Helping the Danish Refugee Council anticipate and prevent forced displacement
The Danish Refugee Council (DRC) is Denmark’s largest non-governmental organization and a global leader in responding to forced displacement. In 2024, DRC reached more than 7.8 million people across Africa, Latin America, Europe, Asia, and the Middle East. Amid continuous and intensifying displacement crises, DRC is advancing anticipatory action frameworks — predictive models that enable faster, smarter, and more efficient humanitarian interventions.
With conflict being a primary driver of displacement, ACLED, a trusted source of global conflict data, is uniquely positioned to support this crucial effort.
Challenge
In today’s humanitarian landscape, displacement crises are intensifying while funding is increasingly constrained. To maximize impact, organizations must act quickly and cost-effectively — not only reacting to crises but anticipating them. But this is only possible when backed by reliable, high-quality data that can trigger the right interventions at the right time.
Solution
DRC uses ACLED’s conflict data to feed its machine learning model AHEAD (Anticipatory Humanitarian Action for Displacement). ACLED data, which combine global coverage with years of detailed historical records, underpin indicators on violent events, kidnappings, lootings, extremist attacks, and violence against civilians — enabling AHEAD to spot long-term conflict patterns and predict displacement trends.
Why does DRC choose ACLED data?
- ACLED’s rigorous process for researcher-led data collection ensures comparable conflict data with global coverage, spanning 244 countries and territories.
- The dataset includes at least seven years of available historical data for each of the 244 countries and territories ACLED covers, which is critical for anticipatory action models.
- ACLED offers tailored training sessions and consultations by its experts and data scientists.
- DRC also has access to ACLED's own robust and easy-to-use early warning and forecasting tools.
Impact
By integrating ACLED data into the AHEAD model, the resulting forecasts have enabled DRC to roll out timely measures to mitigate conflict across Africa. For example, AHEAD triggered an early response in May 2024 in Akobo County, South Sudan, that prevented 2,800 people from being displaced — saving €6.60 for every €1 spent, as detailed in this lessons learned report. This cost-effectiveness ratio corresponds with findings from a 2025 UN OCHA report, which concluded that every $1 invested in anticipatory action can yield up to $7 in avoided losses.
ACLED is best placed to equip humanitarian actors like DRC with reliable conflict data, which are essential for anticipatory action. By enabling earlier, evidence-based interventions, these models help prevent forced displacement and reduce costs. The ultimate result is more efficient programming that protects vulnerable communities before crises escalate, maximizing both impact and resources.
What is your key challenge?
Speak to one of our specialists and find out how ACLED can support your data needs.
