Journal article Open Access
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Roukema, Boudewijn F.</dc:creator> <dc:date>2020-07-23</dc:date> <dc:description>Produced and source files for "Anti-clustering in the national SARS-CoV-2 daily infection counts", 2020, by B. F. Roukema subpoisson-252cf1c.pdf - article in pdf format 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 WHO_vs_WP_jumps.dat - plain text table comparing jumpiness of WHO to C19CCTF national case count data phi_N_full.dat - results: clustering parameter phi_i for full national daily infection counts above a threshold of 50 per day phi_N_28days.dat - as for phi_N_full.dat, for the least noisy 28-day subsequence for each country phi_N_14days.dat - as for phi_N_28days.dat, for 14-day subsequences phi_N_07days.dat - as for phi_N_28days.dat, for 7-day subsequences subpoisson-252cf1c-git.bundle - git source package that can be unbundled with 'git clone subpoisson-252cf1c-git.bundle' and used for reproducibility: to download data, do calculations, analyse them, plot them and produce the article pdf subpoisson-252cf1c-arXiv.tar.gz - source package for producing the article pdf, together with the reproducibility package, but without the git history; appropriate for ArXiv 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 Associated resources: ArXiv preprints: arXiv:2007.11779 Software Heritage (swh): swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166 Pop-science description: What is noisiness in SARS-CoV-2 daily infection counts? All the materials here are free-licensed, as stated in the individual files and packages.</dc:description> <dc:identifier>https://zenodo.org/record/3951152</dc:identifier> <dc:identifier>10.5281/zenodo.3951152</dc:identifier> <dc:identifier>oai:zenodo.org:3951152</dc:identifier> <dc:language>eng</dc:language> <dc:relation>arxiv:arXiv:2007.11779</dc:relation> <dc:relation>swh:swh:1:dir:fcc9d6b111e319e51af88502fe6b233dc78d5166</dc:relation> <dc:relation>doi:10.5281/zenodo.3951151</dc:relation> <dc:relation>url:https://zenodo.org/communities/covid-19</dc:relation> <dc:relation>url:https://zenodo.org/communities/ncu</dc:relation> <dc:relation>url:https://zenodo.org/communities/zenodo</dc:relation> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>COVID-19</dc:subject> <dc:subject>Epidemic curve</dc:subject> <dc:subject>Poisson point process</dc:subject> <dc:title>Anti-clustering in the national SARS-CoV-2 daily infection counts</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> <dc:type>publication-article</dc:type> </oai_dc:dc>
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