Using Machine Learning to Direct Limited HIV Programme Resources to Communities with the Greatest Need

A developer from a healthcare NGO demonstrated how machine learning can help direct limited HIV programme resources to communities with the greatest need. By analyzing historical data on testing coverage, ART coverage, missed appointments, and outreach activities, ML models can classify communities into need levels, enabling evidence-based prioritization. The approach emphasizes using routine programme data to identify service gaps and optimize resource allocation.

Imagine working as a Data Analyst in a healthcare Non-Governmental Organization NGO implementing HIV and AIDS programmes across several communities. The organization has limited resources. There may not be enough funding, healthcare workers, testing kits, transport, outreach teams, or community programmes to serve every community at the same intensity. This creates an important question: How can we use data and machine learning to direct limited programme resources to communities with the greatest need? This is where Machine Learning ML can become valuable. Rather than distributing resources equally across all communities, an NGO can use historical programme data to identify communities experiencing greater HIV-related service gaps or higher levels of need. Resources can then be prioritized based on evidence. Machine Learning is a branch of Artificial Intelligence that enables computers to learn patterns from data and use those patterns to make predictions or support decisions. Instead of manually creating rules for every situation, you provide the algorithm with historical data and allow it to identify relationships within that data. For example, the NGO could have this information about different communities: | Community | HIV Testing Coverage | ART Coverage | Missed Appointments | Outreach Activities | |---|---|---|---|---| | Community A | 85% | 90% | 5% | High | | Community B | 52% | 61% | 25% | Low | | Community C | 70% | 75% | 15% | Medium | | Community D | 40% | 55% | 32% | Low | Looking at this data, Community D appears to have greater programme gaps than Community A. However, in a real programme, the decision should not be based on one indicator alone. Machine learning can analyse many variables simultaneously to identify communities that may require greater attention. HIV programmes operate in environments where resources are often limited. An NGO may have: For example: Community A may have 90% ART coverage and strong appointment retention, while Community B may have 55% ART coverage and substantial treatment interruptions. Giving both communities exactly the same level of support may not produce the greatest programme impact. A data-driven approach asks: Where will an additional unit of resources potentially make the greatest difference? Before building a machine learning model, an one needs appropriate data. WHO's HIV strategic information guidance emphasizes the importance of using routine programme data to understand service access, coverage and gaps across the HIV prevention, testing and treatment cascade. Potential community-level variables could include: The purpose is not to collect every possible variable. The organization should identify variables that are relevant to the programme question and ethically appropriate to use. The first step is to convert the broad question into a measurable problem. Instead of asking: "Which communities need help?" we could ask: "Which communities have the greatest combination of HIV service gaps and unmet programme needs?" We could then create a target variable such as: Community Need Level Low Medium High The machine learning model will then learn from historical programme data and classify communities into these categories. For example: Community A → Low Need Community B → High Need Community C → Medium Need Community D → High Need This provides programme managers with a starting point for prioritization. One possible approach is supervised learning . Suppose the NGO has historical data from 100 communities. For each community, we know: Programme experts could define historical need categories based on agreed programme criteria. The model then learns patterns associated with: High Need Medium Need Low Need When new programme data becomes available, the model can estimate the likely need category for each community. Imagine an NGO has funding for only 10 community outreach campaigns , but there are 30 communities that could potentially receive them. A simple approach would be to distribute the campaigns equally. However, machine learning could help identify communities where programme gaps are greatest. Suppose the model produces: | Community | Predicted Need | Priority Score | |---|---|---| | A | Low | 22 | | B | High | 91 | | C | Medium | 58 | | D | High | 87 | | E | Low | 18 | | F | High | 94 | The programme team could prioritize communities F, B and D for additional assessment and potential intervention. This does not mean that the model automatically decides where resources go. Instead, it provides evidence to support programme managers in making better decisions . Machine learning does not always need to predict a known outcome. An NGO could also use unsupervised learning , particularly clustering. Clustering groups communities that have similar characteristics. For example, a clustering model could identify: Cluster 1 High HIV testing High ART coverage Low treatment interruption Strong outreach Cluster 2 Low HIV testing Low ART coverage High treatment interruption Limited outreach Cluster 3 Moderate testing Moderate ART coverage Moderate treatment interruption Moderate outreach Cluster 2 may become an important focus for further programme investigation. The organization could then ask: What is happening in these communities that is contributing to these service gaps? This is an important point:machine learning identifies patterns, but programme teams still need to understand the reasons behind those patterns. Another practical approach is to develop a priority score . For example, an NGO could combine several programme indicators: Priority Score = Testing Gap + ART Coverage Gap + Viral Load Monitoring Gap + Treatment Interruption + Outreach Gap + Access Challenges The exact weighting would need to be agreed upon by programme specialists and validated using local evidence. For example: | Indicator | Community A | Community B | |---|---|---| | Testing gap | Low | High | | ART coverage gap | Low | High | | Viral load gap | Low | Medium | | Treatment interruption | Low | High | | Outreach gap | Low | High | | Overall priority | Low | High | Machine learning could then improve this approach by learning from historical outcomes and identifying which combinations of factors are most strongly associated with poor programme performance. A machine learning model is useful only if its predictions lead to appropriate action. The process could look like this: Programme Data ↓ Data Cleaning ↓ Exploratory Data Analysis ↓ Machine Learning Model ↓ Identify High-Need Communities ↓ Validate With Programme Teams ↓ Prioritize Resources ↓ Implement Intervention ↓ Monitor Results ↓ Update Model and Strategy For example: The model identifies Community B as high priority because of: The NGO could respond by providing: The organization would then monitor whether these interventions improve programme outcomes. Machine learning cannot solve poor-quality data. Suppose one community reports HIV testing correctly while another community consistently under-reports testing. The model may incorrectly conclude that the second community has a greater need. Therefore, before using machine learning for resource allocation, the NGO should assess: WHO's recent guidance on HIV surveillance emphasizes the importance of improving the quality, consistency and reliability of routine HIV data so that it can support public health action. Better data leads to more reliable analysis and more defensible decisions. One of the biggest mistakes would be to assume: "The model says Community B is high priority, therefore Community B automatically receives all the resources." That is not how responsible machine learning should work. The model provides evidence, but programme managers should consider additional information. For example: The final decision should combine: Data + Machine Learning + Programme Expertise + Community Knowledge HIV data requires strong privacy and confidentiality protections. WHO guidance emphasizes data security, privacy, confidentiality, appropriate access and governance when using digital and person-centred HIV data. Where possible, resource allocation models should work with aggregated community-level information rather than unnecessary individual-level identifiers. For example, instead of exposing individual client records: Client ID: 12345 HIV Status: Positive the resource allocation model may work with: Community: District A ART Coverage: 62% Treatment Interruption: 21% A machine learning model can reproduce biases present in historical data. For example, if certain communities have historically received fewer services, the data may make their programme outcomes look different for reasons that are not entirely related to underlying need. The model should therefore be evaluated carefully across different geographic and population groups. A community being classified as "high need" should lead to more appropriate support , not stigma or punishment. The purpose should be: Identify gaps → provide support → improve outcomes. Resource allocation should not stop after resources are distributed. The NGO should evaluate whether the intervention produced improvement. For example, before intervention: HIV Testing Coverage = 52% ART Coverage = 61% Treatment Interruption = 25% After six months: HIV Testing Coverage = 70% ART Coverage = 74% Treatment Interruption = 15% The organization can then assess whether the intervention appears to have improved programme outcomes. This creates a feedback loop: Predict ↓ Prioritize ↓ Intervene ↓ Measure ↓ Learn ↓ Improve This is where machine learning can become part of a continuous programme improvement cycle. Without data-driven prioritization, a programme manager may ask: "Where should we send our outreach team next month?" With machine learning and programme data, the question becomes: "Which communities currently show the greatest combination of service gaps, access challenges and unmet need, and what intervention is most appropriate for each community?" That is a much stronger decision-making process. For example: Limited Resources ↓ Identify Programme Gaps ↓ Use Historical Data ↓ Apply Machine Learning ↓ Rank/Group Communities ↓ Validate Results ↓ Prioritize High-Need Areas ↓ Deploy Resources ↓ Measure Impact This approach helps an NGO move from equal distribution of resources to evidence-informed prioritization based on need and potential programme impact . Machine learning is powerful, but it is not a perfect solution. A model may fail because: Therefore, machine learning should be treated as a decision-support tool , not an automatic resource allocation system. World Health Organization WHO . 2022 . Consolidated guidelines on person-centred HIV strategic information: Strengthening routine data for impact. WHO. The guidelines emphasize using routine data to identify service gaps and improve HIV programme decision-making. World Health Organization WHO . 2020 . Consolidated HIV strategic information guidelines: Driving impact through programme monitoring and management. WHO. The guidance highlights the importance of collecting, analysing and using data to identify gaps in HIV service access, coverage and quality. UNAIDS. 2026 . Resource Needs Estimation Tool. UNAIDS. The tool demonstrates how data-driven resource estimation can support HIV programme planning and allocation across prevention, testing, treatment and other programme areas.