Data needs for evidence-based decisions: a tuberculosis modeler's ‘wish list’ [Review article]

Abstract

Summary Infectious disease models are important tools for understanding epidemiology and supporting policy decisions for disease control. In the case of tuberculosis (TB), such models have informed our understanding and control strategies for over 40 years, but the primary assumptions of these models – and their most urgent data needs – remain obscure to many TB researchers and control officers. The structure and parameter values of TB models are informed by observational studies and experiments, but the evidence base in support of these models remains incomplete. Speaking from the perspective of infectious disease modelers addressing the broader TB research and control communities, we describe the basic structure common to most TB models and present a “wish list” that would improve the evidence foundation upon which these models are built. As a comprehensive TB research agenda is formulated, we argue that the data needs of infectious disease models – our primary long-term decision-making tools – should figure prominently.

Progression from latent infection to active disease in dynamic tuberculosis transmission models: a systematic review of the validity of modelling assumptions

“…For consumers of modelling studies, our results suggest that the findings of these studies should not be accepted uncritically. Although major gaps exist in the evidence base for constructing and evaluating the validity of these models, 15 it is still important (perhaps more important) to make the best use of the evidence that is available. Greater confidence might be placed in analyses in which modelling approaches are clearly explained and justified with reference to the available evidence and that can reproduce data relevant to the setting and population being modelled.…”

“…These models represent the mechanisms of transmission, natural history, and health system interactions that generate tuberculosis outcomes. 13,14 Despite more than a century of epidemiological research into tuberculosis, concrete evidence for these underlying processes is imperfect, 15 and studies have taken various approaches for constructing and parameterising transmission models. This variation can be consequential: in a modelling collaboration examining the post-2015 End TB Strategy, 16 variation in epidemiological assumptions was identified as a cause of the wide range of estimates produced for the health impact 17 and cost-effectiveness 18 of expanded tuberculosis control.…”

Delays in diagnosis and treatment of pulmonary tuberculosis in India: a systematic review

“…Here, there are several unknowns. 5, 39, 40 How big a reduction in diagnostic delay is necessary before a meaningful decline in TB incidence occurs? Do delays before diagnosis translate directly to duration of infectiousness or do most transmission events cluster toward the beginning or end of the infectious period?…”

Understanding the incremental value of novel diagnostic tests for tuberculosis

“…Mathematical models can be a useful tool in helping to demonstrate how these dimensions relate to the impact of diagnostic tests on TB transmission [20][21][22] . Figure 1a depicts the simplest, and most commonly used [23][24][25] , conceptualization of TB diagnosis in mathematical models so far.…”