30 research outputs found

    Critical behavior in interdependent spatial spreading processes with distinct characteristic time scales

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    AbstractThe spread of an infectious disease is well approximated by metapopulation networks connected by human mobility flow and upon which an epidemiological model is defined. In order to account for travel restrictions or cancellation we introduce a model with a parameter that explicitly indicates the ratio between the time scales of the intervening processes. We study the critical properties of the epidemic process and its dependence on such a parameter. We find that the critical threshold separating the absorbing state from the active state depends on the scale parameter and exhibits a critical behavior itself: a metacritical point – a critical value in the curve of critical points – reflected in the behavior of the attack rate measured for a wide range of empirical metapopulation systems. Our results have potential policy implications, since they establish a non-trivial critical behavior between temporal scales of reaction (epidemic spread) and diffusion (human mobility) processes

    Physics of interdependent dynamical processes.

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    La emergencia de fenómenos colectivos a escalas macroscópicas no observados en escalas microscópicas cuestiona la validez de las teorías reduccionistas. Para explicar estos fenómenos se necesitan enfoques sistémicos que den cuenta de los patrones de interacción no triviales existentes entre los constituyentes de los sistemas sociales, biológicos o económicos, lo que ha dado lugar al nacimiento de la disciplina conocida como ciencia de los sistemas complejos. Una vía habitual para caracterizar los sistemas complejos ha sido la búsqueda de la conexión entre la estructura de interacciones y el comportamiento colectivo observado en sistemas reales mediante el estudio individual de dinámicas aisladas. No obstante, los sistemas complejos no son inmutables y se encuentran constantemente intercambiando información mediante estímulos internos y externos. Esta tesis se centra en la adaptación de modelos sobre diferentes dinámicas en el campo de los sistemas complejos para caracterizar el impacto de este flujo de información, ya sea entre escalas microscópicas y macroscópicas de un mismo sistema o mediante la existencia de interdependencias entre procesos dinámicos que se propagan de forma simultánea.La primera parte de la tesis aborda el estudio dinámicas acopladas en redes de contacto estáticas. Adaptando los modelos compartimentales introducidos en el siglo XX a la naturaleza de cada dinámica, caracterizamos cuatro problemas diferentes: la propagación de patógenos que interactúan, cuya coexistencia puede ser beneficiosa o perjudicial para su evolución, el control de brotes epidémicos con el uso del rastreo de contactos digital, la aparición de movimientos sociales desencadenados por pequeñas minorías sociales bien coordinadas y la competencia entre honestidad y la corrupción en las sociedades modernas. En todas estas dinámicas, encontramos que el flujo de información cambia las propiedades críticas del sistema así como algunas de las conclusiones extraídas sobre el papel de la estructura de contactos al estudiar cada dinámica de forma individual.La segunda parte de la tesis se centra en el impacto de la movilidad recurrente en la propagación de epidemias en entornos urbanos. Derivamos un modelo sencillo que permite incorporar fácilmente la distribución de la población en las ciudades reales y sus patrones habituales de desplazamiento sin ninguna pérdida de información. Demostramos que los efectos de las políticas de contención basadas en la reducción de la movilidad no son universales y dependen en gran medida de las características estructurales de las ciudades y los parámetros epidemiológicos del virus circulante en la población. En particular, descubrimos y caracterizamos un nuevo fenómeno, el detrimento epidémico, que refleja el efecto beneficioso de la movilidad en algunos escenarios para contener un brote epidémico. Por último, exploramos tres casos de estudio reales, mostrando que nuestro modelo permite capturar algunos de los mecanismos que han convertido a los núcleos urbanos en importantes focos de contagio en recientes epidemias y que el modelo desarrollado puede servir como base para desarrollar marcos teóricos más realistas que reproducen la evolución de distintas enfermedades como la COVID-19 o el dengue.<br /

    Dynamical Patterns of Cattle Trade Movements

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    Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

    Dynamical Patterns of Cattle Trade Movements

    Get PDF
    Despite their importance for the spread of zoonotic diseases, our understanding of the dynamical aspects characterizing the movements of farmed animal populations remains limited as these systems are traditionally studied as static objects and through simplified approximations. By leveraging on the network science approach, here we are able for the first time to fully analyze the longitudinal dataset of Italian cattle movements that reports the mobility of individual animals among farms on a daily basis. The complexity and inter-relations between topology, function and dynamical nature of the system are characterized at different spatial and time resolutions, in order to uncover patterns and vulnerabilities fundamental for the definition of targeted prevention and control measures for zoonotic diseases. Results show how the stationarity of statistical distributions coexists with a strong and non-trivial evolutionary dynamics at the node and link levels, on all timescales. Traditional static views of the displacement network hide important patterns of structural changes affecting nodes' centrality and farms' spreading potential, thus limiting the efficiency of interventions based on partial longitudinal information. By fully taking into account the longitudinal dimension, we propose a novel definition of dynamical motifs that is able to uncover the presence of a temporal arrow describing the evolution of the system and the causality patterns of its displacements, shedding light on mechanisms that may play a crucial role in the definition of preventive actions

    Mathematical and computational approaches to contagion dynamics on networks

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    In this thesis, we firstly introduce the basic terminology and concepts needed for the the following chapters. In particular we introduce the basics of graph/network theory, epidemiological models (both well mixed and on networks), and mobility models (the gravity and radiation models). After the introduction of these topics, we propose a general framework for epidemiological network models from which the known individual-based and pair-based models can be derived. We then introduce a more exact pair-based model by showing previous iterations are a linearised version of it, and then we extend it further to the temporal setting. Next, we present a meta-population model for the spread of COVID-19 in Ireland which makes use of temporal commuting patters generated from the radiation model. Finally, we analyse a year worth of Irish cattle trade data. We then fit a number of mobility models and show that an altered version of the radiation model, which we call the generalised radiation model, is able to accurately reproduce the distance distribution of cattle trades in the country

    Networks, Epidemics and Collective Behavior: from Physics to Data Science

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    In the final quarter of the XX century the classical reductionist approach that had been driving the development of physics was questioned. Instead, it was proposed that systems were arranged in hierarchies so that the upper level had to convey to the rules of the lower level, but at the same time it could also exhibit its own laws that could not be inferred from the ones of its fundamental constituents. This observation led to the creation of a new field known as complex systems. This novel view was, however, not restricted to purely physical systems. It was soon noticed that very different systems covering a huge array of fields, from ecology to sociology or economics, could also be analyzed as complex systems. Furthermore, it allowed physicists to contribute with their knowledge and methods in the development of research in those areas. In this thesis we tackle problems covering three areas of complex systems: networks, which are one of the main mathematical tools used to study complex systems; epidemic spreading, which is one of the fields in which the application of a complex systems perspective has been more successful; and the study of collective behavior, which has attracted a lot of attention since data from human behavior in huge amounts has been made available thanks to social networks. In fact, data is also the main driver of our discussion of the other two areas. In particular, we use novel sources of data to challenge some of the classical assumptions that have been made in the study of networks as well as in the development of models of epidemic spreading. In the case of networks, the problem of null models is addressed using tools coming from statistical physics. We show that anomalies in networks can be just a consequence of model oversimplification. Then, we extend the framework to generate contact networks for the spreading of diseases in populations in which both the contact structure and the age distribution of the population are important. Next, we follow the historical development of mathematical epidemiology and revisit the assumptions that were made when there was no data about the real behavior of this kind of systems. We show that one of the most important quantities used in this kind of studies, the basic reproduction number, is not properly defined for real systems. Similarly, we extend the theoretical framework of epidemic spreading on directed networks to multilayer systems. Furthermore, we show that the challenge of incorporating data to models is not only restricted to the problem of obtaining it, but that it is also really important to be aware of its characteristics to do it properly.Lastly, we conclude the thesis studying two examples of collective behavior using data extracted from online systems. We do so using techniques that were originally developed for other purposes, such as earthquake prediction. Yet, we demonstrate that they can also be used to study this new type of systems. Furthermore, we show that, despite their unique characteristics, they possess properties similar to the ones that have been observed in the offline world. This not only means that modern societies are intertwined with the online world, but it also signals that if we aim to understand socio-technical systems a holistic approach, as the one proposed by complex systems, is indispensable.<br /
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