Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach
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Abstract
Background
Although COVID-19 has been well controlled in China, it is rapidly spreading outside the country and may have catastrophic results globally without implementation of necessary mitigation measures. Because the COVID-19 outbreak has made comprehensive and profound impacts on the world, an accurate prediction of its epidemic trend is significant. Although many studies have predicted the COVID-19 epidemic trend, most have used early-stage data and focused on Chinese cases.
Methods
We first built models to predict daily numbers of cumulative confirmed cases (CCCs), new cases (NCs), and death cases (DCs) of COVID-19 in China based on data from January 20, 2020, to March 1, 2020. Based on these models, we built models to predict the epidemic trend across the world (outside China). We also built models to predict the epidemic trend in Italy, Spain, Germany, France, UK, and USA where COVID-19 is rapidly spreading.
Results
The COVID-19 outbreak will have peaked on February 22, 2020, in China and will peak on May 22, 2020, across the world. It will be basically under control in early April 2020 in China and late August 2020 across the world. The total number of COVID-19 cases will reach around 89,000 in China and 6,126,000 across the world during the epidemic. Around 4,000 and 290,000 people will die of COVID-19 in China and across the world, respectively. The COVID-19 outbreak will have peaked recently in Italy and will peak in Spain, Germany, France, UK, and USA within two weeks.
Conclusion
The COVID-19 outbreak is controllable in the foreseeable future if comprehensive and stringent control measures are taken.
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SciScore for 10.1101/2020.03.18.20038117: (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
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: We detected the following sentences addressing limitations in the study:A limitation of this study is that in deriving predictive models for global cases, we did not take into account factors other than demographics that are associated with the spread and outbreak of COVID-19. These include …
SciScore for 10.1101/2020.03.18.20038117: (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
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: We detected the following sentences addressing limitations in the study:A limitation of this study is that in deriving predictive models for global cases, we did not take into account factors other than demographics that are associated with the spread and outbreak of COVID-19. These include politics, economy, culture, education, health facilities, geographic position, and race. Overall, the African countries have a low number of COVID-19 cases; the number of cases is also small in the South Asian countries, including India, Pakistan, Bangladesh, and Sri Lanka, despite their high population density. In contrast, the East Asian countries, including China, South Korea, and Japan, have reported a large number of cases, and Europe and USA have replaced China as the centers of the COVID-19 outbreak. The reasons for notably different COVID-19 epidemic sizes in various regions and countries are worth further investigation.
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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SciScore for 10.1101/2020.03.18.20038117: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
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).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure …
SciScore for 10.1101/2020.03.18.20038117: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
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).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.
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