COVID-19 Epidemics Monitored Through the Logarithmic Growth Rate and SIR Model
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Abstract
The SIR model is often used to analyse and forecast an epidemic. In this model, the number of patients exponentially increases and decreases in the early and late phases; hence the logarithmic growth rate K is constant at the phases. However, in the case of COVID-19 epidemics, K never remains constant but increases and decreases linearly. Simulation showed that a situation in which smaller epidemics were repeated with short time intervals makes the changes in K; it also showed relationship between K to the mean infectious time τ and the basic reproduction number R0. Using this relationship, we analysed epidemic data from 279 countries and regions. The changes in K represented the state of the epidemics and were several weeks to a month ahead of the changes in the number of confirmed cases. If the negative peaks of K could not be reduced to 0.1, the number of patients remained high. To control the epidemic, it was important to observe K daily, not to allow K to remain positive continuously and to terminate a peak with a series of K-negative days. To accomplish this, it was necessary to shorten τ by finding and isolating a patient earlier.
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SciScore for 10.1101/2021.09.13.21263483: (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: Thank you for sharing your data.
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 …
SciScore for 10.1101/2021.09.13.21263483: (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: Thank you for sharing your data.
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.
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