Summaries, Analysis and Simulations of COVID-19 Epidemics in Shanghai
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Shanghai is the best city to prevent the spread of COVID-19 infection in China. Since February 20, 2020, Shanghai has experienced five waves of COVID-19. Out of a total of 388 patients with COVID-19 symptoms, 381 were cured and seven died. Medical staff achieved zero infection. This paper summarizes, analyzes and simulates COVID-19 epidemics in Shanghai. The simulation results show that for five waves of epidemics, after reaching the infection turning point, the blocking rate of symptomatic infection is over 99%. The administration needs to maintain the prevention and control implemented 7 days after reaching the infection turning point until the new infection goes away.
Article activity feed
-
SciScore for 10.1101/2022.01.11.22269050: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter:…
SciScore for 10.1101/2022.01.11.22269050: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-