Agent-Based Simulation of a Tuberculosis Epidemic


Tuberculosis (TB) transmission is a key factor for disease-control policy, but the timing and distribution of transmission and the role of social contacts remain obscure. We develop an agent-based epidemic simulation of a TB in a single population, and consider a hierarchically structured contact network in three levels, typical of airborne diseases. The parameters are adopted from the literature, and the model is calibrated to a setting of high TB incidence. We model the dynamics of transmission at the individual level, and study the timing of secondary infections from a single source throughout the duration of the disease. We compare the patterns of transmission among different networks and discuss implications. Sensitivity analysis of outputs indicates the robustness of the results to variations in the parameter values.

Epidemiology Study with Agent-Based Simulation

TB is an airborne disease transmitted through infectious contact with an active case. TB transmission is one of the key determinants of epidemic severity, and has important implications for design, implementation, and scaling-up of control interventions (e.g., improved diagnosis, active case finding) that aim to reduce the rate of transmission. Unlike other airborne diseases such as influenza, however, TB transmission is not directly measurable, i.e., available diagnostic techniques cannot estimate the original timing of infection in diagnosed cases. TB also has a predilection for establishment of a latent state that is non-infectious and asymptomatic, but may progress to active, infectious disease at any time. As a result, one cannot reliably differentiate between primary infection with rapid progression to active disease, re-infection following a remote initial infection, or reactivation of a previous latent infection. Such limita-tions pose several challenges to the study of transmission dynamics across populations, including the lack of informative data to trace the chain of transmission across various contact networks in retrospective studies. The prospective following a cohort population, on the other hand, are prohibitively expensive and restricted by the time and budget constraints. In such settings, the relationships between the duration of disease, symptom burden, contact networks, diagnosis/treatment, and patients’ infectiousness remain obscure (Dowdy, Dye, and Cohen 2012).

We propose an epidemic agent-based simulation model for disease (TB) transmission dynamics study and to find out the role of various contact networks. Our model simulates the TB epidemic course across a single population and uses a hierarchical network of contacts in three levels, typical to the transmission of airborne diseases (Mossong et al. 2005). Parameters are chosen from the literature, and the model is calibrated to a setting of high TB incidence. We use our model to study the transmission dynamics at an individual level with regard to the timing and distribution of secondary infections from a single source. The average time for disease diffusion to reach 50% of infections at an individual level is estimated, and the timing patterns are compared among different networks. We perform sensitivity analysis of results with regard to multiple parameter values, and discuss the implications for TB control policy.

Agent Based Simulation in Epidemiology - disease transmission dynamics simulation model