{"id":20262,"date":"2020-07-05T17:37:55","date_gmt":"2020-07-05T09:37:55","guid":{"rendered":"https:\/\/swarma.org\/?p=20262"},"modified":"2020-07-05T17:37:55","modified_gmt":"2020-07-05T09:37:55","slug":"%e7%a4%be%e4%bc%9a%e4%ba%92%e5%8a%a8%e7%9a%84%e7%bd%91%e7%bb%9c%e7%bb%93%e6%9e%84%e5%8a%a8%e5%8a%9b%e5%ad%a6%e6%a8%a1%e5%9e%8b-%e7%bd%91%e7%bb%9c%e7%a7%91%e5%ad%a6%e8%ae%ba%e6%96%87%e9%80%9f","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=20262","title":{"rendered":"\u793e\u4f1a\u4e92\u52a8\u7684\u7f51\u7edc\u7ed3\u6784\u52a8\u529b\u5b66\u6a21\u578b | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901220\u7bc7"},"content":{"rendered":"
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    C19-tranet: SARS-CoV-2\u5168\u7403\u7d22\u5f15\u75c5\u4f8b\u4f20\u64ad\u7f51<\/span>\u7edc\uff1b<\/h2>\n<\/li>\n<\/ul>\n


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    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Network structured kinetic models of social interactions<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15452<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Martin Burger<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>The aim of this paper is to study the derivation of appropriate meso- and macroscopic models for interactions as appearing in social processes. There are two main characteristics the models take into account, namely a network structure of interactions, which we treat by an appropriate mesoscopic description, and a different role of interacting agents. The latter differs from interactions treated in classical statistical mechanics in the sense that the agents do not have symmetric roles, but there is rather an active and a passive agent. We will demonstrate how a certain form of kinetic equations can be obtained to describe such interactions at a mesoscopic level and moreover obtain macroscopic models from monokinetics solutions of those. The derivation naturally leads to systems of nonlocal reaction-diffusion equations (or in a suitable limit local versions thereof), which can explain spatial phase separation phenomena found to emerge from the microscopic interactions. We will highlight the approach in three examples, namely the evolution and coarsening of dialects in human language, the construction of social norms, and the spread of an epidemic.<\/span><\/section>\n
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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Heterogeneous impact of a lockdown on inter-municipality mobility<\/span><\/h2>\n

    \u5730\u5740<\/strong>\uff1a<\/strong><\/span><\/h2>\n

    http:\/\/arxiv.org\/abs\/2006.15724<\/span><\/p>\n

    \u4f5c\u8005\uff1a<\/strong><\/span><\/p>\n

    H. P. M. Melo,J. Henriques,R. Carvalho,T. Verma,J. P. da Cruz,N. A. M. Araujo<\/span><\/p>\n


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    Abstract\uff1a<\/strong>Without a vaccine, the fight against the spreading of the coronavirus has focused on maintaining physical distance. To study the impact of such measures on inter-municipality traffic, we analyze a mobile dataset with the daily flow of people in Portugal in March and April 2020. We find that the reduction in inter-municipality traffic depends strongly on its initial outflow. In municipalities where the mobility is low, the outflow reduced by10\u221220% and this decrease was independent of the population size. Whereas, for municipalities of high mobility, the reduction was a monotonic increasing function of the population size and it even exceeded 60% for the largest municipalities. As a consequence of such heterogeneities, there were significant structural changes on the most probable paths for the spreading of the virus, which must be considered when modeling the impact of control measures.<\/span><\/p>\n

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    \u6709\u8272\u566a\u58f0\u65f6\u95f4\u5e8f\u5217\u4f5c\u4e3a\u4eba\u5de5<\/strong><\/strong><\/span><\/p>\n

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    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Coloured noise time series as appropriate models for environmental variation in artificial evolutionary systems<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.16204<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Matt Grove,James M. Borg,Fiona Polack<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Ecological, environmental and geophysical time series consistently exhibit the characteristics of coloured (1\/f^b{eta}) noise. Here we briefly survey the literature on coloured noise, population persistence and related evolutionary dynamics, before introducing coloured noise as an appropriate model for environmental variation in artificial evolutionary systems. To illustrate and explore the effects of different noise colours, a simple evolutionary model that examines the trade-off between specialism and generalism in fluctuating environments is applied. The results of the model clearly demonstrate a need for greater generalism as environmental variability becomes `whiter’, whilst specialisation is favoured as environmental variability becomes `redder’. Pink noise, sitting midway between white and red noise, is shown to be the point at which the pressures for generalism and specialism balance, providing some insight in to why `pinker’ noise is increasingly being seen as an appropriate model of typical environmental variability. We go on to discuss how the results presented here feed in to a wider discussion on evolutionary responses to fluctuating environments. Ultimately we argue that Artificial Life as a field should embrace the use of coloured noise to produce models of environmental variability.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u751f\u6001\u3001\u73af\u5883\u548c\u5730\u7403\u7269\u7406\u65f6\u95f4\u5e8f\u5217\u59cb\u7ec8\u5448\u73b0\u6709\u8272\u566a\u58f0\u7279\u5f81\u3002\u5728\u4ecb\u7ecd\u6709\u8272\u566a\u58f0\u4f5c\u4e3a\u4eba\u5de5\u8fdb\u5316\u7cfb\u7edf\u4e2d\u73af\u5883\u53d8\u5316\u7684\u5408\u9002\u6a21\u578b\u4e4b\u524d\uff0c\u6211\u4eec\u7b80\u8981\u5730\u56de\u987e\u4e86\u6709\u8272\u566a\u58f0\u3001\u79cd\u7fa4\u6301\u7eed\u6027\u548c\u76f8\u5173\u7684\u8fdb\u5316\u52a8\u529b\u5b66\u7684\u6587\u732e\u3002\u4e3a\u4e86\u8bf4\u660e\u548c\u63a2\u7d22\u4e0d\u540c\u7684\u566a\u97f3\u989c\u8272\u7684\u5f71\u54cd\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u8fdb\u5316\u6a21\u578b\uff0c\u5ba1\u67e5\u4e4b\u95f4\u7684\u6743\u8861\u4e13\u4e1a\u548c\u4e00\u822c\u5728\u6ce2\u52a8\u7684\u73af\u5883\u4e2d\u4f7f\u7528\u3002\u8be5\u6a21\u578b\u7684\u7ed3\u679c\u6e05\u695a\u5730\u8868\u660e\uff0c\u9700\u8981\u66f4\u5927\u7684\u4e00\u822c\u6027\uff0c\u56e0\u4e3a\u73af\u5883\u53d8\u5f02\u53d8\u5f97\u201c\u767d\u8272\u201d \uff0c\u800c\u4e13\u4e1a\u5316\u662f\u6709\u5229\u7684\uff0c\u56e0\u4e3a\u73af\u5883\u53d8\u5f02\u53d8\u5f97\u201c\u7ea2\u8272\u201d\u3002\u7c89\u7ea2\u566a\u58f0\uff0c\u4ecb\u4e8e\u767d\u566a\u58f0\u548c\u7ea2\u566a\u58f0\u4e4b\u95f4\uff0c\u88ab\u8bc1\u660e\u662f\u4e00\u822c\u5316\u548c\u4e13\u4e1a\u5316\u7684\u538b\u529b\u5e73\u8861\u70b9\uff0c\u63d0\u4f9b\u4e86\u4e00\u4e9b\u6d1e\u5bdf\u529b\uff0c\u4e3a\u4ec0\u4e48\u7c89\u7ea2\u566a\u58f0\u8d8a\u6765\u8d8a\u88ab\u89c6\u4e3a\u5178\u578b\u73af\u5883\u53d8\u5316\u7684\u9002\u5f53\u6a21\u578b\u3002\u6211\u4eec\u7ee7\u7eed\u8ba8\u8bba\u8fd9\u91cc\u5c55\u793a\u7684\u7ed3\u679c\u5982\u4f55\u4e3a\u66f4\u5e7f\u6cdb\u7684\u5173\u4e8e\u5bf9\u6ce2\u52a8\u73af\u5883\u7684\u8fdb\u5316\u53cd\u5e94\u7684\u8ba8\u8bba\u63d0\u4f9b\u652f\u6301\u3002\u6700\u7ec8\uff0c\u6211\u4eec\u8ba4\u4e3a\uff0c\u4eba\u5de5\u751f\u547d\u4f5c\u4e3a\u4e00\u4e2a\u9886\u57df\uff0c\u5e94\u8be5\u5305\u62ec\u6709\u8272\u566a\u97f3\u7684\u4f7f\u7528\uff0c\u4ee5\u4ea7\u751f\u73af\u5883\u53ef\u53d8\u6027\u7684\u6a21\u578b\u3002<\/span><\/section>\n

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    \u5229\u7528\u6d41\u52a8\u6027\u6570\u636e\u8bbe\u8ba1\u5e94\u5bf9<\/strong><\/span><\/p>\n

    \u82f1\u683c\u5170\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u5927\u6d41\u884c\u7684\u6700\u4f73\u5c01\u9501\u7b56\u7565<\/strong><\/span><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic in England<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.16059<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Ritabrata Dutta,Susana Gomes,Dante Kalise,Lorenzo Pacchiardi<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>A mathematical model for the COVID-19 pandemic spread in England is presented. The model integrates age-structured Susceptible-Exposed-Infected-Removed dynamics with real mobile phone data accounting for the population mobility. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u63d0\u51fa\u4e86\u4e00\u4e2a\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u82f1\u683c\u5170\u4f20\u64ad\u7684\u6570\u5b66\u6a21\u578b\u3002\u8be5\u6a21\u578b\u96c6\u6210\u4e86\u5e74\u9f84\u7ed3\u6784\u7684\u6613\u611f-\u66b4\u9732-\u611f\u67d3-\u79fb\u9664\u52a8\u6001\u4e0e\u771f\u5b9e\u7684\u79fb\u52a8\u7535\u8bdd\u6570\u636e\u7684\u4eba\u53e3\u6d41\u52a8\u3002\u52a8\u6001\u6a21\u578b\u8c03\u6574\u91c7\u7528\u8fd1\u4f3c\u8d1d\u53f6\u65af\u8ba1\u7b97\u3002\u6700\u4f73\u7684\u5c01\u9501\u548c\u9000\u51fa\u7b56\u7565\u662f\u57fa\u4e8e\u975e\u7ebf\u6027\u6a21\u578b\u9884\u4f30\u8ba1\u63a7\u5236\uff0c\u53d7\u5230\u516c\u5171\u536b\u751f\u548c\u793e\u4f1a\u7ecf\u6d4e\u56e0\u7d20\u7684\u7ea6\u675f\u3002\u901a\u8fc7\u5bf9\u8be5\u65b9\u6cd5\u7684\u5e7f\u6cdb\u8ba1\u7b97\u9a8c\u8bc1\uff0c\u8868\u660e\u6709\u53ef\u80fd\u8ba1\u7b97\u51fa\u5177\u6709\u5b9e\u9645\u964d\u4f4e\u7684\u6d41\u52a8\u6027\u503c\u7684\u7a33\u5065\u9000\u51fa\u7b56\u7565\uff0c\u4ece\u800c\u4e3a\u516c\u5171\u51b3\u7b56\u63d0\u4f9b\u4f9d\u636e\u3002<\/span><\/section>\n

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    \u57fa\u4e8e\u7a7a\u8154\u4e3b\u65b9\u7a0b\u7684<\/strong><\/span><\/p>\n

    \u4f20\u67d3\u75c5\u6a21\u578b\u52a8\u529b\u5b66<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Dynamics of epidemic models from cavity master equations<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15881<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Ernesto Ortega,David Machado,Alejandro Lage-Castellanos<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>We apply the cavity master equation (CME) approach to epidemics models. We explore mostly the susceptible-infectious-susceptible (SIS) model, which can be readily treated with the CME as a two-state. We show that this approach is more accurate than individual based and pair based mean field methods, and a previously published dynamic message passing scheme. We explore average case predictions and extend the cavity master equation to SIR and SIRS models.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u5c06\u7a7a\u8154\u4e3b\u65b9\u7a0b(CME)\u65b9\u6cd5\u5e94\u7528\u4e8e\u6d41\u884c\u75c5\u6a21\u578b\u3002\u6211\u4eec\u4e3b\u8981\u63a2\u8ba8\u6613\u611f-\u611f\u67d3-\u6613\u611f(SIS)\u6a21\u578b\uff0c\u5b83\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u5904\u7406 CME \u4f5c\u4e3a\u4e00\u4e2a\u4e24\u6001\u3002\u5b9e\u9a8c\u7ed3\u679c\u8868\u660e\uff0c\u8be5\u65b9\u6cd5\u6bd4\u57fa\u4e8e\u4e2a\u4f53\u7684\u548c\u57fa\u4e8e\u5bf9\u7684\u5747\u503c\u57df\u65b9\u6cd5\u4ee5\u53ca\u5148\u524d\u53d1\u8868\u7684\u52a8\u6001\u6d88\u606f\u4f20\u9012\u65b9\u6848\u5177\u6709\u66f4\u9ad8\u7684\u7cbe\u5ea6\u3002\u6211\u4eec\u63a2\u8ba8\u4e86\u5e73\u5747\u75c5\u4f8b\u9884\u6d4b\uff0c\u5e76\u5c06\u8154\u4e3b\u65b9\u7a0b\u63a8\u5e7f\u5230 SIR \u548c SIRS \u6a21\u578b\u3002<\/span><\/section>\n

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    \u7f51\u7edc\u5143\u4eba\u53e3\u6d41\u884c\u75c5\u7684\u5206\u5e03\u5f0f<\/strong><\/span><\/p>\n

    \u94fe\u8def\u5220\u9664\u7b56\u7565\u53ca\u5176\u5728\u65b0\u578b<\/strong><\/strong><\/span><\/p>\n

    \u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5\u63a7\u5236\u4e2d\u7684\u5e94\u7528<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.16221<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Fangzhou Liu,Yuhong Chen,Tong Liu,Zibo Zhou,Dong Xue,Martin Buss<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>In this paper, we investigate the distributed link removal strategy for networked meta-population epidemics. In particular, a deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. In order to curb the spread of epidemics, we present the spectrum-based optimization problem involving the Perron-Frobenius eigenvalue of the matrix constructed by the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the reported infected and recovered data in each state of Germany. The numerical experiment shows that the infected percentage can be significantly reduced by using the distributed link removal strategy.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u672c\u6587\u7814\u7a76\u4e86\u7f51\u7edc\u5316\u5143\u7fa4\u4f53\u6d41\u884c\u75c5\u7684\u5206\u5e03\u5f0f\u94fe\u8def\u5220\u9664\u7b56\u7565\u3002\u7279\u522b\u5730\uff0c\u6211\u4eec\u8003\u8651\u4e86\u4e00\u4e2a\u786e\u5b9a\u6027\u7684\u7f51\u7edc\u5316\u6613\u611f-\u611f\u67d3-\u5eb7\u590d(SIR)\u6a21\u578b\u6765\u63cf\u8ff0\u4f20\u67d3\u75c5\u7684\u6f14\u5316\u8fc7\u7a0b\u3002\u4e3a\u4e86\u6291\u5236\u6d41\u884c\u75c5\u7684\u4f20\u64ad\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u57fa\u4e8e\u9891\u8c31\u7684\u6700\u4f73\u5316\u95ee\u9898\uff0c\u5305\u62ec\u7531\u7f51\u7edc\u62d3\u6251\u548c\u8f6c\u79fb\u7387\u6784\u9020\u7684\u77e9\u9635\u7684 Perron-Frobenius \u7279\u5f81\u503c\u3002\u63d0\u51fa\u4e86\u4e00\u79cd\u6539\u8fdb\u7684\u5206\u5e03\u5f0f\u94fe\u8def\u5220\u9664\u7b56\u7565\uff0c\u8be5\u7b56\u7565\u53ef\u5e94\u7528\u4e8e\u5e26\u6743\u6709\u5411\u56fe\u4e0a\u5177\u6709\u5f02\u6784\u8f6c\u79fb\u7387\u7684 SIR \u6a21\u578b\u3002\u63d0\u8bae\u7684\u65b9\u6cd5\u662f\u901a\u8fc7\u4f7f\u7528\u5fb7\u56fd\u6bcf\u4e2a\u5dde\u62a5\u544a\u7684\u611f\u67d3\u548c\u6062\u590d\u6570\u636e\u6765\u5b9e\u65bd\u63a7\u5236\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u3002\u6570\u503c\u5b9e\u9a8c\u8868\u660e\uff0c\u91c7\u7528\u5206\u5e03\u5f0f\u94fe\u8def\u5220\u9664\u7b56\u7565\u53ef\u4ee5\u663e\u8457\u964d\u4f4e\u611f\u67d3\u7387\u3002<\/span><\/section>\n

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    \u4f60\u80fd\u611f\u67d3\u591a\u5c11\u79cd?<\/strong><\/span><\/p>\n

    \u7b80\u5355(\u6734\u7d20)\u7684\u65b9\u6cd5\u4f30\u8ba1\u7e41\u6b96\u6570\u91cf<\/strong><\/span><\/strong><\/p>\n

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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    How many can you infect? Simple (and naive) methods of estimating the reproduction number<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15706<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    H. Susanto,V. R. Tjahjono,A. Hasan,M. F. Kasim,N. Nuraini,E. R. M. Putri,R. Kusdiantara,H. Kurniawan<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>This is a pedagogical paper on estimating the number of people that can be infected by one infectious person during an epidemic outbreak, known as the reproduction number. Knowing the number is crucial for developing policy responses. There are generally two types of such a number, i.e., basic and effective (or instantaneous). While basic reproduction number is the average expected number of cases directly generated by one case in a population where all individuals are susceptible, effective reproduction number is the number of cases generated in the current state of a population. In this paper, we exploit the deterministic susceptible-infected-removed (SIR) model to estimate them through three different numerical approximations. We apply the methods to the pandemic COVID-19 in Italy to provide insights into the spread of the disease in the country. We see that the effect of the national lockdown in slowing down the disease exponential growth appeared about two weeks after the implementation date. We also discuss available improvements to the simple (and naive) methods that have been made by researchers in the field. Authors of this paper are members of the SimcovID (Simulasi dan Pemodelan COVID-19 Indonesia) collaboration.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u8fd9\u662f\u4e00\u4efd\u5173\u4e8e\u4f30\u8ba1\u5728\u6d41\u884c\u75c5\u7206\u53d1\u671f\u95f4\u53ef\u80fd\u88ab\u4e00\u4e2a\u4f20\u67d3\u4eba\u611f\u67d3\u7684\u4eba\u6570\u7684\u6559\u5b66\u8bba\u6587\uff0c\u79f0\u4e3a\u7e41\u6b96\u6570\u3002\u4e86\u89e3\u8fd9\u4e2a\u6570\u5b57\u5bf9\u4e8e\u5236\u5b9a\u5e94\u5bf9\u653f\u7b56\u81f3\u5173\u91cd\u8981\u3002\u8fd9\u79cd\u6570\u5b57\u901a\u5e38\u6709\u4e24\u79cd\u7c7b\u578b\uff0c\u5373\u57fa\u672c\u7684\u548c\u6709\u6548\u7684(\u6216\u77ac\u65f6\u7684)\u3002\u57fa\u672c\u4f20\u67d3\u6570\u662f\u6240\u6709\u4e2a\u4f53\u90fd\u6613\u611f\u7684\u4eba\u7fa4\u4e2d\u4e00\u4e2a\u75c5\u4f8b\u76f4\u63a5\u4ea7\u751f\u7684\u5e73\u5747\u9884\u671f\u75c5\u4f8b\u6570\uff0c\u6709\u6548\u751f\u6b96\u6570\u662f\u5728\u5f53\u524d\u4eba\u7fa4\u72b6\u6001\u4e0b\u4ea7\u751f\u7684\u75c5\u4f8b\u6570\u3002\u672c\u6587\u5229\u7528\u786e\u5b9a\u6027\u6613\u611f\u67d3-\u611f\u67d3-\u53bb\u9664(SIR)\u6a21\u578b\uff0c\u901a\u8fc7\u4e09\u79cd\u4e0d\u540c\u7684\u6570\u503c\u903c\u8fd1\u65b9\u6cd5\u5bf9\u5176\u8fdb\u884c\u4f30\u8ba1\u3002\u6211\u4eec\u5c06\u8fd9\u4e9b\u65b9\u6cd5\u5e94\u7528\u4e8e\u610f\u5927\u5229\u7684\u5927\u6d41\u884c\u6027\u6d41\u611f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff0c\u4ee5\u6df1\u5165\u4e86\u89e3\u8be5\u75be\u75c5\u5728\u8be5\u56fd\u7684\u4f20\u64ad\u60c5\u51b5\u3002\u6211\u4eec\u770b\u5230\uff0c\u56fd\u5bb6\u9632\u8303\u6307\u6570\u589e\u957f\u5728\u5ef6\u7f13\u75be\u75c5\u4f20\u64ad\u65b9\u9762\u7684\u6548\u679c\u5728\u5b9e\u65bd\u65e5\u671f\u540e\u5927\u7ea6\u4e24\u5468\u51fa\u73b0\u3002\u6211\u4eec\u8fd8\u8ba8\u8bba\u4e86\u5bf9\u8be5\u9886\u57df\u7814\u7a76\u4eba\u5458\u63d0\u51fa\u7684\u7b80\u5355(\u548c\u6734\u7d20)\u65b9\u6cd5\u7684\u53ef\u7528\u6539\u8fdb\u3002\u672c\u6587\u4f5c\u8005\u662f\u5370\u5ea6\u5c3c\u897f\u4e9a\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u534f\u4f1a\u7684\u6210\u5458\u3002<\/span><\/section>\n

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    \u63a9\u76d6\u4e00\u822c\u4eba\u53e3\u53ef\u80fd\u4f1a\u51cf\u5c11<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7684\u7206\u53d1<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Masking the general population might attenuate COVID-19 outbreaks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    https:\/\/arxiv.org\/abs\/2006.15626<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Bjorn Johansson<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>The effect of masking the general population on a COVID-19 epidemic is estimated by computer simulation using two separate state-of-the-art web-based softwares, one of them calibrated for the SARS-CoV-2 virus. The questions addressed are these: 1. Can mask use by the general population limit the spread of SARS-CoV-2 in a country? 2. What types of masks exist, and how elaborate must a mask be to be effective against COVID-19? 3. Does the mask have to be applied early in an epidemic? 4. A brief general discussion of masks and some possible future research questions regarding masks and SARS-CoV-2. Results are as follows: (1) The results indicate that any type of mask, even simple home-made ones, may be effective. Masks use seems to have an effect in lowering new patients even the protective effect of each mask (here dubbed “one-mask protection”) is low. Strict adherence to mask use does not appear to be critical. However, increasing the one-mask protection to > 50% was found to be advantageous. Masks seemed able to reduce overflow of capacity, e.g. of intensive care. As the default parameters of the software included another intervention, it seems possible to combine mask and other interventions. (2) Masks do seem to reduce the number of new cases even if introduced at a late stage in an epidemic. However, early implementation helps reduce the cumulative and total number of cases. (3) The simulations suggest that it might be possible to eliminate a COVID-19 outbreak by widespread mask use during a limited period. The results from these simulations are encouraging, but do not necessarily represent the real-life situation, so it is suggested that clinical trials of masks are now carried out while continuously monitoring effects and side-effects.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u901a\u8fc7\u4f7f\u7528\u4e24\u4e2a\u72ec\u7acb\u7684\u6700\u5148\u8fdb\u7684\u57fa\u4e8e\u7f51\u7edc\u7684\u8f6f\u4ef6\uff0c\u5176\u4e2d\u4e00\u4e2a\u8f6f\u4ef6\u662f\u4e3a SARS-CoV-2\u75c5\u6bd2\u6821\u51c6\u7684\uff0c\u7531\u8ba1\u7b97\u673a\u6a21\u62df\u536b\u751f\u7ec4\u7ec7\u4f30\u8ba1\u4e86\u63a9\u76d6\u4e00\u822c\u4eba\u7fa4\u5bf9\u4e8e\u4e00\u573a\u975e\u5178\u578b\u80ba\u708e\u6d41\u884c\u75c5\u7684\u5f71\u54cd\u3002\u8fd9\u4e9b\u95ee\u9898\u662f: 1\u3002\u4e00\u822c\u4eba\u7fa4\u4f7f\u7528\u53e3\u7f69\u80fd\u9650\u5236SARS-CoV-2\u5728\u4e00\u4e2a\u56fd\u5bb6\u7684\u4f20\u64ad\u5417\uff1f2\u3002\u5b58\u5728\u54ea\u4e9b\u7c7b\u578b\u7684\u9762\u5177\uff0c\u4e00\u4e2a\u9762\u5177\u9700\u8981\u591a\u4e48\u7cbe\u81f4\u624d\u80fd\u6709\u6548\u5730\u5bf9\u6297\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\uff1f3\u3002\u9762\u5177\u662f\u5426\u5fc5\u987b\u5728\u6d41\u884c\u75c5\u7684\u65e9\u671f\u4f7f\u7528\uff1f4\u3002\u4e00\u4e2a\u7b80\u77ed\u7684\u4e00\u822c\u6027\u8ba8\u8bba\u7684\u53e3\u7f69\u548c\u4e00\u4e9b\u53ef\u80fd\u7684\u672a\u6765\u7814\u7a76\u95ee\u9898\u6709\u5173\u53e3\u7f69\u548c SARS-CoV-2\u3002\u7ed3\u679c\u5982\u4e0b: (1)\u7ed3\u679c\u8868\u660e\uff0c\u4efb\u4f55\u7c7b\u578b\u7684\u53e3\u7f69\uff0c\u5373\u4f7f\u662f\u7b80\u5355\u7684\u81ea\u5236\u53e3\u7f69\uff0c\u90fd\u53ef\u80fd\u662f\u6709\u6548\u7684\u3002\u53e3\u7f69\u7684\u4f7f\u7528\u4f3c\u4e4e\u6709\u964d\u4f4e\u65b0\u75c5\u4eba\u6570\u91cf\u7684\u4f5c\u7528\uff0c\u5373\u4f7f\u6bcf\u4e2a\u53e3\u7f69\u7684\u4fdd\u62a4\u4f5c\u7528(\u8fd9\u91cc\u79f0\u4e3a\u201c\u5355\u4e00\u53e3\u7f69\u4fdd\u62a4\u201d)\u90fd\u5f88\u4f4e\u3002\u4e25\u683c\u9075\u5b88\u9762\u819c\u7684\u4f7f\u7528\u4f3c\u4e4e\u5e76\u4e0d\u91cd\u8981\u3002\u7136\u800c\uff0c\u589e\u52a0\u4e00\u4e2a\u63a9\u819c\u4fdd\u62a4\u5230\u5927\u4e8e50% \u88ab\u53d1\u73b0\u662f\u6709\u5229\u7684\u3002\u53e3\u7f69\u4f3c\u4e4e\u80fd\u591f\u51cf\u5c11\u5bb9\u91cf\u7684\u6ea2\u51fa\uff0c\u4f8b\u5982\u91cd\u75c7\u76d1\u62a4\u3002\u7531\u4e8e\u8be5\u8f6f\u4ef6\u7684\u9ed8\u8ba4\u53c2\u6570\u5305\u62ec\u53e6\u4e00\u9879\u5e72\u9884\u63aa\u65bd\uff0c\u56e0\u6b64\u4f3c\u4e4e\u53ef\u4ee5\u5c06\u9762\u5177\u548c\u5176\u4ed6\u5e72\u9884\u63aa\u65bd\u7ed3\u5408\u8d77\u6765\u3002(2)\u5373\u4f7f\u5728\u6d41\u884c\u75c5\u7684\u540e\u671f\u9636\u6bb5\u5f15\u5165\u53e3\u7f69\uff0c\u53e3\u7f69\u4f3c\u4e4e\u786e\u5b9e\u53ef\u4ee5\u51cf\u5c11\u65b0\u75c5\u4f8b\u7684\u6570\u91cf\u3002\u7136\u800c\uff0c\u65e9\u671f\u5b9e\u65bd\u6709\u52a9\u4e8e\u51cf\u5c11\u7d2f\u79ef\u548c\u603b\u6570\u7684\u6848\u4ef6\u3002(3)\u6a21\u62df\u7ed3\u679c\u663e\u793a\uff0c\u5728\u6709\u9650\u7684\u65f6\u95f4\u5185\u5e7f\u6cdb\u4f7f\u7528\u53e3\u7f69\uff0c\u53ef\u80fd\u53ef\u4ee5\u6d88\u9664\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u7206\u53d1\u3002\u8fd9\u4e9b\u6a21\u62df\u7684\u7ed3\u679c\u4ee4\u4eba\u9f13\u821e\uff0c\u4f46\u4e0d\u4e00\u5b9a\u4ee3\u8868\u771f\u5b9e\u7684\u60c5\u51b5\uff0c\u56e0\u6b64\u5efa\u8bae\u73b0\u5728\u8fdb\u884c\u53e3\u7f69\u7684\u4e34\u5e8a\u8bd5\u9a8c\uff0c\u540c\u65f6\u4e0d\u65ad\u76d1\u6d4b\u5f71\u54cd\u548c\u526f\u4f5c\u7528\u3002<\/span><\/section>\n

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    \u7c7b\u4f3c COVID-19\u6d41\u884c\u75c5<\/strong><\/span><\/p>\n

    \u7684\u8fc7\u5ea6\u6b7b\u4ea1\u7387\u6a21\u578b<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Modelling Excess Mortality in Covid-19-like Epidemics<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15583<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Zdzislaw Burda<\/span><\/section>\n

    <\/section>\n
    A<\/strong>bstract\uff1a<\/strong>We discuss a stochastic model to assess cumulative excess deaths during Covid-19-like epidemics for various non-pharmaceutic interventions. The model simulates three interrelated stochastic processes: epidemic spreading, availability of respiratory ventilators and changes in death statistics. Epidemic may spread either locally or globally. The local mode simulates virus transmission through contacts in the vicinity of the place of residence while the global mode simulates virus transmission through social mixing in public places, sport arenas, airports, etc, where many people meet, who live in remote geographic locations. Epidemic is modelled as a discrete time stochastic process on random geometric networks. In the simulations we assume that the basic reproduction number is<\/span><\/nobr>R0=2.5 and the infectious period lasts ca. ten days. We also assume that the virus leads to severe acute respiratory syndrome in about one percent of cases, which in turn almost surely lead to respiratory default and death, unless the patient receives an appropriate medical treatment supported by respiratory ventilation. For other parameters, like mortality rate or the number of respiratory ventilators per million of inhabitants, we take values typical for developed countries. We simulate populations of <\/span><\/nobr>105\u2212106 people. We compare different strategies: do-nothing, social distancing, reduction of social mixing and lockdown, assuming that there is no vaccine and no efficient medicine. The results of the simulations show that strategies that slow down the spread of epidemic too much are inefficient in reducing the cumulative excess of deaths. A hybrid strategy in which lockdown is in place for some time and is then completely released is inefficient as well.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u6211\u4eec\u8ba8\u8bba\u4e86\u4e00\u4e2a\u968f\u673a\u6a21\u578b\u6765\u8bc4\u4f30\u5404\u79cd\u975e\u836f\u7269\u5e72\u9884\u63aa\u65bd\u7684\u7c7b ovid-19\u6d41\u884c\u75c5\u7d2f\u79ef\u8d85\u989d\u6b7b\u4ea1\u3002\u8be5\u6a21\u578b\u6a21\u62df\u4e86\u4e09\u4e2a\u76f8\u4e92\u5173\u8054\u7684\u968f\u673a\u8fc7\u7a0b: \u6d41\u884c\u75c5\u4f20\u64ad\u3001\u547c\u5438\u673a\u7684\u6709\u6548\u6027\u548c\u6b7b\u4ea1\u7edf\u8ba1\u6570\u636e\u7684\u53d8\u5316\u3002\u6d41\u884c\u75c5\u53ef\u80fd\u5728\u672c\u5730\u6216\u5168\u7403\u4f20\u64ad\u3002\u672c\u5730\u6a21\u5f0f\u6a21\u62df\u75c5\u6bd2\u901a\u8fc7\u5c45\u4f4f\u5730\u9644\u8fd1\u7684\u63a5\u89e6\u4f20\u64ad\uff0c\u800c\u5168\u7403\u6a21\u5f0f\u6a21\u62df\u75c5\u6bd2\u901a\u8fc7\u5728\u516c\u5171\u573a\u6240\u3001\u8fd0\u52a8\u573a\u6240\u3001\u673a\u573a\u7b49\u5730\u7684\u793e\u4f1a\u6df7\u5408\u4f20\u64ad\uff0c\u5728\u8fd9\u4e9b\u5730\u65b9\u6709\u8bb8\u591a\u4eba\u76f8\u9047\uff0c\u4ed6\u4eec\u5c45\u4f4f\u5728\u504f\u8fdc\u7684\u5730\u7406\u4f4d\u7f6e\u3002\u5c06\u4f20\u67d3\u75c5\u6a21\u578b\u5316\u4e3a\u968f\u673a\u51e0\u4f55\u7f51\u7edc\u4e0a\u7684\u79bb\u6563\u65f6\u95f4\u968f\u673a\u8fc7\u7a0b\u3002\u5728\u6a21\u62df\u4e2d\u6211\u4eec\u5047\u8bbe\u57fa\u672c\u4f20\u67d3\u6570<\/span><\/nobr>R0=2.5 \u611f\u67d3\u671f\u6301\u7eed\u5927\u7ea6\u3002\u5341\u5929\u3002\u6211\u4eec\u8fd8\u5047\u8bbe\uff0c\u5728\u5927\u7ea6\u767e\u5206\u4e4b\u4e00\u7684\u75c5\u4f8b\u4e2d\uff0c\u75c5\u6bd2\u4f1a\u5bfc\u81f4\u4e25\u91cd\u6025\u6027\u553f\u5438\u7efc\u5408\u75c7\uff0c\u8fd9\u51e0\u4e4e\u80af\u5b9a\u4f1a\u5bfc\u81f4\u547c\u5438\u7cfb\u7edf\u7f3a\u9677\u548c\u6b7b\u4ea1\uff0c\u9664\u975e\u75c5\u4eba\u63a5\u53d7\u4e86\u547c\u5438\u901a\u6c14\u652f\u6301\u7684\u9002\u5f53\u6cbb\u7597\u3002\u5bf9\u4e8e\u5176\u4ed6\u53c2\u6570\uff0c\u5982\u6b7b\u4ea1\u7387\u6216\u6bcf\u767e\u4e07\u5c45\u6c11\u547c\u5438\u5668\u7684\u6570\u91cf\uff0c\u6211\u4eec\u91c7\u7528\u53d1\u8fbe\u56fd\u5bb6\u7684\u5178\u578b\u503c\u3002\u6211\u4eec\u6a21\u62df\u4e86<\/span><\/nobr>105\u2212106 \u4eba\u3002\u6211\u4eec\u6bd4\u8f83\u4e86\u4e0d\u540c\u7684\u7b56\u7565: \u4ec0\u4e48\u90fd\u4e0d\u505a\uff0c\u793e\u4f1a\u758f\u8fdc\uff0c\u51cf\u5c11\u793e\u4f1a\u6df7\u5408\u548c\u5c01\u9501\uff0c\u5047\u8bbe\u6ca1\u6709\u75ab\u82d7\u548c\u6709\u6548\u7684\u836f\u7269\u3002\u6a21\u62df\u7ed3\u679c\u8868\u660e\uff0c\u8fc7\u5ea6\u51cf\u7f13\u6d41\u884c\u75c5\u8513\u5ef6\u7684\u7b56\u7565\u5728\u51cf\u5c11\u7d2f\u79ef\u8fc7\u591a\u6b7b\u4ea1\u65b9\u9762\u6548\u7387\u4e0d\u9ad8\u3002\u4e00\u4e2a\u6df7\u5408\u7b56\u7565\uff0c\u5176\u4e2d\u9501\u5b9a\u662f\u5728\u9002\u5f53\u7684\u5730\u65b9\u4e00\u6bb5\u65f6\u95f4\uff0c\u7136\u540e\u5b8c\u5168\u91ca\u653e\u662f\u4f4e\u6548\u7684\u3002<\/span><\/section>\n

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    \u6709\u7b26\u53f7\u7f51\u7edc\u4e2d<\/strong><\/span><\/p>\n

    \u7528\u4e8e\u793e\u533a\u68c0\u6d4b\u7684\u975e\u56de\u6eaf\u7b97\u5b50<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Non-backtracking Operator for Community Detection in Signed Networks<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15471<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Zhaoyue Zhong,Xiangrong Wang,Cunquan Qu,Guanghui Wang<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Community detection or clustering is crucial for understanding the structure of complex systems. In some networks, nodes are allowed to be linked by either ‘positive’ or ‘negative’ edges. Such networks are called signed networks. Discovering communities in signed networks is more challenging. Inspired by the structural balance theory, in this article, we innovatively propose a non-backtracking operator for signed networks. Besides, we theoretically derive a detectability threshold and prove the feasibility of the non-backtracking operator in community detection. Simulation results demonstrate that the non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based algorithm, and shows great potential to detect communities with or without overlap.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u793e\u533a\u68c0\u6d4b\u6216\u805a\u7c7b\u5bf9\u4e8e\u7406\u89e3\u590d\u6742\u7cfb\u7edf\u7684\u7ed3\u6784\u81f3\u5173\u91cd\u8981\u3002\u5728\u4e00\u4e9b\u7f51\u7edc\u4e2d\uff0c\u8282\u70b9\u53ef\u4ee5\u901a\u8fc7\u201c\u6b63\u201d\u6216\u201c\u8d1f\u201d\u8fb9\u8fde\u63a5\u3002\u8fd9\u79cd\u7f51\u7edc\u79f0\u4e3a\u6709\u7b26\u53f7\u7f51\u7edc\u3002\u5728\u7b7e\u540d\u7f51\u7edc\u4e2d\u53d1\u73b0\u793e\u533a\u66f4\u5177\u6311\u6218\u6027\u3002\u53d7\u7ed3\u6784\u5e73\u8861\u7406\u8bba\u7684\u542f\u53d1\uff0c\u672c\u6587\u521b\u65b0\u6027\u5730\u63d0\u51fa\u4e86\u4e00\u79cd\u6709\u7b26\u53f7\u7f51\u7edc\u7684\u65e0\u56de\u6eaf\u7b97\u5b50\u3002\u6b64\u5916\uff0c\u6211\u4eec\u4ece\u7406\u8bba\u4e0a\u63a8\u5bfc\u4e86\u4e00\u4e2a\u53ef\u63a2\u6d4b\u6027\u9608\u503c\uff0c\u5e76\u8bc1\u660e\u4e86\u975e\u56de\u6eaf\u7b97\u5b50\u5728\u793e\u533a\u68c0\u6d4b\u4e2d\u7684\u53ef\u884c\u6027\u3002\u4eff\u771f\u7ed3\u679c\u8868\u660e\uff0c\u57fa\u4e8e\u975e\u56de\u6eaf\u77e9\u9635\u7684\u65b9\u6cd5\u660e\u663e\u4f18\u4e8e\u57fa\u4e8e\u90bb\u63a5\u77e9\u9635\u7684\u65b9\u6cd5\uff0c\u5728\u68c0\u6d4b\u6709\u6216\u6ca1\u6709\u91cd\u53e0\u7684\u793e\u533a\u65b9\u9762\u663e\u793a\u4e86\u5de8\u5927\u7684\u6f5c\u529b\u3002<\/span><\/section>\n


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    \u5f02\u8d28\u6027\u5bf9\u8d85\u56fe\u4f20\u67d3\u6a21\u578b\u7684\u5f71\u54cd<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    The effect of heterogeneity on hypergraph contagion models<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15453<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Nicholas Landry,Juan G. Restrepo<\/span><\/section>\n

    <\/section>\n
    A<\/strong>bstract\uff1a<\/strong>The dynamics of network social contagion processes such as opinion formation and epidemic spreading is often mediated by interactions between multiple nodes. Previous results have shown that these higher-order interactions can profoundly modify the dynamics of contagion processes, resulting in bistability, hysteresis, and explosive transitions. In this paper, we present and analyze a degree-based mean-field description of the dynamics of the SIS model on hypergraphs, i.e., networks with higher-order interactions. We derive a hyperdegree-based mean-field equation to describe the contagion dynamics, and illustrate its applicability with the example of a hypergraph where contagion is mediated by both links (pairwise interactions) and triangles (three-way interactions). We consider two different mechanisms of higher-order contagion and healing, and the cases where links and triangles connect preferentially to the same nodes, or are chosen independently of each other. We find that, when links and triangles are chosen independently, heterogeneity in the link degree distribution can suppress explosive transitions. In addition, explosive transitions are more likely to occur when node and triangle connections are positively correlated when compared to the case when they are chosen independently of each other. We verify these results with microscopic simulations of the contagion process and with analytic predictions derived from the mean-field model. Our results show that the structure of higher-order interactions can have important effects on contagion processes on hypergraphs.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u7f51\u7edc\u793e\u4f1a\u4f20\u67d3\u7684\u52a8\u6001\u8fc7\u7a0b\uff0c\u5982\u8206\u8bba\u5f62\u6210\u548c\u6d41\u884c\u75c5\u4f20\u64ad\uff0c\u5f80\u5f80\u662f\u7531\u591a\u4e2a\u8282\u70b9\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u4ecb\u5bfc\u7684\u3002\u4ee5\u5f80\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u8fd9\u4e9b\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u80fd\u591f\u6df1\u523b\u5730\u6539\u53d8\u4f20\u67d3\u8fc7\u7a0b\u7684\u52a8\u529b\u5b66\uff0c\u4ece\u800c\u5bfc\u81f4\u53cc\u7a33\u6001\u3001\u6ede\u540e\u548c\u7206\u70b8\u8dc3\u8fc1\u3002\u672c\u6587\u63d0\u51fa\u5e76\u5206\u6790\u4e86\u8d85\u56fe SIS \u6a21\u578b\u7684\u52a8\u529b\u5b66\u6027\u8d28\u7684\u4e00\u79cd\u57fa\u4e8e\u5ea6\u7684\u5e73\u5747\u573a\u63cf\u8ff0\uff0c\u5373\u5177\u6709\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u7684\u7f51\u7edc\u3002\u6211\u4eec\u63a8\u5bfc\u4e86\u4e00\u4e2a\u57fa\u4e8e\u8d85\u5ea6\u7684\u5e73\u5747\u573a\u65b9\u7a0b\u6765\u63cf\u8ff0\u4f20\u67d3\u52a8\u529b\u5b66\uff0c\u5e76\u4ee5\u4e00\u4e2a\u8d85\u56fe\u4e3a\u4f8b\u8bf4\u660e\u4e86\u5b83\u7684\u9002\u7528\u6027\u3002\u6211\u4eec\u8003\u8651\u4e86\u4e24\u79cd\u4e0d\u540c\u7684\u9ad8\u9636\u4f20\u67d3\u548c\u6108\u5408\u673a\u5236\uff0c\u4ee5\u53ca\u94fe\u63a5\u548c\u4e09\u89d2\u5f62\u4f18\u5148\u8fde\u63a5\u5230\u540c\u4e00\u4e2a\u8282\u70b9\uff0c\u6216\u8005\u5f7c\u6b64\u72ec\u7acb\u9009\u62e9\u7684\u60c5\u51b5\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u5f53\u5355\u72ec\u9009\u62e9\u94fe\u63a5\u548c\u4e09\u89d2\u5f62\u65f6\uff0c\u94fe\u63a5\u5ea6\u5206\u5e03\u7684\u5f02\u8d28\u6027\u53ef\u4ee5\u6291\u5236\u7206\u53d1\u6027\u8dc3\u8fc1\u3002\u6b64\u5916\uff0c\u4e0e\u5355\u72ec\u9009\u62e9\u8282\u70b9\u548c\u4e09\u89d2\u5f62\u8fde\u63a5\u76f8\u6bd4\uff0c\u8282\u70b9\u548c\u4e09\u89d2\u5f62\u8fde\u63a5\u76f8\u4e92\u6b63\u76f8\u5173\u65f6\u66f4\u5bb9\u6613\u53d1\u751f\u7206\u70b8\u6027\u8f6c\u53d8\u3002\u6211\u4eec\u7528\u4f20\u67d3\u8fc7\u7a0b\u7684\u5fae\u89c2\u6a21\u62df\u548c\u7531\u5e73\u5747\u573a\u6a21\u578b\u5bfc\u51fa\u7684\u5206\u6790\u9884\u6d4b\u6765\u9a8c\u8bc1\u8fd9\u4e9b\u7ed3\u679c\u3002\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u9ad8\u9636\u76f8\u4e92\u4f5c\u7528\u7684\u7ed3\u6784\u5bf9\u8d85\u56fe\u7684\u4f20\u67d3\u8fc7\u7a0b\u5177\u6709\u91cd\u8981\u5f71\u54cd\u3002<\/span><\/section>\n

    <\/span><\/section>\n
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    \u91cd\u5212\u9009\u533a\u4e0e\u8ba1\u7b97\u91cd\u5212<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Gerrymandering and computational redistricting<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/1711.04640<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Olivia Guest,Frank J. Kanayet,Bradley C. Love<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Partisan gerrymandering poses a threat to democracy. Moreover, the complexity of the districting task may exceed human capacities. One potential solution is using computational models to automate the districting process by optimizing objective and open criteria, such as how spatially compact districts are. We formulated one such model that minimized pairwise distance between voters within a district. Using US Census Bureau data, we confirmed our prediction that the difference in compactness between the computed and actual districts would be greatest for states that are large and therefore difficult for humans to properly district given their limited capacities. The computed solutions highlighted differences in how humans and machines solve this task with machine solutions more fully optimized and displaying emergent properties not evident in human solutions. These results suggest a division of labour in which humans debate and formulate districting criteria whereas machines optimize the criteria to draw the district boundaries. We discuss how criteria can be expanded beyond notions of compactness to include other factors, such as respecting municipal boundaries, historic communities, relevant legislation, etc.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u515a\u6d3e\u5212\u5206\u9009\u533a\u7684\u505a\u6cd5\u5bf9\u6c11\u4e3b\u6784\u6210\u5a01\u80c1\u3002\u6b64\u5916\uff0c\u533a\u57df\u5212\u5206\u4efb\u52a1\u7684\u590d\u6742\u6027\u53ef\u80fd\u8d85\u51fa\u4e86\u4eba\u7684\u80fd\u529b\u3002\u4e00\u4e2a\u53ef\u80fd\u7684\u89e3\u51b3\u65b9\u6848\u662f\u4f7f\u7528\u8ba1\u7b97\u6a21\u578b\uff0c\u901a\u8fc7\u4f18\u5316\u5ba2\u89c2\u548c\u5f00\u653e\u7684\u6807\u51c6\uff0c\u4f8b\u5982\u7a7a\u95f4\u7d27\u51d1\u7684\u533a\u57df\uff0c\u6765\u81ea\u52a8\u5316\u5206\u533a\u8fc7\u7a0b\u3002\u6211\u4eec\u5efa\u7acb\u4e86\u4e00\u4e2a\u8fd9\u6837\u7684\u6a21\u578b\uff0c\u6700\u5c0f\u5316\u4e86\u4e00\u4e2a\u5730\u533a\u5185\u9009\u6c11\u4e4b\u95f4\u7684\u6210\u5bf9\u8ddd\u79bb\u3002\u4f7f\u7528\u7f8e\u56fd\u4eba\u53e3\u666e\u67e5\u5c40\u7684\u6570\u636e\uff0c\u6211\u4eec\u8bc1\u5b9e\u4e86\u6211\u4eec\u7684\u9884\u6d4b\uff0c\u5373\u8ba1\u7b97\u548c\u5b9e\u9645\u5730\u533a\u4e4b\u95f4\u7684\u7d27\u51d1\u7a0b\u5ea6\u5dee\u5f02\u6700\u5927\u7684\u662f\u90a3\u4e9b\u9762\u79ef\u5f88\u5927\u7684\u5dde\uff0c\u56e0\u6b64\uff0c\u9274\u4e8e\u5176\u6709\u9650\u7684\u5bb9\u91cf\uff0c\u4eba\u4eec\u5f88\u96be\u9002\u5f53\u5730\u9009\u62e9\u5730\u533a\u3002\u8ba1\u7b97\u673a\u89e3\u51b3\u65b9\u6848\u7a81\u51fa\u4e86\u4eba\u7c7b\u548c\u673a\u5668\u5982\u4f55\u89e3\u51b3\u8fd9\u4e2a\u4efb\u52a1\u7684\u5dee\u5f02\uff0c\u673a\u5668\u89e3\u51b3\u65b9\u6848\u5f97\u5230\u4e86\u66f4\u5145\u5206\u7684\u4f18\u5316\uff0c\u5e76\u663e\u793a\u4e86\u5728\u4eba\u7c7b\u89e3\u51b3\u65b9\u6848\u4e2d\u4e0d\u660e\u663e\u7684\u7a81\u53d1\u6027\u3002\u8fd9\u4e9b\u7ed3\u679c\u8868\u660e\u4e86\u4e00\u79cd\u52b3\u52a8\u5206\u5de5\uff0c\u5728\u8fd9\u79cd\u5206\u5de5\u4e2d\uff0c\u4eba\u4eec\u4e89\u8bba\u5e76\u5236\u5b9a\u9009\u533a\u5212\u5206\u6807\u51c6\uff0c\u800c\u673a\u5668\u4f18\u5316\u6807\u51c6\u4ee5\u7ed8\u5236\u5730\u533a\u8fb9\u754c\u3002\u6211\u4eec\u8ba8\u8bba\u5982\u4f55\u5c06\u6807\u51c6\u6269\u5c55\u5230\u7d27\u51d1\u6027\u6982\u5ff5\u4e4b\u5916\uff0c\u4ee5\u5305\u62ec\u5176\u4ed6\u56e0\u7d20\uff0c\u5982\u5c0a\u91cd\u5e02\u653f\u8fb9\u754c\u3001\u5386\u53f2\u793e\u533a\u3001\u76f8\u5173\u7acb\u6cd5\u7b49\u3002<\/span><\/section>\n

    <\/section>\n
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    \u6b27\u6d32\u56fd\u5bb6\u65b0\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e<\/strong><\/span><\/p>\n

    \u6d41\u884c\u75c5\u4f20\u64ad\u548c\u5b9e\u65bd\u6a21\u578b\u7684\u65b9\u6cd5\u5b66<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Methodology for Modelling the new COVID-19 Pandemic Spread and Implementation to European Countries<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15385<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    S. Maltezos<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>After the breakout of the disease caused by the new virus COVID-19, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country’s characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this repulsive disease in the future.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u5728\u65b0\u75c5\u6bd2\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5f15\u8d77\u7684\u75be\u75c5\u7206\u53d1\u4e4b\u540e\uff0c\u4e16\u754c\u4e0a\u5927\u591a\u6570\u56fd\u5bb6\u5df2\u7ecf\u8fdb\u5165\u4e86\u7f13\u89e3\u9636\u6bb5\u3002\u5728\u8fd9\u4e00\u9636\u6bb5\uff0c\u6b27\u6d32\u56fd\u5bb6\u6709\u53ef\u80fd\u5bf9\u6bcf\u65e5\u62a5\u544a\u7684\u75c5\u4f8b\u548c\u5176\u4ed6\u53c2\u6570\u8fdb\u884c\u66f4\u51c6\u786e\u7684\u6570\u636e\u5206\u6790\uff0c\u5e76\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\u8fdb\u884c\u4e86\u5206\u6790\u3002\u57fa\u4e8e\u4e00\u4e2a\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u4eba\u7fa4\u4e2d\u6d41\u884c\u75c5\u7684\u53c2\u6570\u5316\u6a21\u578b\uff0c\u6211\u4eec\u91cd\u70b9\u7814\u7a76\u4e86\u75be\u75c5\u7684\u4f20\u64ad\uff0c\u5e76\u7814\u7a76\u4e86\u6240\u5f97\u5230\u7684\u66f2\u7ebf\uff0c\u4ee5\u53ca\u56fd\u5bb6\u7279\u5f81\u548c\u53c2\u6570\u4e4b\u95f4\u7684\u53ef\u80fd\u76f8\u5173\u6027\u3002\u6211\u4eec\u8fd8\u53d1\u5c55\u4e86\u4e00\u79cd\u65b9\u6cd5\uff0c\u5c06\u6211\u4eec\u7684\u6a21\u578b\u8026\u5408\u5230\u57fa\u4e8e sir \u7684\u6a21\u578b\u4e2d\uff0c\u786e\u5b9a\u53c2\u6570\u7a7a\u95f4\u7684\u57fa\u672c\u548c\u6709\u6548\u518d\u751f\u6570\u3002\u6240\u5f97\u5230\u7684\u7ed3\u679c\u548c\u7ed3\u8bba\u5bf9\u4e8e\u8fd9\u79cd\u4ee4\u4eba\u538c\u6076\u7684\u75be\u75c5\u5728\u672a\u6765\u7684\u590d\u53d1\u53ef\u80fd\u662f\u6709\u7528\u7684\u3002<\/span><\/section>\n


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    \u79d1\u9645\u6574\u5408\u53ca\u6280\u672f\u5f71\u54cd<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Interdisciplinary research and technological impact<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15383<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Qing Ke<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Interdisciplinary research has been considered as a solution to today’s complex societal challenges. While its relationship with scientific impact has been extensively studied, the technological impact of interdisciplinary research remains unexplored. Here, we examine how interdisciplinarity is associated with technological impact at the paper level. We measure the degree of interdisciplinarity of a paper using three popular indicators, namely variety, balance, and disparity, and track how it gets cited by patented technologies over time. Drawing on a large sample of biomedical papers published in 18 years, we find that papers that cites more fields (variety) and whose distributions over those cited fields are more even (balance) are more likely to receive patent citations, but both effects can be offset if papers draw upon more distant fields (disparity). Those associations are consistent across different citation-window lengths. Additional analysis that focuses on the subset of papers with at least one patent citation reveals that the intensity of their technological impact, as measured as the number of patent citations, increases with balance and disparity. Our work may have policy implications for interdisciplinary research and scientific and technology impact.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u79d1\u9645\u6574\u5408\u4e00\u76f4\u88ab\u8ba4\u4e3a\u662f\u89e3\u51b3\u5f53\u4eca\u590d\u6742\u7684\u793e\u4f1a\u6311\u6218\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u867d\u7136\u5b83\u4e0e\u79d1\u5b66\u5f71\u54cd\u7684\u5173\u7cfb\u5df2\u7ecf\u88ab\u5e7f\u6cdb\u7814\u7a76\uff0c\u4f46\u662f\u79d1\u9645\u6574\u5408\u7684\u6280\u672f\u5f71\u54cd\u4ecd\u7136\u6ca1\u6709\u88ab\u63a2\u7d22\u3002\u5728\u8fd9\u91cc\uff0c\u6211\u4eec\u5c06\u4ece\u7eb8\u5f20\u5c42\u9762\u63a2\u8ba8\u79d1\u9645\u6574\u5408\u4e0e\u6280\u672f\u5f71\u54cd\u4e4b\u95f4\u7684\u5173\u7cfb\u3002\u6211\u4eec\u4f7f\u75283\u4e2a\u6d41\u884c\u7684\u6307\u6807\uff0c\u5373\u591a\u6837\u6027\u3001\u5e73\u8861\u6027\u548c\u5dee\u5f02\u6027\u6765\u8861\u91cf\u4e00\u7bc7\u8bba\u6587\u7684\u79d1\u9645\u6574\u5408\u7a0b\u5ea6\uff0c\u5e76\u8ddf\u8e2a\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u5b83\u662f\u5982\u4f55\u88ab\u4e13\u5229\u6280\u672f\u5f15\u7528\u7684\u3002\u57fa\u4e8e18\u5e74\u95f4\u53d1\u8868\u7684\u5927\u91cf\u751f\u7269\u533b\u5b66\u8bba\u6587\u6837\u672c\uff0c\u6211\u4eec\u53d1\u73b0\u5f15\u7528\u66f4\u591a\u9886\u57df(\u591a\u6837\u6027)\u7684\u8bba\u6587\u4ee5\u53ca\u5176\u5728\u8fd9\u4e9b\u88ab\u5f15\u7528\u9886\u57df\u7684\u5206\u5e03\u66f4\u5747\u5300(\u5e73\u8861)\u7684\u8bba\u6587\u66f4\u6709\u53ef\u80fd\u83b7\u5f97\u4e13\u5229\u5f15\u7528\uff0c\u4f46\u662f\u5982\u679c\u8bba\u6587\u5f15\u7528\u66f4\u8fdc\u7684\u9886\u57df(\u4e0d\u5e73\u8861) \uff0c\u8fd9\u4e24\u79cd\u5f71\u54cd\u90fd\u53ef\u4ee5\u88ab\u62b5\u6d88\u3002\u8fd9\u4e9b\u5173\u8054\u5728\u4e0d\u540c\u7684\u5f15\u7528\u7a97\u53e3\u957f\u5ea6\u4e0a\u662f\u4e00\u81f4\u7684\u3002\u5173\u6ce8\u81f3\u5c11\u6709\u4e00\u4e2a\u4e13\u5229\u5f15\u7528\u7684\u8bba\u6587\u5b50\u96c6\u7684\u9644\u52a0\u5206\u6790\u8868\u660e\uff0c\u5b83\u4eec\u7684\u6280\u672f\u5f71\u54cd\u529b\u7684\u5f3a\u5ea6—- \u4ee5\u4e13\u5229\u5f15\u7528\u7684\u6570\u91cf\u8861\u91cf—- \u968f\u7740\u5e73\u8861\u548c\u5dee\u5f02\u7684\u589e\u52a0\u800c\u589e\u52a0\u3002\u6211\u4eec\u7684\u5de5\u4f5c\u53ef\u80fd\u4f1a\u5bf9\u79d1\u9645\u6574\u5408\u548c\u79d1\u5b66\u6280\u672f\u7684\u5f71\u54cd\u4ea7\u751f\u653f\u7b56\u5f71\u54cd\u3002<\/span><\/section>\n

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    \u4f20\u67d3\u75c5\u4f20\u64ad\u7684\u65f6\u7a7a\u9884\u6d4b\u6a21\u578b\u6846\u67b6<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Spatio-temporal predictive modeling framework for infectious disease spread<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15336<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Sashikumaar Ganesan,Deepak Subramani<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>A novel predictive modeling framework for the spread of infectious diseases using high dimensional partial differential equations is developed and implemented. A scalar function representing the infected population is defined on a high-dimensional space and its evolution over all directions is described by a population balance equation (PBE). New infections are introduced among the susceptible population from non-quarantined infected population based on their interaction, adherence to distancing norms, hygiene levels and any other societal interventions. Moreover, recovery, death, immunity and all aforementioned parameters are modeled on the high-dimensional space. To epitomize the capabilities and features of the above framework, prognostic estimates of Covid-19 spread using a six-dimensional (time, 2D space, infection severity, duration of infection, and population age) PBE is presented. Further, scenario analysis for different policy interventions and population behavior is presented, throwing more insights into the spatio-temporal spread of infections across disease age, intensity and age of population. These insights could be used for science-informed policy planning.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u63d0\u51fa\u5e76\u5b9e\u73b0\u4e86\u4e00\u79cd\u57fa\u4e8e\u9ad8\u7ef4\u504f\u5fae\u5206\u65b9\u7a0b\u7684\u4f20\u67d3\u75c5\u6269\u6563\u9884\u6d4b\u6a21\u578b\u6846\u67b6\u3002\u5728\u9ad8\u7ef4\u7a7a\u95f4\u4e2d\u5b9a\u4e49\u4e86\u4e00\u4e2a\u8868\u793a\u88ab\u611f\u67d3\u79cd\u7fa4\u7684\u6807\u91cf\u51fd\u6570\uff0c\u5e76\u7528\u79cd\u7fa4\u5e73\u8861\u65b9\u7a0b(PBE)\u63cf\u8ff0\u5176\u5728\u5404\u4e2a\u65b9\u5411\u4e0a\u7684\u6f14\u5316\u8fc7\u7a0b\u3002\u6839\u636e\u76f8\u4e92\u4f5c\u7528\u3001\u9075\u5b88\u8ddd\u79bb\u89c4\u8303\u3001\u536b\u751f\u6c34\u5e73\u548c\u4efb\u4f55\u5176\u4ed6\u793e\u4f1a\u5e72\u9884\u63aa\u65bd\uff0c\u5728\u6613\u53d7\u611f\u67d3\u4eba\u53e3\u4e0e\u672a\u9694\u79bb\u7684\u53d7\u611f\u67d3\u4eba\u53e3\u4e4b\u95f4\u5f15\u5165\u65b0\u7684\u611f\u67d3\u3002\u6b64\u5916\uff0c\u6062\u590d\uff0c\u6b7b\u4ea1\uff0c\u514d\u75ab\u548c\u4e0a\u8ff0\u6240\u6709\u53c2\u6570\u5efa\u6a21\u7684\u9ad8\u7ef4\u7a7a\u95f4\u3002\u6982\u62ec\u4e0a\u8ff0\u6846\u67b6\u7684\u80fd\u529b\u548c\u7279\u70b9\uff0c\u4f7f\u7528\u4e00\u4e2a\u516d\u7ef4(\u65f6\u95f4\uff0c\u4e8c\u7ef4\u7a7a\u95f4\uff0c\u611f\u67d3\u4e25\u91cd\u7a0b\u5ea6\uff0c\u611f\u67d3\u6301\u7eed\u65f6\u95f4\u548c\u4eba\u53e3\u5e74\u9f84) PBE \u7684\u9884\u540e\u4f30\u8ba1\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u3002\u6b64\u5916\uff0c\u60c5\u666f\u5206\u6790\u7684\u4e0d\u540c\u653f\u7b56\u5e72\u9884\u548c\u4eba\u53e3\u884c\u4e3a\u63d0\u51fa\uff0c\u6295\u5165\u66f4\u591a\u7684\u65f6\u7a7a\u4f20\u64ad\u611f\u67d3\u7684\u75be\u75c5\u5e74\u9f84\uff0c\u5f3a\u5ea6\u548c\u5e74\u9f84\u7684\u4eba\u53e3\u3002\u8fd9\u4e9b\u89c1\u89e3\u53ef\u7528\u4e8e\u79d1\u5b66\u77e5\u60c5\u7684\u653f\u7b56\u89c4\u5212\u3002<\/span><\/section>\n

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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4<\/strong><\/span><\/p>\n

    \u7f8e\u56fd\u7a7a\u6c14\u8d28\u91cf\u548c\u4eba\u53e3\u6d41\u52a8\u7684\u53d8\u5316<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Changes in air quality and human mobility in the U.S. during the COVID-19 pandemic<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15279<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Cristina L. Archer,Guido Cervone,Maryam Golbazi,Nicolas Al Fahel,Carolynne Hultquist<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>The first goal of this study is to quantify the magnitude and spatial variability of air quality changes in the US during the COVID-19 pandemic. We focus on two federally regulated pollutants, nitrogen dioxide (NO2), and fine particulate matter (PM2.5). Observed concentrations at all available ground monitoring sites (240 and 480 for NO2 and PM2.5, respectively) were compared between April 2020 and April of the prior five years, 2015-2019, as the baseline. Large statistically significant decreases in NO2 concentrations were found at more than 65% of the monitoring sites, with an average drop of 2 ppb when compared to the mean of the previous five years. The same patterns are confirmed by satellite-derived NO2 column totals from NASA OMI. PM2.5 concentrations from the ground monitoring sites, however, were more likely to be higher. The second goal of this study is to explain the different responses of the two pollutants during the COVID-19 pandemic. The hypothesis put forward is that the shelter-in-place measures affected peoples’ driving patterns most dramatically, thus passenger vehicle NO2 emissions were reduced. Commercial vehicles and electricity demand for all purposes remained relatively unchanged, thus PM2.5 concentrations did not drop significantly. To establish a correlation between the observed NO2 changes and the extent to which people were sheltering in place, we use a mobility index, which was produced and made public by Descartes Labs. This mobility index aggregates cell phone usage at the county level to capture changes in human movement over time. We found a strong correlation between the observed decreases in NO2 concentrations and decreases in human mobility. By contrast, no discernible pattern was detected between mobility and PM2.5 concentrations changes, suggesting that decreases in personal-vehicle traffic alone may not be effective at reducing PM2.5 pollution.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u8fd9\u9879\u7814\u7a76\u7684\u7b2c\u4e00\u4e2a\u76ee\u6807\u662f\u91cf\u5316\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c\u671f\u95f4\u7f8e\u56fd\u7a7a\u6c14\u8d28\u91cf\u53d8\u5316\u7684\u89c4\u6a21\u548c\u7a7a\u95f4\u53d8\u5f02\u6027\u3002\u6211\u4eec\u91cd\u70b9\u7814\u7a76\u4e86\u4e24\u79cd\u8054\u90a6\u653f\u5e9c\u89c4\u5b9a\u7684\u6c61\u67d3\u7269: \u4e8c\u6c27\u5316\u6c2e(NO2)\u548c\u7ec6\u9897\u7c92\u7269(PM2.5)\u3002\u5c062020\u5e744\u6708\u81f32015-2019\u5e74\u524d\u4e94\u5e74\u76844\u6708\u671f\u95f4\u5728\u6240\u6709\u53ef\u7528\u5730\u9762\u76d1\u6d4b\u70b9(NO2\u548c PM2.5\u5206\u522b\u4e3a240\u548c480)\u7684\u89c2\u6d4b\u6d53\u5ea6\u4f5c\u4e3a\u57fa\u51c6\u3002\u572865% \u4ee5\u4e0a\u7684\u76d1\u6d4b\u70b9\u53d1\u73b0 NO2\u6d53\u5ea6\u6709\u663e\u8457\u7684\u7edf\u8ba1\u663e\u8457\u4e0b\u964d\uff0c\u4e0e\u524d\u4e94\u5e74\u7684\u5e73\u5747\u503c\u76f8\u6bd4\uff0c\u5e73\u5747\u4e0b\u964d\u4e862 ppb\u3002\u7f8e\u56fd\u5b87\u822a\u5c40\u8fd1\u5730\u89c2\u6d4b\u536b\u661f\u63d0\u4f9b\u7684 NO2\u5217\u603b\u6570\u4e5f\u8bc1\u5b9e\u4e86\u540c\u6837\u7684\u6a21\u5f0f\u3002\u7136\u800c\uff0c\u6765\u81ea\u5730\u9762\u76d1\u6d4b\u70b9\u7684 PM2.5\u6d53\u5ea6\u66f4\u53ef\u80fd\u66f4\u9ad8\u3002\u8fd9\u9879\u7814\u7a76\u7684\u7b2c\u4e8c\u4e2a\u76ee\u6807\u662f\u89e3\u91ca\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u671f\u95f4\u8fd9\u4e24\u79cd\u6c61\u67d3\u7269\u7684\u4e0d\u540c\u53cd\u5e94\u3002\u63d0\u51fa\u7684\u5047\u8bbe\u662f\uff0c\u5c31\u5730\u907f\u96be\u63aa\u65bd\u5bf9\u4eba\u4eec\u7684\u9a7e\u9a76\u6a21\u5f0f\u5f71\u54cd\u6700\u5927\uff0c\u4ece\u800c\u51cf\u5c11\u4e86\u4e58\u7528\u8f66\u4e8c\u6c27\u5316\u6c2e\u7684\u6392\u653e\u3002\u5404\u79cd\u7528\u9014\u7684\u5546\u7528\u8f66\u8f86\u548c\u7535\u529b\u9700\u6c42\u4fdd\u6301\u76f8\u5bf9\u4e0d\u53d8\uff0c\u56e0\u6b64 PM2.5\u6d53\u5ea6\u6ca1\u6709\u663e\u8457\u4e0b\u964d\u3002\u4e3a\u4e86\u5efa\u7acb\u89c2\u5bdf\u5230\u7684 NO2\u53d8\u5316\u548c\u4eba\u4eec\u5728\u907f\u96be\u6240\u7684\u7a0b\u5ea6\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u6211\u4eec\u4f7f\u7528\u4e86\u7b1b\u5361\u5c14\u5b9e\u9a8c\u5ba4\u5236\u4f5c\u5e76\u516c\u5e03\u7684\u6d41\u52a8\u6027\u6307\u6570\u3002\u8fd9\u4e00\u6d41\u52a8\u6027\u6307\u6570\u6c47\u603b\u4e86\u53bf\u4e00\u7ea7\u7684\u624b\u673a\u4f7f\u7528\u60c5\u51b5\uff0c\u4ee5\u6355\u6349\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u4eba\u7c7b\u6d3b\u52a8\u7684\u53d8\u5316\u3002\u6211\u4eec\u53d1\u73b0\u89c2\u5bdf\u5230\u7684 NO2\u6d53\u5ea6\u4e0b\u964d\u4e0e\u4eba\u7c7b\u6d3b\u52a8\u80fd\u529b\u4e0b\u964d\u4e4b\u95f4\u6709\u5f88\u5f3a\u7684\u76f8\u5173\u6027\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u5728\u6d41\u52a8\u6027\u548c PM2.5\u6d53\u5ea6\u53d8\u5316\u4e4b\u95f4\u6ca1\u6709\u53d1\u73b0\u660e\u663e\u7684\u6a21\u5f0f\uff0c\u8fd9\u8868\u660e\u4ec5\u4ec5\u51cf\u5c11\u79c1\u4eba\u8f66\u8f86\u4ea4\u901a\u53ef\u80fd\u65e0\u6cd5\u6709\u6548\u5730\u51cf\u5c11 PM2.5\u6c61\u67d3\u3002<\/span><\/section>\n

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    \u5df4\u897f\u7684 SARS-CoV-2<\/strong><\/span><\/p>\n

    \u4e0d\u786e\u5b9a\u6027\u4e09\u811a\u67b6: \u5bf9\u5927\u91cf<\/strong><\/strong><\/span><\/p>\n

    \u6f0f\u62a5<\/strong><\/strong>\u7684\u57fa\u4e8e\u6a21\u578b\u7684\u9884\u6d4b\u7684\u8bc4\u4f30<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    The COVID-19 (SARS-CoV-2) Uncertainty Tripod in Brazil: Assessments on model-based predictions with large under-reporting<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15268<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Saulo B. Bastos,Marcelo M. Morato,Daniel O. Cajueiro anda Julio E Normey-Rico<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>The COVID-19 pandemic (SARS-CoV-2 virus) is the defying global health crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We propose an adapted Susceptible-Infected-Recovered (SIR) model which explicitly incorporates the under-reporting and the response of the population to public policies (such as confinement measures, widespread use of masks, etc) to cast short-term and long-term predictions. Large amounts of uncertainty could provide misleading models and predictions. In this paper, we discuss the role of uncertainty in these prediction, which is illustrated regarding three key aspects. First, assuming that the number of infected individuals is under-reported, we demonstrate an anticipation regarding the peak of infection. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic. Second, considering that the actual amount of deaths is larger than what is being register, then demonstrate the increase of the mortality rates. Third, when consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the effect of the “COVID-19 under-reporting tripod”, i.e. the under-reporting in terms of infected individuals, of deaths and the true mortality rate. If two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u75c5\u6bd2\u5927\u6d41\u884c(SARS-CoV-2\u75c5\u6bd2)\u662f\u6211\u4eec\u8fd9\u4e2a\u65f6\u4ee3\u7684\u5168\u7403\u5065\u5eb7\u5371\u673a\u3002\u7531\u4e8e\u7f3a\u4e4f\u5927\u89c4\u6a21\u68c0\u6d4b\u4ee5\u53ca\u65e0\u75c7\u72b6\u4e2a\u4f53\u7684\u76f8\u5173\u5b58\u5728\uff0c\u5df4\u897f\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u7684\u73b0\u6709\u6570\u636e\u5728\u611f\u67d3\u4eba\u6570\u548c\u6b7b\u4ea1\u4eba\u6570\u65b9\u9762\u5927\u591a\u88ab\u4f4e\u62a5\u3002\u6211\u4eec\u63d0\u51fa\u4e00\u4e2a\u7ecf\u8fc7\u8c03\u6574\u7684\u6613\u611f\u67d3-\u611f\u67d3-\u5eb7\u590d(SIR)\u6a21\u578b\uff0c\u8be5\u6a21\u578b\u660e\u786e\u7eb3\u5165\u4e86\u6f0f\u62a5\u548c\u4eba\u53e3\u5bf9\u516c\u5171\u653f\u7b56(\u4f8b\u5982\u9650\u5236\u63aa\u65bd\u3001\u5e7f\u6cdb\u4f7f\u7528\u53e3\u7f69\u7b49)\u7684\u53cd\u5e94\uff0c\u4ee5\u8fdb\u884c\u77ed\u671f\u548c\u957f\u671f\u9884\u6d4b\u3002\u5927\u91cf\u7684\u4e0d\u786e\u5b9a\u6027\u53ef\u80fd\u4f1a\u63d0\u4f9b\u8bef\u5bfc\u6027\u7684\u6a21\u578b\u548c\u9884\u6d4b\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u8ba8\u8bba\u4e86\u4e0d\u786e\u5b9a\u6027\u5728\u8fd9\u4e9b\u9884\u6d4b\u4e2d\u7684\u4f5c\u7528\uff0c\u5e76\u4ece\u4e09\u4e2a\u5173\u952e\u65b9\u9762\u52a0\u4ee5\u8bf4\u660e\u3002\u9996\u5148\uff0c\u5047\u8bbe\u611f\u67d3\u4eba\u6570\u88ab\u4f4e\u62a5\uff0c\u6211\u4eec\u8868\u73b0\u51fa\u5bf9\u611f\u67d3\u9ad8\u5cf0\u671f\u7684\u9884\u671f\u3002\u6b64\u5916\uff0c\u867d\u7136\u53ea\u6709\u4e00\u7c7b\u611f\u67d3\u8005\u7684\u6a21\u578b\u4ea7\u751f\u5cf0\u503c\u589e\u52a0\u7684\u9884\u6d4b\uff0c\u4f46\u662f\u540c\u65f6\u8003\u8651\u6709\u75c7\u72b6\u548c\u65e0\u75c7\u72b6\u611f\u67d3\u8005\u7684\u6a21\u578b\u8868\u660e\u75c7\u72b6\u5cf0\u503c\u51cf\u5c11\u3002\u5176\u6b21\uff0c\u8003\u8651\u5230\u5b9e\u9645\u6b7b\u4ea1\u4eba\u6570\u5927\u4e8e\u767b\u8bb0\u7684\u4eba\u6570\uff0c\u7136\u540e\u8bc1\u660e\u6b7b\u4ea1\u7387\u7684\u589e\u52a0\u3002\u7b2c\u4e09\uff0c\u5f53\u8003\u8651\u4e00\u822c\u7684\u6f0f\u62a5\u6570\u636e\u65f6\uff0c\u6211\u4eec\u6f14\u793a\u4e86\u4f20\u8f93\u548c\u6062\u590d\u7387\u6a21\u578b\u53c2\u6570\u5982\u4f55\u5b9a\u6027\u548c\u5b9a\u91cf\u5730\u53d8\u5316\u3002\u6211\u4eec\u8fd8\u8c03\u67e5\u4e86\u201c\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5065\u5eb7\u62a5\u544a\u4e0d\u8db3\u4e09\u811a\u67b6\u201d\u7684\u5f71\u54cd\uff0c\u5373\u5728\u611f\u67d3\u4e2a\u4f53\u3001\u6b7b\u4ea1\u548c\u771f\u5b9e\u6b7b\u4ea1\u7387\u65b9\u9762\u7684\u62a5\u544a\u4e0d\u8db3\u3002\u5982\u679c\u8fd9\u4e9b\u56e0\u7d20\u4e2d\u6709\u4e24\u4e2a\u662f\u5df2\u77e5\u7684\uff0c\u53ea\u8981\u6bd4\u4f8b\u4fdd\u6301\u4e0d\u53d8\uff0c\u5c31\u53ef\u4ee5\u63a8\u65ad\u51fa\u5176\u4f59\u90e8\u5206\u3002\u62df\u8bae\u7684\u529e\u6cd5\u4f7f\u4eba\u4eec\u80fd\u591f\u901a\u8fc7\u8bc4\u4f30\u89c2\u5bdf\u5230\u7684\u548c\u771f\u5b9e\u7684\u6b7b\u4ea1\u7387\u6765\u786e\u5b9a\u4e0d\u786e\u5b9a\u7684\u7a0b\u5ea6\u3002<\/span><\/section>\n

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    Axelrod \u6a21\u578b\u7684\u4e00\u79cd\u65b0\u7684\u89e3\u6790\u516c\u5f0f<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    A novel analytical formulation of the Axelrod model<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15241<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Luc\u00eda Pedraza,Sebasti\u00e1n Pinto,Juan Pablo Pinasco,Pablo Balenzuela<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>The Axelrod model of cultural dissemination has been widely studied in the field of statistical mechanics. The traditional version of this agent-based model is to assign a cultural vector of<\/span><\/nobr>F components to each agent, where each component can take one of <\/span><\/nobr>Q cultural trait. In this work, we introduce a novel set of mean field master equations to describe the model for <\/span><\/nobr>F=2 and <\/span><\/nobr>F=3 in complete graphs where all indirect interactions are explicitly calculated. We find that the transition between different macroscopic states is driven by initial conditions (set by parameter <\/span><\/nobr>Q) and the size of the system <\/span><\/nobr>N, who measures the balance between linear and cubic terms in master equations. We also find that this analytical approach fully agrees with simulations where the system does not break up during the dynamics and a scaling relation related to missing links reestablishes the agreement when this happens.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u6587\u5316\u4f20\u64ad\u7684\u963f\u514b\u585e\u5c14\u7f57\u5fb7\u6a21\u5f0f\u5728\u7edf\u8ba1\u529b\u5b66\u9886\u57df\u5f97\u5230\u4e86\u5e7f\u6cdb\u7684\u7814\u7a76\u3002\u8fd9\u4e2a\u4e2a\u4f53\u4e3a\u672c\u6a21\u578b\u7684\u4f20\u7edf\u7248\u672c\u662f\u6307\u5b9a\u4e00\u4e2a\u6587\u5316\u8f7d\u4f53<\/span><\/nobr>F \u6bcf\u4e2a\u7ec4\u4ef6\u53ef\u4ee5\u4ece\u6bcf\u4e2a\u4ee3\u7406\u4e2d<\/span><\/nobr>Q \u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u5f15\u5165\u4e00\u7ec4\u5e73\u5747\u573a\u4e3b\u65b9\u7a0b\u6765\u63cf\u8ff0\u6a21\u578b<\/span><\/nobr>F=2 \u53ca<\/span><\/nobr>F=3 \uff08\u5728\u5b8c\u5168\u56fe\u4e2d\uff0c\u6240\u6709\u7684\u95f4\u63a5\u76f8\u4e92\u4f5c\u7528\u90fd\u88ab\u660e\u786e\u8ba1\u7b97\u51fa\u6765\u3002\u6211\u4eec\u53d1\u73b0\u4e0d\u540c\u5b8f\u89c2\u72b6\u6001\u4e4b\u95f4\u7684\u8f6c\u53d8\u662f\u7531\u521d\u59cb\u6761\u4ef6(\u53c2\u6570\u8bbe\u7f6e)\u9a71\u52a8\u7684<\/span><\/nobr>Q)\u53ca\u7cfb\u7edf\u7684\u89c4\u6a21<\/span><\/nobr>N,\u4ed6\u5728\u4e3b\u65b9\u7a0b\u4e2d\u6d4b\u91cf\u4e86\u7ebf\u6027\u9879\u548c\u7acb\u65b9\u9879\u4e4b\u95f4\u7684\u5e73\u8861\u3002\u6211\u4eec\u8fd8\u53d1\u73b0\uff0c\u8fd9\u79cd\u5206\u6790\u65b9\u6cd5\u5b8c\u5168\u7b26\u5408\u6a21\u62df\u7684\u60c5\u51b5\uff0c\u5373\u7cfb\u7edf\u5728\u52a8\u6001\u8fc7\u7a0b\u4e2d\u4e0d\u4f1a\u5d29\u6e83\uff0c\u4e0e\u7f3a\u5931\u73af\u8282\u6709\u5173\u7684\u6807\u5ea6\u5173\u7cfb\u5728\u53d1\u751f\u8fd9\u79cd\u60c5\u51b5\u65f6\u91cd\u65b0\u5efa\u7acb\u4e86\u4e00\u81f4\u6027\u3002<\/span><\/section>\n


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    \u4f7f\u7528\u4f4d\u7f6e\u6570\u636e\u63ed\u793a<\/strong><\/span><\/p>\n

    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u611f\u5927\u6d41\u884c<\/strong><\/span><\/p>\n

    \u671f\u95f4\u6d41\u52a8\u6027\u51cf\u5c11\u7684\u793e\u4f1a\u7ecf\u6d4e\u5dee\u8ddd<\/strong><\/span><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f<\/strong>\u6587\u6807\u9898\uff1a<\/strong><\/span><\/h2>\n

    Uncovering socioeconomic gaps in mobility reduction during the COVID-19 pandemic using location data<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15195<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Samuel P. Fraiberger,Pablo Astudillo,Lorenzo Candeago,Alex Chunet,Nicholas K. W. Jones,Maham Faisal Khan,Bruno Lepri,Nancy Lozano Gracia,Lorenzo Lucchini,Emanuele Massaro,Aleister Montfort<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Using smartphone location data from Colombia, Mexico, and Indonesia, we investigate how non-pharmaceutical policy interventions intended to mitigate the spread of the COVID-19 pandemic impact human mobility. In all three countries, we find that following the implementation of mobility restriction measures, human movement decreased substantially. Importantly, we also uncover large and persistent differences in mobility reduction between wealth groups: on average, users in the top decile of wealth reduced their mobility up to twice as much as users in the bottom decile. For decision-makers seeking to efficiently allocate resources to response efforts, these findings highlight that smartphone location data can be leveraged to tailor policies to the needs of specific socioeconomic groups, especially the most vulnerable.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u901a\u8fc7\u4f7f\u7528\u6765\u81ea\u54e5\u4f26\u6bd4\u4e9a\u3001\u58a8\u897f\u54e5\u548c\u5370\u5ea6\u5c3c\u897f\u4e9a\u7684\u667a\u80fd\u624b\u673a\u5b9a\u4f4d\u6570\u636e\uff0c\u6211\u4eec\u8c03\u67e5\u4e86\u65e8\u5728\u51cf\u7f13\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u6d41\u884c\u75c5\u8513\u5ef6\u7684\u975e\u836f\u7269\u653f\u7b56\u5e72\u9884\u63aa\u65bd\u5bf9\u4eba\u7c7b\u6d41\u52a8\u6027\u7684\u5f71\u54cd\u3002\u5728\u8fd9\u4e09\u4e2a\u56fd\u5bb6\u4e2d\uff0c\u6211\u4eec\u53d1\u73b0\u968f\u7740\u6d41\u52a8\u9650\u5236\u63aa\u65bd\u7684\u5b9e\u65bd\uff0c\u4eba\u5458\u6d41\u52a8\u663e\u8457\u51cf\u5c11\u3002\u91cd\u8981\u7684\u662f\uff0c\u6211\u4eec\u8fd8\u53d1\u73b0\u4e86\u4e0d\u540c\u8d22\u5bcc\u7fa4\u4f53\u4e4b\u95f4\u5728\u6d41\u52a8\u6027\u51cf\u5c11\u65b9\u9762\u7684\u5de8\u5927\u800c\u6301\u4e45\u7684\u5dee\u5f02: \u5e73\u5747\u800c\u8a00\uff0c\u8d22\u5bcc\u9876\u5c4210\u5206\u4f4d\u6570\u7684\u7528\u6237\u6d41\u52a8\u6027\u51cf\u5c11\u7684\u5e45\u5ea6\u662f\u5e95\u5c4210\u5206\u4f4d\u6570\u7528\u6237\u7684\u4e24\u500d\u3002\u5bf9\u4e8e\u5bfb\u6c42\u6709\u6548\u5206\u914d\u8d44\u6e90\u5e94\u5bf9\u5de5\u4f5c\u7684\u51b3\u7b56\u8005\u6765\u8bf4\uff0c\u8fd9\u4e9b\u8c03\u67e5\u7ed3\u679c\u7a81\u51fa\u8868\u660e\uff0c\u53ef\u4ee5\u5229\u7528\u667a\u80fd\u624b\u673a\u7684\u4f4d\u7f6e\u6570\u636e\u6765\u8c03\u6574\u653f\u7b56\uff0c\u4ee5\u6ee1\u8db3\u7279\u5b9a\u793e\u4f1a\u7ecf\u6d4e\u7fa4\u4f53\uff0c\u7279\u522b\u662f\u6700\u5f31\u52bf\u7fa4\u4f53\u7684\u9700\u6c42\u3002<\/span><\/section>\n


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    C19-tranet: <\/strong><\/span><\/p>\n

    SARS-CoV-2\u5168\u7403\u7d22\u5f15\u75c5\u4f8b\u4f20\u64ad\u7f51\u7edc<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/span><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


    <\/section>\n

    \u539f\u6587\u6807\u9898\uff1a<\/strong>
    <\/span><\/h2>\n

    C19-TraNet: an empirical, global index-case transmission network of SARS-CoV-2<\/span><\/h2>\n

    \u5730\u5740\uff1a<\/strong><\/span><\/h2>\n
    http:\/\/arxiv.org\/abs\/2006.15162<\/span><\/section>\n
    \u4f5c\u8005\uff1a<\/strong><\/span><\/section>\n
    Vikram Singh,Vikram Singh<\/span><\/section>\n

    <\/section>\n
    Abstract\uff1a<\/strong>Originating in Wuhan, the novel coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to its rapid and simultaneous spread to the neighboring and distantly located countries. To gain the systems level understanding of the role of global transmission routes in the COVID-19 spread, in this study, we have developed the first, empirical, global, index-case transmission network of SARS-CoV-2 termed as C19-TraNet. We manually curated the travel history of country wise index-cases using government press releases, their official social media handles and online news reports to construct this C19-TraNet that is a spatio-temporal, sparse, growing network comprising of 187 nodes and 199 edges and follows a power-law degree distribution. To model the growing C19-TraNet, a novel stochastic scale free (SSF) algorithm is proposed that accounts for stochastic addition of both nodes as well as edges at each time step. A peculiar connectivity pattern in C19-TraNet is observed, characterized by a fourth degree polynomial growth curve, that significantly diverges from the average random connectivity pattern obtained from an ensemble of its 1,000 SSF realizations. Partitioning the C19-TraNet, using edge betweenness, it is found that most of the large communities are comprised of a heterogeneous mixture of countries belonging to different world regions suggesting that there are no spatial constraints on the spread of disease. This work characterizes the superspreaders that have very quickly transported the virus, through multiple transmission routes, to long range geographical locations alongwith their local neighborhoods.<\/span><\/section>\n
    \u6458\u8981\uff1a<\/strong>\u8fd9\u79cd\u540d\u4e3a SARS-CoV-2\u7684\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u8d77\u6e90\u4e8e\u6b66\u6c49\uff0c\u7531\u4e8e\u5176\u8fc5\u901f\u5e76\u540c\u65f6\u4f20\u64ad\u5230\u90bb\u56fd\u548c\u8ddd\u79bb\u8f83\u8fdc\u7684\u56fd\u5bb6\uff0c\u9707\u60ca\u4e86\u5168\u7403\u7684\u533b\u7597\u4fdd\u5065\u7cfb\u7edf\u3002\u4e3a\u4e86\u5728\u7cfb\u7edf\u6c34\u5e73\u4e0a\u7406\u89e3\u5168\u7403\u4f20\u64ad\u8def\u5f84\u5728\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u4e2d\u7684\u4f5c\u7528\uff0c\u5728\u8fd9\u9879\u7814\u7a76\u4e2d\uff0c\u6211\u4eec\u5f00\u53d1\u4e86\u7b2c\u4e00\u4e2a\uff0c\u7ecf\u9a8c\u7684\uff0c\u5168\u7403\u7684\uff0c\u6307\u6570\u60c5\u51b5\u4e0b\u7684 SARS-CoV-2\u4f20\u64ad\u7f51\u7edc\u79f0\u4e3a C19-TraNet\u3002\u6211\u4eec\u4f7f\u7528\u653f\u5e9c\u65b0\u95fb\u7a3f\u3001\u5176\u5b98\u65b9\u793e\u4ea4\u5a92\u4f53\u5904\u7406\u548c\u5728\u7ebf\u65b0\u95fb\u62a5\u9053\u624b\u5de5\u6574\u7406\u4e86\u5404\u56fd\u7d22\u5f15\u6848\u4f8b\u7684\u65c5\u884c\u5386\u53f2\uff0c\u6784\u5efa\u4e86\u8fd9\u4e2a C19-TraNet \u7f51\u7edc\uff0c\u8be5\u7f51\u7edc\u662f\u4e00\u4e2a\u65f6\u7a7a\u3001\u7a00\u758f\u3001\u4e0d\u65ad\u589e\u957f\u7684\u7f51\u7edc\uff0c\u7531187\u4e2a\u8282\u70b9\u548c199\u6761\u8fb9\u7ec4\u6210\uff0c\u9075\u5faa\u5e42\u5f8b\u5ea6\u5206\u5e03\u3002\u4e3a\u4e86\u5bf9\u751f\u957f\u4e2d\u7684 C19-TraNet \u8fdb\u884c\u5efa\u6a21\uff0c\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u968f\u673a\u65e0\u6807\u5ea6(SSF)\u7b97\u6cd5\uff0c\u8be5\u7b97\u6cd5\u8003\u8651\u4e86\u8282\u70b9\u548c\u8fb9\u5728\u6bcf\u4e2a\u65f6\u95f4\u6b65\u7684\u968f\u673a\u76f8\u52a0\u3002\u5728 C19-TraNet \u4e2d\u89c2\u5bdf\u5230\u4e00\u79cd\u7279\u6b8a\u7684\u8fde\u901a\u6027\u6a21\u5f0f\uff0c\u62e5\u6709\u5c5e\u6027\u4e3a\u4e00\u4e2a\u56db\u6b21\u591a\u9879\u5f0f\u589e\u957f\u66f2\u7ebf\uff0c\u8fd9\u79cd\u8fde\u901a\u6027\u6a21\u5f0f\u4e0e\u51761000\u4e2a SSF \u5b9e\u73b0\u7684\u96c6\u5408\u6240\u5f97\u5230\u7684\u5e73\u5747\u968f\u673a\u8fde\u901a\u6027\u6a21\u5f0f\u5b58\u5728\u663e\u8457\u5dee\u5f02\u3002\u5bf9 C19-TraNet \u8fdb\u884c\u8fb9\u754c\u5206\u5272\uff0c\u53d1\u73b0\u5927\u591a\u6570\u5927\u578b\u793e\u533a\u7531\u5c5e\u4e8e\u4e0d\u540c\u4e16\u754c\u533a\u57df\u7684\u4e0d\u540c\u56fd\u5bb6\u6df7\u5408\u7ec4\u6210\uff0c\u8fd9\u8868\u660e\u75be\u75c5\u7684\u4f20\u64ad\u6ca1\u6709\u7a7a\u95f4\u9650\u5236\u3002\u8fd9\u9879\u5de5\u4f5c\u63cf\u8ff0\u4e86\u8d85\u7ea7\u4f20\u64ad\u8005\u7684\u7279\u5f81\uff0c\u4ed6\u4eec\u901a\u8fc7\u591a\u79cd\u4f20\u64ad\u9014\u5f84\uff0c\u5c06\u75c5\u6bd2\u8fc5\u901f\u4f20\u64ad\u5230\u8fdc\u8ddd\u79bb\u7684\u5730\u7406\u4f4d\u7f6e\u4ee5\u53ca\u4ed6\u4eec\u7684\u90bb\u8fd1\u5730\u533a\u3002<\/span><\/section>\n


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    \u6765\u6e90\uff1a\u96c6\u667a\u6591\u56fe<\/section>\n
    \u7f16\u8f91\uff1a\u738b\u5efa\u840d<\/section>\n<\/section>\n<\/blockquote>\n
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    \u8fd1\u671f\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u9012<\/strong><\/span><\/strong><\/p>\n

    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n


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    <\/strong>GPT-GNN: \u56fe\u5f62\u795e\u7ecf\u7f51\u7edc\u7684\u751f\u6210\u6027\u9884\u8bad\u7ec3 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901235\u7bc7<\/span><\/a>
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    <\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

    \u5173\u95ed\u548c\u91cd\u65b0\u5f00\u653e: \u5b66\u6821\u5728\u6b27\u6d32\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u4f20\u64ad\u4e2d\u7684\u4f5c\u7528 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901233\u7bc7<\/span><\/strong><\/a>
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    \u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5728\u4e0d\u540c\u793e\u533a\u4f20\u64ad\u7684 SIR \u6a21\u578b\u5047\u8bbe | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901230\u7bc7<\/strong><\/a>
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    \u5229\u7528\u77ac\u6001\u52a8\u529b\u5b66\u548c\u6270\u52a8\uff0c\u63a8\u5bfc\u52a8\u529b\u7cfb\u7edf\u56e0\u679c\u7f51\u7edc |\u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901225\u7bc7<\/strong><\/a>
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    \u5b66\u4e60\u590d\u6742\u591a\u5c3a\u5ea6\u7cfb\u7edf\u7684\u6709\u6548\u52a8\u529b\u5b66 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901214\u7bc7<\/strong><\/a>
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    \u79bb\u6563\u56fe\u6a21\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5b66\u4e60 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901221\u7bc7<\/strong><\/a>
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    \u96c6\u667a\u4ff1\u4e50\u90e8QQ\u7fa4\uff5c877391004<\/span><\/strong><\/p>\n

    \u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org
    <\/span><\/strong><\/p>\n

    \n

    \u25c6 <\/span>\u25c6 <\/span>\u25c6<\/span><\/span><\/strong><\/strong><\/h1>\n<\/section>\n

    \u641c\u7d22\u516c\u4f17\u53f7\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/span><\/p>\n


    <\/p>\n

    \u52a0\u5165\u201c\u6ca1\u6709\u56f4\u5899\u7684\u7814\u7a76\u6240\u201d<\/span><\/strong><\/span><\/p>\n

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    <\/p>\n<\/section>\n

    \u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section><\/div>\n","protected":false},"excerpt":{"rendered":"

    \u672c\u6587\u7531\u673a\u5668\u7ffb\u8bd1\uff0c\u4ec5\u4f9b\u53c2\u8003\uff0c\u611f\u5174\u8da3\u8bf7\u67e5\u9605\u8bba\u6587\u539f\u6587 \u6838\u5fc3\u901f\u9012 \u793e\u4f1a\u4e92\u52a8\u7684\u7f51\u7edc\u7ed3\u6784\u52a8\u529b\u5b66\u6a21\u578b\uff1b \u5c01\u9501\u5bf9\u57ce\u5e02\u95f4\u6d41\u52a8\u6027\u7684\u5f02\u8d28\u6027\u5f71\u54cd\uff1b \u6709\u8272\u566a\u58f0\u65f6\u95f4\u5e8f\u5217\u4f5c\u4e3a\u4eba\u5de5\u8fdb\u5316\u7cfb\u7edf\u4e2d\u73af\u5883\u53d8\u5316\u7684\u5408\u9002\u6a21\u578b\uff1b \u5229\u7528\u6d41\u52a8\u6027\u6570\u636e\u8bbe\u8ba1\u5e94\u5bf9\u82f1\u683c\u5170\u65b0\u578b\u51a0\u72b6\u75c5\u6bd2\u80ba\u708e\u5927\u6d41\u884c\u7684\u6700\u4f73\u5c01\u9501\u7b56\u7565\uff1b \u57fa\u4e8e\u7a7a\u8154\u4e3b\u65b9\u7a0b\u7684\u4f20\u67d3\u75c5\u6a21\u578b\u52a8\u529b\u5b66\uff1b \u7f51\u7edc\u5143\u4eba\u53e3\u6d41\u884c\u75c5…<\/p>\n","protected":false},"author":1,"featured_media":20260,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20262"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=20262"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/20262\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/20260"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=20262"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=20262"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=20262"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=20262"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}