Rule of thumb in human intelligence for assessing the COVID-19 outbreak in Japan
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Abstract
Background
The COVID-19 outbreak in Japan exhibited its third peak at the end of 2020. Mathematical modelling and developed AI cannot explain several peaks in a single year.
Object
This study was conducted to evaluate a rule of thumb for prediction from past wave experiences.
Method
We rescaled the number of newly infected patients as 100% at the peak and checked similarities among waves. Then we extrapolated the courses of the third and later waves.
Results
Results show some similarity around the second and the third waves. Based on this similarity, we expected the bottom of the third wave will show 2131 newly positive patients including asymptomatic patients at around the end of February, 2021.
Discussion and Conclusion
We can infer the course of the third wave from similarity with the second wave. Mathematical modelling has been unable to do it, even when AI was used for prediction. Performance of the rule of thumb used with human intelligence might be superior to that of AI under these circumstances.
Article activity feed
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SciScore for 10.1101/2021.01.20.21250204: (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
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…
SciScore for 10.1101/2021.01.20.21250204: (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
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.
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