社会网络分析法及其在动物行为学研究中的应用

近年来,社会网络分析法被广泛用于动物行为学研究。它通过量化动物社会关系中的特定属性 (如中心度和中心势),可辨识动物群体中的关键个体及其在群体中的作用,揭示动物社会交往的形成机制,深化人们对动物社会性起源、个体行为与种群动态格局间关系的理解。本文简要介绍了社会网络分析法的发展史和基本概念,然后阐述了如何建立网络及选择常用指标,强调了创建原模型的必要性及途径。最后着重评述了社会网络分析法在动物行为学研究中的应用现状,并提出未来应当关注直接身体接触 (如打斗和理毛) 和非肢体接触 (如声音通讯) 等不同交往形式的变动过程,以此构建社会交往的动态网络。同时也需要加强种间社交网络的研究,以便增进人们对社会行为的生态功能以及合作理论等问题的理解。     

Susceptible-infected-recovered epidemics in dynamic contact networks

Contact patterns in populations fundamentally influence the spread of infectious diseases. Current mathematical methods for epidemiological forecasting on networks largely assume that contacts between individuals are fixed, at least for the duration of an outbreak. In reality, contact patterns may be quite fluid, with individuals frequently making and breaking social or sexual relationships. Here, we develop a mathematical approach to predicting disease transmission on dynamic networks in which each individual has a characteristic behaviour (typical contact number), but the identities of their contacts change in time. We show that dynamic contact patterns shape epidemiological dynamics in ways that cannot be adequately captured in static network models or mass-action models. Our new model interpolates smoothly between static network models and mass-action models using a mixing parameter, thereby providing a bridge between disparate classes of epidemiological models. Using epidemiological and sexual contact data from an Atlanta high school, we demonstrate the application of this method for forecasting and controlling sexually transmitted disease outbreaks.     

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.     

Impact of Preventive Responses to Epidemics in Rural Regions

Various epidemics have arisen in rural locations through human-animal interaction, such as the H1N1 outbreak of 2009. Through collaboration with local government officials, we have surveyed a rural county and its communities and collected a dataset characterizing the rural population. From the respondents’ answers, we build a social (face-to-face) contact network. With this network, we explore the potential spread of epidemics through a Susceptible-Latent-Infected-Recovered (SLIR) disease model. We simulate an exact model of a stochastic SLIR Poisson process with disease parameters representing a typical influenza-like illness. We test vaccine distribution strategies under limited resources. We examine global and location-based distribution strategies, as a way to reach critical individuals in the rural setting. We demonstrate that locations can be identified through contact metrics for use in vaccination strategies to control contagious diseases.     

Contact networks and transmission of an intestinal pathogen in bumble bee (Bombus impatiens) colonies

In socially living animals, individuals interact through complex networks of contact that may influence the spread of disease. Whereas traditional epidemiological models typically assume no social structure, network theory suggests that an individual’s location in the network determines its risk of infection. Empirical, especially experimental, studies of disease spread on networks are lacking, however, largely due to a shortage of amenable study systems. We used automated video-tracking to quantify networks of physical contact among individuals within colonies of the social bumble bee Bombus impatiens. We explored the effects of network structure on pathogen transmission in naturally and artificially infected hives. We show for the first time that contact network structure determines the spread of a contagious pathogen (Crithidia bombi) in social insect colonies. Differences in rates of infection among colonies resulted largely from differences in network density among hives. Within colonies, a bee’s rate of contact with infected nestmates emerged as the only significant predictor of infection risk. The activity of bees, in terms of their movement rates and division of labour (e.g., brood care, nest care, foraging), did not influence risk of infection. Our results suggest that contact networks may have an important influence on the transmission of pathogens in social insects and, possibly, other social animals.     

Social encounter networks: characterizing Great Britain

A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual''s contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents'' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact.     

Infectious disease transmission and contact networks in wildlife and livestock

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools.     

The dynamic nature of contact networks in infectious disease epidemiology

Although contact network models have yielded important insights into infectious disease transmission and control throughout the last decade, researchers have just begun to explore the dynamic nature of contact patterns and their epidemiological significance. Most network models have assumed that contacts are static through time. Developing more realistic models of the social interactions that underlie the spread of infectious diseases thus remains an important challenge for both data gatherers and modelers. In this article, we review some recent data-driven and process-driven approaches that capture the dynamics of human contact, and discuss future challenges for the field.     

Effects of heterogeneous and clustered contact patterns on infectious disease dynamics

The spread of infectious diseases fundamentally depends on the pattern of contacts between individuals. Although studies of contact networks have shown that heterogeneity in the number of contacts and the duration of contacts can have far-reaching epidemiological consequences, models often assume that contacts are chosen at random and thereby ignore the sociological, temporal and/or spatial clustering of contacts. Here we investigate the simultaneous effects of heterogeneous and clustered contact patterns on epidemic dynamics. To model population structure, we generalize the configuration model which has a tunable degree distribution (number of contacts per node) and level of clustering (number of three cliques). To model epidemic dynamics for this class of random graph, we derive a tractable, low-dimensional system of ordinary differential equations that accounts for the effects of network structure on the course of the epidemic. We find that the interaction between clustering and the degree distribution is complex. Clustering always slows an epidemic, but simultaneously increasing clustering and the variance of the degree distribution can increase final epidemic size. We also show that bond percolation-based approximations can be highly biased if one incorrectly assumes that infectious periods are homogeneous, and the magnitude of this bias increases with the amount of clustering in the network. We apply this approach to model the high clustering of contacts within households, using contact parameters estimated from survey data of social interactions, and we identify conditions under which network models that do not account for household structure will be biased.     

Exploring the Link between Genetic Relatedness r and Social Contact Structure k in Animal Social Networks

Our understanding of how cooperation can arise in a population of selfish individuals has been greatly advanced by theory. More than one approach has been used to explore the effect of population structure. Inclusive fitness theory uses genetic relatedness r to express the role of population structure. Evolutionary graph theory models the evolution of cooperation on network structures and focuses on the number of interacting partners k as a quantity of interest. Here we use empirical data from a hierarchically structured animal contact network to examine the interplay between independent, measurable proxies for these key parameters. We find strong inverse correlations between estimates of r and k over three levels of social organization, suggesting that genetic relatedness and social contact structure capture similar structural information in a real population.     

Revealing mechanisms of infectious disease spread through empirical contact networks

The spread of pathogens fundamentally depends on the underlying contacts between individuals. Modeling the dynamics of infectious disease spread through contact networks, however, can be challenging due to limited knowledge of how an infectious disease spreads and its transmission rate. We developed a novel statistical tool, INoDS (Identifying contact Networks of infectious Disease Spread) that estimates the transmission rate of an infectious disease outbreak, establishes epidemiological relevance of a contact network in explaining the observed pattern of infectious disease spread and enables model comparison between different contact network hypotheses. We show that our tool is robust to incomplete data and can be easily applied to datasets where infection timings of individuals are unknown. We tested the reliability of INoDS using simulation experiments of disease spread on a synthetic contact network and find that it is robust to incomplete data and is reliable under different settings of network dynamics and disease contagiousness compared with previous approaches. We demonstrate the applicability of our method in two host-pathogen systems: Crithidia bombi in bumblebee colonies and Salmonella in wild Australian sleepy lizard populations. INoDS thus provides a novel and reliable statistical tool for identifying transmission pathways of infectious disease spread. In addition, application of INoDS extends to understanding the spread of novel or emerging infectious disease, an alternative approach to laboratory transmission experiments, and overcoming common data-collection constraints.     

Contact switching as a control strategy for epidemic outbreaks

We study the effects of switching social contacts as a strategy to control epidemic outbreaks. Connections between susceptible and infective individuals can be broken by either individual, and then reconnected to a randomly chosen member of the population. It is assumed that the reconnecting individual has no previous information on the epidemiological condition of the new contact. We show that reconnection can completely suppress the disease, both by continuous and discontinuous transitions between the endemic and the infection-free states. For diseases with an asymptomatic phase, we analyze the conditions for the suppression of the disease, and show that—even when these conditions are not met—the increase of the endemic infection level is usually rather small. We conclude that, within some simple epidemiological models, contact switching is a quite robust and effective control strategy. This suggests that it may also be an efficient method in more complex situations.     

Epidemic Spread on Weighted Networks

The contact structure between hosts shapes disease spread. Most network-based models used in epidemiology tend to ignore heterogeneity in the weighting of contacts between two individuals. However, this assumption is known to be at odds with the data for many networks (e.g. sexual contact networks) and to have a critical influence on epidemics'' behavior. One of the reasons why models usually ignore heterogeneity in transmission is that we currently lack tools to analyze weighted networks, such that most studies rely on numerical simulations. Here, we present a novel framework to estimate key epidemiological variables, such as the rate of early epidemic expansion () and the basic reproductive ratio (), from joint probability distributions of number of partners (contacts) and number of interaction events through which contacts are weighted. These distributions are much easier to infer than the exact shape of the network, which makes the approach widely applicable. The framework also allows for a derivation of the full time course of epidemic prevalence and contact behaviour, which we validate with numerical simulations on networks. Overall, incorporating more realistic contact networks into epidemiological models can improve our understanding of the emergence and spread of infectious diseases.     

The impact of imitation on vaccination behavior in social contact networks

Previous game-theoretic studies of vaccination behavior typically have often assumed that populations are homogeneously mixed and that individuals are fully rational. In reality, there is heterogeneity in the number of contacts per individual, and individuals tend to imitate others who appear to have adopted successful strategies. Here, we use network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics. We integrate contact network epidemiological models with a framework for decision-making, within which individuals make their decisions either based purely on payoff maximization or by imitating the vaccination behavior of a social contact. Simulations suggest that when the cost of vaccination is high imitation behavior may decrease vaccination coverage. However, when the cost of vaccination is small relative to that of infection, imitation behavior increases vaccination coverage, but, surprisingly, also increases the magnitude of epidemics through the clustering of non-vaccinators within the network. Thus, imitation behavior may impede the eradication of infectious diseases. Calculations that ignore behavioral clustering caused by imitation may significantly underestimate the levels of vaccination coverage required to attain herd immunity.     

House finches (Carpodacus mexicanus) balance investment in behavioural and immunological defences against pathogens

Infection with parasites and pathogens is costly for hosts, causing loss of nutritional resources, reproductive potential, tissue integrity and even life. In response, animals have evolved behavioural and immunological strategies to avoid infection by pathogens and infestation by parasites. Scientists generally study these strategies in isolation from each other; however, since these defences entail costs, host individuals should benefit from balancing investment in these strategies, and understanding of infectious disease dynamics would benefit from studying the relationship between them. Here, we show that Carpodacus mexicanus (house finches) avoid sick individuals. Moreover, we show that individuals investing less in behavioural defences invest more in immune defences. Such variation has important implications for the dynamics of pathogen spread through populations, and ultimately the course of epidemics. A deeper understanding of individual- and population-level disease defence strategies will improve our ability to understand, model and predict the outcomes of pathogen spread in wildlife.     

Dominance rank predicts social network position across developmental stages in rhesus monkeys

Social network analysis is increasingly common in studying complex interactions among individuals. Across a range of primates, high-ranking adults are generally more socially connected, which results in better fitness outcomes. However, it still remains unclear whether this relationship between social network position and dominance rank emerges in infancy and whether, in species with a social transmission of dominance rank, social network positions are driven by the presence of the mother. To fill this gap, we first explored whether dominance ranks were related to social network position, measured via eigenvector centrality, in infants, juveniles, and adults in a troop of semi-free-ranging rhesus macaques (Macaca mulatta). We then examined relationships between dominance rank and eigenvector centrality in a peer-only group of yearlings who were reared with their mothers in either a rich, socially complex environment of multigenerational (MG) kin support or a unigenerational group of mothers and their infants from birth through 8 months. In Experiment 1, we found that mother's network position predicted offspring network position and that dominants across all age categories were more central in affiliative networks (social contact, social grooming, and social play). Experiment 2 showed that high-ranking yearlings in a peer-only group were more central only in the social contact network. Moreover, yearlings reared in a socially complex environment of MG kin support were more central. Our findings suggest that the relationship between dominance rank and social network position begins early in life, and that complex early social environments can promote later social competency. Our data add to the growing body of evidence that the presence/absence of the mother and kin influence how dominance rank affects social network position. These findings have important implications for the role of caregivers in the social status of developing primates, which ultimately ties to health and fitness outcomes.     

Inferring a District-Based Hierarchical Structure of Social Contacts from Census Data

Researchers have recently paid attention to social contact patterns among individuals due to their useful applications in such areas as epidemic evaluation and control, public health decisions, chronic disease research and social network research. Although some studies have estimated social contact patterns from social networks and surveys, few have considered how to infer the hierarchical structure of social contacts directly from census data. In this paper, we focus on inferring an individual’s social contact patterns from detailed census data, and generate various types of social contact patterns such as hierarchical-district-structure-based, cross-district and age-district-based patterns. We evaluate newly generated contact patterns derived from detailed 2011 Hong Kong census data by incorporating them into a model and simulation of the 2009 Hong Kong H1N1 epidemic. We then compare the newly generated social contact patterns with the mixing patterns that are often used in the literature, and draw the following conclusions. First, the generation of social contact patterns based on a hierarchical district structure allows for simulations at different district levels. Second, the newly generated social contact patterns reflect individuals social contacts. Third, the newly generated social contact patterns improve the accuracy of the SEIR-based epidemic model.     

Coexistence and specialization of pathogen strains on contact networks

The coexistence of different pathogen strains has implications for pathogen variability and disease control and has been explained in a number of different ways. We use contact networks, which represent interactions between individuals through which infection could be transmitted, to investigate strain coexistence. For sexually transmitted diseases the structure of contact networks has received detailed study and has been shown to be a vital determinant of the epidemiological dynamics. By using analytical pairwise models and stochastic simulations, we demonstrate that network structure also has a profound influence on the interaction between pathogen strains. In particular, when the population is serially monogamous, fully cross-reactive strains can coexist, with different strains dominating in network regions with different characteristics. Furthermore, we observe specialization of different strains in different risk groups within the network, suggesting the existence of diverging evolutionary pressures.