Visualizing Contact Networks Evolved To Fit Epidemic Profiles

被引：0

作者：

Ashlock, Wendy ^{[1
]}

机构：

[1] Ashlock & McGuinness Consulting Inc, Guelph, ON, Canada

来源：

2020 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB) | 2020年

关键词：

epidemic modelling; contact networks; graph visualization; COVID-19;

D O I：

暂无

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

A method for finding contact networks that fit epidemic profiles has been developed in previous work. This paper applies that method to real world data from the 2020 COVID-19 pandemic and presents three ways to visualize the resulting networks: the Step View illuminates how the epidemic progresses through time; the Community View illustrates how the epidemic moves through communities; and the Hub View highlights the role of hub nodes (superspreaders) in the network. These visualizations are intended to aid researchers who want to use evolved networks to design epidemic interventions. They will help them choose networks from many replicates that best suit the community they are studying, and they will help them interpret results.

引用

页码：26 / 33

页数：8

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