceived risk, limiting their community relevance. Some studies have showed that including variables such as education, circumcision, and urban residency improved model performance. 26 27 However, data standardisation across countries remains a major challenge. To bridge the gap between modelling and implementation, hybrid strategies are needed. Some advocates have suggested that predictive strategies should be embedded into culturally sensitive, youth-friendly, and community-led service platforms to ensure that risk identification leads to actionable and equitable PrEP delivery. 32 33 Strategies Adopted to Enhance PrEP Initiation and Continuation and Lessons for Broader LMICs The scoping review by Ekwunife et al. 36 highlights strategies to improve pre-exposure prophylaxis (PrEP) initiation and continuation among adolescent girls and young women (AGYW) in LMICs. Recognising AGYW’s heightened HIV risk in Africa, the authors propose a three-pillar framework built around demand creation, PrEP initiation, and PrEP continuation. Demand Creation To stimulate demand, social marketing campaigns and behaviour-centred design are emphasised. These efforts reframe PrEP as a tool of empowerment and protection rather than solely biomedical intervention. Peer influencer networks also play a key role by challenging stigma, providing relatable narratives, and communicating PrEP benefits via culturally relevant channels and youth-friendly platforms, including social media. PrEP Initiation Common barriers such as limited knowledge, stigma, cost, and fear of side effects necessitate multifaceted strategies involving education, resource allocation, insurance coverage, and technology-driven solutions to increase accessibility. 37 The importance of decision-support tools and adolescent-friendly health services to facilitate informed PrEP decisions, address adherence or side-effect concerns, and improve trust in prevention services is also stressed. 36 AGYW are more likely to initiate PrEP in youth-focused or private clinics than in general family planning settings, highlighting the value of privacy, confidentiality, and nonjudgmental service environments. Establishing dedicated youth spaces or partnering with trusted community providers can further lower barriers. PrEP Continuation Retention remains a major challenge due to AGYW’s rapidly changing life circumstances. Recommended interventions include SMS adherence reminders, drug-level feedback, peer-support clubs, and conditional economic incentives. Integrating psychosocial support and counselling into follow-up visits reinforces adherence and addresses stigma, mobility, and partner disclosure concerns. Differentiated service delivery (DSD) models such as community pharmacies, mobile outreach, and telemedicine bring services closer to where AGYW live and work. These decentralised approaches enhance privacy, flexibility, and convenience, particularly in rural or underserved areas. Lessons from Broader LMIC Contexts and Other Key Populations While the focus of Ekwunife et al. 36 is on AGYW, similar strategies have been successfully applied to other key populations at high risk of HIV in LMICs. For instance, studies from Kenya and South Africa have shown that peer-led education, mobile PrEP clinics, and community drop-in centres have significantly increased PrEP uptake and adherence amongst MSM’s and FSWs. 38 39 These interventions are often accompanied by stigma reduction campaigns and linkage to other services, such as STI screening and psychosocial support. In Nigeria, key population-led clinics have emerged as a promising model for delivering PrEP to people who inject drugs (PWID) and HIV-serodiscordant couples. These clinics are typically staffed by members of the key populations themselves, fostering a non-judgmental and inclusive environment that encourages retention in care. 40 In addition, digital tools have been widely adopted to support adherence and monitoring. In Uganda, the use of real-time adherence devices such as Wisepill and social media-based support platforms (e.g., WhatsApp groups) has shown promise in improving adherence among MSM. Meanwhile, in Zimbabwe and Zambia, community-based PrEP delivery through mobile health vans and integrated sexual and reproductive health (SRH)-HIV services has facilitated better outreach and engagement, particularly among underserved and mobile populations. 41 These lessons highlight the need for population-specific tailored, flexible delivery mechanisms and strong community engagement to ensure that PrEP is not only accessible but also acceptable and sustainable. Suggested Strategies Using Boosters for PrEP Delivery in High-Risk Populations Employing Implementation Research (IR) Integrating implementation research (IR) into HIV prevention efforts can substantially improve pre-exposure prophylaxis (PrEP) delivery and effectiveness, particularly among high-risk groups (HRGs) in Africa and globally. This scoping review highlights the importance of adopting IR principles to address gaps in PrEP service delivery and uptake. Through methods such as literature reviews, stakeholder engagement, and contextual analyses, IR identifies key challenges including limited Demographic and Health Survey (DHS) data, stock management issues, and programmatic inefficiencies. Implementation strategies such as training healthcare professionals, supporting community health workers, and developing tailored educational programmes on demographic data use, digital tools, and predictive models can strengthen PrEP services. 36 42 – 44 When aligned with target groups’ needs, IR interventions have shown measurable improvements in PrEP initiation, adherence and retention. Predictive modelling can amplify these efforts in resource-limited settings by stratifying populations based on HIV risk and enabling efficient, locally tailored interventions while overcoming stigma, low awareness, and ethical barriers. Exploring both users’ and non-users’ perspectives enhances understanding of PrEP acceptability, feasibility, and service gaps. Evidence from initiatives such as Australia’s EPIC-New South Wales—achieving a 25% HIV incidence reduction within one year, demonstrates the potential of high-coverage, targeted PrEP programmes. 45 However, uptake and adherence remain suboptimal across many LMICs in Africa due to stigma, cost, poor integration, and limited provider engagement. 46 47 Community-based and telemedicine-supported PrEP programmes have proven feasible, acceptable, and scalable but require further adaptation to reach underrepresented groups, including women, people who inject drugs, and ethnic minorities. 43 48 Ultimately, IR especially when guided by predictive modelling offers a critical pathway for adapting HIV prevention strategies to local contexts, enhancing programme efficiency, and ensuring PrEP reaches those at highest risk, thereby maximising its public health impact. 45 46 48 Role of Community Engagement Community engagement through youth-friendly, culturally competent, and socially responsive approaches is a powerful strategy to strengthen PrEP delivery among high-risk HIV populations in Africa, including MSM, adolescences youth and adults, and female sex workers (FSWs). These methods reduce barriers such as stigma, discrimination, and distrust of health systems that often impede HIV prevention. Evidence shows that community-based interventions using non-judgmental communication and culturally sensitive messaging normalise PrEP use and increase confidence in prevention efforts, particularly in underserved or conservative settings. 49 50 Culturally relevant health education delivered by peer educators, community influencers, and digital platforms raises awareness, dispels myths, and supports informed decision-making around PrEP. 51 Youth-friendly, confidential services and mobile outreach reduce access gaps caused by transportation or service limitations. 52 53 Direct community involvement in programme design fosters ownership, sustainability, and effectiveness. Models such as decentralised services, peer-led interventions, and integration with sexual and reproductive health services have further enhanced accessibility. 33 54 Engagement of trusted leaders, use of safe spaces, and youth-trained providers also improve uptake and adherence. 32 55 56 Despite notable gains, challenges like stigma, side effects, limited resources, and community instability persist, requiring continuous adaptation, stakeholder engagement, and innovative demand-creation strategies. Expanding PrEP delivery to schools, youth zones, and community venues has yielded positive outcomes. 33 54 Ultimately, community engagement is central to scaling PrEP and reducing HIV incidence across Africa’s most vulnerable populations. Using the Health Systems Strengthening (HSS) Approach Strengthening health systems is critical for effectively using predictive models to enhance HIV PrEP uptake among high-risk groups. Machine learning-based HIV risk prediction models integrated into electronic health records (EHRs) improve identification of PrEP-eligible individuals and prescription rates, often outperforming traditional risk assessments while maintaining equitable sensitivity across diverse populations. 30 57 – 60 Mathematical and simulation studies show that targeted PrEP delivery combined with HIV testing and behavioural interventions can substantially reduce new infections in high-burden settings. 61 62 Differentiated service delivery models such as peer-led and telemedicine-assisted programmes further improve PrEP adherence and retention. 43 Successful implementation depends on integrating predictive tools into clinical workflows, training regimens, and addressing stigma, trust, and digital literacy concerns. 63 Investment in digital infrastructure, provider capacity, and community engagement will enable ethical, precise, and scalable predictive analytics for PrEP delivery in resource-limited African contexts. 60 58 30 Download figure Open in new tab Figure 3. Suggested Framework for improving worldwide PrEP adoption using the 3 Booters approach across Africa Conclusion Predictive modelling offers a powerful, data-driven approach to identify high-risk populations for HIV and guide targeted PrEP interventions in African LMICs. By integrating demographic, behavioural, clinical, and contextual data, these models can improve efficiency, cost-effectiveness, and scalability of HIV prevention. Machine learning approaches such as XGBoost, recurrent neural networks, and deep learning show strong potential for predicting HIV risk and informing early interventions. Complementary strategies, including implementation research, community engagement, and health systems strengthening, are essential to overcome structural and financial barriers, sustain adherence, and optimise programme impact. Tailoring predictive tools to key populations and socio-behavioural contexts enables more precise, equitable PrEP delivery, accelerating progress toward reduced HIV incidence and improved public health outcomes across the continent. Contributions MZP conceptualized the study, database search and wrote the study’s first draft. AO also executed the study search and redrafting of the manuscript, while MS did the article’s final review and corrections Funding This study was performed without any external resources aside from that of the authors Competing Interest The authors had none to declare Patient consent for publication Not applicable Ethics approval There was no human contact or need for primary records for this scoping review Data Availability The required data for this work has been captured in the work APPENDICES View this table: View inline View popup Download powerpoint Table A.1: Databases and search terms for scoping review References 1. ↵ Nagai H , Ankomah A , Fuseini K , et al. HIV pre-exposure prophylaxis uptake among high-risk population in sub-Saharan Africa: A systematic review and meta-analysis . AIDS Patient Care and STDs . 2024 Feb 1 ; 38 ( 2 ): 70 – 81 . OpenUrl CrossRef PubMed 2. ↵ GBD 2015 HIV Collaborators . Estimates of global, regional, and national incidence, prevalence, and mortality of HIV, 1980–2015: the Global Burden of Disease Study 2015 . The lancet HIV . 2016 Aug 1 ; 3 ( 8 ): e361 – 87 . OpenUrl 3. ↵ Swinkels H , Nguyen A , Gulick P . HIV and AIDS . StatPearls . 2024 Jul 27. 4. ↵ Phillips K . Review of the AVERT, a Global Information and Education on HIV and AIDS Resource . J of Cons Health on the Internet . 2019 Jul 3 ; 23 ( 3 ): 290 – 8 . OpenUrl 5. Geneva, U. N. A. I. D. S. UNAIDS Report on the Global AIDS Epidemic Shows that 2020 Targets will not be MET Because of Deeply Unequal Success; COVID-19 Risks Blowing HIV Progress Way Off Course . UNAIDS ; 2020 : 5 – 7 . https://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2020/july/20200706_global-aids-report . 6. ↵ ONUSIDA/UNAIDS, 2020 Global HIV & AIDS statistics—2020 fact sheet | UNAIDS . Unaids.Org . https://www.unaids.org/en/resources/fact-sheet 7. ↵ Chou R , Spencer H , Bougatsos C , et al. Preexposure Prophylaxis for the Prevention of HIV: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force . JAMA . 2023 : 22; 330 ( 8 ): 746 – 763 OpenUrl CrossRef PubMed 8. Kabapy AF , Shatat HZ , Abd EllWahab EW . Attributes of HIV infection over decades (1982– 2018): A systematic review and metalanalysis . Transboundary and emerging diseases . 2020 Nov ; 67 ( 6 ): 2372 – 88 . OpenUrl 9. Joint United Nations Programme on HIV/AIDS (UNAIDS) . The path that ends AIDS: UNAIDS global AIDS update 2023 . Geneva : UNAIDS ; 2023 . 10. ↵ Sanni OF , Umoh P , Kalaiwo A , et al. Pre-exposure prophylaxis and HIV prevention among key populations in Nigeria . Int J of Mat and Child Health & AIDS . 2024 Jun 28 ; 13 : e013 . OpenUrl 11. ↵ Sundareshan V , Swinkels HM , Nguyen AD , et al. Preexposure prophylaxis for HIV prevention. InStatPearls [Internet] 2024 Jan 28 . StatPearls Publishing . 12. ↵ World Health Organization , Pan American Health Organization . Pre-exposure prophylaxis (PrEP) . Washington (DC) : PAHO ; 2022 . https://www.paho.org/en/topics/combination-hiv-prevention/pre-exposure-prophylaxis-prep 13. ↵ Joint United Nations Programme on HIV/AIDS (UNAIDS) . Fact Sheet 2024—Latest Global and Regional HIV Statistics on the Status of the AIDS Epidemic . Joint United Nations Programme on HIV/AIDS (UNAIDS) : Geneva, Switzerland . 2024 Dec 14 . 14. ↵ Olushola A , Mart J , Alao V . Predictive Modelling For Disease Outbreak Prediction [Internet] . 2023 15. ↵ Hillis A , Germain J , Hope V , et al. Pre-exposure prophylaxis (PrEP) for HIV prevention among men who have sex with men (MSM): a scoping review on PrEP service delivery and programming . AIDS and Behavior . 2020 Nov ; 24 ( 11 ): 3056 – 70 . OpenUrl CrossRef PubMed 16. ↵ Fahey CA , Wei L , Njau PF , et al. Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania . PLOS Global Public Health . 2022 Sep 16 ; 2 ( 9 ): e0000720 . OpenUrl PubMed 17. ↵ Arksey H , O’Malley L . Scoping studies: towards a methodological framework . Int J Soc Res Methodol 2005 ; 8 : 19 – 32 . OpenUrl CrossRef 18. ↵ Tricco AC , Lillie E , Zarin W , et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation . Ann Intern Med 2018 ; 169 : 467 – 73 . OpenUrl CrossRef PubMed 19. ↵ Hunidzarira P , Brown ER , Chirenje ZM , et al. Population-Level Correlation Between Incidence of Curable Sexually Transmitted Infections and Human Immunodeficiency Virus (HIV)-1 Among African Women Participating in HIV-1 Pre-Exposure Prophylaxis Trials . The Journal of Infectious Diseases . 2022 Sep 15 ; 226 ( 6 ): 1069 – 74 . OpenUrl PubMed 20. ↵ Siyamayambo C , Phalane E , Phaswana-Mafuya RN . Diagnosis and Management of Sexually Transmitted Infections Using Artificial Intelligence Applications Among Key and General Populations in Sub-Saharan Africa: A Systematic Review and Meta-Analysis . Algorithms . 2025 Mar 7 ; 18 ( 3 ): 151 . OpenUrl 21. ↵ Grant H . The scale-up of PrEP for HIV prevention in high-risk women in sub-Saharan Africa: use of mathematical modelling to inform policy making ( Doctoral dissertation, London School of Hygiene & Tropical Medicine ). 22. ↵ Belete DM , Huchaiah MD . A Deep Learning Approaches for Modeling and Predicting of HIV Test Results Using EDHS Dataset . Future Opportunities and Tools for Emerging Challenges for HIV/AIDS Control . 2023 Feb 8 : 63 . 23. Birri Makota RB , Musenge E . Predicting HIV infection in the decade (2005–2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach . PLOS Digital Health . 2023 Jun 7 ; 2 ( 6 ): e0000260 . OpenUrl 24. ↵ Chingombe , I. ; Dzinamarira , T. ; Cuadros , D , et al. Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data , Recurrent Neural Networks, and Machine Learning Techniques. Trop. Med. Infect. Dis . 2022 , 7 , 231 . OpenUrl PubMed 25. Oladokun OO . Predicting HIV status among women in South Africa using machine learning: comparing decision tree model and logistic regression ( Master’s thesis, University of the Witwatersrand , Johannesburg (South Africa) . 26. ↵ Orel E , Esra R , Estill J , et al. Machine learning to identify socio-behavioural predictors of HIV positivity in East and Southern Africa . medRxiv . 2020 Jan 27 : 2020 – 01 . 27. ↵ Mutai CK , McSharry PE , Ngaruye I , et al. Use of unsupervised machine learning to characterise HIV predictors in sub-Saharan Africa . BMC Infectious Diseases . 2023 Jul 19 ; 23 ( 1 ): 482 . OpenUrl PubMed 28. ↵ Balzer LB , Havlir DV , Kamya MR , et al. Machine learning to identify persons at high-risk of human immunodeficiency virus acquisition in rural Kenya and Uganda . Clin Infect Dis . 2020 Nov 1 ; 71 ( 9 ): 2326 – 33 . OpenUrl PubMed 29. ↵ Rosenberg NE , Shook-Sa BE , Young AM , et al. A Human Immunodeficiency Virus Type 1 Risk Assessment Tool for Women Aged 15–49 Years in African Countries: A Pooled Analysis Across 15 Nationally Representative Surveys . Clinical Infectious Diseases . 2024 Nov 15 ; 79 ( 5 ): 1223 – 32 . OpenUrl PubMed 30. ↵ Romero S , Alvarez KS , Nijhawan AE , Nethi A , Bistransin K , King HL . Association of an HIV-Prediction Model with Uptake of Preexposure Prophylaxis . Applied Clinical Informatics . 2025 May ; 16 ( 03 ): 507 – 15 . OpenUrl PubMed 31. ↵ Rugira E , Biracyaza E , Umubyeyi A . Uptake and persistence on HIV pre-exposure prophylaxis among female sex workers and men having sex with men in Kigali, Rwanda: a retrospective cross-sectional study design . Patient preference and adherence . 2023 Dec 31 : 2353 – 64 . 32. ↵ Nagai H , Ankomah A , Fuseini K , et al. HIV pre-exposure prophylaxis uptake among high-risk population in sub-Saharan Africa: A systematic review and meta-analysis . AIDS Patient Care and STDs . 2024 Feb 1 ; 38 ( 2 ): 70 – 81 . OpenUrl CrossRef PubMed 33. ↵ Nicol E , Basera W , Turawa E , et al. Assessing PrEP Initiation and Adherence Among High-Risk, Sexually Active Adolescents and Young Adults: A Population-Based Prospective Study Across Diverse Service Delivery Models in a High HIV Prevalent District in South Africa . AIDS and Behavior . 2025 May 2 : 1 – 22 . 34. ↵ IN DANGER: UNAIDS Global AIDS Update 2022 . Geneva : Joint United Nations Programme on HIV/AIDS ; 2022 . Licence: CC BY-NC-SA 3.0 IGO. 35. ↵ World Health Organization . Serving the needs of key populations: Case examples of innovation and good practice on HIV prevention, diagnosis, treatment and care . InServing the needs of key populations: case examples of innovation and good practice on HIV prevention, diagnosis, treatment and care 2017 . 36. ↵ Ekwunife OI , Ejie IL , Okelu V , et al. Interventions to increase the uptake and continuation of pre-exposure prophylaxis (PrEP) by adolescent girls and young women at high risk of HIV in low-income and middle-income countries: a scoping review . BMJ global health . 2022 Dec 1 ; 7 ( 12 ): e009474 . OpenUrl Abstract / FREE Full Text 37. ↵ Dave P . Exploring the Effectiveness of Pre-Exposure Prophylaxis (PrEP) Use for HIV Prevention among High-Risk Populations . Asian J of Pharm Res and Dev . 2024 Apr 15 ; 12 ( 2 ): 1 – 6 . OpenUrl 38. ↵ Irungu EM , Mugwanya KK , Mugo NR , et al. Integration of pre-exposure prophylaxis services into public HIV care clinics in Kenya: a pragmatic stepped-wedge randomised trial . The Lancet Global Health . 2021 Dec 1 ; 9 ( 12 ): e1730 – 9 . OpenUrl 39. ↵ Velloza J , Donnell D , Hosek S , et al. Alignment of PrEP adherence with periods of HIV risk among adolescent girls and young women in South Africa and Zimbabwe: a secondary analysis of the HPTN 082 randomised controlled trial . The Lancet HIV . 2022 Oct 1 ; 9 ( 10 ): e680 – 9 . OpenUrl PubMed 40. ↵ Ogunbajo A , Tsai AC , Kanki PJ , et al. Acceptability of and preferences for long-acting injectable HIV PrEP and other PrEP modalities among sexual minority men in Nigeria, Africa . AIDS and Behavior . 2022 Jul ; 26 ( 7 ): 2363 – 75 . OpenUrl PubMed 41. ↵ Rucinski KB , Schwartz SR , Mishra S , et al. High HIV prevalence and low HIV-service engagement among young women who sell sex: a pooled analysis across 9 sub-Saharan African countries . JAIDS . 2020 Oct 1 ; 85 ( 2 ): 148 – 55 . OpenUrl PubMed 42. ↵ Scott RK , Deyarmond M , Marwitz S , et al. Implementation of an educational intervention to improve HIV pre-exposure prophylaxis services for women in an urban sexual health clinic . AIDS patient care and STDs . 2023 Sep 1 ; 37 ( 9 ): 447 – 57 . OpenUrl PubMed 43. ↵ Wu Y , Chen H , Zhan J , Liu J , Li Y , Cai W , Liu S , Liang N , Lan G . A Differentiated HIV Pre-Exposure Prophylaxis Delivery Model for High-Risk Groups in Nanning City , South China: Findings from a Pilot Program. AIDS Edu and Prev . 2024 Dec ; 36 ( 6 ): 428 – 45 . OpenUrl 44. ↵ Kamitani E , Higa DH , Crepaz N , et al. Identifying best practices for increasing HIV pre-exposure prophylaxis (PrEP) use and persistence in the United States: a systematic review . AIDS and Behavior . 2024 Jul ; 28 ( 7 ): 2340 – 9 . OpenUrl PubMed 45. ↵ Grulich AE , Guy R , Amin J , et al. Population-level effectiveness of rapid, targeted, high-coverage roll-out of HIV pre-exposure prophylaxis in men who have sex with men: the EPIC-NSW prospective cohort study . Lancet HIV . 2018 Nov ; 5 ( 11 ): e629 – 37 . OpenUrl 46. ↵ Merle JL , Benbow N , Li DH , et al. Improving delivery and use of HIV pre-exposure prophylaxis in the US: a systematic review of implementation strategies and adjunctive interventions . AIDS and Behavior . 2024 Jul ; 28 ( 7 ): 2321 – 39 . OpenUrl PubMed 47. ↵ Li DH , Benbow N , Keiser B , et al. Determinants of implementation for HIV pre-exposure prophylaxis based on an updated consolidated framework for implementation research: a systematic review . JAIDS . 2022 Jul 1 ; 90 ( S1 ): S235 – 46 . OpenUrl PubMed 48. ↵ Iniesta C , Coll P , Barberá MJ , et al. Implementation of pre-exposure prophylaxis programme in Spain. Feasibility of four different delivery models . PLoS One . 2021 Feb 8 ; 16 ( 2 ): e0246129 . OpenUrl CrossRef PubMed 49. ↵ Rousseau E , Bekker LG , Julies RF , Celum C , Morton J , Johnson R , Baeten JM , O’Malley G . A community-based mobile clinic model delivering PrEP for HIV prevention to adolescent girls and young women in Cape Town, South Africa . BMC health services research . 2021 Aug 28 ; 21 ( 1 ): 888 . OpenUrl PubMed 50. ↵ MedinalMarino A , Bezuidenhout D , Ngwepe P , et al. Acceptability and feasibility of leveraging communitylbased HIV counselling and testing platforms for samelday oral PrEP initiation among adolescent girls and young women in Eastern Cape , South Africa. J Inter AIDS Soc . 2022 Jul ; 25 ( 7 ): e25968 . OpenUrl 51. ↵ Obeagu EI , Obeagu GU . Preventive measures against HIV among Uganda’s youth: Strategies, implementation, and effectiveness . Medicine . 2024 Nov 1 ; 103 ( 44 ): e40317 . OpenUrl PubMed 52. ↵ Kakande ER , Ayieko J , Sunday H , et al. A communitylbased dynamic choice model for HIV prevention improves PrEP and PEP coverage in rural Uganda and Kenya: a cluster randomized trial . J Inter AIDS Soc . 2023 Dec ; 26 ( 12 ): e26195 . OpenUrl 53. ↵ Butler V , Kutywayo A , Martin CE , et al. Implementing differentiated and integrated HIV prevention services for adolescent girls and young women: experiences from oral PrEP rollout in primary care services in South Africa . J Adol Health . 2023 Dec 1 ; 73 ( 6 ): S58 – 66 . OpenUrl PubMed 54. ↵ Naidoo NP , Mthimkulu N , Jama N , et al. Community PrEP delivery for adolescent girls and young women: insights from the DREAMS PrEP Choice Study in Johannesburg , South Africa. Frontiers in Reproductive Health . 2025 Apr 4 ; 7 : 1474067 . OpenUrl PubMed 55. ↵ Jackson-Gibson M , Ezema AU , Orero W , et al. Facilitators and barriers to HIV pre-exposure prophylaxis (PrEP) uptake through a community-based intervention strategy among adolescent girls and young women in Seme Sub-County, Kisumu, Kenya . BMC public health . 2021 Jul 1 ; 21 ( 1 ): 1284 . OpenUrl CrossRef PubMed 56. ↵ Chen-Charles J , Joseph Davey D , Toska E , et al. PrEP uptake and utilisation among adolescent girls and young women in Sub-Saharan Africa: A scoping review . AIDS and Behavior . 2025 Feb 28 : 1 – 21 . 57. ↵ Marcus JL , Hurley LB , Krakower DS , et al. Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study . The lancet HIV . 2019 Oct 1 ; 6 ( 10 ): e688 – 95 . OpenUrl PubMed 58. ↵ May SB , Giordano TP , Gottlieb A . Generalizable pipeline for constructing HIV risk prediction models across electronic health record systems . JAMIA . 2024 Mar 1 ; 31 ( 3 ): 666 – 73 . OpenUrl PubMed 59. Gruber S , Krakower D , Menchaca JT , et al. Using electronic health records to identify candidates for human immunodeficiency virus prelexposure prophylaxis: An application of super learning to risk prediction when the outcome is rare . Statistics in medicine . 2020 Oct 15 ; 39 ( 23 ): 3059 – 73 . OpenUrl CrossRef PubMed 60. ↵ Burns C , Pung L , Witt D , et al. Hiv risk-prediction model using electronic health record data in a southern cohort . Topics in Antiviral Medicine . 2021 ; 29 . 61. ↵ Garcia-Cremades M , Hendrix CW , Jayachandran P , et al. Modeling the Probability of HIV Infection over Time in High-Risk Seronegative Participants Receiving Placebo in Five Randomized Double-Blind Placebo-Controlled HIV Pre-Exposure Prophylaxis Trials: A Patient-Level Pooled Analysis . Pharmaceutics . 2022 Aug 27 ; 14 ( 9 ): 1801 . OpenUrl PubMed 62. ↵ Tollett Q , Safdar S , Gumel AB . Dynamics of a two-group model for assessing the impacts of pre-exposure prophylaxis, testing and risk behaviour change on the spread and control of HIV/AIDS in an MSM population . Infectious Disease Modelling . 2024 Mar 1 ; 9 ( 1 ): 103 – 27 . OpenUrl PubMed 63. ↵ Gilkey MB , Marcus JL , Garrell JM , et al. Using HIV risk prediction tools to identify candidates for pre-exposure prophylaxis: perspectives from patients and primary care providers . AIDS pat care and STDs . 2019 Aug 1 ; 33 ( 8 ): 372 – 8 . OpenUrl View the discussion thread. 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Share Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review Martin Zang Pam , Alex Odoom , Marian Serbeh medRxiv 2025.09.20.25336235; doi: https://doi.org/10.1101/2025.09.20.25336235 Share This Article: Copy Citation Tools Predictive Modelling’s role in Improving Pre-exposure Prophylaxis (PrEP) Uptake in High-Risk HIV Groups in Africa: An Integrative Scoping Review Martin Zang Pam , Alex Odoom , Marian Serbeh medRxiv 2025.09.20.25336235; doi: https://doi.org/10.1101/2025.09.20.25336235 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area HIV/AIDS Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffdc9a2f9e58650',t:'MTc3OTQ3MzUzMQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();