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Journal article Open Access

Anti-clustering in the national SARS-CoV-2 daily infection counts

Roukema, Boudewijn F.


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Produced and source files for &quot;Anti-clustering in the national SARS-CoV-2 daily infection counts&quot;, 2020, by B. F. Roukema</p>\n\n<ul>\n\t<li>subpoisson-02548e9.pdf - article in pdf format</li>\n\t<li>WP_C19CCTF_SARSCoV2.dat - Wikipedia COVID-19 Case Count Task Force (C19CCTF) medical cases chart data on national daily SARS-CoV-2 infections in csv format; URLs of the specific versions of the source pages are included</li>\n\t<li>WHO_vs_WP_jumps.dat - plain text table comparing jumpiness of WHO to C19CCTF national case count data</li>\n\t<li>phi_N_full.dat - results: clustering parameter phi_i for full national daily infection counts above a threshold of 50 per day</li>\n\t<li>phi_N_28days.dat - as for phi_N_full.dat, for the least noisy 28-day subsequence for each country</li>\n\t<li>phi_N_14days.dat - as for phi_N_28days.dat, for 14-day subsequences</li>\n\t<li>phi_N_07days.dat - as for phi_N_28days.dat, for 7-day subsequences</li>\n\t<li>four files phi_N*_jhu.dat which are equivalent to the main tables, but for the JHU CSSE data, as listed in the appendix of the article</li>\n\t<li>subpoisson-02548e9-git.bundle - git source package that can be unbundled with &#39;git clone subpoisson-02548e9-git.bundle&#39; and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the article pdf</li>\n\t<li>subpoisson-02548e9-arXiv.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv</li>\n\t<li>software-252cf1c.tar.gz - this should contain all the software, apart from a minimal POSIX-compatible system, needed for compiling and installing the software used in producing this paper</li>\n</ul>\n\n<p>Associated resources:</p>\n\n<ul>\n\t<li>ArXiv preprints: <a href=\"https://arXiv.org/abs/2007.11779\">arXiv:2007.11779</a></li>\n\t<li>Software Heritage (swh): <a href=\"https://archive.softwareheritage.org/swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166\">swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166</a></li>\n\t<li>Pop-science description: <a href=\"https://codeberg.org/boud/subpoisson/src/branch/subpoisson/README-popular-science.md\">What is noisiness in SARS-CoV-2 daily infection counts?</a></li>\n</ul>\n\n<p>All the materials here are free-licensed, as stated in the individual files and packages.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Nicolaus Copernicus University", 
      "@id": "https://orcid.org/0000-0002-3772-0250", 
      "@type": "Person", 
      "name": "Roukema, Boudewijn F."
    }
  ], 
  "headline": "Anti-clustering in the national SARS-CoV-2 daily infection counts", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-07-23", 
  "url": "https://zenodo.org/record/3990666", 
  "version": "252cf1c", 
  "keywords": [
    "COVID-19", 
    "Data validation", 
    "Poisson point process", 
    "SARS-CoV-2"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3990666", 
  "@id": "https://doi.org/10.5281/zenodo.3990666", 
  "@type": "ScholarlyArticle", 
  "name": "Anti-clustering in the national SARS-CoV-2 daily infection counts"
}
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