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Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

Authors :
Català Sabaté, Martí
Cardona Iglesias, Pere Joan
Prats Soler, Clara
Alonso Muñoz, Sergio
Álvarez Lacalle, Enrique
Conesa Ortega, David
Perramon Malavez, Aida
Echebarría Domínguez, Blas
López Codina, Daniel
Universitat Politècnica de Catalunya. Departament de Física
Universitat Politècnica de Catalunya. Doctorat en Física Computacional i Aplicada
Universitat Politècnica de Catalunya. BIOCOM-SC - Grup de Biologia Computacional i Sistemes Complexos
Publication Year :
2021

Abstract

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We provide some figures and tables with several indexes and indicators as well as predictions for the next days. We also include a brief analysis of the global situation, as well as the highlights for European countries. On Fridays, we prepare a full analysis of a specific topic. As for the predictions, we employ a validated empirical model based on the Gompertz growth. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-14 days later. We show an individual report with 7 graphs and a summary table with the main indicators for different countries and regions. Only reliable predictions that accomplish with the quality criteria are included. Methodological details are given in the Methods section, at the end of the report. These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00

Details

Language :
English
Database :
OpenAIRE
Accession number :
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