A mathematical dashboard for the analysis of Italian COVID ‐19 epidemic data
| Autor: | Alfio Quarteroni, Nicola Parolini, Giovanni Ardenghi, Luca Dedè |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data Analysis
FOS: Computer and information sciences 2019-20 coronavirus outbreak Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Dashboard (business) Biomedical Engineering Statistics - Applications Data visualization Research Article ‐ Applications FOS: Mathematics Humans Applications (stat.AP) Mathematics - Numerical Analysis Quantitative Biology - Populations and Evolution Epidemics Molecular Biology Research Article ‐ Application SARS-CoV-2 business.industry Data Visualization Applied Mathematics Populations and Evolution (q-bio.PE) COVID-19 Numerical Analysis (math.NA) Data science Geography Italy Computational Theory and Mathematics FOS: Biological sciences Modeling and Simulation business Software |
| Zdroj: | International Journal for Numerical Methods in Biomedical Engineering |
| ISSN: | 2040-7947 2040-7939 |
| DOI: | 10.1002/cnm.3513 |
| Popis: | An analysis of the COVID‐19 epidemic is proposed on the basis of the epiMOX dashboard (publicly accessible at https://www.epimox.polimi.it) that deals with data of the epidemic trends and outbreaks in Italy from late February 2020. Our analysis provides an immediate appreciation of the past epidemic development, together with its current trends by fostering a deeper interpretation of available data through several critical epidemic indicators. In addition, we complement the epiMOX dashboard with a predictive tool based on an epidemiological compartmental model, named SUIHTER, for the forecast on the near future epidemic evolution. The epiMOX dashboard (https://www.epimox.polimi.it) enables data analysis of the COVID‐19 epidemic in Italy from late February 2020.epiMOX fosters immediate appreciation of the past and current epidemic trends through critical indicators.epiMOX predicts near future epidemic evolution based on an epidemiological compartmental model named SUIHTER. |
| Databáze: | OpenAIRE |
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