Arrival Time Statistics in Global Disease Spread

Metapopulation models describing cities with different populations coupled by the travel of individuals are of great importance in the understanding of disease spread on a large scale. An important example is the Rvachev-Longini model [{\it Math. Biosci.} {\bf 75}, 3-22 (1985)] which is widely used in computational epidemiology. Few analytical results are however available and in particular little is known about paths followed by epidemics and disease arrival times. We study the arrival time of a disease in a city as a function of the starting seed of the epidemics. We propose an analytical Ansatz, test it in the case of a spreading on the world wide air transportation network, and show that it predicts accurately the arrival order of a disease in world-wide cities

Analytic solution of a static scale-free network model

We present a detailed analytical study of a paradigmatic scale-free network model, the Static Model. Analytical expressions for its main properties are derived by using the hidden variables formalism. We map the model into a canonic hidden variables one, and solve the latter. The good agreement between our predictions and extensive simulations of the original model suggests that the mapping is exact in the infinite network size limit. One of the most remarkable findings of this study is the presence of relevant disassortative correlations, which are induced by the physical condition of absence of self and multiple connections.Comment: 8 pages, 4 figure

Epidemic variability in complex networks

We study numerically the variability of the outbreak of diseases on complex networks. We use a SI model to simulate the disease spreading at short times, in homogeneous and in scale-free networks. In both cases, we study the effect of initial conditions on the epidemic's dynamics and its variability. The results display a time regime during which the prevalence exhibits a large sensitivity to noise. We also investigate the dependence of the infection time on nodes' degree and distance to the seed. In particular, we show that the infection time of hubs have large fluctuations which limit their reliability as early-detection stations. Finally, we discuss the effect of the multiplicity of shortest paths between two nodes on the infection time. Furthermore, we demonstrate that the existence of even longer paths reduces the average infection time. These different results could be of use for the design of time-dependent containment strategies

Weighted evolving networks: coupling topology and weights dynamics

We propose a model for the growth of weighted networks that couples the establishment of new edges and vertices and the weights' dynamical evolution. The model is based on a simple weight-driven dynamics and generates networks exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a non-trivial time evolution of vertices' properties and scale-free behavior for the weight, strength and degree distributions.Comment: 4 pages, 4 figure

Social inertia in collaboration networks

This work is a study of the properties of collaboration networks employing the formalism of weighted graphs to represent their one-mode projection. The weight of the edges is directly the number of times that a partnership has been repeated. This representation allows us to define the concept of "social inertia" that measures the tendency of authors to keep on collaborating with previous partners. We use a collection of empirical datasets to analyze several aspects of the social inertia: 1) its probability distribution, 2) its correlation with other properties, and 3) the correlations of the inertia between neighbors in the network. We also contrast these empirical results with the predictions of a recently proposed theoretical model for the growth of collaboration networks.Comment: 7 pages, 5 figure

Modeling the evolution of weighted networks

We present a general model for the growth of weighted networks in which the structural growth is coupled with the edges' weight dynamical evolution. The model is based on a simple weight-driven dynamics and a weights' reinforcement mechanism coupled to the local network growth. That coupling can be generalized in order to include the effect of additional randomness and non-linearities which can be present in real-world networks. The model generates weighted graphs exhibiting the statistical properties observed in several real-world systems. In particular, the model yields a non-trivial time evolution of vertices properties and scale-free behavior with exponents depending on the microscopic parameters characterizing the coupling rules. Very interestingly, the generated graphs spontaneously achieve a complex hierarchical architecture characterized by clustering and connectivity correlations varying as a function of the vertices' degree

Velocity and hierarchical spread of epidemic outbreaks in scale-free networks

We study the effect of the connectivity pattern of complex networks on the propagation dynamics of epidemics. The growth time scale of outbreaks is inversely proportional to the network degree fluctuations, signaling that epidemics spread almost instantaneously in networks with scale-free degree distributions. This feature is associated with an epidemic propagation that follows a precise hierarchical dynamics. Once the highly connected hubs are reached, the infection pervades the network in a progressive cascade across smaller degree classes. The present results are relevant for the development of adaptive containment strategies.Comment: 4 pages, 4 figures, final versio

Efficient routing on complex networks

In this letter, we propose a new routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called {\it efficient path}, which considers the possible congestion in the nodes along actual paths. Since the nodes with largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our new strategy, can be as redistributing traffic load in central nodes to other non-central nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis.Comment: 4 pages, 4 figure

Head-to-head comparison of 10 plasma phospho-tau assays in prodromal Alzheimer\u27s disease

Plasma phospho-tau (p-tau) species have emerged as the most promising blood-based biomarkers of Alzheimer\u27s disease. Here, we performed a head-to-head comparison of p-tau181, p-tau217 and p-tau231 measured using 10 assays to detect abnormal brain amyloid-β (Aβ) status and predict future progression to Alzheimer\u27s dementia. The study included 135 patients with baseline diagnosis of mild cognitive impairment (mean age 72.4 years; 60.7% women) who were followed for an average of 4.9 years. Seventy-one participants had abnormal Aβ-status (i.e. abnormal CSF Aβ42/40) at baseline; and 45 of these Aβ-positive participants progressed to Alzheimer\u27s dementia during follow-up. P-tau concentrations were determined in baseline plasma and CSF. P-tau217 and p-tau181 were both measured using immunoassays developed by Lilly Research Laboratories (Lilly) and mass spectrometry assays developed at Washington University (WashU). P-tau217 was also analysed using Simoa immunoassay developed by Janssen Research and Development (Janss). P-tau181 was measured using Simoa immunoassay from ADxNeurosciences (ADx), Lumipulse immunoassay from Fujirebio (Fuji) and Splex immunoassay from Mesoscale Discovery (Splex). Both p-tau181 and p-tau231 were quantified using Simoa immunoassay developed at the University of Gothenburg (UGOT). We found that the mass spectrometry-based p-tau217 (p-tau217WashU) exhibited significantly better performance than all other plasma p-tau biomarkers when detecting abnormal Aβ status [area under curve (AUC) = 0.947; Pdiff \u3c 0.015] or progression to Alzheimer\u27s dementia (AUC = 0.932; Pdiff \u3c 0.027). Among immunoassays, p-tau217Lilly had the highest AUCs (0.886-0.889), which was not significantly different from the AUCs of p-tau217Janss, p-tau181ADx and p-tau181WashU (AUCrange 0.835-0.872; Pdiff \u3e 0.09), but higher compared with AUC of p-tau231UGOT, p-tau181Lilly, p-tau181UGOT, p-tau181Fuji and p-tau181Splex (AUCrange 0.642-0.813; Pdiff ≤ 0.029). Correlations between plasma and CSF values were strongest for p-tau217WashU (R = 0.891) followed by p-tau217Lilly (R = 0.755; Pdiff = 0.003 versus p-tau217WashU) and weak to moderate for the rest of the p-tau biomarkers (Rrange 0.320-0.669). In conclusion, our findings suggest that among all tested plasma p-tau assays, mass spectrometry-based measures of p-tau217 perform best when identifying mild cognitive impairment patients with abnormal brain Aβ or those who will subsequently progress to Alzheimer\u27s dementia. Several other assays (p-tau217Lilly, p-tau217Janss, p-tau181ADx and p-tau181WashU) showed relatively high and consistent accuracy across both outcomes. The results further indicate that the highest performing assays have performance metrics that rival the gold standards of Aβ-PET and CSF. If further validated, our findings will have significant impacts in diagnosis, screening and treatment for Alzheimer\u27s dementia in the future

The Swiss Board Directors Network in 2009

We study the networks formed by the directors of the most important Swiss boards and the boards themselves for the year 2009. The networks are obtained by projection from the original bipartite graph. We highlight a number of important statistical features of those networks such as degree distribution, weight distribution, and several centrality measures as well as their interrelationships. While similar statistics were already known for other board systems, and are comparable here, we have extended the study with a careful investigation of director and board centrality, a k-core analysis, and a simulation of the speed of information propagation and its relationships with the topological aspects of the network such as clustering and link weight and betweenness. The overall picture that emerges is one in which the topological structure of the Swiss board and director networks has evolved in such a way that special actors and links between actors play a fundamental role in the flow of information among distant parts of the network. This is shown in particular by the centrality measures and by the simulation of a simple epidemic process on the directors network.Comment: Submitted to The European Physical Journal