Modeling epidemics on a regular tree graph

Q3 Mathematics
C. Seibold, H. Callender
{"title":"Modeling epidemics on a regular tree graph","authors":"C. Seibold, H. Callender","doi":"10.1080/23737867.2016.1185979","DOIUrl":null,"url":null,"abstract":"We will first provide a brief introduction to models of disease transmission on so-called contact networks, which can be represented by various structures from the mathematical field of graph theory. These models allow for exploration of stochastic effects and incorporation of more biological detail than the classical compartment-based ordinary differential equation models, which usually assume both homogeneity in the population and uniform mixing. In particular, we use an agent-based modelling platform to compare theoretical predictions from mathematical epidemiology to results obtained from simulations of disease transmission on a regular tree graph. We also demonstrate how this graph reveals connections between network structure and the spread of infectious diseases. Specifically, we discuss results for how certain properties of the tree graph, such as network diameter and density, alter the duration of an outbreak.","PeriodicalId":37222,"journal":{"name":"Letters in Biomathematics","volume":"3 1","pages":"59 - 74"},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23737867.2016.1185979","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Letters in Biomathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23737867.2016.1185979","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 7

Abstract

We will first provide a brief introduction to models of disease transmission on so-called contact networks, which can be represented by various structures from the mathematical field of graph theory. These models allow for exploration of stochastic effects and incorporation of more biological detail than the classical compartment-based ordinary differential equation models, which usually assume both homogeneity in the population and uniform mixing. In particular, we use an agent-based modelling platform to compare theoretical predictions from mathematical epidemiology to results obtained from simulations of disease transmission on a regular tree graph. We also demonstrate how this graph reveals connections between network structure and the spread of infectious diseases. Specifically, we discuss results for how certain properties of the tree graph, such as network diameter and density, alter the duration of an outbreak.
在规则树图上对流行病进行建模
我们将首先简要介绍所谓的接触网络上的疾病传播模型,接触网络可以用图论数学领域的各种结构来表示。这些模型允许探索随机效应,并比经典的基于隔间的常微分方程模型包含更多的生物细节,后者通常假设种群的均匀性和均匀混合。特别是,我们使用基于主体的建模平台来比较数学流行病学的理论预测与常规树图上疾病传播模拟的结果。我们还展示了这张图如何揭示了网络结构与传染病传播之间的联系。具体来说,我们将讨论树图的某些属性(如网络直径和密度)如何改变爆发持续时间的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Letters in Biomathematics
Letters in Biomathematics Mathematics-Statistics and Probability
CiteScore
2.00
自引率
0.00%
发文量
0
审稿时长
14 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信