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COVID-19: A Comparative Study of Contagions Peaks in Cities from Europe and the Americas.

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  • Additional Information
    • Source:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE
    • Publication Information:
      Original Publication: Basel : MDPI, c2004-
    • Subject Terms:
      COVID-19*/epidemiology; Humans ; SARS-CoV-2 ; Cities/epidemiology ; Quarantine ; Europe/epidemiology ; China/epidemiology
    • Abstract:
      Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a group of viruses that provoke illnesses ranging from the common cold to more serious illnesses such as pneumonia. COVID-19 started in China and spread rapidly from a single city to an entire country in just 30 days and to the rest of the world in no more than 3 months. Several studies have tried to model the behavior of COVID-19 in diverse regions, based on differential equations of the SIR and stochastic SIR type, and their extensions. In this article, a statistical analysis of daily confirmed COVID-19 cases reported in eleven different cities in Europe and America is conducted. Log-linear models are proposed to model the rise or drop in the number of positive cases reported daily. A classification analysis of the estimated slopes is performed, allowing a comparison of the eleven cities at different epidemic peaks. By rescaling the curves, similar behaviors among rises and drops in different cities are found, independent of socioeconomic conditions, type of quarantine measures taken, whether more or less restrictive. The log-linear model appears to be suitable for modeling the incidence of COVID-19 both in rises and drops.
    • References:
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    • Contributed Indexing:
      Keywords: COVID; cluster analysis; regression model
    • Publication Date:
      Date Created: 20221223 Date Completed: 20221226 Latest Revision: 20230120
    • Publication Date:
      20240513
    • Accession Number:
      PMC9779244
    • Accession Number:
      10.3390/ijerph192416953
    • Accession Number:
      36554833