The Effect of Weather Pattern on the Second Wave of Coronavirus: A cross study between cold and tropical climates of France, Italy, Colombia, and Brazil
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Abstract
This study aims to explore and understand the common belief that COVID infection rate is highly dependent on either the outside temperature and/or the humidity. Thirty-six regions/states from two humid-tropical countries, namely Brazil and Colombia and two countries with temperate climate, France and Italy, are studied over the period of October to December. Daily outside temperature, relative humidity and hospitalization/cases are analyzed using Spearman’s correlation. The eighteen cold regions of France and Italy has seen an average drop in temperature from 10°C to 6°C and 17°C to 7°C, respectively, and France recorded an addition of 2.3 million cases, while Italy recorded an addition of 1.8 million cases. Outside temperature did not fluctuate much in tropical countries, but Brazil and Colombia added 4.17 million and 1.1 million cases, respectively. Köppen–Geiger classification showed the differences in weather pattern between the four countries, and the analysis showed that there is very weak correlation between either outside weather and/or relative humidity alone to the COVID-19 pandemic.
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SciScore for 10.1101/2021.12.28.21268496: (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
Software and Algorithms Sentences Resources ], Google [51] and Microsoft Bing[52]. Googlesuggested: (Google, RRID:SCR_017097)Therefore, careful consideration has been made to locate correct WMO/WBAN stations within the given latitude and longitude combinations for each of the 36 regions/states and the weather data were accurately collected and matched with the COVID datasets using the MATLAB algorithm. MATLABsuggested: (MATLAB, RRID:SCR_001622)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 LimitationRecogni…SciScore for 10.1101/2021.12.28.21268496: (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
Software and Algorithms Sentences Resources ], Google [51] and Microsoft Bing[52]. Googlesuggested: (Google, RRID:SCR_017097)Therefore, careful consideration has been made to locate correct WMO/WBAN stations within the given latitude and longitude combinations for each of the 36 regions/states and the weather data were accurately collected and matched with the COVID datasets using the MATLAB algorithm. MATLABsuggested: (MATLAB, RRID:SCR_001622)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: Please consider improving the rainbow (“jet”) colormap(s) used on page 12. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.
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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