Modeling tuberculosis in Barcelona. A solution to speed-up agent-based simulations

Tuberculosis remains one of the world's deadliest infectious diseases. About one-third of the world's population is infected with tuberculosis bacteria. Understanding the dynamics of transmission at different spatial scales is critical to progress in its control. We present an agent-based model for tuberculosis epidemics in Barcelona, which has an observatory on this disease. Our model considers high heterogeneity within the population, including risk factors for developing an active disease, and it tracks the individual behavior once diagnosed. We incorporated the immunodeficiency and smoking/alcoholism, as well as the individual's origin (foreigner or not) for its contagion and infection as risks factors. We implemented the model in Netlogo, a useful tool for interaction with physicians. However, the platform has some computational limitations, and we propose a solution to overcome them.

[1]  M. Murray,et al.  Determinants of cluster distribution in the molecular epidemiology of tuberculosis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Cesare Furlanello,et al.  Modeling socio-demography to capture tuberculosis transmission dynamics in a low burden setting. , 2011, Journal of theoretical biology.

[3]  W. David Kelton,et al.  Estimating the proportion of tuberculosis recent transmission via simulation , 2014, Proceedings of the Winter Simulation Conference 2014.

[4]  Uri Wilensky,et al.  NetLogo: A simple environment for modeling complexity , 2014 .

[5]  Hazel R. Parry,et al.  A comparative analysis of parallel processing and super-individual methods for improving the computational performance of a large individual-based model , 2008 .

[6]  D. Dowdy,et al.  Mathematical Modelling and Tuberculosis: Advances in Diagnostics and Novel Therapies , 2015, Advances in medicine.

[7]  P. Cardona Revisiting the Natural History of Tuberculosis The Inclusion of Constant Reinfection , Host Tolerance , and Damage-Response Frameworks Leads to a Better Understanding of Latent Infection and its Evolution towards Active Disease , 2010 .

[8]  Clara Prats,et al.  Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis. , 2009, International journal of food microbiology.

[9]  J. Valls,et al.  Individual-Based Modeling of Tuberculosis in a User-Friendly Interface: Understanding the Epidemiological Role of Population Heterogeneity in a City , 2016, Front. Microbiol..

[10]  P. Cardona Revisiting the Natural History of Tuberculosis , 2010, Archivum Immunologiae et Therapiae Experimentalis.

[11]  G. Marks,et al.  Contact Tracing of Tuberculosis: A Systematic Review of Transmission Modelling Studies , 2013, PloS one.

[12]  H. Van Dyke Parunak,et al.  Concurrent Modeling of Alternative Worlds with Polyagents , 2006, MABS.

[13]  W. David Kelton,et al.  An agent-based simulation of a Tuberculosis epidemic: Understanding the timing of transmission , 2013, 2013 Winter Simulations Conference (WSC).

[14]  Alexandre Souto Martinez,et al.  An agent-based computational model of the spread of tuberculosis , 2011 .