Forecasting displacement and solutions for decision-making in volatile contexts: a case study from Ukraine

Abstract Following the large-scale Russian invasion in February 2022, policymakers and humanitarian actors urgently sought to anticipate displacement flows within Ukraine. However, existing internal displacement data systems had not been adapted to contexts as dynamic as a full-fledged war marked by uneven trigger events. A year and a half later, policymakers and practitioners continue to seek forecasts, needing to anticipate how many internally displaced persons (IDPs) can be expected to return to their areas of origin and how many will choose to stay and seek a durable solution in their place of displacement. This article presents a case study of an anticipatory approach deployed by the International Organization for Migration (IOM) Mission in Ukraine since March 2022, delivering nationwide displacement figures less than 3 weeks following the invasion alongside near real-time data on mobility intentions as well as key data anticipating the timing, direction, and volume of future flows and needs related to IDP return and (re)integration. The authors review pre-existing mobility forecasting approaches, then discuss practical experiences with mobility prediction applications in the Ukraine response using the Ukraine General Population Survey (GPS), including in program and policy design related to facilitating durable solutions to displacement. The authors focus on the usability and ethics of the approach, already considered for replication in other displacement contexts.