Age-structured model for Tuberculosis intervention planning

Kennedy Houck, a junior from Ursinus College, worked with Paige Miller in the lab of Dr. John Drake to study age-based interventions for Tuberculosis.

Abstract:  Tuberculosis (TB) represents a widespread public health concern.  The World Health Organization’s “End TB Strategy” has set the goal for global TB eradication by 2050.  Previous studies have suggested that current public health intervention strategies may not achieve this goal in many parts of the world that experience high TB incidence rates.  The goal of this project was to determine whether age-based interventions could enhance current interventions, which are currently implemented.  A standard TB model, which includes five state variables (Susceptible, Latent, Infectious, Noninfectious, and Removed), was modified to include 16 different age classes, and parameterized with previously published information for India and South Africa.  The model was run for 500 years until equilibrium was reached.  Once equilibrium was reached, 18 different interventions, all simulating faster rates of testing and treating, or shorter infectious periods, among active TB cases, were tested by calculating the rate of decrease of TB cases in each population over time.  A “baseline” scenario where the rate of treatment was held constant was compared to interventions where the infectious period was reduced by 10, 50, 70, and 90% independently for either a specific age class or overall (i.e. a “blanket strategy”).  To test the validity of model predictions, we calculated the correlation between the stable age distribution of cases at equilibrium and WHO TB prevalence data.  In general, age-targeted interventions were found to be more effective at reducing TB cases than the “blanket” strategy.  In India, targeting 15-19 year olds was predicting to result in the greatest overall decline in incidence of both latent and active TB at all intervention levels.  In South Africa, targeting 10-14 year olds was predicted to result in the greatest overall decline of latent TB at all intervention levels; however, targeting 10-14 year olds at lower intervention levels and a blanket strategy at higher intervention levels, were more effective at reducing infectious TB burden.  These results suggest that age-based interventions may complement current public health interventions by further reducing TB burden to achieve WHO eradication goals.  Future studies should utilize a more detailed model for TB dynamics to generate a more realistic prediction.

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