A continuous-time Markov chain framework for population size estimation from multi-list data: accounting for absorbing lists and asymmetric interactions

TL;DR

This paper presents a continuous-time Markov chain framework for population size estimation from multi-list data, effectively modeling directional interactions and absorbing lists like death records, with demonstrated empirical advantages.

Contribution

It introduces a novel Markov chain approach that accounts for absorbing lists and asymmetric interactions, extending existing models for more accurate population estimates.

Findings

Markov chain and log-linear models are equivalent under list independence.

Accounting for absorbing lists reduces bias in population estimates.

Empirical results show improved accuracy with the proposed model.

Abstract

We introduce a continuous-time Markov chain framework for estimating population size from multi-list data, which allows directional interactions to be modelled and can accommodate absorbing lists, such as death records, or more general data collection processes. The standard model of the continuous-time Markov chain framework and the log-linear model for multi-list data are equivalent when lists are independent and we show empirically that they give similar results in the presence of dependencies between lists. Through a simulation study, we highlight the need to account for an absorbing list by using the Markov model or the log-linear model with forced absorbing interactions, observing biased estimates of the population size otherwise. We motivate our approach with an epidemiological dataset concerning individuals suffering from a first ever stroke in North-West England, in which one of the lists is a death record. We illustrate a further use of our approach by considering a case of ordered lists on drug use data from the City of London.