Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/145359
Title: How to optimize the supply and allocation of medical emergency resources during public health emergencies
Authors: Wang, Chunyu
Deng, Yue
Yuan, Ziheng
Zhang, Chijun
Zhang, Fan
Cai, Qing
Gao, Chao
Kurths, Jurgen
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Source: Wang, C., Deng, Y., Yuan, Z., Zhang, C., Zhang, F., Cai, Q., . . . Kurths, J. (2020). How to optimize the supply and allocation of medical emergency resources during public health emergencies. Frontiers in Physics, 8, 383-. doi:10.3389/fphy.2020.00383
Journal: Frontiers in Physics
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.
URI: https://hdl.handle.net/10356/145359
ISSN: 2296-424X
DOI: 10.3389/fphy.2020.00383
Rights: © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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