Selected article for: "country level and time series"

Author: Marco Tulio Pacheco Coelho; Joao Fabricio Mota Rodrigues; Anderson Matos Medina; Paulo Scalco; Levi Carina Terribile; Bruno Vilela; Jose Alexandre Felizola Diniz-Filho; Ricardo Dobrovolski
Title: Exponential phase of covid19 expansion is not driven by climate at global scale
  • Document date: 2020_4_6
  • ID: f0ahn4iu_20
    Snippet: The country-level network (Fig 1) is a directed weighted graph where the links are the number of connections between 226 countries which is collapsed for the 44 countries that had more than 100 cases and for which time series data had at least 10 days after the 100 th case . Afterward, we measured the countries centrality in the network using the Eigenvector Centrality (Bonacich, 1987) , hereafter centrality, that weights the We evaluated the rel.....
    Document: The country-level network (Fig 1) is a directed weighted graph where the links are the number of connections between 226 countries which is collapsed for the 44 countries that had more than 100 cases and for which time series data had at least 10 days after the 100 th case . Afterward, we measured the countries centrality in the network using the Eigenvector Centrality (Bonacich, 1987) , hereafter centrality, that weights the We evaluated the relationship between the predictors (climatic, socioeconomic and transport data) and our growth rate parameter using a standard multiple regression (OLS) after taking into consideration the distribution of the original predictors as well . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

    Search related documents:
    Co phrase search for related documents
    • cc NC ND International license and growth rate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • cc NC ND International license and growth rate parameter: 1
    • cc NC ND International license and International license: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • cc NC ND International license and th case: 1, 2, 3, 4, 5, 6
    • country centrality and growth rate: 1
    • country centrality and network country centrality: 1, 2
    • country level and growth rate: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
    • country level and International license: 1, 2, 3, 4, 5, 6
    • country level and multiple regression: 1, 2, 3, 4, 5, 6
    • country level and th case: 1
    • growth rate and International license: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • growth rate and multiple regression: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • growth rate and network country centrality: 1
    • growth rate and th case: 1, 2
    • growth rate parameter and International license: 1
    • International license and th case: 1, 2, 3, 4, 5, 6, 7, 8
    • multiple regression and standard multiple regression: 1, 2, 3, 4, 5, 6, 7, 8, 9