Dynamical and bursty interactions in social networks

We present a modeling framework for dynamical and bursty contact networks made of agents in social interaction. We consider agents' behavior at short time scales, in which the contact network is formed by disconnected cliques of different sizes. At each time a random agent can make a transition from being isolated to being part of a group, or vice-versa. Different distributions of contact times and inter-contact times between individuals are obtained by considering transition probabilities with memory effects, i.e. the transition probabilities for each agent depend both on its state (isolated or interacting) and on the time elapsed since the last change of state. The model lends itself to analytical and numerical investigations. The modeling framework can be easily extended, and paves the way for systematic investigations of dynamical processes occurring on rapidly evolving dynamical networks, such as the propagation of an information, or spreading of diseases

Social network dynamics of face-to-face interactions

The recent availability of data describing social networks is changing our understanding of the "microscopic structure" of a social tie. A social tie indeed is an aggregated outcome of many social interactions such as face-to-face conversations or phone-calls. Analysis of data on face-to-face interactions shows that such events, as many other human activities, are bursty, with very heterogeneous durations. In this paper we present a model for social interactions at short time scales, aimed at describing contexts such as conference venues in which individuals interact in small groups. We present a detailed anayltical and numerical study of the model's dynamical properties, and show that it reproduces important features of empirical data. The model allows for many generalizations toward an increasingly realistic description of social interactions. In particular in this paper we investigate the case where the agents have intrinsic heterogeneities in their social behavior, or where dynamic variations of the local number of individuals are included. Finally we propose this model as a very flexible framework to investigate how dynamical processes unfold in social networks.Comment: 20 pages, 25 figure

What's in a crowd? Analysis of face-to-face behavioral networks

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing important differences as well as striking similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

The spread of infectious diseases crucially depends on the pattern of contacts among individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. Few empirical studies are however available that provide estimates of the number and duration of contacts among social groups. Moreover, their space and time resolution are limited, so that data is not explicit at the person-to-person level, and the dynamical aspect of the contacts is disregarded. Here, we want to assess the role of data-driven dynamic contact patterns among individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. We consider high resolution data of face-to-face interactions between the attendees of a conference, obtained from the deployment of an infrastructure based on Radio Frequency Identification (RFID) devices that assess mutual face-to-face proximity. The spread of epidemics along these interactions is simulated through an SEIR model, using both the dynamical network of contacts defined by the collected data, and two aggregated versions of such network, in order to assess the role of the data temporal aspects. We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation which retains only the topology of the contact network fails in reproducing the size of the epidemic. These results have important implications in understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics

High-resolution measurements of face-to-face contact patterns in a primary school

Little quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6-12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices. Data on face-to-face interactions were collected on October 1st and 2nd, 2009. We recorded 77,602 contact events between 242 individuals. Each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis. The observed properties of the contact patterns between school children are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics

Réseaux de proximité humaine : analyse, modélisation et processus dynamiques

Modern technologies allow to access to more and more detailed information on human interactions. In this context, the SocioPatterns collaboration has allowed to develop an infrastructure based on radio-identi cation devices, that records human proximity patterns at a fine grained resolution, among voluntary individuals. This infrastructure has been deployed in diverse contexts, such as scienti c conferences, a museum, a primary school, or a hospital department. The mere analysis of these data represents a high stake for the study of human dynamics and raises fundamental issues such as the need of adequate tools and analysis techniques. This thesis presents the statistical characterization of physical proximity dynamics, put into relation with the context and other available metadata such as the age, the gender of participants or the structure of their virtual social networks. Although contact patterns considerably di ffer amongst the various contexts, the empirical distributions of interaction durations and of inter-contact times are very similar. An agent-based model, presented in this thesis, suggests simple microscopic interaction rules able to produce the complex macrostructure of interaction durations. In the last place, the characterization of contact dynamics constitutes a determining step for understanding spreading mechanisms of diseases such as the in uenza. The human proximity data have allowed to analyze the level of information needed on contact dynamics for the elaboration of epidemiological models of contagion. Such models allow to better estimate the impact of public health strategies, e.g. the closure of school classes and targeted vaccinations.Les technologies modernes permettent d'avoir des renseignements toujours plus précis sur les interactions entre individus. Dans ce contexte, la collaboration SocioPatterns a permis de développer une infrastructure mesurant, avec une très grande résolution temporelle, la proximité face-à-face d'individus volontaires, portant des badges de radio-identi cation. Cette infrastructure a été déployée dans divers contextes, tels que des conférences scienti ques, un musée, une école ou encore un service hospitalier. La simple analyse de ces données représente un enjeu majeur pour l'étude de la dynamique humaine et soulève des questions aussi fondamentales que la recherche d'outils et de techniques d'analyse adaptés. Cette thèse présente la caractérisation statistique de la dynamique de proximité physique, mise en relation avec le contexte et les autres métadonnées disponibles, telles que l'âge, le sexe des individus, ou bien la structure de leurs réseaux sociaux virtuels. Si la structure des contacts diff ère considérablement selon le contexte, les distributions empiriques des durées des interactions et entre interactions sont très similaires. Un modèle individu-centré, présenté dans cette thèse, propose des règles d'interactions microscopiques simples susceptibles de donner lieu à cette structure macroscopique complexe des temps d'interaction. Enfin, la caractérisation de la dynamique des contacts entre individus constitue une étape cruciale pour comprendre les mécanismes de propagation de maladies telles que la grippe dans une population. Les données de proximité humaine ont permis d'étudier la quantité d'informations nécessaires sur la dynamique des contacts pour la construction de modèles épidémiologiques de contagion. De tels modèles permettent de mieux estimer a priori l'impact de stratégies de santé publique telles que la fermeture de classes et les vaccinations ciblées

Les parents séparés d’enfants mineurs : quel niveau de vie après une rupture conjugale ?

Fontaine Maëlle, Stehlé Juliette. Les parents séparés d’enfants mineurs : quel niveau de vie après une rupture conjugale ?. In: Politiques sociales et familiales, n°117, 2014. Dossier « La résidence alternée ». pp. 80-86