Selected article for: "disease prevent and infection rate"
Author: Qihui Yang; Chunlin Yi; Aram Vajdi; Lee W Cohnstaedt; Hongyu Wu; Xiaolong Guo; Caterina M Scoglio
Title: Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
  • Document date: 2020_3_30
    • ID: kcb68hue_46
    Snippet: is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 The estimation of infection rate β from January 15 th to February 27 th is presented as the blue line in Fig.3 . The orange line in Fig. 3 presents the removing rate. From January 15 th to January 27 th , the infection rate β is increasing. After January 27th, β starts to decrease. The self-quarantine policy was carried ou.....
    Document: is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 The estimation of infection rate β from January 15 th to February 27 th is presented as the blue line in Fig.3 . The orange line in Fig. 3 presents the removing rate. From January 15 th to January 27 th , the infection rate β is increasing. After January 27th, β starts to decrease. The self-quarantine policy was carried out on January 23 rd in Wuhan and a series of administrative actions were taken to prevent the disease from spreading. Considering that there are several days delay from the infection to detection, the estimated β matches with the real situation. On February 12 th , because Wuhan authorities changed the case definition to include symptomatic patients in addition to tested ones, there was a huge increase in cases, which results in a huge increase in estimated infection rate β as well. Therefore, the estimated β on February 12 th is inaccurate. After February 14 th , the infection rate β is less than the removing rate γ, which means the epidemic had peaked in Wuhan.

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