Spatial Spread of Tuberculosis through Neighborhoods Segregated by Socioeconomic Position: A Stochastic Automata Model

David H Rehkopf ,Tamara Awerbuch-Friedlander,Alice Furumoto‐Dawson ,Anthony E Kiszewski

doi:10.1155/2015/583819

Abstract

Transmission of the agent of tuberculosis,Mycobacterium tuberculosis, is dependent on social context. A discrete spatial model representing neighborhoods segregated by levels of crowding and immunocompetence is constructed and used to evaluate prevention strategies, based on a number of assumptions about the spatial dynamics of tuberculosis. A cellular automata model is used to (a) construct neighborhoods of different densities, (b) model stochastically local interactions among individuals, and (c) model the spread of tuberculosis within and across neighborhoods over time. Since infected people may become progressively sick but also heal through treatment, the transition among stages was modeled with transition probabilities. A moderate level of successful treatment (40%) dramatically reduced the number of infections across all neighborhoods. Increasing the treatment in neighborhoods of a lower socioeconomic level from 40% to 90% results in an additional decrease of approximately 25% in the number of infected individuals overall. In conclusion, we find that a combination of a moderate level of successful treatment across all areas with more focused treatment efforts in lower socioeconomic areas resulted in the least number of infections over time.

Highlights

Introduction and BackgroundCellular automata and stochastic automata are methods that allow us to study the dynamics of the population at large, based on local interactions among neighbors, as shown in models of physical systems [1]
Rising rates of comorbidities that undermine the immuno-competence of populations; the migration of populations dislocated by conflict, market failures and economic transitions [11, 12, 13] ; incarceration, homelessness and crowding in inadequate housing for disadvantaged groups living in high income inequity countries [14]; de-funding of barely adequate screening and treatment programs; the emergence of drug-resistant strains out of inadequately treated prison, HIV positive, and homeless populations – all contribute to sustained levels of tuberculosis disease (TB) in the U.S and worldwide
2.1 Cellular Automata Models for epidemic spread, whether stochastic or deterministic, have been based on interactions between infected populations and susceptible ones, incorporating other epidemiological features such as latency, and acquisition and subsequent loss of immunity[26, 27, 28, 29, 30]; a similar approach was used for studying the intrinsic transmission dynamics of tuberculosis based on a multi-century analysis [31]

Summary

Introduction

Introduction and BackgroundCellular automata (if a deterministic model) and stochastic automata (if implemented with varying probabilities of transmission) are methods that allow us to study the dynamics of the population at large, based on local interactions among neighbors, as shown in models of physical systems [1]. Rising rates of comorbidities that undermine the immuno-competence of populations; the migration of populations dislocated by conflict, market failures and economic transitions [11, 12, 13] ; incarceration, homelessness and crowding in inadequate housing for disadvantaged groups living in high income inequity countries [14]; de-funding of barely adequate screening and treatment programs; the emergence of drug-resistant strains out of inadequately treated prison, HIV positive, and homeless populations – all contribute to sustained levels of tuberculosis disease (TB) in the U.S and worldwide Combinations of conditions such as these have produced alarming spikes in active TB case rates and TB mortality across the globe. One such spike occurred in the US in the late 1980s, fueled by economic downturn, lagging public health control efforts, and the rising incidence of what was a relatively new and not well understood disease - HIV infection[15, 16]

Methods

Results

Conclusion