Understanding SARSCOV-2 propagation, impacting factors to derive possible scenarios and simulations
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Objectives
We aimed to analyze factors impacting the Covid-19 epidemic on a macro level, comparing multiple countries across the world, and verifying the occurrence at a micro level through cluster analysis.
Design
Statistical analysis of large datasets.
Methods
We used publicly available large world datasets (1-11). Data was transformed to fit parametric distributions prior to statistical analyses, which were performed with Student’s t-test, linear regression and post-hoc tests. Especially for ordinary least squares regression, natural logarithmic transformations were done to remediate normality violations in the standardized residuals.
Results
The severity of the epidemic was most strongly related to exposure to ultraviolet light and extrapolated levels of vitamin D and to the health of the population, especially with regards to obesity. We found no county with an obesity level < 8% with a severe epidemic. We also found that countries where the population benefited from sun exposure or vitamin D supplementation and spent time outside fared well. Factors related to increased propagation of the virus included the use of heating ventilation and air conditioning (HVAC), population density, poorly aerated gatherings, relative humidity, timely policies of closing clustering places until aeration was improved, and daily amount of ridership on public transportation, especially subways. Population lockdowns, masks, and blood type did not provide much explanatory power. The excess mortality observed is within the ranges of severe past influenza epidemics of 2016/2017 or 1999/2000.
Conclusions
Our study suggested that prevention measures should be directed to improving aeration systems, enhancing diets and exercise, and ensuring adequate levels of vitamin D. Further research on masking is indicated as our study could not separate policies from how well they were actually followed.
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors’
Strengths and Limitations of the Study
-
The Study examines large datasets across countries to look for macrotrends in management of the Covid-19 outbreak.
-
The Study cannot necessarily establish causation but rather correlation.
-
The Study raises some novel possibilities for further studies in relation to country-wide and individual-level susceptibility to Covid-19 and to other epidemics in general.
-
The Study raises questions about some political policies based upon country-level comparisons and suggests some areas for exploration of prevention policies.
Article activity feed
-
-
-
-
SciScore for 10.1101/2020.09.07.20190066: (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
Software and Algorithms Sentences Resources Microsoft Excel for Mac v 16.39 (20071200) (© 2020 Microsoft, Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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 Tri…
SciScore for 10.1101/2020.09.07.20190066: (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
Software and Algorithms Sentences Resources Microsoft Excel for Mac v 16.39 (20071200) (© 2020 Microsoft, Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)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.
-