Intervention strategies for tuberculosis control

Mathematics and Statistics: Dylan Shepardson (Mount Holyoke College)

Tuberculosis (TB) is one of the most deadly infectious diseases in the world (the World Health Organization says the only other single infectious agent that kills more people worldwide in a given year than mycobacterium tuberculosis is HIV). With the emergence of multidrug-resistant TB, and with 1/3 or the world’s population estimated to be latently infected with TB and at risk of progression to active TB disease, there is a tremendous need for improved strategies to control TB. We are developing mathematical models that can be used to develop and assess alternative strategies for TB control.

One project involves using mixed-integer optimization and machine learning techniques to develop simple screening rules that can be used in low-resource settings to predict which patients are at highest risk for TB, and a second project involves developing individual-based stochastic simulations to model the spread of TB through a population. Interested students should have some background in computer programming. Mathematics through linear algebra or differential equations would be very helpful.