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Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (preprint, 2020), "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications"
No full textThis upload contains all replication material for "Ultraviolet radiation decreases COVID-19 growth rates: Global causal estimates and seasonal implications" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded to Zenodo as soon as they are available).
Authors: Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.
Code is located within CCHMP_covid_climate_code_release.zip, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script.
Please find the code needed to replicate the main findings of the paper described below:
Plots of data: R and Stata scripts to make figures 1B, S1, S2, S3, S4 and S13 can be found within “code/analysis/data_plots/”.
Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, S6, S7, S8 and S9 can be found within “code/analysis/regressions/”
Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S5 and S10 can be found within “code/analysis/seasonal_sim/”.
SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S11 and S12 can be found within “code/analysis/SEIR/”.
Data are located within CCHMP_covid_climate_data_release.zip
Data and code release for Carleton, Cornetet, Huybers, Meng & Proctor (PNAS, 2020), "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates"
No full text<p>This upload contains all replication material for "Global evidence for ultraviolet radiation decreasing COVID-19 growth rates" (PNAS, 2020). Please note that previous versions of this upload provided data and code for the pre-print version of the article, which changed somewhat through the peer review process. </p>
<p><strong>Authors:</strong> Tamma Carleton, Jules Cornetet, Peter Huybers, Kyle C. Meng, Jonathan Proctor.</p>
<p><strong>Code is located within CCHMP_covid_climate_code_release.zip</strong>, and is written in R, Stata, and Matlab. The working directory should be set to the repository folder at the top of each script (all other filepaths are relative).</p>
<p>Please find the code needed to replicate the main findings of the paper described below:</p>
<ul>
<li>Plots of data: R and Stata scripts to make figures 1B, 2A/B/C, S1, S2, and S3, can be found within “code/analysis/data_plots/”.</li>
<li>Regression analysis: Stata scripts to run the distributed lag regressions and plot the results in figures 2, 3C, S5, S6, S7, S8, S10, and S14, as well as Table S1, can be found within “code/analysis/regressions/”. R scripts for data analysis and plotting for figures 3A/B and S9 are also within "code/analysis/regressions/".</li>
<li>Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 4, S4 and S11 can be found within “code/analysis/seasonal_sim/”.</li>
<li>SEIR simulations: Matlab scripts to replicate the SEIR simulations shown in figures S12 and S13 can be found within “code/analysis/SEIR/”.</li>
</ul>
<p><strong>Data are located within CCHMP_covid_climate_data_release.zip.</strong></p>
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