Data needs for evidence-based decisions: a tuberculosis modeler's 'wish list'

Abstract

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.

Figure 1. Simplified TB Model

The basic structure that is common to many compartmental transmission models of TB. Health states are represented by boxes and transitions are indicated by arrows. We highlight assumptions necessary to estimate rates associated with four basic processes (in circles): infection, rapid progression, reactivation, and treatment/recovery. Mortality (not shown) also occurs from each box.

Figure 2. The Effective Reproduction Number (R_e) and the Transmission Chain of TB

Each box represents a person in the TB transmission chain. Parameters that have the greatest influence on R_e, and thus on projected TB incidence and mortality, in mathematical models typically affect either the rate of TB transmission per infectious person-year (A), probability of developing infectious TB (B), or duration of infectiousness (C).

Figure 3. Sensitivity and Uncertainty of Model Outcomes with Parameter Variation

Bars represent the change in steady-state TB incidence in a simplified model of TB transmission (Figure 1) that would occur with specified increases (black bars) and decreases (white bars) in model parameters. The model is calibrated to globally representative TB incidence and prevalence rates (Box 1). Panel A shows sensitivity of the model to a one-way 25% change in each parameter value given in Box 1. Panel B shows corresponding changes in steady-state incidence when parameters are varied across a reasonable uncertainty range, as specified (100% reduction corresponds to R_e<1 or eventual elimination, and rightward-pointing arrows denote changes greater than 150%). In both analyses, the 7 most influential parameters describe either the probability of progression to active TB, the rate of TB transmission per infectious person-year, or the duration of TB infectiousness. In a low-incidence scenario (incidence 5 per 100,000/year), the rate of endogenous reactivation was proportionally more important (ranked fourth in panel B), and the degree of protection afforded by latent infection was less important (ranked last), but other findings were similar. Within existing levels of uncertainty or heterogeneity, the TB transmission rate, probability of rapid progression, and diagnosis/treatment rate could each generate steady-state TB incidence rates across all reasonable values, demonstrating the importance of appropriately specifying these parameters in setting-specific models. The 2 least influential parameters describe non-TB mortality and relapse, and were not included in the “wish list” (Box 2). ARTI, annual risk of TB infection; CDR, case detection rate (defined as the proportion of all patients with TB who are detected)