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A simple model for the total number of SARS-CoV-2 infections on a national level.
Blanco, N; Stafford, K A; Lavoie, M C; Brandenburg, A; Górna, M W; Merski, M.
  • Blanco N; Center for International Health, Education, and Biosecurity, Institute of Human Virology-University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Stafford KA; Center for International Health, Education, and Biosecurity, Institute of Human Virology-University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Lavoie MC; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Brandenburg A; Center for International Health, Education, and Biosecurity, Institute of Human Virology-University of Maryland School of Medicine, Baltimore, Maryland, USA.
  • Górna MW; Nordita, KTH Royal Institute of Technology and Stockholm University, SE-10691, Stockholm, Sweden.
  • Merski M; Structural Biology Group, Biological and Chemical Research Centre, Department of Chemistry, University of Warsaw, Warsaw, Poland.
Epidemiol Infect ; 149: e80, 2021 03 25.
Article in English | MEDLINE | ID: covidwho-1211252
ABSTRACT
This study aimed to identify an appropriate simple mathematical model to fit the number of coronavirus disease 2019 (COVID-19) cases at the national level for the early portion of the pandemic, before significant public health interventions could be enacted. The total number of cases for the COVID-19 epidemic over time in 28 countries was analysed and fit to several simple rate models. The resulting model parameters were used to extrapolate projections for more recent data. While the Gompertz growth model (mean R2 = 0.998) best fit the current data, uncertainties in the eventual case limit introduced significant model errors. However, the quadratic rate model (mean R2 = 0.992) fit the current data best for 25 (89%) countries as determined by R2 values of the remaining models. Projection to the future using the simple quadratic model accurately forecast the number of future total number of cases 50% of the time up to 10 days in advance. Extrapolation to the future with the simple exponential model significantly overpredicted the total number of future cases. These results demonstrate that accurate future predictions of the case load in a given country can be made using this very simple model.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Logistic Models / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2021 Document Type: Article Affiliation country: S0950268821000649

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Logistic Models / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Epidemiol Infect Journal subject: Communicable Diseases / Epidemiology Year: 2021 Document Type: Article Affiliation country: S0950268821000649