التفاصيل البيبلوغرافية
| العنوان: |
How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies |
| المؤلفون: |
Chunyu Wang, Yue Deng, Ziheng Yuan, Chijun Zhang, Fan Zhang, Qing Cai, Chao Gao, Jurgen Kurths |
| المساهمون: |
School of Mechanical and Aerospace Engineering |
| المصدر: |
Frontiers in Physics, Vol 8 (2020) |
| بيانات النشر: |
Frontiers Media SA, 2020. |
| سنة النشر: |
2020 |
| مصطلحات موضوعية: |
Computer science, Process (engineering), Materials Science (miscellaneous), Control (management), Crossover, Population, Biophysics, General Physics and Astronomy, Computational Epidemiology, 01 natural sciences, Multi-objective optimization, Local optimum, 0103 physical sciences, medicine, Computational epidemiology, Physical and Theoretical Chemistry, 010306 general physics, education, Mathematical Physics, emergence management, education.field_of_study, COVID-19, computational epidemiology, medicine.disease, lcsh:QC1-999, Megacity, multi-objective optimization, medical emergency resources, Mechanical engineering [Engineering], Medical emergency, epidemic propagation, lcsh:Physics |
| الوصف: |
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 © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| تدمد: |
2296-424X |
| DOI: |
10.3389/fphy.2020.00383 |
| الوصول الحر: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af0c2aaa257558b04e5d2205210052ccTest |
| حقوق: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....af0c2aaa257558b04e5d2205210052cc |
| قاعدة البيانات: |
OpenAIRE |
