How host heterogeneity governs tuberculosis reinfection?

Abstract

Recurrent episodes of tuberculosis (TB) can be due to relapse of latent infection or exogenous reinfection, and discrimination is crucial for control planning. Molecular genotyping of Mycobacterium tuberculosis isolates offers concrete opportunities to measure the relative contribution of reinfection in recurrent disease. Here, a mathematical model of TB transmission is fitted to data from 14 molecular epidemiology studies, enabling the estimation of relevant epidemiological parameters. Meta-analysis reveals that rates of reinfection after successful treatment are higher than rates of new TB, raising an important question about the underlying mechanism. We formulate two alternative mechanisms within our model framework: (i) infection increases susceptibility to reinfection or (ii) infection affects individuals differentially, thereby recruiting high-risk individuals to the group at risk for reinfection. The second mechanism is better supported by the fittings to the data, suggesting that reinfection rates are inflated through a population phenomenon that occurs in the presence of heterogeneity in individual risk of infection. As a result, rates of reinfection are higher when measured at the population level even though they might be lower at the individual level. Finally, differential host recruitment is modulated by transmission intensity, being less pronounced when incidence is high.

Figure 1.

Diagram illustrating host selection. In a population with a heterogeneous susceptibility profile, infection tends to affect individuals at higher risk. This skews the distribution of recovered individuals towards higher susceptibility, which explains the increased reinfection rates in this subpopulation. Host selection is modulated by transmission intensity and its effect is weaker under higher transmission.

Figure 2.

Proportion (p) of reinfection among recurrent tuberculosis (TB) cases as a function of TB incidence. Open circles correspond to the data described in table 2. Curves correspond to the best-fitting model under the two assumptions: homogeneous host population (dotted) and heterogeneous host population (dashed).

Figure 3.

Model predictions for the ratio (κ) of the rate of reinfection tuberculosis (TB) after successful treatment by the rate of new TB as a function of TB incidence. Open circle corresponds to the Cape Town study [4]. Curves are generated by the best-fitting models identified in figure 2: homogeneous host population (dotted) and heterogeneous host population (dashed).