Biology subjects

Arregui, S.

Publications and source records attributed to Arregui, S..

3 recordsLinked to original sources

bioRxiv2018

Projecting social contact matrices to different demographic structures

The modeling of large-scale communicable epidemics has greatly benefited in the last years from the increasing availability of highly detailed data. Particularly, in order to achieve quantitative descriptions of the evolution of epidemics, contact networks and mixing patterns are key. These heterogeneous patterns depend on several factors such as location, socioeconomic conditions, time, and age. This last factor has been shown to encapsulate a large fraction of the observed inter-individual variation in contact patterns, an observation validated by different measurements of age-dependent contact matrices. Recently, several works have studied how to project those matrices to areas where empiric data is not available. However, the dependence of contact matrices on demographic structures and their time evolution has been largely neglected. In this work, we tackle the problem of how to transform an empirical contact matrix that has been obtained for a given demographic structure into a different contact matrix that is compatible with a different demography. The methodology discussed here allows extrapolating a contact structure measured in a particular area to any other whose demographic structure is known, as well as to obtain the time evolution of contact matrices as a function of the demographic dynamics of the populations they refer to. To quantify the effect of considering time-dynamics of contact patterns on disease modeling, we implemented a Susceptible-Exposed-Infected-Recovered (SEIR) model on 16 different countries and evaluated the impact of neglecting the temporal evolution of mixing patterns. Our results show that simulated disease incidence rates, both at the aggregated and age-specific levels, are significantly dependent on contact structures variation driven by demographic evolution. The present work opens the path to eliminate technical biases from model-based impact evaluations of future epidemic threats and warns against the use of contact matrices to model diseases without correcting for demographic evolution or geographic variations.\n\nAuthor summaryLarge scale epidemic outbreaks represent an ever increasing threat to humankind. In order to anticipate eventual pandemics, mathematical modeling should not only have the capacity to model in real time an ongoing disease, but also to predict the evolution of potential outbreaks in different locations and times. To this end, computational frameworks need to incorporate, among other ingredients, realistic contact patterns into the models. This not only implies anticipating the demographic structure of the populations under study, but also understanding how demographic evolution reshapes social mixing patterns along time. Here we present a mathematical framework to solve this problem and test our modeling approach on 16 different empirical contact matrices. We also evaluate the impact of an eventual future outbreak by simulating a SEIR scenario in the countries analyzed. Our results show that using outdated or imported contact matrices that do not take into account demographic structure or its evolution can lead to largely misleading conclusions.

epidemiology↗

bioRxiv2018

Design principles for TB vaccines’ clinical trials based on spreading dynamics

Tuberculosis (TB) is one of the most complex diseases from the perspective of mathematical epidemiology. Individuals recently infected with the bacillus Mycobacterium tuberculosis can either develop TB directly in a matter of several weeks, or enter into an asymptomatic latent TB infection state (LTBI) that only occasionally derives into active disease, sometimes even decades after the infection event. The possible interruptions that a vaccine might provoke on these two mechanisms are indistinguishable in phase II clinical trials. In this work, we present a new methodology that allows differentiating vaccines that slow down the progression to disease from vaccines that prevent it. By introducing a stochastic framework for simulating synthetic clinical trials based on transmission models, we show how the method proposed here contributes both to reduce uncertainty in vaccine characterization and impact forecasts as well as to assist the design of clinical trials, improving their probabilities of success.

epidemiology↗

bioRxiv2017

A data-driven model for the assessment of age-dependent patterns of Tuberculosis burden and impact evaluation of novel vaccines.

In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations' age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age-strata that are common to all air-borne diseases, but still typically neglected in the TB case. Furthermore, whilst demographic structures of many countries are rapidly aging, demographic dynamics is pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels prospected for the next decades in the areas of the world that are most hit by the disease nowadays. Similarly, we show that considering realistic patterns of contacts among individuals in different age-strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions.\n\nSignificance StatementEven though tuberculosis (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 transmission models at describing the relationship between demography and TB transmission. Here, we present a data-driven epidemiological model that, unlike previous approaches, explicitly contemplates relevant aspects of the coupling between agestructure and TB dynamics, such as demographic evolution and contact heterogeneities. Using our model, we identify substantial biases in epidemiological forecasts rooted in an inadequate description of these aspects, both at the level of aggregated incidence and mortality rates and their distribution across age-strata.

epidemiology↗