In Tuberculosis (TB), given the complexity of its transmission dynamics, observations of reduced epidemiological risk associated with preventive interventions can be difficult to translate into mechanistic interpretations. Specifically, in clinical trials of vaccine efficacy, a readout of protection against TB disease can be mapped to multiple dynamical mechanisms, an issue that has been overlooked so far. Here, we describe this limitation and its effect on model-based evaluations of vaccine impact. Furthermore, we propose a methodology to analyze efficacy trials that circumvents it, leveraging a combination of compartmental models and stochastic simulations. Using our approach, we can disentangle the different possible mechanisms of action underlying vaccine protection effects against TB, conditioned to trial design, size, and duration. Our results unlock a deeper interpretation of the data emanating from efficacy trials of TB vaccines, which renders them more interpretable in terms of transmission models and translates into explicit recommendations for vaccine developers.

Background The ongoing COVID-19 pandemic has greatly disrupted our everyday life, forcing the adoption of non-pharmaceutical interventions in many countries and putting public health services and healthcare systems worldwide under stress. These circumstances are leading to unintended effects such as the increase in the burden of other diseases. Methods Here, using a data-driven epidemiological model for tuberculosis (TB) spreading, we describe the expected rise in TB incidence and mortality if COVID-associated changes in TB notification are sustained and attributable entirely to disrupted diagnosis and treatment adherence. Results Our calculations show that the reduction in diagnosis of new TB cases due to the COVID-19 pandemic could result in 228k (CI 187–276) excess deaths in India, 111k (CI 93–134) in Indonesia, 27k (CI 21–33) in Pakistan, and 12k (CI 9–18) in Kenya. Conclusions We show that it is possible to reverse these excess deaths by increasing the pre-covid diagnosis capabilities from 15 to 50% for 2 to 4 years. This would prevent almost all TB-related excess mortality that could be caused by the COVID-19 pandemic if no additional preventative measures are introduced. Our work therefore provides guidelines for mitigating the impact of COVID-19 on tuberculosis epidemic in the years to come.

In the development of vaccines against tuberculosis (TB), a number of factors represent burdensome difficulties for the design and interpretation of randomized control trials (RCTs) of vaccine efficacy. Among them, the complexity of the transmission chain of TB allows the co-existence of several routes to disease that can be observed within the populations from where vaccine efficacy trial participants are sampled. This makes it difficult to link trial-derived readouts of vaccine efficacy to specific vaccine mechanistic descriptions, since, intuitively, the same efficacy readouts may lean on the ability of a vaccine to arrest only some, but not all, the possible routes to disease. This increases uncertainty in evaluations of vaccine impact based on transmission models, since different vaccine descriptions of the same efficacy readout typically lead to different impact forecasts. In this work, we develop a Bayesian framework to evaluate the relative compatibility of different vaccine descriptions with the observations emanating from a randomized clinical trial (RCT) of vaccine efficacy, offering an unbiased framework to estimate vaccine impact even when the specific mechanisms of action of the given vaccine are not explicitly known. The type of RCTs considered here, conducted on IGRA+ individuals, emerged as a promising design architecture after the encouraging results reported for the vaccine M72/AS01E clinical trial, which we use here as a case study.

The ongoing COVID-19 pandemic has greatly disrupted our everyday life, forcing the adoption of non-pharmaceutical interventions in many countries worldwide and putting public health services and healthcare systems worldwide under stress. These circumstances are leading to unintended effects such as the increase in the burden of other diseases. Here, using a data-driven epidemiological model for Tuberculosis (TB) spreading, we describe the expected rise in TB incidence and mortality that can be attributable to the impact of COVID-19 on TB surveillance and treatment in four high-burden countries. Our calculations show that the reduction in the diagnosis of new TB cases due to the COVID-19 pandemic could result in 824250 (CI 702416-940873) excess deaths in India, 288064 (CI 245932-343311) in Indonesia, 145872 (CI 120734-171542) in Pakistan, and 37603 (CI 27852-52411) in Kenya. Furthermore, we show that it is possible to revert such unflattering TB burden scenarios by increasing the pre-covid diagnosis capabilities by at least 75% during 2 to 4 years. This would prevent almost all TB-related excess mortality caused by the COVID-19 pandemic, which will be observed if nothing is done to prevent it. Our work, therefore, provides guidelines for mitigating the impact of COVID-19 on tuberculosis epidemic in the years to come.