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Bayesian Projection of Extant Refugee and Asylum Seeker Populations.
Herbert P Susmann1, Adrian E Raftery2
1Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, New York University, New York, NY, USA.
Demography
|December 5, 2025
Summary
Forecasting refugee and asylum seeker populations is challenging. This study introduces a Bayesian time-series model to project future forced migration numbers, showing good accuracy for 1-, 5-, and 10-year predictions.
Area of Science:
- Demography
- Forced Migration Studies
- Statistical Modeling
Background:
- Estimating future migration patterns is crucial in demography.
- Forced migration, including refugees and asylum seekers, significantly impacts global migration but is difficult to forecast.
- Accurate projections are needed for policy and resource allocation.
Purpose of the Study:
- To develop and validate a novel modeling pipeline for projecting refugee and asylum seeker populations.
- To provide reliable forecasts for countries of origin with large displaced populations.
- To improve the understanding of forced migration dynamics.
Main Methods:
- Utilized Bayesian hierarchical time-series modeling.
- Applied an interrupted logistic process model to simulate population growth and decline phases.
- Incorporated data from the United Nations High Commissioner for Refugees (UNHCR).
Main Results:
- The proposed modeling pipeline demonstrated good performance in validation exercises.
- Achieved accurate forecasts at 1-, 5-, and 10-year horizons.
- Generated projections for 35 countries of origin.
Conclusions:
- The Bayesian time-series approach offers a robust method for forecasting forced migration.
- The model successfully captures the complex dynamics of refugee and asylum seeker population changes.
- Findings provide valuable data for humanitarian organizations and policymakers.
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