Population size estimation based upon ratios of recapture probabilities

TL;DR

This paper introduces a new method for estimating the size of elusive populations using ratios of recapture probabilities within a Poisson-Gamma model framework, applicable across various fields like medicine, ecology, and social sciences.

Contribution

It proposes a novel approach based on ratios of neighboring Poisson-Gamma probabilities, offering a flexible and effective alternative for population size estimation with zero-truncated data.

Findings

The method effectively estimates hidden population sizes in diverse applications.

Ratios of neighboring probabilities are linearly related, simplifying calculations.

The approach outperforms traditional models like the homogeneous Poisson.

Abstract

Estimating the size of an elusive target population is of prominent interest in many areas in the life and social sciences. Our aim is to provide an efficient and workable method to estimate the unknown population size, given the frequency distribution of counts of repeated identifications of units of the population of interest. This counting variable is necessarily zero-truncated, since units that have never been identified are not in the sample. We consider several applications: clinical medicine, where interest is in estimating patients with adenomatous polyps which have been overlooked by the diagnostic procedure; drug user studies, where interest is in estimating the number of hidden drug users which are not identified; veterinary surveillance of scrapie in the UK, where interest is in estimating the hidden amount of scrapie; and entomology and microbial ecology, where interest is in estimating the number of unobserved species of organisms. In all these examples, simple models such as the homogenous Poisson are not appropriate since they do not account for present and latent heterogeneity. The Poisson-Gamma (negative binomial) model provides a flexible alternative and often leads to well-fitting models. It has a long history and was recently used in the development of the Chao-Bunge estimator. Here we use a different property of the Poisson-Gamma model: if we consider ratios of neighboring Poisson-Gamma probabilities, then these are linearly related to the counts of repeated identifications.