A model simulation study on effects of intervention measures in Wuhan COVID-19 epidemic
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Abstract
Background
In the beginning of January 2020, new unknown virus pneumonia cases started to emerge in local hospitals in Wuhan, China. This virus epidemic quickly became a public health emergency of international concern by the WHO. Enormous amount of medical supplies as well as healthcare personals from other provinces were mobilized to support Wuhan. This current work tent to help people understanding how infectious disease spread and the purpose and consequences of various efforts based on simulation model.
Method
a simulation model was created using known parameters. R0 set to 3 and mean incubation time to be 7.5days. the epidemic was divided to 3 periods. Simulation would run 50 times to mimic different patient0 status. Personal activity index was used to mimic different level of control measures. 141427709 simulated patients were created. Cumulation number of patients at the end of period 1 (day50) is 2868.7±1739.0. Total infected patients could be 913396.5 ± 559099.9 by the end of period 2 (day70) in free transmission state. And at day90, total patients number is 913396.5 ± 559099.9.
Conclusion
COVID-19 is a novel severe respiratory disease. This will put great burden on the shoulder of healthcare workers as well as on medical hardware and supplements. Current strict control measures help to contain disease from spreading. An early detecting, reporting and fast reacting system needs to be setup to prevent future unknown infectious disease.
Article activity feed
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SciScore for 10.1101/2020.02.14.20023168: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Based on these parameters a simple simulation model was accomplished with python 3.6. pythonsuggested: (IPython, RRID:SCR_001658)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 …
SciScore for 10.1101/2020.02.14.20023168: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Based on these parameters a simple simulation model was accomplished with python 3.6. pythonsuggested: (IPython, RRID:SCR_001658)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.
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