Asymmetrically interacting spreading dynamics on complex layered networks

The spread of disease through a physical-contact network and the spread of information about the disease on a communication network are two intimately related dynamical processes. We investigate the asymmetrical interplay between the two types of spreading dynamics, each occurring on its own layer, by focusing on the two fundamental quantities underlying any spreading process: epidemic threshold and the final infection ratio. We find that an epidemic outbreak on the contact layer can induce an outbreak on the communication layer, and information spreading can effectively raise the epidemic threshold. When structural correlation exists between the two layers, the information threshold remains unchanged but the epidemic threshold can be enhanced, making the contact layer more resilient to epidemic outbreak. We develop a physical theory to understand the intricate interplay between the two types of spreading dynamics.Comment: 29 pages, 14 figure

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward

Correlated network of networks enhances robustness against catastrophic failures

Networks in nature rarely function in isolation but instead interact with one another with a form of a network of networks (NoN). A network of networks with interdependency between distinct networks contains instability of abrupt collapse related to the global rule of activation. As a remedy of the collapse instability, here we investigate a model of correlated NoN. We find that the collapse instability can be removed when hubs provide the majority of interconnections and interconnections are convergent between hubs. Thus, our study identifies a stable structure of correlated NoN against catastrophic failures. Our result further suggests a plausible way to enhance network robustness by manipulating connection patterns, along with other methods such as controlling the state of node based on a local rule

Statistical physics of vaccination

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research

The multiple faces of leukocyte interstitial migration

Spatiotemporal control of leukocyte dynamics within tissues is critical for successful innate and adaptive immune responses. Homeostatic trafficking and coordinated infiltration into and within sites of inflammation and infection rely on signaling in response to extracellular cues that in turn controls a variety of intracellular protein networks regulating leukocyte motility, migration, chemotaxis, positioning, and cell–cell interaction. In contrast to mesenchymal cells, leukocytes migrate in an amoeboid fashion by rapid cycles of actin polymerization and actomyosin contraction, and their migration in tissues is generally referred to as low adhesive and nonproteolytic. The interplay of actin network expansion, contraction, and adhesion shapes the exact mode of amoeboid migration, and in this review, we explore how leukocyte subsets potentially harness the same basic biomechanical mechanisms in a cell-type-specific manner. Most of our detailed understanding of these processes derives from in vitro migration studies in three-dimensional gels and confined spaces that mimic geometrical aspects of physiological tissues. We summarize these in vitro results and then critically compare them to data from intravital imaging of leukocyte interstitial migration in mouse tissues. We outline the technical challenges of obtaining conclusive mechanistic results from intravital studies, discuss leukocyte migration strategies in vivo, and present examples of mode switching during physiological interstitial migration. These findings are also placed in the context of leukocyte migration defects in primary immunodeficiencies. This overview of both in vitro and in vivo studies highlights recent progress in understanding the molecular and biophysical mechanisms that shape robust leukocyte migration responses in physiologically complex and heterogeneous environments

Contagion dynamics in multilevel and structured populations.

Existen numerosos procesos de contagio sobre redes, como la propagación de epidemias, los rumores, la información u otros fenómenos no lineales propios de los sistemas complejos humanos. Desde la perspectiva de la modelización matemática, los procesos de contagio en poblaciones estructuradas se están volviendo cada vez más sofisticados en lo que respecta al tipo de interacciones no triviales involucradas en ellos. Los modelos han evolucionado desde los simples métodos compartimentales a modelos estructurados en los que se tienen en cuenta las heterogeneidades de la población. Además, para visualizar estas jerarquías y heterogeneidades de los sistemas complejos humanos, también consideramos la representación multicapa de las poblaciones. En esta tesis, intentamos explorar la punta del iceberg en lo que respecta a procesos de contagio sobre poblaciones basándonos en varios modelos matemáticos. Nuestro objetivo es entender la complejidad de las dinámicas de contagio en poblaciones estructuradas y multinivel.En el primer capítulo, nos centramos en presentar el desarrollo de algunas de las teorías principales que se utilizan para estudiar los sistemas complejos. El descubrimiento de las interacciones no lineales hizo que le método del reduccionismo fuese cuestionado, dado que el comportamiento general no puede describirse como una simple superposición de pequeñas escalas. La ciencia de redes busca caracterizar los sistemas complejos de diversos campos. Al mismo tiempo, la teoría de grafos proporciona las herramientas matemáticas necesarias para describir redes realistas. Discutiremos algunas de las cantidades fundamentales y las métricas más relevantes para la caracterización de la estructura de la red, así como varios ejemplos de modelos de red. Además, repasaremos brevemente los principios básicos de las redes multicapa que rompen la limitación de un solo tipo de conexión existente en las redes monocapa, estableciendo la base para explorar y generalizar estos conceptos.A continuación, estudiaremos procesos dinámicos comenzando por una breve introducción a los modelos matemáticos que se usarán durante el resto de la tesis. En el caso de la ecuación maestra, resaltaremos el rol de los procesos de Markov así como la aproximación de campo medio, sin centrarnos en sus soluciones completas. Los métodos de modelización y las reglas de actualización que se utilizan en las simulaciones numéricas también se presentan en detalle. En esta tesis, nos centraremos en el problema de la propagación de epidemias sobre redes, un tema que despierta gran interés en el campo de los procesos de propagación y contagio. Después de revisar las propiedades y los resultados teóricos de algunos de los modelos epidemiológicos típicos, con varias simplificaciones desde el punto de vista matemático, exploraremos varias medidas importantes en el campo de la epidemiología, i.e., el número reproductivo básico y la inmunidad de grupo. Después, implementaremos un modelo clásico de epidemias sobre redes multicapa para explorar el papel que juega la direccionalidad utilizando funciones generatrices. Terminaremos el capítulo 2 modelizando un tipo especial de procesos de contagio social, en particular, utilizaremos un modelo compartimental para estudiar la propagación de la corrupción. Prestaremos atención a las condiciones críticas para que surja este tipo de comportamiento desarrollando la aproximación de campo medio y comparando sus predicciones con simulaciones. Es más, extenderemos el modelo de corrupción a un sistema de dos capas en el que los flujos de contagio pueden ser diferentes en cada capa para investigar el papel que juega el solapamiento de enlaces y las correlaciones de grado entre capas en la evolución de las actividades honestas y corruptas.Resulta evidente que la complejidad de los sistemas humanos del mundo real afecta la precisión con la que se pueden predecir las epidemias y algunas propiedades específicas de los sistemas. Sin embargo, debido al desarrollo de la ciencia de datos, fuentes de datos masivas y muy informativas pueden utilizarse para enriquecer la topología de la red de forma que se acerque a los sistemas reales. En al tercera parte de esta tesis, comenzaremos describiendo los retos y las oportunidades que han surgido durante el desarrollo de la ciencia de datos. A continuación, intentaremos conseguir una imagen más realista de la estructura interna de las redes de contacto utilizando datos reales. Además, ilustraremos la importancia de utilizar una perspectiva conducida por los datos en lo que respecta a la modelización de redes a la hora de estudiar la propagación de epidemias en redes de contacto. En este caso, la variabilidad de patrones de interacción que surge de la heterogeneidad de la población, sus comportamientos sociales, etc. puede ser capturada correctamente.Bajo este mismo desarrollo teórico, consideraremos la edad de los individuos y sus patrones de interacción social para generar redes multicapa con estructura de edad para estudiar el problema de la inmunidad de grupo del SARS-CoV-2 y evaluar el impacto que tres estrategias de vacunación pueden tener a la hora de eliminar la transmisión de la panedmia. Después, para explorar la dinámica de las enfermedades que se propagan en entornos hospitalarios (HAI, por sus siglas en inglés) cuando los pacientes están recibiendo tratamiento en ellos, utilizaremos una colección de datos espacio-temporales recogida en tres hospitales de Canadá para generar las redes de interacción entre los trabajadores hospitalarios (HCWs). Nos centraremos en determinar cuantitativemente el riesgo de que las HAIs se propaguen por las diferentes unidades de un hospital y los varios grupos de HCWs, respectivamente. Calcularemos el riesgo de las unidades espaciales usando el tiempo de llegada de la enfermedad y el número de infecciones producidas en cada unidad. En el caso de los HCWs, la probabilidad de infectarse y el número de reproducción efectivo son usados como indicador del riesgo de HCWs.Concluiremos la tesis presentando nuestras conclusiones y discutiendo algunos de los restos que quedan por explorar en el futuro.<br /

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Adaptive dynamical networks

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks