Evidence map›Paper›PMID 38505694›Full record

ArticlePNAS nexus2024

An agent-based framework to study forced migration: A case study of Ukraine.

Zakaria Mehrab, Logan Stundal, Srinivasan Venkatramanan, Samarth Swarup, Bryan Lewis, Henning S Mortveit, Christopher L Barrett, Abhishek Pandey, Chad R Wells, Alison P Galvani and 4 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
9.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 5 institutions in 1 country.

Zakaria MehrabBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0002-5617-8524
Logan StundalBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-0444-4154
Srinivasan VenkatramananBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.
Samarth SwarupBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-3615-1663
Bryan LewisBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-0793-6082
Henning S MortveitBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-3363-2947
Christopher L BarrettBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-2518-0039
Abhishek PandeyCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520, USA.ORCID https://orcid.org/0000-0003-4578-7487
Chad R WellsCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520, USA.ORCID https://orcid.org/0000-0003-1370-8350
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT 06520, USA.
Burton H SingerEmerging Pathogens Institute, University of Florida, Gainesville, FL 32610, USA.ORCID https://orcid.org/0000-0002-5295-5971
David LeblangDepartment of Political Science, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-1315-3092
Rita R ColwellCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.ORCID https://orcid.org/0000-0001-5432-1502
Madhav V MaratheBiocomplexity Institute & Initiative, University of Virginia, Charlottesville, VA 22904, USA.ORCID https://orcid.org/0000-0003-1653-0658
Biocom · USYale University · USUniversity of Florida · USUniversity of Maryland, College Park · USUniversity of Virginia · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

agent-based modelingdigital twinforced migrationpolicy analysissocial theoriesUkraine

Identifiers

PMID38505694
PMCPMC10949908
OpenAlexW4392952602

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.