Modeling the USA Winter 2021 CoVID-19 Resurgence
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Abstract
The current USA 2021 CoVID-19 Winter Resurgence is modeled here with the same function used for analyzing prior USA CoVID-19 waves: Here, N ( t ) gives the total number of CoVID-19 cases above the previous baseline, and t R sets the initial t dbl = t R (ln 2) pandemic t dbl doubling time. Larger α S values indicate that uninfected people are improving their pandemic mitigation efforts, such as Social Distancing and vaccinations ; while δ 0 > 0 accelerates the post-peak tail-off , and is empirically associated with mask-wearing . The pandemic wave end is when N ( t ) no longer increases.
The USA Summer 2021 resurgence results from our prior medrxiv . org preprints * were used as a baseline. By 11/15/2021, an additional cases above baseline were found, signaling the USA Winter 2021 resurgence. This CoVID-19 wave is still in its initial stages. Presently, our analysis indicates that this CoVID-19 wave can infect virtually all susceptible persons; just like the initial stage of the USA Summer 2021 resurgence. Data up through 12/30/2021 gives these paremeter values: These values are identical to the prior 2020 USA Winter Resurgence results. Also, the and the values are similar. However, while the Winter 2020 Resurgence showed a significant mask-wearing effect , this initial USA Winter 2021 Resurgence shows practically no mask-wearing effects . If mask-wearing were to quickly rise to the Winter 2020 levels, it would give these projected totals: More robust mask-wearing and enhanced Social Distancing measures could further reduce these values ( with 3 Figures ).
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SciScore for 10.1101/2022.01.06.22268868: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis 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:…
SciScore for 10.1101/2022.01.06.22268868: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis 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.
Results from scite Reference Check: We found no unreliable references.
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