About: Objective: The aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID−19) in major endemic areas of China. Methods The provinces that reported more than 500 cases of COVID−19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID−19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID−19, respectively. The latent class of R0 was analyzed using a latent profile analysis model. Results The median R0 calculated from SARS and COVID−19 parameters were 1.84 − 3.18 and 1.74 − 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID−19 parameters (Z = −4.782 − −4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly. Conclusion The results indicated that overall trend of R0 has been falling with the strengthening of China's comprehensive prevention and control measures for COVID−19, however, presents regional differences.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • Objective: The aim of the study is to analyze the latent class of basic reproduction number (R0) trend of 2019 novel coronavirus disease (COVID−19) in major endemic areas of China. Methods The provinces that reported more than 500 cases of COVID−19 till February 18, 2020 were selected as the major endemic area. The Verhulst model was used to fit the growth rate of cumulative confirmed cases. The R0 of COVID−19 was calculated using the parameters of severe acute respiratory syndrome (SARS) and COVID−19, respectively. The latent class of R0 was analyzed using a latent profile analysis model. Results The median R0 calculated from SARS and COVID−19 parameters were 1.84 − 3.18 and 1.74 − 2.91, respectively. The R0 calculated from the SARS parameters was greater than that of calculated from the COVID−19 parameters (Z = −4.782 − −4.623, P < 0.01). Both R0 can be divided into three latent classes. The initial value of R0 in class 1 (Shandong Province, Sichuan Province and Chongqing Municipality) was relatively low and decreases slowly. The initial value of R0 in class 2 (Anhui Province, Hunan Province, Jiangxi Province, Henan Province, Zhejiang Province, Guangdong Province and Jiangsu Province) was relatively high and decreases rapidly. Moreover, the initial value of R0 of class 3 (Hubei Province) was between that of class 1 and class 2, but the higher level of R0 lasts longer and decreases slowly. Conclusion The results indicated that overall trend of R0 has been falling with the strengthening of China's comprehensive prevention and control measures for COVID−19, however, presents regional differences.
Subject
  • Epidemiology
  • Pandemics
  • BRICS nations
  • Classification algorithms
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software