Estimating Effect-sizes to Infer if COVID-19 transmission rates were low because of Masks, Heat or High because of Air-conditioners, Tests
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Abstract
How does one interpret the observed increase or decrease in COVID-19 case rates? Did the compliance to the non-pharmaceutical interventions, seasonal changes in the temperature influence the transmission rates or are they purely an artefact of the number of tests? To answer these questions, we estimate the effect-sizes from these different factors on the reproduction ratios (R t ) from the different states of the USA during March 9 to August 9. Ideally R t should be less than 1 to keep the pandemic under control and our model predicts many of these factors contributed significantly to the R t ’s: Post-lockdown opening of the restaurants and nightclubs contributed 0.04 (CI 0.04-0.04) and 0.11 (CI. 0.11-0.11) to R t . The mask mandates helped reduce R t by 0.28 (CI 0.28-0.29)), whereas the testing rates which may have influenced the number of infections observed, did not influence R t beyond 10,000 daily tests 0.07 (CI -0.57-0.42). In our attempt to understand the role of temperature, the contribution to the R t was found to increase on both sides of 55 F, which we infer as a reflection of the climatization needs. A further analysis using the cooling and heating needs showed contributions of 0.24 (CI 0.18-0.31) and 0.31 (CI 0.28-0.33) respectively. The work thus illustrates a data-driven approach for estimating the effect-sizes on the graded policies, and the possibility of prioritizing the interventions, if necessary by weighing the economic costs and ease of acceptance with them.
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SciScore for 10.1101/2021.01.15.21249896: (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 The Python implementation of SHAP (https://github.com/slundberg/shap) was used for our interpretable AI analyses as well as for generating figures. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
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.
R…
SciScore for 10.1101/2021.01.15.21249896: (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 The Python implementation of SHAP (https://github.com/slundberg/shap) was used for our interpretable AI analyses as well as for generating figures. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
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.
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