Probabilistic Estimation of Hidden Migrant Fatalities Along the Central Mediterranean Route

TL;DR

This paper introduces a Bayesian probabilistic model to estimate the true number of migrant fatalities along the Central Mediterranean route, accounting for unobserved incidents and incomplete data, providing more accurate fatality estimates.

Contribution

It develops a novel time-stratified latent-class Bayesian framework for estimating unobserved migrant fatalities, improving upon existing methods with a principled probabilistic approach.

Findings

Estimated total fatalities: 30,426-39,172 with 95% credible intervals.

Approximately 66%-85% of fatalities are captured in observed data.

Unreported fatalities were mainly between 2014 and 2016.

Abstract

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.