AI could give the Horn of Africa early warning against climate-driven outbreaks

Researchers have laid out a detailed blueprint for how artificial intelligence could transform outbreak prediction across the Horn of Africa, a region where droughts, floods, conflict and displacement converge to accelerate climate-sensitive infectious diseases faster than conventional surveillance can track them. Writing in the journal Globalization and Health, the authors present a Perspective rather than the results of a deployed system: no AI early warning platform yet exists for the region, and no datasets were generated or analyzed in the study. What the paper offers instead is a five-layer conceptual architecture, an assessment of barriers specific to fragile and conflict-affected states, and a phased policy roadmap anchored in the Intergovernmental Authority on Development, the regional bloc known as IGAD.

The urgency, according to the authors, stems from a convergence of hazards. Between 2020 and 2024, the region endured its most severe drought in four decades, followed immediately by catastrophic El Niño flooding. That hydrological whiplash has coincided with concurrent outbreaks of cholera in Somalia and Ethiopia, dengue fever in Sudan, and a malaria upsurge in the Kenyan highlands. The eight countries of the Horn—Djibouti, Eritrea, Ethiopia, Kenya, Somalia, South Sudan, Sudan and Uganda—are described as navigating a poly-crisis in which climate change, conflict and fragile health systems accelerate disease emergence beyond the pace of passive surveillance, which relies on retrospective case counting at health facilities. By the time a cholera cluster is confirmed in a remote displacement camp or a malaria spike registers in another district, the authors argue, the window for containment has often closed.

The technical case for AI rests on fusing data streams that currently sit in separate institutional silos. Meteorological agencies such as the IGAD Climate Prediction and Applications Center hold sophisticated satellite observations of rainfall anomalies, while ministries of health maintain disease records, but no automated mechanism links the two. The proposed framework would combine satellite earth observations—including precipitation estimates from CHIRPS, land surface temperature from MODIS and Landsat, and vegetation indices—with vulnerability indicators such as displacement tracking data from the International Organization for Migration, acute malnutrition classifications and water and sanitation coverage maps. Machine learning models in the analytics layer would then fuse hazard and vulnerability streams to classify risk probabilities, feeding a decision-support layer that translates those probabilities into operational alerts and logistics triggers, such as pre-positioning oral rehydration salts or initiating vector control.

The paper draws on operational precedents elsewhere. Brazil’s InfoDengue platform integrates meteorological data, social media analysis and clinical notifications to produce weekly dengue risk maps. In Singapore, forecasting models using LASSO and support vector machines have predicted dengue trends up to three months in advance. For malaria, the Malaria Anticipation Project piloted by Médecins Sans Frontières in South Sudan demonstrated the viability of using focused environmental data to predict caseloads in conflict zones, while the iDEWS project in Ethiopia explored integrating precipitation lags into district-level planning. A key lesson from cholera models in Yemen and Bangladesh and from European heat-mortality systems is that climate data alone are insufficient: models must incorporate displacement, conflict intensity and infrastructure variables that often override pure climatic drivers.

On model selection, the authors recommend ensemble tree-based methods—Random Forest, XGBoost and LightGBM—over deep learning for most of the region, because many districts have fewer than five years of consistent surveillance data. These models handle missing data better, require less training data and offer interpretability through feature importance rankings, which the authors consider crucial for building trust with health ministries. Deep learning approaches such as long short-term memory networks may suit countries with longer time series, like Kenya’s malaria records dating to 2000, but risk overfitting in data-scarce settings. The authors also flag severe class imbalance, with outbreak weeks representing less than five percent of observations, and propose techniques such as synthetic minority oversampling and cost-sensitive learning, alongside time-series and spatial cross-validation to handle the non-stationarity introduced by El Niño cycles.

The barriers the paper catalogs are substantial. Ground-truth epidemiological data are often fragmented, paper-based or nonexistent in territories controlled by non-state armed groups, creating what the authors call surveillance black holes. Conflict in Sudan has severed reporting lines and destroyed health facilities. High-latency internet in rural Ethiopia and South Sudan impedes real-time data upload, and the region faces a critical shortage of professionals cross-trained in data science, public health and meteorology—fewer than fifty across all eight IGAD member states, with no university offering a dedicated climate health informatics program. Governance fragmentation separates climate and health data custodians, and unresolved ethical risks loom large: call-detail-record tracking of displaced populations poses privacy dangers, surveillance infrastructure built for health could be repurposed for security or political control, and a dependency on Global North technology firms raises what the authors term a risk of digital colonialism.

To mitigate these risks, the framework wraps the entire system in a governance and ethics layer enforcing bias audits and privacy standards, including the African Union Convention on Cyber Security. The authors advocate federated learning so that algorithms train on decentralized data without raw records crossing borders, neutral regional hosting by IGAD or Africa CDC rather than bilateral donors, open-source code published for government audit, and participatory processes in which pastoralist communities and frontline health workers help define alert thresholds. Privacy-by-design standards on geospatial resolution are meant to ensure that mapped hotspots cannot pinpoint vulnerable settlements in conflict zones.

The implementation pathway begins with integration rather than replacement. The authors propose embedding a CHEWS module within DHIS2, the District Health Information Software 2 platform already used by all Horn of Africa countries, which would ingest weekly case reports already entered by district officers, pull climate data from ICPAC interfaces in the background and push color-coded risk flags back into familiar dashboards. In low-connectivity pastoralist regions such as Turkana, Afar and rural Somalia, alerts would travel by SMS through gateways like RapidPro or FrontlineSMS, with two-way messaging allowing health workers to report surveillance zeros or early clusters. A phased rollout would pilot the system in three to five border districts in the first year, expand to ten to fifteen high-burden districts in the second, and move toward national scale thereafter, with evaluation at each phase.

Financing is estimated at 200 to 300 million dollars over ten years, plus 15 to 20 million dollars annually for operations, which the authors argue is modest compared with the economic toll of recurrent epidemics, and could be reduced by 40 to 60 percent through regional coordination instead of duplicated country-by-country approaches. The roadmap also calls for postgraduate training programs, embedded advisors within ministries, retention mechanisms for skilled staff, and a research agenda including cluster-randomized trials measuring whether early warnings actually reduce morbidity rather than merely improving model accuracy. The authors are careful to frame the entire proposal as prospective: the technology exists and has demonstrated efficacy elsewhere, but whether an AI-enabled early warning system can function when internet fails and governments collapse in the Horn of Africa remains an open question that only phased, evaluated implementation can answer.

Subject of Research: A proposed artificial intelligence framework and policy roadmap for climate-health early warning systems in the Horn of Africa

Article Title: Artificial intelligence for climate–health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action

Article References: Alasow, A. A., Abdi, Y. H., Hersi, A. A., & Boakye, M. (2026). Artificial intelligence for climate–health early warning systems in the Horn of Africa: opportunities, challenges, and a roadmap for action. Globalization and Health, 22(1), Article 63. https://doi.org/10.1186/s12992-026-01233-9

Image Credits: AI Generated

DOI: 10.1186/s12992-026-01233-9

Keywords: artificial intelligence, early warning systems, Horn of Africa, climate-sensitive infectious diseases, IGAD, disease surveillance, machine learning, cholera, malaria, dengue, data sovereignty, conflict-affected states