Individual-Based Modeling of Tuberculosis in a User-Friendly Interface: Understanding the Epidemiological Role of Population Heterogeneity in a City

Abstract

For millennia tuberculosis (TB) has shown a successful strategy to survive, making it one of the world’s deadliest infectious diseases. This resilient behavior is based not only on remaining hidden in most of the infected population, but also by showing slow evolution in most sick people. The course of the disease within a population is highly related to its heterogeneity. Thus, classic epidemiological approaches with a top-down perspective have not succeeded in understanding its dynamics. In the past decade a few individual-based models were built, but most of them preserved a top-down view that makes it difficult to study a heterogeneous population. We propose an individual-based model developed with a bottom-up approach to studying the dynamics of pulmonary TB in a certain population, considered constant. Individuals may belong to the following classes: healthy, infected, sick, under treatment, and treated with a probability of relapse. Several variables and parameters account for their age, origin (native or immigrant), immunodeficiency, diabetes, and other risk factors (smoking and alcoholism). The time within each infection state is controlled, and sick individuals may show a cavitated disease or not that conditions infectiousness. It was implemented in NetLogo because it allows non-modelers to perform virtual experiments with a user-friendly interface. The simulation was conducted with data from Ciutat Vella, a district of Barcelona with an incidence of 67 TB cases per 100,000 inhabitants in 2013. Several virtual experiments were performed to relate the disease dynamics with the structure of the infected subpopulation (e.g., the distribution of infected times). Moreover, the short-term effect of health control policies on modifying that structure was studied. Results show that the characteristics of the population are crucial for the local epidemiology of TB. The developed user-friendly tool is ready to test control strategies of disease in any city in the short-term.

Stochastic Analysis of a Deterministic and Seasonally-Forced Sei Model for Improved Disease Spread Simulation

“…These are firstly the Lipschitz criteria and secondly the stability analysis of the model with graphical analysis and phase space portraits. It is hoped that the proposed analysis and the results will aid mathematical biologists in conducting research in different domains where stochastic modelling is applicable [31][32][33][34][35][36][37][38][39][40][41][42]. Trends in current human settlement, together with rapidly expanded urban areas, exploding population density, and limited socio-economic resources, suggest that the human factors, in addition to climate factors are important components in understanding current and future risks of disease transmission [1,32].…”

Evaluation of the Burden and Intervention Strategies of TB-HIV Co-Infection in West Africa

“…The flow diagram, subprocess and sub models are shown in Figure 4, adapted from [10]. Some actions can only happen if some conditions are fulfilled.…”

“…These additional testing sites increased TB case detection by 92%, with 65% of patients referred for appropriate treatment operational in Nigeria. These additional testing sites increased TB case detection by 92%, with 65% of patients referred for appropriate treatment [10] ( Figure 1).…”

Approaching parallel computing to simulating population dynamics in demography

“…For instance, Ahmad and Billimek (2005) developed a system dynamics model which analyzes policies to reduce the harmful effects of tobacco on population health. Saysel et al (2002) Prats et al (2016) showed how discrete-event models can be used to evaluate public policies in the transmission of tuberculosis in big cities. Eubank (2002) and Rao and Chernyakhovsky (2008) showed with their development of specialized simulation tools that parallel discrete-event simulation was required for large-scale epidemiological models.…”