Global disease spread: statistics and estimation of arrival times

We study metapopulation models for the spread of epidemics in which different subpopulations (cities) are connected by fluxes of individuals (travelers). This framework allows one to describe the spread of a disease on a large scale and we focus here on the computation of the arrival time of a disease as a function of the properties of the seed of the epidemics and of the characteristics of the network connecting the various subpopulations. Using analytical and numerical arguments, we introduce an easily computable quantity which approximates this average arrival time. We show on the example of a disease spread on the world-wide airport network that this quantity predicts with a good accuracy the order of arrival of the disease in the various subpopulations in each realization of epidemic scenario, and not only for an average over realizations. Finally, this quantity might be useful in the identification of the dominant paths of the disease spread.     

Structure,syntax and “small-world” organization in the complex songs of California Thrashers (Toxostoma redivivum)

We describe songs of the California Thrasher (Toxostoma redivivum), a territorial, monogamous species whose complex songs are composed of extended sequences of phonetically diverse phrases. We take a network approach, so that network nodes represent specific phrases, and links or transitions between nodes describe a subgroup structure that reveals the syntax of phrases within the songs. We found that individual birds have large and largely distinct repertoires, with limited phrase sharing between neighbours and repertoire similarity decaying between individuals with distance apart, decaying also over time within individuals. During song sequences, only a limited number of phrases (ca. 15–20) were found to be actually “in play” at any given time; these phrases can be grouped into themes within which transitions are much more common than among them, a feature contributing to a small-world structure. It appears that such “small-world themes” arise abruptly, while old themes are abandoned more gradually during extended song sequences; most individual thrashers switch among 3–4 themes over the course of several successive songs, and some small-world themes appear to have specific roles in starting or ending thrasher songs.     

Neural field model of binocular rivalry waves

We present a neural field model of binocular rivalry waves in visual cortex. For each eye we consider a one-dimensional network of neurons that respond maximally to a particular feature of the corresponding image such as the orientation of a grating stimulus. Recurrent connections within each one-dimensional network are assumed to be excitatory, whereas connections between the two networks are inhibitory (cross-inhibition). Slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We derive an analytical expression for the speed of a binocular rivalry wave as a function of various neurophysiological parameters, and show how properties of the wave are consistent with the wave-like propagation of perceptual dominance observed in recent psychophysical experiments. In addition to providing an analytical framework for studying binocular rivalry waves, we show how neural field methods provide insights into the mechanisms underlying the generation of the waves. In particular, we highlight the important role of slow adaptation in providing a “symmetry breaking mechanism” that allows waves to propagate.     

Optimal link removal for epidemic mitigation: a two-way partitioning approach

The structure of the contact network through which a disease spreads may influence the optimal use of resources for epidemic control. In this work, we explore how to minimize the spread of infection via quarantining with limited resources. In particular, we examine which links should be removed from the contact network, given a constraint on the number of removable links, such that the number of nodes which are no longer at risk for infection is maximized. We show how this problem can be posed as a non-convex quadratically constrained quadratic program (QCQP), and we use this formulation to derive a link removal algorithm. The performance of our QCQP-based algorithm is validated on small Erd?s–Renyi and small-world random graphs, and then tested on larger, more realistic networks, including a real-world network of injection drug use. We show that our approach achieves near optimal performance and out-performs other intuitive link removal algorithms, such as removing links in order of edge centrality.     

Connectivity,reproduction number,and mobility interact to determine communities’ epidemiological superspreader potential in a metapopulation network

Disease epidemic outbreaks on human metapopulation networks are often driven by a small number of superspreader nodes, which are primarily responsible for spreading the disease throughout the network. Superspreader nodes typically are characterized either by their locations within the network, by their degree of connectivity and centrality, or by their habitat suitability for the disease, described by their reproduction number (R). Here we introduce a model that considers simultaneously the effects of network properties and R on superspreaders, as opposed to previous research which considered each factor separately. This type of model is applicable to diseases for which habitat suitability varies by climate or land cover, and for direct transmitted diseases for which population density and mitigation practices influences R. We present analytical models that quantify the superspreader capacity of a population node by two measures: probability-dependent superspreader capacity, the expected number of neighboring nodes to which the node in consideration will randomly spread the disease per epidemic generation, and time-dependent superspreader capacity, the rate at which the node spreads the disease to each of its neighbors. We validate our analytical models with a Monte Carlo analysis of repeated stochastic Susceptible-Infected-Recovered (SIR) simulations on randomly generated human population networks, and we use a random forest statistical model to relate superspreader risk to connectivity, R, centrality, clustering, and diffusion. We demonstrate that either degree of connectivity or R above a certain threshold are sufficient conditions for a node to have a moderate superspreader risk factor, but both are necessary for a node to have a high-risk factor. The statistical model presented in this article can be used to predict the location of superspreader events in future epidemics, and to predict the effectiveness of mitigation strategies that seek to reduce the value of R, alter host movements, or both.     

Mitochondrial trafficking and morphology in neuronal injury

Alterations in mitochondrial function may have a central role in the pathogenesis of many neurodegenerative diseases. The study of mitochondrial dysfunction has typically focused on ATP generation, calcium homeostasis and the production of reactive oxygen species. However, there is a growing appreciation of the dynamic nature of mitochondria within cells. Mitochondria are highly motile organelles, and also constantly undergo fission and fusion. This raises the possibility that impairment of mitochondrial dynamics could contribute to the pathogenesis of neuronal injury. In this review we describe the mechanisms that govern mitochondrial movement, fission and fusion. The key proteins that are involved in mitochondrial fission and fusion have also been linked to some inherited neurological diseases, including autosomal dominant optic atrophy and Charcot–Marie–Tooth disease 2A. We will discuss the evidence that altered movement, fission and fusion are associated with impaired neuronal viability. There is a growing collection of literature that links impaired mitochondrial dynamics to a number of disease models. Additionally, the concept that the failure to deliver a functional mitochondrion to the appropriate site within a neuron could contribute to neuronal dysfunction provides an attractive framework for understanding the mechanisms underlying neurologic disease. However, it remains difficult to clearly establish that altered mitochondrial dynamics clearly represent a cause of neuronal dysfunction.     

Instability in Evolutionary Games

Background

Phenomena of instability are widely observed in many dissimilar systems, with punctuated equilibrium in biological evolution and economic crises being noticeable examples. Recent studies suggested that such instabilities, quantified by the abrupt changes of the composition of individuals, could result within the framework of a collection of individuals interacting through the prisoner''s dilemma and incorporating three mechanisms: (i) imitation and mutation, (ii) preferred selection on successful individuals, and (iii) networking effects.

Methodology/Principal Findings

We study the importance of each mechanism using simplified models. The models are studied numerically and analytically via rate equations and mean-field approximation. It is shown that imitation and mutation alone can lead to the instability on the number of cooperators, and preferred selection modifies the instability in an asymmetric way. The co-evolution of network topology and game dynamics is not necessary to the occurrence of instability and the network topology is found to have almost no impact on instability if new links are added in a global manner. The results are valid in both the contexts of the snowdrift game and prisoner''s dilemma.

Conclusions/Significance

The imitation and mutation mechanism, which gives a heterogeneous rate of change in the system''s composition, is the dominating reason of the instability on the number of cooperators. The effects of payoffs and network topology are relatively insignificant. Our work refines the understanding on the driving forces of system instability.     

Spatial graphs as templates for habitat networks in boreal landscapes

Network topology serves as a useful model for biological systems at various scales. Contrary to many biological systems, spatial reference is crucial for habitat networks. Boreal forest landscapes provide a wide gradient of spatial patterns and, thus, unique network structures. Assuming forest-dwelling organisms in general aim to minimize travel distances during foraging, dispersal, etc., linear links across the landscape matrix constitute expected movement routes among forested areas in boreal landscapes. We quantified the number and length of links in a set of 57 boreal forest landscapes for four hierarchically nested graphs in order to compare the incremental changes in characteristics of resulting graph measures. The forest cover graphs consisted of the same set of forest patches, and hierarchical link types extracted from real landscapes: nearest neighbour graph (NN), minimum spanning tree (MST), Gabriel graph (GG) and minimum planar graph (MPG). Most of the links in graphs were NN and GG links. Commonly links were 100–200?m in length, but link lengths particularly in the GG and MPG shorten when the proportion of forest in landscapes increased. Most nodes had 3–5 links each, but the number of links per node depended on node size and the proportion of forest cover. GG and MPG graphs retain the topology of the underlying node layout. Changes in node pattern alter the NN and MST graphs more than GG and MPG. Variation in regional network topologies is likely to affect connectivity patterns in a landscape and, thus, many ecological processes that occur at a local scale. An appropriate network analysis enables the discovery and comparison of distinctive network patterns. Understanding network topologies provide practical tools for land use planning and biodiversity management of broader areas that target functional habitat networks.     

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper.     

Monogamous networks and the spread of sexually transmitted diseases

Patterns of sexual mixing and heterogeneity in the number of sexual partners can have a huge effect on the spread of a sexually transmitted disease (STD). The sexual mixing network identifies all partnerships within a population over a given period and is a powerful tool in the study of such infections. Previous models assumed all links within the network to be concurrent active partnerships. We present a novel modelling approach in which we adapt the notion of a sexual contact network to a monogamous population by allowing the nature of the links to change. We use the underlying network to represent potential sexual partnerships, only some of which are active at any one time. Thus serial monogamy can be modelled while maintaining the patterns of mixing displayed by the population.     

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network‐based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time‐points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue‐generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases‐specific, stage‐specific, severity‐specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.     

Ambiguity in Social Network Data for Presence,Sensitive-Attribute,Degree and Relationship Privacy Protection

Maintaining privacy in network data publishing is a major challenge. This is because known characteristics of individuals can be used to extract new information about them. Recently, researchers have developed privacy methods based on k-anonymity and l-diversity to prevent re-identification or sensitive label disclosure through certain structural information. However, most of these studies have considered only structural information and have been developed for undirected networks. Furthermore, most existing approaches rely on generalization and node clustering so may entail significant information loss as all properties of all members of each group are generalized to the same value. In this paper, we introduce a framework for protecting sensitive attribute, degree (the number of connected entities), and relationships, as well as the presence of individuals in directed social network data whose nodes contain attributes. First, we define a privacy model that specifies privacy requirements for the above private information. Then, we introduce the technique of Ambiguity in Social Network data (ASN) based on anatomy, which specifies how to publish social network data. To employ ASN, individuals are partitioned into groups. Then, ASN publishes exact values of properties of individuals of each group with common group ID in several tables. The lossy join of those tables based on group ID injects uncertainty to reconstruct the original network. We also show how to measure different privacy requirements in ASN. Simulation results on real and synthetic datasets demonstrate that our framework, which protects from four types of private information disclosure, preserves data utility in tabular, topological and spectrum aspects of networks at a satisfactory level.     

Spatial structure of an individual‐based plant–pollinator network

The influence of space on the structure (e.g. modularity) of complex ecological networks remains largely unknown. Here, we sampled an individual‐based plant–pollinator network by following the movements and flower visits of marked bumblebee individuals within a population of thistle plants for which the identities and spatial locations of stems were mapped in a 50 × 50 m study plot. The plant–pollinator network was dominated by parasitic male bumblebees and had a significantly modular structure, with four identified modules being clearly separated in space. This indicated that individual flower visitors opted for the fine‐scale division of resources, even within a local site. However, spatial mapping of network modules and movements of bumblebee individuals also showed an overlap in the dense center of the plant patch. Model selection based on Akaike information criterion with traits as predictor variables revealed that thistle stems with high numbers of flower heads and many close neighbours were particularly important for connecting individuals within the modules. In contrast, tall plants and those near the patch center were crucial for connecting the different modules to each other. This demonstrated that individual‐based plant–pollinator networks are influenced by both the spatial structure of plant populations and individual‐specific plant traits. Additionally, bumblebee individuals with long observation times were important for both the connectivity between and within modules. The latter suggests that bumblebee individuals will still show locally restricted movements within sub‐patches of plant populations even if they are observed over a prolonged time period. Our individual‐based and animal‐centered approach of sampling ecological networks opens up new avenues for incorporating foraging behaviour and intra‐specific trait variation into analyses of plant–animal interactions across space.     

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.     

A general procedure for predicting rates of inbreeding in populations undergoing mass selection

Predictions of rates of inbreeding (DeltaF), based on the concept of long-term genetic contributions assuming the infinitesimal model, are developed for populations with discrete or overlapping generations undergoing mass selection. Phenotypes of individuals are assumed to be recorded prior to reproductive age and to remain constant over time. The prediction method accounts for inheritance of selective advantage both within and between age classes and for changing selection intensities with age. Terms corresponding to previous methods that assume constant selection intensity with age are identified. Predictions are accurate (relative errors < or =8%), except for cases with extreme selection intensities in females in combination with high heritability. With overlapping generations DeltaF reaches a maximum when parents are equally distributed over age classes, which is mainly due to selection of the same individuals in consecutive years. DeltaF/year decreases much more slowly compared to DeltaF/generation as the number of younger individuals increases, whereas the decrease is more similar as the number of older individuals increases. The minimum DeltaF (per year or per generation) is obtained when most parents were in the later age classes, which is mainly due to an increased number of parents per generation. With overlapping generations, the relationship between heritability and DeltaF is dependent on the age structure of the population.     

Parasites, democratization, and the liberalization of values across contemporary countries

The countries of the world vary in their position along the autocracy–democracy continuum of values. Traditionally, scholars explain this variation as based on resource distribution and disparity among nations. We provide a different framework for understanding the autocracy–democracy dimension and related value dimensions, one that is complementary (not alternative) to the research tradition, but more encompassing, involving both evolutionary (ultimate) and proximate causation of the values. We hypothesize that the variation in values pertaining to autocracy–democracy arises fundamentally out of human (Homo sapiens) species‐typical psychological adaptation that manifests contingently, producing values and associated behaviours that functioned adaptively in human evolutionary history to cope with local levels of infectious diseases. We test this parasite hypothesis of democratization using publicly available data measuring democratization, collectivism–individualism, gender egalitarianism, property rights, sexual restrictiveness, and parasite prevalence across many countries of the world. Parasite prevalence across countries is based on a validated index of the severity of 22 important human diseases. We show that, as the hypothesis predicts, collectivism (hence, conservatism), autocracy, women’s subordination relative to men’s status, and women’s sexual restrictiveness are values that positively covary, and that correspond with high prevalence of infectious disease. Apparently, the psychology of xenophobia and ethnocentrism links these values to avoidance and management of parasites. Also as predicted, we show that the antipoles of each of the above values—individualism (hence, liberalism), democracy, and women’s rights, freedom and increased participation in casual sex—are a positively covarying set of values in countries with relatively low parasite stress. Beyond the cross‐national support for the parasite hypothesis of democratization, it is consistent with the geographic location at high latitudes (and hence reduced parasite stress) of the early democratic transitions in Britain, France and the U.S.A. It, too, is consistent with the marked increase in the liberalization of social values in the West in the 1950s and 1960s (in part, the sexual revolution), regions that, a generation or two earlier, experienced dramatically reduced infectious diseases as a result of antibiotics, vaccinations, food‐ and water‐safety practices, and increased sanitation. Moreover, we hypothesize that the generation and diffusion of innovations (in thought, action and technology) within and among regions, which is associated positively with democratization, is causally related to parasite stress. Finally, we hypothesize that past selection in the context of morbidity and mortality resulting from parasitic disease crafted many of the aspects of social psychology unique to humans.     

Inferring infection hazard in wildlife populations by linking data across individual and population scales

Our ability to infer unobservable disease‐dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time‐averaged value and are based on population‐level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within‐host processes to FOI is needed. Specifically, within‐host antibody kinetics in wildlife hosts can be short‐lived and produce patterns that are repeatable across individuals, suggesting individual‐level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population‐level FOI signal can be recovered from individual‐level antibody kinetics, despite substantial individual‐level variation. In addition to improving inference, the cross‐scale quantitative antibody approach we describe can reveal insights into drivers of individual‐based variation in disease response, and the role of poorly understood processes such as secondary infections, in population‐level dynamics of disease.     

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.     

Role of Relaxation Time Scale in Noisy Signal Transduction

Intra-cellular fluctuations, mainly triggered by gene expression, are an inevitable phenomenon observed in living cells. It influences generation of phenotypic diversity in genetically identical cells. Such variation of cellular components is beneficial in some contexts but detrimental in others. To quantify the fluctuations in a gene product, we undertake an analytical scheme for studying few naturally abundant linear as well as branched chain network motifs. We solve the Langevin equations associated with each motif under the purview of linear noise approximation and derive the expressions for Fano factor and mutual information in close analytical form. Both quantifiable expressions exclusively depend on the relaxation time (decay rate constant) and steady state population of the network components. We investigate the effect of relaxation time constraints on Fano factor and mutual information to indentify a time scale domain where a network can recognize the fluctuations associated with the input signal more reliably. We also show how input population affects both quantities. We extend our calculation to long chain linear motif and show that with increasing chain length, the Fano factor value increases but the mutual information processing capability decreases. In this type of motif, the intermediate components act as a noise filter that tune up input fluctuations and maintain optimum fluctuations in the output. For branched chain motifs, both quantities vary within a large scale due to their network architecture and facilitate survival of living system in diverse environmental conditions.