Malaria in 2026
In 2026, malaria remains a major global public health challenge. Despite substantial progress over the past two decades, gains have been fragile and uneven and progress has stalled. Estimated global malaria cases rose from 241 million in 2020 to 249 million in 2022, with deaths increasing alongside. The WHO African Region bears the highest burden, accounting for nearly 90% of cases.
The reasons for this stagnation are complex: changes in transmission hotspots and high-risk populations; source-sink dynamics driven by human mobility; gaps in spatiotemporal data coverage; changes in vector behaviour following sustained interventions; the emergence of drug-resistant parasites and insecticide-resistant mosquitoes; altered dynamics under a changing climate; and declining global health funding.
Read the full World Malaria Report 2025 (WHO) →
What we do
MalarAI develops and applies high-resolution risk mapping, spatiotemporal modelling, and decision-support tools that integrate surveillance, climate, mobility, genomics, and environmental data to support targeted, evidence-based malaria elimination. Our tools are designed to be transferable across endemic settings, from island pre-elimination contexts to high-burden transmission settings.
We are building an AutoML pipeline for near real-time data integration and analysis, enabling national malaria programmes to move from reactive to proactive intervention strategies, identifying micro-sources and sinks, mapping importation corridors, tracking the spread of drug and insecticide resistance, and targeting resources precisely where transmission actually occurs.
How we work
Open and reproducible methods with transparent assumptions and explicit representation of uncertainty. All code and data products are published openly.
Partnership-driven research, co-designed with national malaria programmes, regional partners, and local collaborators. Workshops with stakeholders shape the research agenda from the outset.
Actionable outputs, designed for real-world planning, targeting, and evaluation. Dashboards, early warning alerts, and intervention guidance, not just academic papers.
Capacity building: training programme staff to own, maintain, and interpret AI tools beyond the life of the project. The goal is local ownership, not dependency.
Where we work
MalarAI tools are being developed and validated in Zanzibar, Tanzania, with a framework designed to be transferable across endemic and pre-elimination settings.
Zanzibar, Tanzania: Pilot site
Two decades of intensive malaria control in Zanzibar produced a rapid initial decline in transmission, but progress has stagnated. The annual parasite incidence has remained stubbornly persistent, characterised by seasonally and geographically heterogeneous low-level transmission. Transmission is increasingly outdoors, concentrated among mobile high-risk populations, with a high proportion of imported infections driven by human mobility between Zanzibar and mainland Tanzania. MalarAI is developing AI-driven tools in direct partnership with the Zanzibar Malaria Elimination Programme (ZAMEP) to identify the true sources of transmission and enable precision targeting of interventions.
Funding
Our pilot project is funded by the Web Science Institute Pilot Project Fund 2025-26 at the University of Southampton, providing pump-priming support for data landscape assessment, AutoML feasibility testing, and stakeholder co-design workshops. We are actively pursuing full programme funding from a range of international funders.