The EMOD TB model is available for download. New features include TB specific diagnostics, treatment, and dynamic modeling of multiple pathways to care.These features allow the modeler to ask a wide variety of policy-relevant questions including what impact an intervention might have, what indicators might be the most important to track during an epidemic, and what groups serve as the main sources of transmission.
The TB model has been used to address the interventions necessary to achieve the 2025 and 2035 TB Global Targets in China, India, and South Africa.
Our TB research is developing multi-scale modeling tools to estimate the impact of new and existing strategies for preventing, diagnosing and treating TB. Our work includes the following areas:
- Reaching 2025 and 2035 Global Targets
- Pathways to Care
- TB/HIV Co-Infection and Within-Host Disease Dynamics
Reaching 2025 and 2035 Global Targets
In May 2014 the World Health Assembly approved ambitious TB control targets for 2025 including a 40% reduction in incidence and a 75% reduction in mortality. We are building a calibrated model of TB transmission for three of the highest burden countries: China, India and South Africa. The model enables us to understand sources of infection, define the role of high-risk groups and explore the potential epidemiological benefits of various intervention strategies including improved disease surveillance and deployment of new drugs and diagnostics.
Pathways to Care
Effective detection and treatment of TB require a strong health system infrastructure and continuity of care, as infected individuals may go undetected for long periods of time and current treatment regimens require a minimum of six months' adherence. To better understand how shortening the diagnostic delay and improving treatment adherence can help reduce overall TB burden, we explicitly model the patient treatment-seeking pathway and healthcare system within each country.
TB/HIV Co-Infection and Within-Host Disease Dynamics
While a case of TB infection may lie dormant as latent TB for years, individual factors such as HIV co-infection may shorten the time to re-activation or alter disease presentation. To account for this effect in high HIV burden countries such as South Africa, we are developing an extended TB model that includes HIV co-infection, variations in host immunity, and the effect on time to re-activation and response to drug treatment. This model helps us understand how within-host disease dynamics could be targeted to reduce population-level TB burden.