Understanding the effects of populations’ aging and heterogeneous contact patterns on TB transmission dynamics.

The work, led by ISI Research Leader Prof. Yamir Moreno together with researchers from the University of Zaragoza and the St. Justine Hospital in Montreal, Canada, presents a new model for the spreading of tuberculosis that helps to understand the effects of population aging and contact patterns in the transmission dynamics of Tuberculosis.

The main result of the study, which will have important applications for the design of optimal vaccination campaigns, is that the incidence of tuberculosis worldwide could increase in the coming decades.

Despite its reduced incidence in the contemporary developed world, tuberculosis (TB) is still a major public health problem worldwide. The World Health Organization (WHO), which organizes the World TB Day this March 24th, estimates that this contagious bacterial disease was responsible for over than 1.5 million deaths worldwide in 2016 alone, most of which concentrated, dramatically, in developing countries. These numbers put TB, along with AIDS and malaria, as one of the deadliest infectious diseases today. As such, the WHO places TB eradication as a priority goal to accomplish within the next decades, which is fueling the efforts of an active research community working on the development of novel interventions against the disease. That includes the exploration of novel diagnosis tools, the development of antibiotics, prophylactic measures to protect infected individuals against the active form of the disease and, most noticeably, new vaccines aimed at outperforming the current TB vaccine BCG.
However, the development of these new interventions against the disease is hindered by a series of particularities that make TB not just one of the deadliest, but also one of the infectious diseases that are hardest to understand and fight. Our current knowledge of the mechanisms defining the ability of the human immune system to respond to the TB pathogen is remarkably limited, which often makes it difficult to foresee the potential of these novel interventions from laboratory experiments. This forces researchers to design clinical trials involving large cohorts of individuals in high burden countries to get estimates of the efficacy of their interventions, and to use mathematical models of TB spreading to interpret and extrapolate the results of such trials. In this context, the combination of disciplines as disparate as immunology, microbiology, epidemiology and complexity sciences is key to generating models able to produce impact forecasts and evaluations of cost-effectiveness for novel epidemiological interventions.
These mathematical models of disease spreading must integrate geo-demographic, epidemiological and sociological data to simulate the transmission dynamics of the TB pathogen through human populations. Admittedly, such a task is not exempt from its own issues. Paradigmatically, even though TB is acknowledged as a strongly age-dependent disease, it remains unclear how TB epidemics would react, in the following decades, to the generalized aging that human populations are experiencing worldwide. This situation is partly caused by the limitations of current spreading models at describing the relationship between demography and TB transmission. Specifically, classical models in TB epidemiology that aim at producing TB forecasts at supra-national scales operate under the assumption of static demographic structures, and they assume that the interactions between individuals causing the propagation of the pathogen occur homogeneously among all age strata.
In a new work published by S. Arregui et al. this week in the Proceedings of the Natural Academy of Sciences (PNAS), a group of researchers of the University of Zaragoza (Spain) and the Hospital Sainte Justine (in Montreal, Canada) led by ISI Research Leader Prof. Yamir Moreno, present a novel epidemiological model of TB transmission that challenge those simplifying assumptions by incorporating empirical data. On the one hand, the authors integrate publicly available country-wise empiric demographic prospects into their TB model to tackle a question that had so far remained elusive for TB modelers, namely, what are the expected effects of populations’ aging on the evolution of the TB epidemics. On the other hand, they incorporate recent survey-based data regarding contact patterns among age groups into their formalism, mimicking, for the first time, what has been done with remarkable implications in other communicable respiratory diseases such as flu. According to their results, incorporating populations’ aging into current TB models has the disturbing effect of raising the burden levels predicted for the next decades, especially in the countries where this aging is more intense, thus slowing down the pace at which the epidemic has been shrinking in the last decades. Under this view, the socio-economic and Public Health improvements that made possible the recent decline of TB worldwide would need to be intensified in many countries if the goal of TB eradication is to be pursued before 2050, at the same time that global aging of human populations unfolds.
Furthermore, in this work, Arregui et al. quantify how the distribution of contagions between age strata described by transmission models is extensively reshaped once the assumption of contacts homogeneity is substituted by the integration of empiric data on age-dependent contact patterns. These results are crucially relevant for the evaluation of any epidemiological intervention focused on a particular age group, such as an eventual novel vaccine targeting adolescents. The impact evaluation of novel vaccines is, in fact, the main foreseen application of the model here presented, which will constitute a valuable tool to explore and compare different immunization campaigns and vaccines in the years to come. Some of the co-authors of the study are currently working on the development of MTBVAC, which is the first novel vaccine candidate, based on an attenuated strain of the TB pathogen, in clinical trials in humans. Once these trials are concluded, as well as others from other teams working on other prospective vaccines, the model presented in this work will be instrumental to predict and compare their expected impacts.
“Data-driven model for the assessment of Mycobacterium tuberculosis transmission in evolving demographic structures”
Sergio Arregui, María José Iglesias, Sofía Samper, Dessislava Marinova, Carlos Martin, Joaquín Sanz and Yamir Moreno.
Proceeding of the National Academy of Sciences USA (PNAS), http://www.pnas.org/cgi/doi/10.1073/pnas.1720606115