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Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

Published online by Cambridge University Press:  27 August 2019

Benjamin Q. Huynh Open the ORCID record for Benjamin Q. Huynh [Opens in a new window]  and
Sanjay Basu
Benjamin Q. Huynh*
Affiliation:
Stanford University, Department of Medicine, Stanford, California.
Sanjay Basu
Affiliation:
Harvard Medical School, Center for Primary Care, Cambridge, Massachusetts Imperial College London, School of Public Health, London, England
*
Correspondence and reprint requests to Benjamin Q. Huynh, 615 Crothers Way, Office 211, Stanford, CA 94305, USA (e-mail: [email protected])
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Abstract

Objectives:

Armed conflict has contributed to an unprecedented number of internally displaced persons (IDPs), individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts.

Methods:

We modeled monthly IDP migration between provinces within Syria and within Yemen using data on food prices, fuel prices, wages, location, time, and conflict reports. We compared machine learning methods with baseline persistence methods of forecasting.

Results:

We found a machine learning approach that more accurately forecast migration trends than baseline persistence methods. A random forest model outperformed the best persistence model in terms of root mean square error of log migration by 26% and 17% for the Syria and Yemen datasets, respectively.

Conclusions:

Integrating diverse data sources into a machine learning model appears to improve IDP migration prediction. Further work should examine whether implementation of such models can enable proactive aid allocation for IDPs in anticipation of forecast arrivals.

Keywords

SyriaYemenmachine learninginternal displacementforecasting
Type
Brief Report
Information
Disaster Medicine and Public Health Preparedness , Volume 14 , Issue 3 , June 2020 , pp. 302 - 307
Copyright
Copyright © 2019 Society for Disaster Medicine and Public Health, Inc.

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References

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