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

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 employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. 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 tendencies. Note, however, that the effects of the measures' control that start on a given day are not observed until approximately 5-7 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. Next, we show a report with 8 graphs and a table with the short-term predictions for (1) European Union and its countries, (2) other countries, (3) Spain and its autonomous communities. We are currently adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold: Group A: countries that have reported more than 100 cumulated cases for 10 consecutive days or more ¿ 3-5 days prediction; Group B: countries that have reported more than 100 cumulated cases for 7 to 9 consecutive days ¿ 2 days prediction; Group C: countries that have reported more than 100 cumulated cases for 4 to 6 days ¿ 1 d ay prediction. We have introduced a change in fittings, that are now weighted at some points. The whole methodology employed in the inform is explained in the last pages of this document. ; 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 ; Postprint (published version)