Inmate population models with nonhomogeneous sentence lengths and their effects in an epidemiological model

TL;DR

This paper develops a structured inmate population model incorporating sentence lengths and demonstrates how neglecting this structure can lead to underestimating disease prevalence in prisons, with applications to the Chilean system.

Contribution

It introduces a transport equation-based inmate population model with sentence length structure and integrates an SIS epidemiological model considering new inmate entries.

Findings

Ignoring sentence length structure underestimates steady-state prevalence.

The threshold for underestimation depends on the basic reproduction number.

Analytical and numerical examples illustrate the impact of sentence length distributions.

Abstract

In this work, we develop an inmate population model with a sentencing length structure. The sentence length structure of new inmates represents the problem data and can usually be estimated from the histograms corresponding to the conviction times that are sentenced in a given population. We obtain a transport equation, typically known as the McKendrick equation, the homogenous version of which is included in population models with age structures. Using this equation, we compute the inmate population and entry/exit rates in equilibrium, which are the values to consider in the design of a penitentiary system. With data from the Chilean penitentiary system, we illustrate how to perform these computations. In classifying the inmate population into two groups of sentence lengths (short and long), we incorporate the SIS (susceptible-infected-susceptible) epidemiological model, which considers the entry of infective individuals. We show that a failure to consider the structure of the sentence lengths -- as is common in epidemiological models developed for inmate populations -- for prevalences of new inmates below a certain threshold induces an underestimation of the prevalence in the prison population at steady state. The threshold depends on the basic reproduction number associated with the nonstructured SIS model with no entry of new inmates. We illustrate our findings with analytical and numerical examples for different distributions of sentencing lengths.