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Investigating time, strength, and duration of measures in controlling the spread of COVID-19 using a networked meta-population model.
Zhang, Jiang; Dong, Lei; Zhang, Yanbo; Chen, Xinyue; Yao, Guiqing; Han, Zhangang.
  • Zhang J; School of Systems Science, Beijing Normal University, Beijing, 100875 China.
  • Dong L; School of Earth and Space Sciences, Institute of Remote Sensing and Geographical Information Systems, Peking University, Beijing, 100871 China.
  • Zhang Y; Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
  • Chen X; School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85281 USA.
  • Yao G; School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275 China.
  • Han Z; Department of Health Sciences, University of Leicester, Leicester, LE1 7RH UK.
Nonlinear Dyn ; 101(3): 1789-1800, 2020.
Article in English | MEDLINE | ID: covidwho-1906360
ABSTRACT
Policy makers around the world are facing unprecedented challenges in making decisions on when and what degrees of measures should be implemented to tackle the COVID-19 pandemic. Here, using a nationwide mobile phone dataset, we developed a networked meta-population model to simulate the impact of intervention in controlling the spread of the virus in China by varying the effectiveness of transmission reduction and the timing of intervention start and relaxation. We estimated basic reproduction number and transition probabilities between health states based on reported cases. Our model demonstrates that both the time of initiating an intervention and its effectiveness had a very large impact on controlling the epidemic, and the current Chinese intense social distancing intervention has reduced the impact substantially but would have been even more effective had it started earlier. The optimal duration of the control measures to avoid resurgence was estimated to be 2 months, although would need to be longer under less effective controls.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: Nonlinear Dyn Year: 2020 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study Language: English Journal: Nonlinear Dyn Year: 2020 Document Type: Article