共查询到18条相似文献,搜索用时 156 毫秒
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《应用数学学报》2020,(2)
2019年底武汉突发的新型冠状病毒疾病(COVID-19)疫情已进入"全球大流行"状态.本文基于Li等(New England Journal of Medicine,2020,382:1199-1207)关于2019年12月10日至2020年1月4日期间武汉最早的425例确诊病例数据,使用似然函数估计方法得到了COVID-19疫情的基本再生数为2.42,平均序列间隔为8.85天.为了刻画疾病瞬时传播能力的地域差异,利用湖北省内各地市和中国境内各省市的每日疫情报告数据(来自各省市卫生健康委员会网站),由统计模型得到了各地在2020年2月间的每日瞬时再生数估计值.进一步,基于瞬时再生数定义了一个新的疫情控制效能公式,定性评估了各地区采取的防控策略对COVID-19实时传播能力的影响.结果提示现阶段境外输入病例造成国内疫情复发的风险依然很大,COVID-19传播具有较明显的地域差异,但引起差异的因素值得后续进一步持续关注. 相似文献
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《中国科学:数学》2020,(7)
基于全国和湖北省新型冠状病毒肺炎(COVID-19)疫情报告数据以及百度人口迁徙与分布大数据,本文构建武汉及周边15个疫情严重城市的COVID-19传播复杂网络模型,重点分析武汉及周边地区复工的可能时间节点和复工对二次暴发风险的影响.首先基于各个城市的累计病例数估计1月23日武汉的累计病例数,得到不同时期湖北省16个主要城市控制再生数的估计值,揭示了早期的传播风险较大和目前的传播风险小(控制再生数的值小于1).本文基于2019年同期的流动网络结构和流动量模拟整个网络模型,给出2020年2月17日、2月24日和3月2日的复工对各个城市疫情的影响.主要结论显示,在较强的防控措施和自我防护下,2020年3月2日复工将在一段时间内不会引起疫情的二次暴发. 相似文献
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《中国科学:数学》2020,(8)
新型冠状病毒肺炎(COVID-19)疫情已经蔓延至全国各地,包括陕西省在内很多省份的早期疫情均以输入病例为主,后期的疫情在严格的防控措施下也已呈下降趋势.评价防控措施的有效性、分析人口流动对疫情的影响对于研究陕西省(或其他以输入病例为主的地区)疫情和未来应对突发性传染病有着重要的意义.根据陕西省卫生健康委员会(简称卫健委)公布的详实数据信息可以挖掘传播链(感染树),得到从发病到首诊、入院、确诊的中位持续时间,每日潜伏者类、感染者类、治疗者类的具体人数和感染者状态转移的空间分布.本文计算确定COVID-19疫情的控制再生数(1.48–1.69),并发展新的统计推断方法获得陕西省严控措施下的有效再生数;进而提出一个全新的融入了公共卫生干预和输入病例的离散随机COVID-19疫情传播模型,通过多源数据实现了模型的参数化,分析不同的流动模式、输入人口中感染者的比例对二次暴发风险的影响.主要结论显示,间歇性的人口流动、密切关注和有效隔离流动人口中的感染者能有效降低二次暴发的风险,为有序组织复工、复学提供决策支持. 相似文献
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报道于2019年12月底的新型冠状病毒肺炎(COVID-19)疫情, 由于2020年春运期间人口的大规模流动, 使得其迅速蔓延.自2020年1月23日起, 我国采取了各种措施使得疫情得到了有效的控制, 例如武汉封城、确诊病例的密切接触者跟踪隔离、湖北人员的居家隔离等.该文基于COVID-19在山西省的实际传播情况, 建立了具有输入病例和确诊病例密切接触者跟踪隔离的动力学模型.在不考虑输入病例的情况下, 分析了模型的动力学行为.利用山西省COVID-19病例数据, 计算了实时再生数, 发现山西省2020 年1月25日全省封村封街道有效控制了COVID-19疫情的传播, 即实时再生数小于1, 从宏观角度验证了防控措施的有效性.进一步通过模型的数值拟合得到: 早期染病者隔离14天的防控策略是合理有效的; 武汉封城时间越早, 染病者的规模越小; 跟踪隔离到大量确诊病例的接触者时, 染病者的规模越小. 相似文献
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科学地预测疫情发展趋势对疫情防控至关重要.在新时滞动力学模型(TDD-NCP)的基础上,提出基于随机动力学的时滞卷积模型和离散卷积模型,并基于中国疾病预防控制中心的相关研究结果及公开数据以及Wallinga和Lipsitch的工作,反演出COVID-19的重要参数,拟合了武汉及上海市疫情发展趋势. 相似文献
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文章基于Bayes空间计量视角,分析我国GDP增长与投资、消费、出口等因素之间的关系模式,并将区域集聚效应引入模型.研究结果表明:中国经济增长存在空间相关性,表现为外生冲击引起的空间误差自相关,将空间相关和空间异质性因素同时纳入模型后的分析结果显示:消费增长对GDP增长的拉动作用占主导地位,超过投资和出口影响的总和,这与普通回归模型分析结果有着显著的差异;同时,GDP增长的空间计量模型显现出区域集聚效应差异:西部地区的增长显著低于其它区域,东部和中部地区之间差异并不显著. 相似文献
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鉴于新型冠状病毒肺炎(COVID-19)的易感染性与聚集性等特点,基于COVID-19的传播机制,应用故障树分析(FTA)方法,研究了具有不同特征的疫情突发事件风险决策问题,包括疫情突发事件的动态演化过程、多种情景以及应急方案对突发事件的影响。通过分析COVID-19疫情突发事件的演化过程,构建故障树来描述导致突发事件演变的条件与因素之间的逻辑关系,给出了不同的可行应急方案。利用FTA预估出疫情突发事件发生的概率,计算出可行应急方案的整体排序值,获得最优应急方案。最后通过一个COVID-19确诊患者的案例分析,验证了所提出的方法的可行性和有效性。 相似文献
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While the spread of COVID-19 in China is under control, the pandemic is developing rapidly around the world. Due to the normal migration of population, China is facing the high risk from imported cases. The potential specific medicine and vaccine are still in the process of clinical trials. Currently,controlling the impact of imported cases is the key to prevent new outbreak of COVID-19 in China. In this paper, we propose two impulsive systems to describe the impact of multilateral imported cases of COVID-19. Based on the published data, we simulate and analyze the epidemic trends under different control strategies. In particular, we compare four different scenarios and show the corresponding medical burden. The results can be useful in designing appropriate control strategy for imported cases in practice. 相似文献
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Bayesian spatial modeling of genetic population structure 总被引:2,自引:0,他引:2
Natural populations of living organisms often have complex histories consisting of phases of expansion and decline, and the
migratory patterns within them may fluctuate over space and time. When parts of a population become relatively isolated, e.g.,
due to geographical barriers, stochastic forces reshape certain DNA characteristics of the individuals over generations such
that they reflect the restricted migration and mating/reproduction patterns. Such populations are typically termed as genetically
structured and they may be statistically represented in terms of several clusters between which DNA variations differ clearly
from each other. When detailed knowledge of the ancestry of a natural population is lacking, the DNA characteristics of a
sample of current generation individuals often provide a wealth of information in this respect. Several statistical approaches
to model-based clustering of such data have been introduced, and in particular, the Bayesian approach to modeling the genetic
structure of a population has attained a vivid interest among biologists. However, the possibility of utilizing spatial information
from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only very recently.
While the standard Bayesian hierarchical modeling techniques through Markov chain Monte Carlo simulation provide flexible
means for describing even subtle patterns in data, they may also result in computationally challenging procedures in practical
data analysis. Here we develop a method for modeling the spatial genetic structure using a combination of analytical and stochastic
methods. We achieve this by extending a novel theory of Bayesian predictive classification with the spatial information available,
described here in terms of a colored Voronoi tessellation over the sample domain. Our results for real and simulated data
sets illustrate well the benefits of incorporating spatial information to such an analysis. 相似文献
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Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model
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The novel coronavirus pneumonia 2019 (COVID-19) has swept the globe in just a few months with negative social and psychological consequences for public health. So far, the United States has been one of the countries most affected by the epidemic. In this study, 51 states in the United States are divided into 10 state clusters according to relevant factors, and a difference equation model with spatio-temporal dynamic characteristics is established to predict the transmission dynamics of COVID-19 in the 10 state clusters and obtain data on regional aggregation levels (the United States). The study showed that the Pearson Correlation Coefficient between the actual data and the predicted data in the 10 state clusters is between 0.6 and 0.96 (mean R$^{2}$=0.8448), and the mean absolute error (MAE) of the newly confirmed cases in each cluster is between 300 and 1650 (mean MAE=878) and the average forecasting error rate (AFER) of the total confirmed cases in each cluster is between 0.9$\%$ and 3$\%$ (mean AFER=1.57$\%$). These results show that the difference equation model can well predict the changes in the recent confirmed cases of infectious diseases such as COVID-19. 相似文献
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Abayomi Samuel Oke Oluwafemi Isaac Bada Ganiyu Rasaq Victoria Adodo 《Mathematical Methods in the Applied Sciences》2022,45(1):137-149
Coronavirus pandemic (COVID-19) hit the world in December 2019, and only less than 5% of the 15 million cases were recorded in Africa. A major call for concern was the significant rise from 2% in May 2020 to 4.67% by the end of July 15, 2020. This drastic increase calls for quick intervention in the transmission and control strategy of COVID-19 in Africa. A mathematical model to theoretically investigate the consequence of ignoring asymptomatic cases on COVID-19 spread in Africa is proposed in this study. A qualitative analysis of the model is carried out with and without re-infection, and the reproduction number is obtained under re-infection. The results indicate that increasing case detection to detect asymptomatically infected individuals will be very effective in containing and reducing the burden of COVID-19 in Africa. In addition, the fact that it has not been confirmed whether a recovered individual can be re-infected or not, then enforcing a living condition where recovered individuals are not allowed to mix with the susceptible or exposed individuals will help in containing the spread of COVID-19. 相似文献
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该文以新型冠状病毒(SARS-Cov-2)在日本钻石公主号邮轮上传播为例,通过建立简单的易感者-感染者传染病模型,研究在封闭空间中新冠病毒肺炎(COVID-19)的传播机制.动力学分析和数值拟合预测了疾病传播过程和最终结果,讨论了不同隔离措施对疾病传播进程的影响,并给出防控策略建议. 相似文献