Clearing infections could boost TB case-finding impact

Tuberculosis remains a major global health concern, and public health planners use mathematical models to estimate how interventions will change disease trends. Theresa Ryckman and colleagues asked whether a common assumption in many of these models — that everyone who becomes infected faces a lifelong risk of progressing to active disease — might underestimate the benefits of actively finding and treating cases. To explore this, the team built two contrasting models of infection dynamics. One model assumed lifelong progression risk after infection (the Conventional model). The other allowed that some people who are infected actually clear their infections and thus no longer face the same progression risk (the Clearance model). Both models were calibrated to real-world data on tuberculosis burden in India and to empirical estimates of how people progress after infection. By testing these two ways of representing what happens after infection, the researchers aimed to see how sensitive impact projections for population-level interventions would be to that biological realism.

After fitting both models to empirical data from India, the researchers used them to project the effects of community-wide active case-finding campaigns that would cover 75% of the population every two years. They ran projections with and without the mass provision of tuberculosis preventive treatment (TPT). The Clearance model’s posterior distributions of key outputs — including disease incidence, infection prevalence, progression over time, and incidence trends — were more similar to the empirical estimates than those from the Conventional model, indicating better fit. Importantly, the Clearance model projected a substantially greater impact of case-finding on incidence: a 26% reduction after 10 years with a 95% uncertainty interval of 15-41%, compared with an 11% reduction in the Conventional model with a 95% uncertainty interval of 7-24%. These differences emerged from how each model represents the pool of people at risk after infection and how case-finding reduces transmission over time.

The study’s main takeaway is that allowing some infected people to clear their infection changes both model fit to data and the projected population-level benefits of active case-finding. The authors conclude that because clearance of some infections is biologically plausible and because the Clearance model matched empirical patterns better, the real-world epidemiological impact of large-scale case-finding campaigns may be greater than many current models suggest. This matters for decision makers who rely on modeling to weigh investments in community screening, case-finding logistics, and TPT delivery. Models that ignore infection clearance risk underestimating the return on these interventions, potentially influencing priorities and resource allocation. Incorporating more realistic infection dynamics could therefore change expectations about how quickly and how much case-finding can reduce tuberculosis incidence at the population level.