  Population size estimation based on the capture-recapture experiment is an
interesting problem in various fields including epidemiology, criminology,
demography, etc. In many real-life scenarios, there exists inherent
heterogeneity among the individuals and dependency between capture and
recapture attempts. A novel trivariate Bernoulli model is considered to
incorporate these features, and the Bayesian estimation of the model parameters
is suggested using data augmentation. Simulation results show robustness under
model misspecification and the superiority of the performance of the proposed
method over existing competitors. The method is applied to analyse real case
studies on epidemiological surveillance. The results provide interesting
insight on the heterogeneity and dependence involved in the capture-recapture
mechanism. The methodology proposed can assist in effective decision-making and
policy formulation.
