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Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example
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Science & Technology Review | 2021, 39(24) : 96 - 107
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Science & Technology Review | 2021, 39(24): 96-107
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Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example
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QIAN Xuesheng1,2,3, WU Huanyu4, CHEN Cheng5, HUANG Xiaoyan4, TONG Qing5, DAI Weihui1,2
Affiliations
    1. Smart City Research Center, Fudan University, Shanghai 200433, China;
    2. School of Management, Fudan University, Shanghai 200433, China;
    3. Macau Institute of System Engineering, Macau University of Science and Technology, Macau 999078, China;
    4. Shanghai Center for Disease Control and Prevention, Shanghai 200336, China;
    5. Wonders Information Co., Ltd., Shanghai 201112, China
Published: 2021-12-28 doi: 10.3981/j.issn.1000-7857.2021.24.011
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SARS-CoV-2 and its variants, the viruses that cause the COVID-19 pandemic, have some new characteristics, such as the high transmissibility, the long incubation period, the sweeping susceptible population, and the high environmental endurance, so a key question of the pandemic management and control is monitoring the asymptomatic transmission in daily life and socioeconomic activities, especially, the wide-range of cross-region spread. These features pose a great challenge to the traditional pandemic management and control methods and the global public health surveillance and control system. An effective way to tackle this challenge is making full use of the digital technology in the pandemic management and control, and building an accurate regular epidemiological surveillance and smart pandemic management and control system. By analyzing the essential factors of a smart pandemic management and control system, the critical role of the federated learning in the practice of the cross-region and cross-department smart pandemic management and control is shown. According to the classifications of the cross-region pandemic-related data and the pandemic management and control requirements, the technology and the application of the cross-region smart pandemic management and control based on the federated learning are explored. This method is successfully applied in the COVID-19 pandemic management and control in Shanghai and Yangtze River Delta, providing a new pathway for the integrated decision-making and targeted pandemic management and control in China. This method is also instrumental for other countries in the pandemic management and control.
joint pandemic prevention and control  /  smart pandemic management and control  /  smart city  /  urban digital transformation
QIAN Xuesheng, WU Huanyu, CHEN Cheng, HUANG Xiaoyan, TONG Qing, DAI Weihui. Cross-region smart COVID-19 pandemic management and control based on federated learning: With Shanghai as an example[J]. Science & Technology Review, 2021 , 39 (24) : 96 -107 . DOI: 10.3981/j.issn.1000-7857.2021.24.011
Year 2021 volume 39 Issue 24
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doi: 10.3981/j.issn.1000-7857.2021.24.011
  • Receive Date:2021-09-02
  • Online Date:2022-01-08
  • Published:2021-12-28
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  • Received:2021-09-02
  • Revised:2021-09-30
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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