Using Φ-lab’s machine learning algorithms to fight mosquito-borne outbreaks in Brazil and Peru

As the number of dengue and malaria cases rises each year, governments and health authorities are in a race against time. DIRE (Disease Incidence and Resource Estimator) is a digital, predictive data analysis and visualisation platform that transforms climate and epidemiological data into a concrete operational roadmap by using a machine learning approach developed by ESA Φ-lab for UNICEF. This platform will help governments in high-burden regions like Brazil and Peru to shift from reactive crisis management to proactive, life-saving preparation.

Dengue and malaria are two of the most threatening mosquito-borne diseases worldwide, placing an immense burden on global healthcare systems and economies. According to the World Health Organization (WHO), about half of the world’s population is now at risk of dengue, with an estimated 100 to 400 million infections occurring each year.

As for malaria, it remains a leading cause of mortality, particularly among children under five years old in sub-Saharan Africa. The World Malaria Report from 2024 states that, in 2023, there were an estimated 263 million cases and 597 000 deaths globally.

While these two diseases are transmitted by different mosquito species, the causes that lead to their spreading within populations are very similar. Dengue and malaria are both deeply tethered to the environment. Climate change, land use change, deforestation, rapid urbanisation and poor drainage create ‘hotspots’ where mosquitoes thrive, increasing human exposure.

When we talk about infectious diseases, timing is everything. Tools that predict outbreaks are therefore paramount to shift public health action from reactive – responding once people are already sick – to proactive, allowing governments to plan ahead and act before cases spike.

Meet DIRE, a digital, predictive data analysis and visualisation platform for imminent disease epidemics. This tool was funded by Wellcome Trust and developed by the University of California San Diego School of Global Policy and Strategy and New Light Technologies.

DIRE translates disease forecasting into actionable guidance for decision-makers through an interactive map that uses geospatial predictive analytics, showing where dengue and malaria outbreaks are likely to occur and what public resources may be needed to control them.

At the heart of DIRE lies a climate-based ensemble model developed by ESA Φ-lab for UNICEF that uses multiple machine learning approaches and Earth observation products to take account of geographical variations in dengue incidence. The model proved to be more accurate than previous predictive techniques when piloted in Brazil and Peru. This novel approach was selected as one of UNICEF’s top research initiatives of 2022 and one of UNESCO’s Top 100 AI solutions for Sustainable Development Goals.

As the senior author of the study behind Φ-lab’s machine learning approach used in DIRE, Rochelle Schneider (Copernicus Ecosystem Operations Engineer and ESA Φ-lab ambassador) shares her thoughts: “Predicting outbreaks is a challenging work where the complexity is present in data, model, and decision-support layers. By leveraging the machine learning framework we originally developed at Φ-lab, DIRE abstracts these complexities to non-expert users.”

“Seeing this technology transition from the lab to a tool that predicts the needs and resource allocation in Brazil and Peru is the ultimate evidence of Φ-lab’s impact. It aligns with our ‘AI for Good’ mission on creating and implementing new ideas through AI and Earth observation”, Rochelle added.

DIRE focuses on Brazil and Peru, as these two countries have faced persistent, climate-related outbreaks of both dengue and malaria. Its interactive and user-friendly format allow users to view predicted disease risks at multiple geographic levels and see both recent trends and short-term predictions.

Users can select a country (Brazil or Peru) to view past reported cases and projections for the current month and up to two months in advance. DIRE provides a range of socio-economic and environmental indicators that were used by the model and flags regions where predictions are less certain, helping users weigh the risks alongside uncertainty.

“UNICEF and ESA previously pioneered machine learning-based predictive models for dengue outbreaks in Latin America by synthesising UNICEF’s granular field data with ESA Φ-lab’s robust Earth observation and machine learning capabilities. This foundational work garnered significant interest from major entities, including the Wellcome Trust, and ultimately served as the analytical backbone for the DIRE project—a private-public collaboration focused on scalability”, commented Do-Hyung Kim, Data Science Specialist at UNICEF’s Climate and Environment Data Unit.

“It is a compelling testament to our partnership that such research initiatives produce high-quality, open-source algorithms that can be scaled to support diverse regions globally. I hope UNICEF and ESA continue to lead in this space”, Do-Hyung added.

DIRE has come a long way in predicting disease outbreaks and its capabilities go beyond forecasting. This platform also estimates the quantity and the cost of commodities and personnel required for disease control and treatment in each region – for example, the number of vaccines and fumigation kits needed, as well as their costs. With these data, DIRE generates a PDF report to be shared with local authorities who need clear information about the risk and resource readiness.

For Carlos Zegarra Zamalloa, Health Specialist at UNICEF Peru, DIRE is a reflection of the collaborative spirit between all stakeholders involved: “Climate-related outbreaks like dengue and malaria are becoming more frequent and dangerous in Peru, especially for children and pregnant women. In 2025 alone, Peru reported 39,000 dengue cases, with a substantial proportion affected being children; the scale has been overwhelming the current capacity of governments and communities to respond effectively. We were therefore delighted to work together with UC San Diego and New Light Technologies to bring a range of stakeholders together to troubleshoot the problem.”

During this soft launch phase, DIRE’s interface and data quality are undergoing improvement tests. The long-term impact of this platform will be determined by its adoption by local authorities to plan and respond to disease outbreaks, supported by real examples and testimonials of its use in the field.

The DIRE visualisation platform is available here. The technical details about the model are available in this Nature Scientific Report’s article.   

To know more: DIRE, ESA Φ-lab, UNICEF, UNESCO, Wellcome Trust, University of California San Diego School of Global Policy and Strategy, New Light Technologies.

Photo courtesy of Unsplash/John Cameron