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  How to optimize the supply and allocation of medical emergency resources during public health emergencies

Wang, C., Deng, Y., Yuan, Z., Zhang, C., Zhang, F., Cai, Q., Gao, C., Kurths, J. (2020): How to optimize the supply and allocation of medical emergency resources during public health emergencies. - Frontiers in Physics, 8, 383.
https://doi.org/10.3389/fphy.2020.00383

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 Creators:
Wang, Chunyu1, Author
Deng, Yue1, Author
Yuan, Ziheng1, Author
Zhang, Chijun1, Author
Zhang, Fan1, Author
Cai, Qing1, Author
Gao, Chao1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics.

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 Dates: 2020-10-282020-10-28
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3389/fphy.2020.00383
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Organisational keyword: RD4 - Complexity Science
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Frontiers in Physics
Source Genre: Journal, SCI, Scopus, p3, oa
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Pages: - Volume / Issue: 8 Sequence Number: 383 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/140820
Publisher: Frontiers