含时输电网络的空间随机SIR模型 适用于中国新冠肺炎疫情

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摘要翻译：
世界各国政府已经实施了预防措施来防止冠状病毒疾病（新冠肺炎）的传播。在这项研究中，我们考虑了一个多元离散时间马尔可夫模型来分析新冠病毒在中国33个省级地区的传播。这种方法使我们能够评估流动限制政策对疾病传播的影响。我们使用跨地区的日常人员流动数据，并应用贝叶斯框架来估计所提出的模型。结果表明，该疾病在中国的传播主要是由区域内的社区传播驱动的，地方政府采取的封锁政策抑制了该流行病的传播。进一步，我们记录湖北只是疫情早期阶段的震中。到2020年1月下旬，北京和广东等次级震中已经建立，疾病蔓延到相关地区。在实施跨区域人员流动限制后，这些震中的传播大幅下降。
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英文标题：
《A Spatial Stochastic SIR Model for Transmission Networks with
  Application to COVID-19 Epidemic in China》
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作者：
Tatsushi Oka and Wei Wei and Dan Zhu
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Populations and Evolution        种群与进化
分类描述：Population dynamics, spatio-temporal and epidemiological models, dynamic speciation, co-evolution, biodiversity, foodwebs, aging; molecular evolution and phylogeny; directed evolution; origin of life
种群动力学；时空和流行病学模型；动态物种形成；协同进化；生物多样性；食物网；老龄化；分子进化和系统发育；定向进化；生命起源
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
--
一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

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英文摘要：
  Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions.
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PDF链接：
https://arxiv.org/pdf/2008.06051

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关键词：Sir Transmission Quantitative Biodiversity epidemiology 肺炎 spread epicenters was across