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Carleton,Cornetet,Huybers,Meng的数据和代码发布&Proctor(预印本,2020年),"紫外线降低COVID-19增长率的证据:全球估计和季节性影响"

塔马卡尔顿; Cornetet,朱尔斯; 彼得·休伯斯; 孟凯莉C.; 乔纳森·普罗克特


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  <dc:contributor>Carleton, Tamma</dc:contributor>
  <dc:contributor>Cornetet, Jules</dc:contributor>
  <dc:contributor>Huybers, Peter</dc:contributor>
  <dc:contributor>Meng, Kyle C.</dc:contributor>
  <dc:contributor>Proctor, Jonathan</dc:contributor>
  <dc:creator>Carleton, Tamma</dc:creator>
  <dc:creator>Cornetet, Jules</dc:creator>
  <dc:creator>Huybers, Peter</dc:creator>
  <dc:creator>Meng, Kyle C.</dc:creator>
  <dc:creator>Proctor, Jonathan</dc:creator>
  <dc:date>2020-06-17</dc:date>
  <dc:description>此上载包含所有复制材料"紫外线降低COVID-19增长率的证据:全球估计和季节性影响" (preprint). Please note that this manuscript is under review and the data and code are likely to change (updated versions will be uploaded 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 (all other filepaths are relative).

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, 2A/B, S1, S2, S3, and S9 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, S5, S6, S7, S8, and S13, as well as Table S1, can be found within “code/analysis/regressions/”
	Seasonal simulations: R and Stata scripts to replicate the seasonal simulation shown in figures 3, S4 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.</dc:description>
  <dc:identifier>//americinnmankato.com/record/3899594</dc:identifier>
  <dc:identifier>10.5281 / zenodo.3899594</dc:identifier>
  <dc:identifier>oai:zenodo.org:3899594</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:http://ssrn.com/abstract=3588601</dc:relation>
  <dc:relation>doi:10.2139/ssrn.3588601</dc:relation>
  <dc:relation>doi:10.5281 / zenodo.3829621</dc:relation>
  <dc:relation>url://americinnmankato.com/communities/covid-19</dc:relation>
  <dc:relation>url://americinnmankato.com/communities/zenodo</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>//creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>COVID-19</dc:subject>
  <dc:subject>climate</dc:subject>
  <dc:subject>紫外线辐射</dc:subject>
  <dc:title>Carleton,Cornetet,Huybers,Meng的数据和代码发布&amp;Proctor(预印本,2020年),"紫外线降低COVID-19增长率的证据:全球估计和季节性影响"</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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