检索结果-南通市图书馆

Revisiting classical SIR modelling in light of the COVID-19 pandemic

Infectious Disease Modelling 2023年第1期8卷 72-83页

作者： leonid Kalachev erin l.landguth Jon Graham Mathematical Sciences University of MontanaMissoulaUSA Center for Population Health Research School of Public and Community Health SciencesUniversity of MontanaMissoulaUSA

Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health ***,it is important to correctly classify reported data and understand how this impacts estimation of model *** COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling ***:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the *** main focus is on how model prediction results are dependent on data being assigned to the right model ***:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling *** then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,***:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated *** showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus *** a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriat

关键词： Basic disease reproduction number Communicable disease control Coronavirus COVID-19 Disease transmission Epidemics Epidemiology Influenza data Mathematical models Montana SIR models

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：