How could we act earlier?<\/h4>\n\n\n\n
Humanitarian teams now have access to more early warning systems and hazard forecasts than ever before. However, turning alerts into specific, localized anticipatory action remains a challenge.<\/p>\n\n\n\n
UNICEF’s Latin American and the Caribbean Regional Office (LACRO), in collaboration with the FDN, has increasingly been embracing innovative data approaches. As Liliana Carvajal, Data and Monitoring Manager at LACRO, emphasizes, \u201cGrounding frontier data initiatives in real country challenges, rather than pursuing innovation for its own sake, is key to the success of these initiatives\u201d. To this end, the regional office hosted a field assessment mission along with the FDN team to do exactly that: capture stakeholder perspectives and identify critical data gaps directly from those working on the ground.<\/p>\n\n\n\n
Consultations across UNICEF\u2019s programme areas, including health, nutrition, water, sanitation, and hygiene (WASH), emergency response and logistics, revealed that while teams may know a hurricane is approaching, they often lack the operational insights to act proactively.<\/p>\n\n\n\n
\u201cWhen planning for a humanitarian action, we face critical questions that are usually unanswered for weeks,\u201d Mjrko Rennola, Regional Emergency Specialist, explains. \u201cWhich schools are likely to be damaged and how many students will be impacted? Which health centers could flood? Which communities may suffer the interruption of safe water services? Knowing a storm is coming isn\u2019t enough. We need to know what it will do and where, and to act, with no regret.\u201d<\/p>\n\n\n\n
At the same time, traditional funding mechanisms release money only after disasters, not in anticipation of them. Even with sound preparedness planning and humanitarian scenario construction, humanitarian funding often requires detailed damage and needs assessments that usually arrive weeks after impact, when the opportunity for impact mitigation has already passed.<\/p>\n\n\n\n
What\u2019s missing is not the data about what<\/em> is coming, but insight into what it will do<\/em> and who it will affect<\/em>, real-time<\/em>. This is the crucial link that today\u2019s systems fail to provide.<\/p>\n\n\n\n To help address this challenge, UNICEF\u2019s FDN hosted two MIT Sloan Master of Business Analytics students, Caio de Prospero Iglesias and Sidharth Vijay, to develop a prototype for anticipating forecasted hurricane impact. Their \u201ccapstone project\u201d explored how predictive analytics could close this gap, moving from general risk awareness to impact-based forecasting.<\/p>\n\n\n\n Based on stakeholder priorities, the students developed a prototype framework connecting real-time hazard forecasts with data on child vulnerability and infrastructure. It integrates:<\/p>\n\n\n\n Caio de Prospero Iglesias, MIT Sloan Master of Business Analytics ’25, reflects: \u201cThis isn\u2019t about making business processes more efficient. It\u2019s about exploring whether we can use data to save lives. The framework is experimental, but it shows what might be possible if we can connect the dots between weather forecasts and vulnerable children.\u201d<\/p>\n\n\n\n As new hazard forecasts are released almost in real-time, the model recalculates which areas, services, and populations are most likely to be affected. The results are dynamic impact predictions that could help humanitarian actors move from reactive response to proactive decision-making.<\/p>\n\n\n\n In practice, this could transform field operations. For example, AI-enhanced forecasts could predict that a hurricane will likely result in flooding of three rural health clinics in Nicaragua within 48 hours. Field teams could evacuate medical supplies and relocate mobile health services before roads become impassable. Children requiring routine immunisations could be reached proactively, rather than waiting weeks for post-disaster access to resume. These aren’t hypothetical gains. They mark the difference between prevention and emergency response.<\/p>\n\n\n\n Sidharth Vijay, MIT Sloan Master of Business Analytics ’25, adds: \u201cThe real challenge was bridging meteorological forecasting with what humanitarian teams actually need to know. The prototype demonstrates that it\u2019s technically feasible to translate hazard data into location-specific impact predictions.\u201d<\/p>\n\n\n\n Rather than replacing existing systems, the prototype enhances them by adding a layer of analytical foresight, enabling:<\/p>\n\n\n\n Technically, the model draws on global forecasting systems from sources including Google DeepMind’s AI-powered hurricane prediction models, NASA’s artificial intelligence-enhanced landslide monitoring and international flood monitoring services. While the current prototype uses traditional analytical methods, future versions could leverage AI and machine learning to refine impact predictions and automate risk assessments in real-time.<\/p>\n\n\n\n Exploratory yet promising, the prototype adds to the Ahead of the Storm<\/em> body of work, helping to make early action more precise, timely and focused on those who stand to lose the most.<\/p>\n\n\n\n The broader Ahead of the Storm<\/em> initiative is now being piloted in Nicaragua and other Caribbean countries, where national and regional teams have expressed strong interest in using predictive analytics to strengthen preparedness and early response. With frequent hurricanes, floods and landslides, the region faces both urgent need and a powerful case for this approach.<\/p>\n\n\n\n The prototype contributes to this pilot by revealing both the potential and the practical challenges of integrating predictive models into humanitarian operations. But scaling this work requires more technology. It demands institutional alignment, sustained access to reliable forecast data, and a shared commitment to shift from reactive response to forward-looking preparedness.<\/p>\n\n\n\n As Yves Jaques, Chief of the Frontier Data Technology Unit, notes: \u201cWithout modern data infrastructure, even the best predictive models remain theoretical. We need robust systems that swiftly discover and ingest real-time forecasts, process vulnerability data at scale, and deliver actionable insights to field teams in time to act.\u201d<\/p>\n\n\n\n The goal is not to replace existing systems, but to enhance them, giving decision-makers a predictive edge when time is critical.<\/p>\n\n\n\n Flexible by design, the initiative can be adapted to different hazards and contexts, depending on local data availability and technical capacity. As more UNICEF offices engage with predictive approaches, Ahead of the Storm<\/em> offers a foundation for global learning and innovation, reflecting the Frontier Data Network\u2019s core mission: turning data innovation into operational impact through collaboration.<\/p>\n\n\n\n This project also demonstrates the power of science and research in humanitarian work and innovation. Academic engagements bring fresh analytical thinking, cutting-edge methods, and scientific rigor that can transform how humanitarian actors understand and respond to risk. As FDN Principal Researcher Manuel Garc\u00eda-Herranz explains: \u201cStrong engagements with leading research centers and academia are essential in both directions: to ensure that our solutions are grounded in solid science and expertise, and that the world\u2019s brightest minds are engaging with child-centric, high-impact problems\u201d.<\/p>\n\n\n\n The MIT Sloan capstone project did more than deliver a prototype; it demonstrated how academic research can be directly applied to operations and humanitarian challenges, creating a model for future scientific engagements across the sector.<\/p>\n\n\n\n This focus on proactive, anticipatory, and early response also creates opportunities for new partnerships between humanitarian agencies and academic institutions, adapting emerging analytical methods to real operational needs. \u201cUNICEF is actively engaging with diverse private and public sector partners to ensure initiatives like Ahead of the Storm<\/em> can, from the start, build with and learn from global leading experts on data and technology,\u201d says Viviana Canon Tamayo, Frontier Data Network Program Coordinator. \u201cIn the case of this initiative, we are grateful to already count on the immense help of MIT-Sloan, the Development Data Partnership (DDP), and the Spanish Agency for International Development Cooperation (AECID).\u201d<\/p>\n\n\n\n In a world of escalating climate disruption, reactive response is not only insufficient\u2014it\u2019s unsustainable. Children cannot afford to wait for systems to catch up. As pilots like Ahead of the Storm<\/em> advance, they will help uncover what works, what barriers persist and how predictive approaches can be scaled across different contexts.<\/p>\n\n\n\nFrom weather warnings to impact-based forecasting<\/h4>\n\n\n\n
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<\/figure>\n<\/div>\n\n\nFrom pilot to practice: The path forward<\/h4>\n\n\n\n
Acknowledgements<\/h4>\n\n\n\n