Epidemic Modeling of the Spatiotemporal Spread of COVID-19 over an Intercity Population Mobility Network

Yuxi Liu, Shaowen Qin, Zhenhao Zhang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Intercity traveling has been recognized as a leading cause for the continuation of the COVID-19 global pandemic. However, there lacks credible prediction of the spatiotemporal spread of COVID-19 with humans traveling between metropolitan areas. This study attempts to establish a novel framework to simulate human traveling and the spread of virus across an intercity population mobility network. A Markov process was introduced to capture the stochastic nature of travelers’ migration. A backward derivation algorithm was adopted and the Nelder-Mead simplex optimization method applied to overcome the limitation of existing deterministic epidemic models, including the difficulties in estimating the initial susceptible population and the optimal hyper-parameters required for simulation. We conducted two case studies with data from 24 cities in China and Italy. Our framework yielded state-of-the-art accuracy while being modular and scalable, indicating the addition of population mobility and stochasticity significantly improves prediction performance compared to using epidemic data alone. Moreover, our results revealed that transmission patterns of COVID-19 differ significantly with different population mobility, offering valuable information to the understanding of the correlation between traveling activities and COVID-19 transmission.

Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings
EditorsHamido Fujita, Philippe Fournier-Viger, Moonis Ali, Yinglin Wang
Place of PublicationSwitzerland
PublisherSpringer Science and Business Media Deutschland GmbH
Pages147-159
Number of pages13
ISBN (Print)9783031085291
DOIs
Publication statusE-pub ahead of print - 30 Aug 2022
Event35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022 - Kitakyushu, Japan
Duration: 19 Jul 202222 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13343 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022
Country/TerritoryJapan
CityKitakyushu
Period19/07/2222/07/22

Keywords

  • COVID-19
  • Epidemic modeling
  • Population mobility
  • Spatiotemporal analysis

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