Estimating the number of migrants who die or go missing along dangerous routes such as the Central Mediterranean remains challenging as available records are incomplete. Some incidents are never documented, and fatalities associated with such unobserved incidents are absent from observed totals. We propose a Bayesian approach for probabilistic estimation of total migrant fatalities in such settings. Building on recent developments in multiple-systems estimation, we develop a time-stratified latent-class framework that accommodates missing fatality counts for unobserved incidents. We apply the method to recoded incident-level data from the Missing Migrants Project for the Central Mediterranean route from 2014 to 2025, encompassing 25,712 fatalities across 1,562 incidents. Our model yields 95% credible intervals of 30,426-39,172 fatalities and 2,200-2,591 deadly incidents, indicating that approximately 66%-85% of fatalities and 60%-71% of incidents are reflected in the available data. We estimate that unreported fatalities were concentrated between 2014 and 2016. Furthermore, we document that reporting likelihood increases with incident severity, implying that smaller incidents are most likely to remain undetected. While contingent on modeling assumptions and incomplete data, our method provides a broadly applicable and principled alternative to naive data adjustment methods.

翻译：估算地中海中部等危险路线上死亡或失踪的移民人数仍具挑战性，因为现有记录并不完整。部分事件从未被记录，而这类未被观测到的事件所导致的死亡人数也未包含在已知统计中。我们提出一种贝叶斯方法，用于在此类场景下对移民死亡总人数进行概率估算。基于多重系统估算的最新进展，我们开发了一个时间分层潜在类别框架，能够处理未被观测到事件中缺失的死亡人数统计。我们将该方法应用于"失踪移民项目"中2014至2025年间地中海中部航线的事件级编码数据，涵盖1562起事件中的25712例死亡。该模型给出的95%可信区间显示，死亡人数为30426-39172例，致命事件为2200-2591起，表明现有数据反映了约66%-85%的死亡人数和60%-71%的事件。我们估算未报告的死亡事件主要集中在2014年至2016年间。此外，我们发现报告概率随事件严重程度增加而升高，这意味着小型事件最可能未被发现。虽然该方法依赖于模型假设和不完整数据，但相较于简单的数据调整方法，它提供了更广泛适用且更严谨的替代方案。