Abstract
The natural history of human infection with Mycobacterium tuberculosis (Mtb) is highly variable, as is the response to treatment of active tuberculosis. There is presently no direct means to identify individuals in whom Mtb infection has been eradicated, whether by a bactericidal immune response or sterilizing antimicrobial chemotherapy. Mathematical models can assist in such circumstances by measuring or predicting events that cannot be directly observed. The 3 models discussed in this review illustrate instances in which mathematical models were used to identify individuals with innate resistance to Mtb infection, determine the etiologic mechanism of tuberculosis in patients treated with tumor necrosis factor blockers, and predict the risk of relapse in persons undergoing tuberculosis treatment. These examples illustrate the power of various types of mathematic models to increase knowledge and thereby inform interventions in the present global tuberculosis epidemic.
Highlights
Mathematical Models of Tuberculosis Reactivation and RelapseSpecialty section: This article was submitted to Infectious Diseases, a section of the journal Frontiers in Microbiology
The natural history of human infection with Mycobacterium tuberculosis (Mtb) is highly variable
These 3 diverse examples illustrate how mathematic models can help advance our understanding of basic aspects of Mtb biology as they affect drug and vaccine development
Summary
Specialty section: This article was submitted to Infectious Diseases, a section of the journal Frontiers in Microbiology. The 3 models discussed in this review illustrate instances in which mathematical models were used to identify individuals with innate resistance to Mtb infection, determine the etiologic mechanism of tuberculosis in patients treated with tumor necrosis factor blockers, and predict the risk of relapse in persons undergoing tuberculosis treatment. These examples illustrate the power of various types of mathematic models to increase knowledge and thereby inform interventions in the present global tuberculosis epidemic
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