RE-CLAIM-EWS

RE-CLAIM-EWS

Project Information Card

Developing a Gender-Responsive, Regional AI-Driven Early Warning System for Climate-Sensitive Malaria in Ethiopia, Kenya, and Uganda.

Duration: August 2026 to July 2028 | Countries: Ethiopia, Kenya, Uganda | Lead: ABH Partners | Principal Investigator: Amare Deribew (MD, PhD).

About the Project

RE-CLAIM-EWS is a regional research initiative led by ABH Partners in collaboration with Jimma University and AfroEye Research. It uses artificial intelligence (AI), climate information, and malaria surveillance data to predict outbreaks, strengthen preparedness, and support timely, evidence-informed public health decisions.

The project is part of the research portfolio of the Eastern and Southern African Regional Hub for Research and Policy on Climate Change and Health (ESA-RHRCCH). The Hub is managed by Amref Health Africa, with funding from the International Development Research Centre (IDRC) and the UK’s international development programme. It supports African-led research on the health impacts of climate change across ten subgrantee institutions.

 

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The Challenge

Climate change is altering malaria transmission patterns and raising the risk of outbreaks in vulnerable communities. Conventional surveillance often detects outbreaks only after transmission has already increased, which limits the chance to prevent and control them in time.

Our Response

We are developing and validating an AI-driven early warning system that brings together climate, environmental, demographic, and malaria surveillance data. The system is designed to anticipate outbreaks and guide proactive action, with gender equality and health equity built into both the forecasts and the responses.

 

Objectives

  1. Data platform and dashboard: Integrate disaggregated malaria, climate, environmental, and remote-sensing data on a secure regional platform with an interactive decision dashboard.
  2. AI forecasting: Develop and validate AI and machine-learning models that predict malaria risk 1 to 12 weeks ahead, at lead times that are useful in practice.
  3. Equity: Embed gender, age, displacement, and socioeconomic vulnerability in prediction and targeted early action, with a focus on pregnant women, children under five, and displaced communities.
  4. Workforce readiness: Build capacity to interpret dashboard alerts and carry out timely public health responses.
  5. Sustainable scale-up: Assess cost-effectiveness and the economic value of early warnings, and define governance, financing, and policy pathways for regional adoption.

 

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Our Approach

RE-CLAIM-EWS follows a regional hub-and-spoke model. Ethiopia is the hub for model development, calibration, validation, and implementation research. Kenya and Uganda serve as spokes, supporting external validation and testing how well the system works in different climate and transmission settings.

  • Data integration: Historical malaria and climate data from 2000 to 2025, combined with prospective clinical, entomological, and community data.
  • AI forecasting: Hybrid ensemble models generating forecasts at multiple horizons (1, 2, 4, 8, and 12 weeks).
  • Validation: Prospective validation, calibration, and usability testing.
  • Early warning: Risk classes, vulnerability overlays, and uncertainty ranges presented in a user-oriented dashboard.
  • Early action: Operational triggers, readiness measures, and response guidance, with a four-week operational lead time.

Implementation research guided by the Consolidated Framework for Implementation Research (CFIR) will assess feasibility, usefulness, sustainability, and integration into routine decision-making. Economic evaluation, stakeholder engagement, and capacity strengthening run alongside it.

 

Contact

Dr. Amare Deribew, MD, PhD, Principal Investigator
amare@abhpartners.com

 

Expected Outcomes

A validated early warning system and dashboard

  • A validated AI-driven malaria early warning system for Ethiopia, Kenya, and Uganda
  • An interactive dashboard with risk maps, forecasts, confidence and uncertainty ranges, vulnerability overlays, and recommended early actions
  • Earlier, more targeted, and more equitable malaria prevention and preparedness

A ready workforce

  • 60+ malaria experts trained through training of trainers
  • A workforce able to interpret forecasts, verify alerts, and coordinate equitable early action

Sustainable scale-up

  • Economic evidence, including a cost-effectiveness analysis and an investment case
  • Policy and governance recommendations, with at least three pathways per country
  • Four to six policy briefs supporting financing and adoption

Policy and regional impact

  • Evidence to inform national and regional climate-health policies
  • A foundation for scaling AI-enabled early warning across East Africa
  • African-led evidence and regional learning that strengthen the links between climate research, health policy, and practical action

 

 

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