Suppr超能文献

利用易感-感染-康复(SIR)模型,我们能从死亡率和感染病例数据中估算出什么?以新冠疫情为例的研究

What Can We Estimate From Fatality and Infectious Case Data Using the Susceptible-Infected-Removed (SIR) Model? A Case Study of Covid-19 Pandemic.

作者信息

Ahmetolan Semra, Bilge Ayse Humeyra, Demirci Ali, Peker-Dobie Ayse, Ergonul Onder

机构信息

Department of Mathematics, Faculty of Science and Letters, Istanbul Technical University, Istanbul, Turkey.

Department of Industrial Engineering, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey.

出版信息

Front Med (Lausanne). 2020 Sep 3;7:556366. doi: 10.3389/fmed.2020.556366. eCollection 2020.

Abstract

The rapidly spreading Covid-19 that affected almost all countries, was first reported at the end of 2019. As a consequence of its highly infectious nature, countries all over the world have imposed extremely strict measures to control its spread. Since the earliest stages of this major pandemic, academics have done a huge amount of research in order to understand the disease, develop medication, vaccines and tests, and model its spread. Among these studies, a great deal of effort has been invested in the estimation of epidemic parameters in the early stage, for the countries affected by Covid-19, hence to predict the course of the epidemic but the variability of the controls over the course of the epidemic complicated the modeling processes. In this article, the determination of the basic reproduction number, the mean duration of the infectious period, the estimation of the timing of the peak of the epidemic wave is discussed using early phase data. Daily case reports and daily fatalities for China, South Korea, France, Germany, Italy, Spain, Iran, Turkey, the United Kingdom and the United States over the period January 22, 2020-April 18, 2020 are evaluated using the Susceptible-Infected-Removed (SIR) model. For each country, the SIR models fitting cumulative infective case data within 5% error are analyzed. It is observed that the basic reproduction number and the mean duration of the infectious period can be estimated only in cases where the spread of the epidemic is over (for China and South Korea in the present case). Nevertheless, it is shown that the timing of the maximum and timings of the inflection points of the proportion of infected individuals can be robustly estimated from the normalized data. The validation of the estimates by comparing the predictions with actual data has shown that the predictions were realized for all countries except USA, as long as lock-down measures were retained.

摘要

2019年底首次报告了迅速蔓延且几乎影响到所有国家的新型冠状病毒肺炎(Covid-19)。由于其具有高度传染性,世界各国都采取了极其严格的措施来控制其传播。自这场重大疫情的最初阶段以来,学术界开展了大量研究,以了解该疾病、研发药物、疫苗和检测方法,并对其传播进行建模。在这些研究中,为受Covid-19影响的国家在早期阶段投入了大量精力来估计流行参数,从而预测疫情的发展过程,但疫情期间防控措施的变化使建模过程变得复杂。本文利用早期阶段数据讨论了基本再生数的确定、感染期的平均持续时间以及疫情波峰值时间的估计。使用易感-感染-康复(SIR)模型对2020年1月22日至2020年4月18日期间中国、韩国、法国、德国、意大利、西班牙、伊朗、土耳其、英国和美国的每日病例报告和每日死亡人数进行了评估。对每个国家分析了拟合累积感染病例数据且误差在5%以内的SIR模型。可以观察到,只有在疫情传播结束的情况下(在本案例中为中国和韩国)才能估计基本再生数和感染期的平均持续时间。然而,结果表明,从归一化数据中可以可靠地估计感染个体比例的最大值时间和拐点时间。通过将预测结果与实际数据进行比较来验证估计值,结果表明,只要保留封锁措施,除美国外的所有国家的预测都得到了实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0ff/7494820/a603e031459b/fmed-07-556366-g0001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验