Documents

Anticipatory action reports, assessments, evaluations, etc.

Explore the collection below to discover how data, foresight, and community leadership are joining forces to redefine the future of global humanitarian response.

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DRC Global Anticipatory Action Portfolio 21 Mar 2025 PDF 2.7 MB
Lessons Learned: Anticipating displacement in South Sudan and Burkina Faso 07 Mar 2025 PDF 1.2 MB
Assessment of AA for conflict-induced displacement in South Sudan 01 Dec 2024 PDF 1.6 MB
Pushing the boundaries of anticipatory action using machine learning 17 Jan 2025 PDF 905.6 KB
Lessons learned: Anticipatory action response for IDPs exposed to floods in Somalia 24 Dec 2024 PDF 893.4 KB
Lessons Learned from DRC Predictive Modelling 30 Jun 2024 PDF 459.9 KB
AHEAD One pager 01 Oct 2024 PDF 101.8 KB
AHEAD - background note 14 Dec 2023 PDF 141.1 KB

Executive summary

Welcome to the Anticipatory Action (AA) resource library. As global displacement and humanitarian needs reach unprecedented levels due to escalating conflict and climate shocks, traditional reactive aid is no longer sufficient. This collection of reports synthesizes years of groundbreaking research and field implementation, offering a blueprint for shifting the humanitarian sector from a reactive to a proactive, forecast-driven approach.

The Core Synthesis: Predicting and Preventing Crises At the heart of our findings is the powerful combination of artificial intelligence and local human insight. These documents explore how advanced machine learning models, such as Foresight and AHEAD, can accurately forecast displacement at a sub-national level up to 3 to 4 months in advance. However, the data shows that algorithms alone are not enough. The most successful interventions occur when predictive analytics are grounded in grassroots community monitoring—empowering local peace and early warning committees to identify context-specific indicators of impending conflict or disaster.

Real-World Impact and Cost-Effectiveness Across diverse pilot programs, the evidence for Anticipatory Action is compelling:

In climate-induced crises, such as the El Niño flooding in Somalia, distributing unconditional cash transfers just days before disaster struck allowed vulnerable households to secure food and protect their livelihoods, virtually eliminating negative coping strategies like child labor.
In conflict-affected zones, such as Akobo, South Sudan, utilizing forecasts to trigger community-led peace dialogues actively de-escalated tensions and averted mass displacement. This approach not only preserved lives and dignity but proved to be exceptionally cost-effective, saving between €6.6 and €23 in humanitarian response costs for every single euro invested.

Navigating Challenges and Shaping the Future We also critically examine the complex realities of operating ahead of crises. The library provides an honest look at the ethical risks of relying on predictive modeling, such as data misuse and biased responses. Furthermore, it highlights the operational hurdles of acting in active conflict zones like Burkina Faso, where the humanitarian sector must adapt its rigid timelines and adopt broader definitions of acting "earlier" rather than strictly "early". To scale these successes, the reports emphasize the urgent need for flexible donor funding, harmonized multi-hazard forecasting, and deeper integration with long-term peacebuilding and development efforts.

Who Should Read This Library?

Donors and Policymakers: Discover the compelling return-on-investment and strategic value of shifting funding structures toward proactive, preventative frameworks.
Data Scientists and Academics: Explore the mechanics, accuracy, and ethical frontiers of deploying machine learning and predictive analytics in highly volatile humanitarian contexts.
Humanitarian Practitioners and Program Managers: Gain actionable field lessons on how to effectively merge data forecasts with community-led action, navigate complex crisis implementation, and design robust early-warning systems.