covid-19建模中使用的地方性流行病框架(由nunes, caetano, antunes和dias对论文进行讨论)

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Revstat-Statistical Journal Pub Date : 2020-10-01 DOI:10.5167/UZH-198715

M. Dunbar, L. Held

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引用次数: 6

摘要

Nunes等人([54])概述了用于分析流行病的数学模型以及开展研究以获得此类模型参数估计的技术。他们讨论了已用于2019冠状病毒病(COVID-19)分析的SEIR模型。我们的讨论提出了一个基于时间序列分析的建模框架，该框架用于分析传染病监测数据。我们认为，Nunes等人([54])概述的传染病建模的许多目的以及强调的数学建模的好处也可以在我们工作中使用的统计建模技术中找到©2020，国家统计研究所保留所有权利

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Endemic-epidemic framework used in covid-19 modelling (Discussion on the paper by nunes, caetano, antunes and dias)

Nunes et al ([54]) provide an overview of mathematical models used to analyse epidemics and techniques for conducting studies to obtain parameter estimates for such models They discuss the SEIR model which has been used in much coronavirus disease 2019 (COVID-19) analysis Our discussion presents a modelling framework based in time series analysis developed for the analysis of infectious disease surveillance data, as well as our use of the framework in analysing COVID-19 We believe many of the purposes of modelling infectious disease outlined by Nunes et al ([54]) as well as the benefits of mathematical modelling highlighted can also be found in the statistical modelling techniques we use in our work © 2020, National Statistical Institute All rights reserved

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来源期刊

Revstat-Statistical Journal STATISTICS & PROBABILITY-

CiteScore

1.30

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： The aim of REVSTAT Statistical Journal is to publish articles of high scientific content, developing Statistical Science focused on innovative theory, methods and applications in different areas of knowledge.