140 research outputs found

    Global Spatio-temporal Patterns of Influenza in the Post-pandemic Era

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    We study the global spatio-temporal patterns of influenza dynamics. This is achieved by analysing and modelling weekly laboratory confirmed cases of influenza A and B from 138 countries between January 2006 and May 2014. The data were obtained from FluNet, the surveillance network compiled by the the World Health Organization. We report a pattern of {\it skip-and-resurgence} behavior between the years 2011 and 2013 for influenza H1N1/09, the strain responsible for the 2009 pandemic, in Europe and Eastern Asia. In particular, the expected H1N1/09 epidemic outbreak in 2011 failed to occur (or"skipped") in many countries across the globe, although an outbreak occurred in the following year. We also report a pattern of {\it well-synchronized} 2010 winter wave of H1N1/09 in the Northern Hemisphere countries, and a pattern of replacement of strain H1N1/77 by H1N1/09 between the 2009 and 2012 influenza seasons. Using both a statistical and a mechanistic mathematical model, and through fitting the data of 108 countries (108 countries in a statistical model and 10 large populations with a mechanistic model), we discuss the mechanisms that are likely to generate these events taking into account the role of multi-strain dynamics. A basic understanding of these patterns has important public health implications and scientific significance

    Pandemic Dynamics and the Breakdown of Herd Immunity

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    In this note we discuss the issues involved in attempting to model pandemic dynamics. More specifically, we show how it may be possible to make projections for the ongoing H1N1 pandemic as extrapolated from knowledge of seasonal influenza. We derive first-approximation parameter estimates for the SIR model to describe seasonal influenza, and then explore the implications of the existing classical epidemiological theory for the case of a pandemic virus. In particular, we note the dramatic nonlinear increase in attack rate as a function of the percentage of susceptibles initially present in the population. This has severe consequences for the pandemic, given the general lack of immunity in the global population

    Species–area relationships always overestimate extinction rates from habitat loss : comment

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    Author Posting. © Ecological Society of America, 2013. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 94 (2013): 761–763, doi:10.1890/12-0047.1.The species–area relationship summarizes the relationship between the average number of species in a region and its area. This relationship provides a basis for predicting the loss of species associated with loss of habitat (e.g., Pimm and Raven 2000). The approach involves two steps. First, as discussed in more detail below, the species–area relationship is used to predict the number of species that are endemic to the habitat at risk based on its area. Second, these endemic species are assumed to become extinct should this habitat be lost. In a controversial paper, He and Hubbell (2011) argued that the way in which the species–area relationship is used to predict the number of endemic species is incorrect when individual organisms are aggregated in space and argued that this explains a discrepancy between predicted and observed extinction rates associated with habitat loss. The controversy surrounding the paper focused primarily on the second part of their argument (Brooks 2011, Evans et al. 2011, He and Hubbell 2012, Pereira et al. 2012, Thomas and Williamson 2012). Here, we focus on the details underlying the first part.U. Roll is supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities. L. Stone is supported by the Israeli Science Foundation

    Discovering important nodes of complex networks based on laplacian spectra

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    © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the “Laplacian energy” of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.This research was partly supported by the Erasmus+ KA107 grant. AMA, MJ, LS and XY were supported by the Australian Research Council through project No. DP170102303. MJ and XY are also supported by the Australian Research Council through project No. DP200101199. MAF was supported by AGAUR from the Catalan Government under project 2017SGR1087, and by MICINN from the Spanish Government with the European Regional Development Fund under project PGC2018-095471-B-I00Peer ReviewedPostprint (author's final draft

    Onset of a pandemic: characterizing the initial phase of the swine flu (H1N1) epidemic in Israel

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    <p>Abstract</p> <p>Background</p> <p>The swine influenza H1N1 first identified in Mexico, spread rapidly across the globe and is considered the fastest moving pandemic in history. The early phase of an outbreak, in which data is relatively scarce, presents scientific challenges on key issues such as: scale, severity and immunity which are fundamental for establishing sound and rapid policy schemes. Our analysis of an Israeli dataset aims at understanding the spatio-temporal dynamics of H1N1 in its initial phase.</p> <p>Methods</p> <p>We constructed and analyzed a unique dataset from Israel on all confirmed cases (between April 26 to July 7, 2009), representing most swine flu cases in this period. We estimated and characterized fundamental epidemiological features of the pandemic in Israel (e.g. effective reproductive number, age-class distribution, at-risk social groups, infections between sexes, and spatial dynamics). Contact data collected during this stage was used to estimate the generation time distribution of the pandemic.</p> <p>Results</p> <p>We found a low effective reproductive number (<it>R</it><sub><it>e </it></sub>= 1.06), an age-class distribution of infected individuals (skewed towards ages 18-25), at-risk social groups (soldiers and ultra Orthodox Jews), and significant differences in infections between sexes (skewed towards males). In terms of spatial dynamics, the pandemic spread from the central coastal plain of Israel to other regions, with higher infection rates in more densely populated sub-districts with higher income households.</p> <p>Conclusions</p> <p>Analysis of high quality data holds much promise in reducing uncertainty regarding fundamental aspects of the initial phase of an outbreak (e.g. the effective reproductive number R<sub>e</sub>, age-class distribution, at-risk social groups). The formulation for determining the effective reproductive number <it>R</it><sub><it>e </it></sub>used here has many advantages for studying the initial phase of the outbreak since it neither assumes exponential growth of infectives and is independent of the reporting rate. The finding of a low <it>R</it><sub><it>e </it></sub>(close to unity threshold), combined with identification of social groups with high transmission rates would have enabled the containment of swine flu during the summer in Israel. Our unique use of contact data provided new insights into the differential dynamics of influenza in different ages and sexes, and should be promoted in future epidemiological studies. Thus our work highlights the importance of conducting a comprehensive study of the initial stage of a pandemic in real time.</p

    On node ranking in graphs

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    The ranking of nodes in a network according to their ``importance'' is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it is related to the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by the COVID-19 spreading dynamics, in this paper we review the most popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network, that takes into account the community-based structure of society. Also, we generalize a classic benchmark network originally proposed by Newman for ranking nodes in unweighted graphs, to show how ranks change in the weighted case

    Post-lockdown abatement of COVID-19 by fast periodic switching

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    COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github at https://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system