Effective surveillance on the long-term public health impact due to war and
terrorist attacks remain limited. Such health issues are commonly
under-reported, specifically for a large group of individuals. For this
purpose, efficient estimation of the size of the population under the risk of
physical and mental health hazards is of utmost necessity. In this context,
multiple system estimation is a potential strategy that has recently been
applied to quantify under-reported events allowing heterogeneity among the
individuals and dependence between the sources of information. To model such
complex phenomena, a novel trivariate Bernoulli model is developed, and an
estimation methodology using Monte Carlo based EM algorithm is proposed.
Simulation results show superiority of the performance of the proposed method
over existing competitors and robustness under model mis-specifications. The
method is applied to analyze real case studies on the Gulf War and 9/11
Terrorist Attack at World Trade Center, US. The results provide interesting
insights that can assist in effective decision making and policy formulation
for monitoring the health status of post-war survivors.Comment: arXiv admin note: text overlap with arXiv:2105.0867