Checchi Francesco, Koum Besson Emilie Sabine
Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, United Kingdom.
J Migr Health. 2022 Apr 21;5:100105. doi: 10.1016/j.jmh.2022.100105. eCollection 2022.
Yemen has experienced widespread insecurity since 2014, resulting in large-scale internal displacement. In the absence of reliable vital events registration, we tried to reconstruct the evolution of Yemen's population between June 2014 and September 2021, at subdistrict (administrative level 3) resolution, while accounting for growth and internal migration.
We reconstructed subdistrict-month populations starting from June 2014 WorldPop gridded estimates, as a function of assumed birth and death rates, estimated changes in population density, net internal displacement to and from the subdistrict and assumed overlap between internal displacement and WorldPop trends. Available displacement data from the Displacement Tracking Matrix (DTM) project were subjected to extensive cleaning and imputation to resolve missingness, including through machine learning models informed by predictors such as insecurity. We also modelled the evolution of displaced groups before and after assessment points. To represent parameter uncertainty, we complemented the main analysis with sensitivity scenarios.
We estimated that Yemen's population rose from about 26.3 M to 31.1 M during the seven-year analysis period, with considerable pattern differences at sub-national level. We found that some 10 to 14 M Yemenis may have been internally displaced during 2015-2016, about five times United Nations estimates. By contrast, we estimated that the internally displaced population had declined to 1-2 M by September 2021.
This analysis illustrates approaches to analysing the dynamics of displacement, and the application of different models and data streams to supplement incomplete ground observations. Our findings are subject to limitations related to data quality, model inaccuracy and omission of migration outside Yemen. We recommend adaptations to the DTM project to enable more robust estimation.
自2014年以来，也门经历了广泛的不安全状况，导致大规模的国内流离失所。在缺乏可靠的生命事件登记的情况下，我们试图在考虑人口增长和国内迁移的同时，以分区（行政三级）分辨率重建2014年6月至2021年9月期间也门人口的演变情况。
我们从2014年6月的世界人口格网估计数开始，根据假定的出生率和死亡率、估计的人口密度变化、该分区的净国内流离失所进出情况以及假定的国内流离失所与世界人口趋势之间的重叠情况，重建分区月度人口。流离失所跟踪矩阵（DTM）项目提供的现有流离失所数据经过了广泛的清理和插补，以解决数据缺失问题，包括通过由不安全等预测因素提供信息的机器学习模型。我们还对评估点前后流离失所群体的演变情况进行了建模。为了表示参数的不确定性，我们用敏感性情景对主要分析进行了补充。
我们估计，在七年的分析期内，也门人口从约2630万增加到3110万，在国家以下层面存在相当大的模式差异。我们发现，在2015年至2016年期间，约有1000万至1400万也门人可能在国内流离失所，约为联合国估计数的五倍。相比之下，我们估计到2021年9月，国内流离失所人口已降至100万至200万。
本分析说明了分析流离失所动态的方法，以及应用不同模型和数据流来补充不完整的实地观测的情况。我们的研究结果受到数据质量、模型不准确以及未考虑也门境外移民等因素的限制。我们建议对DTM项目进行调整，以实现更可靠的估计。
