A framework for epidemic spreading in multiplex networks of metapopulations

We propose a theoretical framework for the study of epidemics in structured metapopulations, with heterogeneous agents, subjected to recurrent mobility patterns. We propose to represent the heterogeneity in the composition of the metapopulations as layers in a multiplex network, where nodes would correspond to geographical areas and layers account for the mobility patterns of agents of the same class. We analyze both the classical Susceptible-Infected-Susceptible and the Susceptible-Infected-Removed epidemic models within this framework, and compare macroscopic and microscopic indicators of the spreading process with extensive Monte Carlo simulations. Our results are in excellent agreement with the simulations. We also derive an exact expression of the epidemic threshold on this general framework revealing a non-trivial dependence on the mobility parameter. Finally, we use this new formalism to address the spread of diseases in real cities, specifically in the city of Medellin, Colombia, whose population is divided into six socio-economic classes, each one identified with a layer in this multiplex formalism.Comment: 13 pages, 11 figure

Socioeconomic determinants of stay-at-home policies during the first COVID-19 wave

IntroductionThe COVID-19 pandemic has had a significant impact on public health and social systems worldwide. This study aims to evaluate the efficacy of various policies and restrictions implemented by different countries to control the spread of the virus. MethodsTo achieve this objective, a compartmental model is used to quantify the “social permeability” of a population, which reflects the inability of individuals to remain in confinement and continue social mixing allowing the spread of the virus. The model is calibrated to fit and recreate the dynamics of the epidemic spreading of 42 countries, mainly taking into account reported deaths and mobility across the populations. ResultsThe results indicate that low-income countries have a harder time slowing the advance of the pandemic, even if the virus did not initially propagate as fast as in wealthier countries, showing the disparities between countries in their ability to mitigate the spread of the disease and its impact on vulnerable populations. DiscussionThis research contributes to a better understanding of the socioeconomic and environmental factors that affect the spread of the virus and the need for equitable policy measures to address the disparities in the global response to the pandemic

Explosive transitions induced by interdependent contagion-consensus dynamics in multiplex networks

We introduce a model to study the delicate relation between the spreading of information and the formation of opinions in social systems. For this purpose, we propose a two-layer multiplex network model in which consensus dynamics takes place in one layer while information spreading runs across the other one. The two dynamical processes are mutually coupled by considering that the control parameters that govern the dynamical evolution of the state of the nodes inside each layer depend on the dynamical states at the other layer. In particular, we explore the scenario in which consensus is favored by the common adoption of information while information spreading is boosted between agents sharing similar opinions. Numerical simulations together with some analytical results point out that, when the coupling between the dynamics of the two layers is strong enough, a double explosive transition, i.e. an explosive transition both for consensus dynamics and for the information spreading appears. Such explosive transitions lead to bi-stability regions in which the consensus-informed stated and the disagreement-ignorant states are both stable solutions.Comment: 7 pages, 3 figure

The interconnection between independent reactive control policies drives the stringency of local containment

The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic

Socioeconomic determinants of stay-at-home policies during the first COVID-19 wave

IntroductionThe COVID-19 pandemic has had a significant impact on public health and social systems worldwide. This study aims to evaluate the efficacy of various policies and restrictions implemented by different countries to control the spread of the virus.MethodsTo achieve this objective, a compartmental model is used to quantify the “social permeability” of a population, which reflects the inability of individuals to remain in confinement and continue social mixing allowing the spread of the virus. The model is calibrated to fit and recreate the dynamics of the epidemic spreading of 42 countries, mainly taking into account reported deaths and mobility across the populations.ResultsThe results indicate that low-income countries have a harder time slowing the advance of the pandemic, even if the virus did not initially propagate as fast as in wealthier countries, showing the disparities between countries in their ability to mitigate the spread of the disease and its impact on vulnerable populations.DiscussionThis research contributes to a better understanding of the socioeconomic and environmental factors that affect the spread of the virus and the need for equitable policy measures to address the disparities in the global response to the pandemic

Interplay between population density and mobility in determining the spread of epidemics in cities

The increasing agglomeration of people in dense urban areas coupled with the existence of efficient modes of transportation connecting such centers, make cities particularly vulnerable to the spread of epidemics. Here we develop a data-driven approach combines with a meta-population modeling to capture the interplay between population density, mobility and epidemic spreading. We study 163 cities, chosen from four different continents, and report a global trend where the epidemic risk induced by human mobility increases consistently in those cities where mobility flows are predominantly between high population density centers. We apply our framework to the spread of SARS-CoV-2 in the United States, providing a plausible explanation for the observed heterogeneity in the spreading process across cities. Based on this insight, we propose realistic mitigation strategies (less severe than lockdowns), based on modifying the mobility in cities. Our results suggest that an optimal control strategy involves an asymmetric policy that restricts flows entering the most vulnerable areas but allowing residents to continue their usual mobility patterns

Impact of human-human contagions in the spread of vector-borne diseases

This article is aimed at proposing a generalization of the Ross-Macdonald model for the transmission of Vector-borne diseases in which human-to-human contagions are also considered. We first present this generalized model by formulating a mean field theory, checking its validity by comparing to numerical simulations. To make the premises of our model more realistic, we adapt the mean field equations to the case in which human contacts are described by a complex network. In this case we are also able to derive an analytical expression for the epidemic threshold. In both the mean-field and network-based models, we estimate the value of the epidemic threshold which corresponds to the boundary between the disease-free and epidemic regimes. The expression of this threshold allows us to discuss the impact that human-to-human contagions have on the spread of vector-borne diseases.Comment: 7 pages, 5 figure

Procesos de contagio en metapoblaciones y modelos de movilidad.

En este trabajo se introduce un nuevo modelo de contagio de epidemias en poblaciones, que toma características de modelos anteriores y las mejora, teniendo en cuenta la movilidad de otra forma. Se derivan las ecuaciones que gobiernan el sistema a escala mesoscópica y se verifican frente a simulaciones de agentes microscópicos. Se comparan los resultados con el modelo anterior y se explican las diferencias entre ambos y su origen. También se derivan las ecuaciones que permitirán calcular el umbral epidémico de cualquier red de forma analítica a partir de la matriz de origen-destino, y se comprueba que son correctas. Utilizamos dichas ecuaciones para entender con más profundidad la naturaleza del detrimento del umbral epidémico con la movilidad.<br /

Introducción de mecanismos evolutivos en la propagación de epidemias

En los últimos meses, y debido a la pandemia, ha cobrado gran importancia la influencia de las variantes de un patógeno en la evolución de una epidemia. En este trabajo presentamos un modelo que reproduce la dinámica de una epidemia que refleja ese comportamiento. Tras explicar algunos conceptos y modelos fundamentales para comprender bien el trabajo, trataremos de analizar dos modelos que han abordado previamente la dinámica de las variantes. Los resultados de ambos modelos nos ayudarán a motivar la necesidad de proponer una nueva perspectiva de atajar el problema. Finalmente, proponemos un nuevo punto de vista para modelizar la dinámica de una epidemia con variantes de un patógeno. Las conclusiones a las que hemos llegado demuestran la importancia del estudio de las nuevas variantes ante el cambio sustancial que se produce cuando no se tienen en cuenta, y establece la base para futuros modelos que permitirán un estudio a escala global de la evolución de una enfermedad con variantes del patógeno.<br /