Citation

Mehrab Z, Stundal L, Venkatramanan S, Swarup S, Lewis B, Mortveit HS, Barrett CL, Pandey A, Wells CR, Galvani AP, Singer BH, Leblang D, Colwell RR, Marathe MV. PNAS Nexus 2024; 3(3): pgae080.

Copyright

(Copyright © 2024, National Academy of Sciences (USA), Publisher Oxford University Press)

DOI

10.1093/pnasnexus/pgae080

PMID

38505694

PMCID

PMC10949908

Abstract

The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics of forced migration is essential for policy-making and for delivering humanitarian assistance. Existing work is hindered by a reliance on observational data which is only available well after the fact. In this work, we study the efficacy of a data-driven agent-based framework motivated by social and behavioral theory in predicting outflow of migrants as a result of conflict events during the initial phase of the Ukraine war. We discuss policy use cases for the proposed framework by demonstrating how it can leverage refugee demographic details to answer pressing policy questions. We also show how to incorporate conflict forecast scenarios to predict future conflict-induced migration flows. Detailed future migration estimates across various conflict scenarios can both help to reduce policymaker uncertainty and improve allocation and staging of limited humanitarian resources in crisis settings.

Language: en

Keywords

agent-based modeling; digital twin; forced migration; policy analysis; social theories; Ukraine