A Simple Method of Finding an Approximate Pattern of the COVID-19 Spread
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Abstract
We are going to show that the pattern of spread of COVID-19 outside China is not monotonic. We have considered the data outside China because we are going to study the data starting from March 21, and by that time the spread had almost come to a stop in China. We have used for our analysis data on total cases outside China till April 25, 2020, and data from April 26 to April 30 for comparison of forecasts and observed values. Right from the beginning the spread pattern was nonlinear, and by the end of the third week of March the nonlinearity became nearly exponential. The exponential pattern thereafter has changed by around March 28, April 5, April 11 and April 18. Since March 21, the spread is following a nearly exponential pattern of growth changing observably at almost regular intervals of seven days. It is but natural that at some point of time the countries that had been contributing in observably large numbers to the total cases would start to show diminishing growth patterns. Therefore long term forecasts using our method would give us slightly overestimated results. However, for short term forecasting our simple method does work very well when we consider the total number of cases in the world and not in any particular country.
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SciScore for 10.1101/2020.05.24.20112292: (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: 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 …
SciScore for 10.1101/2020.05.24.20112292: (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: 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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