Climate & BCG: Effects on COVID-19 Death Growth Rates
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Abstract
M ultiple studies have suggested the spread of COVID-19 is affected by factors such as climate, BCG vaccinations, pollution and blood type. We perform a joint study of these factors using the death growth rates of 40 regions worldwide with both machine learning and Bayesian methods. We find weak, non-significant (< 3 σ ) evidence for temperature and relative humidity as factors in the spread of COVID-19 but little or no evidence for BCG vaccination prevalence or PM 2.5 pollution. The only variable detected at a statistically significant level (>3 σ ) is the rate of positive COVID-19 tests, with higher positive rates correlating with higher daily growth of deaths.
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SciScore for 10.1101/2020.07.13.20152991: (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:We find marginal evidence for A+ blood type being relevant, subject to the caveats discussed in section (5.4.1). How should our results be taken in the context of the many claims of climate, blood, BCG etc… being significant factors in COVID-19 spread? First, many studies were based on confirmed COVID-19 test cases which, as we discussed earlier, are affected by differences in testing capability and protocols between countries. …
SciScore for 10.1101/2020.07.13.20152991: (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:We find marginal evidence for A+ blood type being relevant, subject to the caveats discussed in section (5.4.1). How should our results be taken in the context of the many claims of climate, blood, BCG etc… being significant factors in COVID-19 spread? First, many studies were based on confirmed COVID-19 test cases which, as we discussed earlier, are affected by differences in testing capability and protocols between countries. Secondly, many of the studies present regressions that do not allow for unmodelled confounding sources of variation in the growth. Hence, if a country shows high growth the algorithm will try to force one of the potential factors under study to explain it. Instead the hierarchical Bayesian framework allows the base growth rate of each country to be different, and hence potential factors will only be given credit for the difference in the growth rate if they provide a genuinely better fit. Further, many studies do not model the intrinsic uncertainties associated with the data as we have done. We too find that the best-fits are non-zero (as can be seen by looking at the peaks of the posteriors in Figure or at the last rows of Table (6). Hence our results are not in disagreement with regression results, the issue is about the statistical significance of such claims. Finally, although we do not detect environmental factors at more than 3σ, it is interesting to examine how big the environmental factors would be if our best-fit parameter values describe real...
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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