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Methodology for modelling the new COVID-19 pandemic spread and implementation to European countries
- Source :
- Infection, Genetics and Evolution
- Publication Year :
- 2021
- Publisher :
- Elsevier B.V., 2021.
-
Abstract
- After the breakout of the disease caused by the new virus COVID-19, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, we investigated probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this repulsive disease in the future.<br />8 pages, 6 figures and 2 tables
- Subjects :
- 0301 basic medicine
Microbiology (medical)
Physics - Physics and Society
2019-20 coronavirus outbreak
Coronavirus disease 2019 (COVID-19)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Gross Domestic Product
030106 microbiology
Large population
FOS: Physical sciences
Epidemic
Physics and Society (physics.soc-ph)
Biology
Microbiology
03 medical and health sciences
Pandemic
Genetics
Econometrics
Humans
Computer Simulation
Quantitative Biology - Populations and Evolution
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Population Density
Models, Statistical
SARS-CoV-2
Populations and Evolution (q-bio.PE)
Outbreak
COVID-19
Semi-Gaussian
Pathogenicity
Europe
030104 developmental biology
Infectious Diseases
FOS: Biological sciences
SIR
Parametrization
Research Paper
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 15677257 and 15671348
- Volume :
- 91
- Database :
- OpenAIRE
- Journal :
- Infection, Genetics and Evolution
- Accession number :
- edsair.doi.dedup.....88d1ba828391d284ddd404a26cca8497