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

Karine Bertin1, Johanna Garzón2, Jaime San Martín3

  • 1Centro de Investigación y Modelamiento de Fenómenos Aleatorios-Valparaíso, Instituto de Ingeniería Matemática, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2362905, Chile.

International Journal of Environmental Research and Public Health
|December 23, 2022
PubMed

Insights

Log-linear models effectively analyze Coronavirus disease 2019 (COVID-19) case trends. This statistical approach reveals similar epidemic rise and drop patterns across diverse global cities, irrespective of socioeconomic factors or quarantine stringency.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Mathematical Modeling

Background:

  • Coronavirus disease 2019 (COVID-19) rapidly spread globally, necessitating effective epidemiological modeling.
  • Existing studies utilized SIR and stochastic SIR models to understand COVID-19 dynamics.
  • The need for statistical models to analyze daily case incidence across diverse urban settings was apparent.

Purpose of the Study:

  • To statistically analyze daily confirmed COVID-19 cases in eleven European and American cities.
  • To propose and apply log-linear models for assessing trends in daily COVID-19 case numbers.
  • To compare epidemic peak behaviors and identify common patterns in case incidence.

Main Methods:

  • Statistical analysis of daily confirmed COVID-19 cases.
  • Application of log-linear models to quantify case incidence trends (rise and drop).
  • Classification analysis of estimated slopes and curve rescaling for cross-city comparison.

Main Results:

  • Log-linear models demonstrated suitability for modeling both increases and decreases in daily COVID-19 cases.
  • Rescaling of epidemic curves revealed similar rise and drop patterns across the eleven studied cities.
  • Observed epidemic behaviors were independent of socioeconomic conditions and quarantine measures.

Conclusions:

  • Log-linear models provide a robust statistical framework for analyzing COVID-19 incidence dynamics.
  • Epidemic curve shapes exhibit universal characteristics across different urban environments.
  • The findings suggest that underlying transmission dynamics may follow consistent patterns despite external interventions.

Keywords:
COVIDcluster analysisregression model

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