Measuring seasonal population dynamics
Understanding seasonal population dynamics, including how people move through landscapes at different times of year, is fundamental to understanding malaria transmission. Agricultural cycles, religious gatherings, school terms, and economic migration all shape when and where people are exposed to malaria risk.
This research stream integrates multiple data sources including smartphone location histories, Meta Activity Space Map data, WorldPop population models, and point-of-interest data to characterise seasonal shifts in population distribution and mobility. These dynamics are analysed within a malaria transmission modelling framework to improve the precision and timeliness of risk mapping across endemic settings.
Fine-scale transmission mapping
In many endemic settings, cases are attributed to place of residence, but a majority of transmission occurs outdoors, away from home. Construction sites, agricultural fields, and evening gathering spots are likely true transmission foci, yet they go unmapped. This research identifies and characterises micro-transmission sources and sinks by combining individual-level location data from malaria-positive cases with parasite genomics, point-of-interest data, and remote sensing.
By tracking where cases spent time during peak vector biting hours and applying spatial clustering algorithms to identify hotspots, we produce fine-scale maps that enable precisely targeted interventions. Our AI-centred, AutoML-driven approach to identifying transmission sources is more resource-efficient than blanket indoor approaches, and designed to be adaptable across diverse transmission settings.
Importation pathway analysis
In pre-elimination settings, a substantial proportion of malaria cases are linked to importation, yet importation is rarely incorporated into routine intervention planning. This research characterises and predicts importation pathways using aggregated mobile phone data, call records, and parasite genomics to identify source regions, travel corridors, entry points, and destination sinks that drive sustained transmission.
Seasonal patterns are critical: travel peaks associated with agricultural calendars, religious events, and holiday periods drive predictable importation waves. Understanding these dynamics enables proactive screening, targeted messaging, and early warning systems calibrated to real importation risk, applicable across island, border, and corridor transmission settings.
Spatiotemporal resistance surveillance
The emergence and spread of antimalarial drug resistance represents one of the greatest threats to malaria elimination. Resistance does not respect national borders, resistant parasite strains move with human populations across porous frontiers, driven by the same mobility dynamics that sustain transmission. Tracking resistance requires understanding not just where resistant genotypes occur, but how they spread through connected populations over time.
MalarAI applies spatiotemporal genomic modelling to map the spread of antimalarial resistance genotypes across health facility networks, integrating human mobility data and cross-border population movement to reconstruct transmission pathways. Genomic approaches, including identity-by-descent and haplotype sharing analysis, provide evidence of cross-border parasite gene flow that mobility data alone cannot capture.
AutoML integration for near real-time analysis
Malaria control programmes now have access to more data than ever, but the scale and complexity of integrating diverse streams often exceeds local analytical capacity. The MalarAI AutoML pipeline continuously processes and harmonises case surveillance, human mobility, parasite genomics, climate, environmental, and point-of-interest data to identify transmission risks, classify micro-sources and sinks, and generate actionable intervention guidance in near real-time.
The pipeline uses an ensemble of modelling approaches including gradient-boosted trees, random forests, neural networks, and spatiotemporal Bayesian models with automated hyperparameter tuning and continuous updating. Outputs include a web-based risk dashboard, short-term case forecasts, importation risk estimates by entry point, and automated briefings for programme staff. The system is designed for local ownership: modular, maintainable, and not dependent on external expertise to operate.