Temperature and Humidity Do Not Influence Global COVID-19 Incidence as Inferred from Causal Models
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Abstract
The relationship between meteorological factors such as temperature and humidity with COVID-19 incidence is still unclear after 6 months of the beginning of the pandemic. Some literature confirms the association of temperature with disease transmission while some oppose the same. This work intends to determine whether there is a causal association between temperature, humidity and Covid-19 cases. Three different causal models were used to capture stochastic, chaotic and symbolic natured time-series data and to provide a robust & unbiased analysis by constructing networks of causal relationships between the variables. Granger-Causality method, Transfer Entropy method & Convergent Cross-Mapping (CCM) was done on data from regions with different temperatures and cases greater than 50,000 as of 13 th May 2020. From the Granger-Causality test we found that in only Canada, the United Kingdom, temperature and daily new infections are causally linked. The same results were obtained from Convergent Cross Mapping for India. Again using Granger-Causality test, we found that in Russia only, relative humidity is causally linked to daily new cases. Thus, a Generalized Additive Model with a smoothing spline function was fitted for these countries to understand the directionality. Using the combined results of the said models, we were able to conclude that there is no evidence of a causal association between temperature, humidity and Covid-19 cases.
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SciScore for 10.1101/2020.06.29.20142307: (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 code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has few limitations, it’s difficult to compute the exposure when it comes to weather conditions as many people are inside their houses due to lockdown and would avoid outdoor exposure. We also think that we have a very short period of observational data and with more data, findings may fluctuate to other sides. Overall we tried to put forth an exhaustive search for causal relationships between weather conditions and new cases …
SciScore for 10.1101/2020.06.29.20142307: (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 code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has few limitations, it’s difficult to compute the exposure when it comes to weather conditions as many people are inside their houses due to lockdown and would avoid outdoor exposure. We also think that we have a very short period of observational data and with more data, findings may fluctuate to other sides. Overall we tried to put forth an exhaustive search for causal relationships between weather conditions and new cases of covid19 based on the available literature on causal time-series analysis and these findings asserts that we didn’t have causal association between temperature and humidity with new cases of covid19.
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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