Capturing the Effects of Transportation on the Spread of COVID-19 with a Multi-Networked SEIR Model

被引：0
|
作者：
Vrabac, Damir [1 ]
Shang, Mingfeng [2 ]
Butler, Brooks [3 ]
Pham, Joseph [2 ]
Stern, Raphael [2 ]
Pare, Philip E. [3 ]
机构：
[1] Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA
[2] Univ Minnesota, Dept Civil Environm & Geoengn, Minneapolis, MN 55455 USA
[3] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA
来源：
2021 AMERICAN CONTROL CONFERENCE (ACC) | 2021年
基金：
美国国家科学基金会;
关键词：
STABILITY;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we present a deterministic discrete-time networked SEIR model that includes a number of transportation networks, and present assumptions under which it is well defined. We analyze the limiting behavior of the model and present necessary and sufficient conditions for estimating the spreading parameters from data. We illustrate these results via simulation and with real COVID-19 data from the Northeast United States, integrating transportation data into the results.
引用
收藏
页码：3152 / 3157
页数：6
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