COVID-LIBERTY, A Machine Learning Computational Framework for the Study of the Covid-19 Pandemic in Europe. Part 1: Building of an Artificial Neural Network and Analysis and Parametrization of Key Factors which Influence the Spread of the Virus.

Abstract

Covid-19 is the most recent strain from the corona virus family that its rapid spread across the globe has caused a pandemic, resulting in over 200,000,000 infections and over 4,000,000 deaths so far. Many countries had to impose full lockdowns, with serious effects in all aspects of everyday life (economic, social etc.). In this paper, a computational framework is introduced, aptly named COVID-LIBERTY, in order to assist the study of the pandemic in Europe. Initially, the mathematics and details of the computational engine of the framework, a feed-forward, back-propagation Artificial Neural Network are presented. 5 European countries with similar population numbers were chosen and we examined the main factors that influence the spread of the virus, in order to be taken into consideration in the simulations. In this way lockdown, seasonal variability and virus effective reproduction were considered. The effectiveness of lockdown in the spread of the virus was examined and the Lockdown Index was introduced. Moreover, the relation of Covid- 19 to seasonal variability was demonstrated and the parametrization of seasonality presented.