Open AccessCOVID-LIBERTY, A Machine Learning Computational Framework for the Study of the Covid-19 Pandemic in Europe. Part 2: Setting up the Framework with Ensemble Modeling
Author(s) -
Nicholas A. Christakis,
Panagiotis Tirchas,
Michael Politis,
Minas Achladianakis,
Eleftherios Avgenikou,
Georgios T. Kossioris
Publication year - 2021
Publication title -
international journal of neural networks and advanced applications
Language(s) - English
Resource type - Journals
ISSN - 2313-0563
DOI - 10.46300/91016.2021.8.4
Subject(s) - covid-19 , pandemic , artificial neural network , computer science , population , order (exchange) , artificial intelligence , machine learning , infectious disease (medical specialty) , disease , virology , medicine , outbreak , economics , finance , pathology , environmental health
The Covid-19 pandemic has caused within a period of one year and eight months over 200,000,000 infections and more than 4,000,000 deaths. It is of paramount importance to design powerful and robust tools in order to be able to predict the evolution of the disease. In this paper, the computational framework COVID-LIBERTY is introduced, in order to assist the study of the pandemic in Europe. In Part 1, important parameters that should be taken into consideration and their parametrizations were given, as well as the details and mathematics of the computational engine of COVID-LIBERTY, a feed-forward, back-propagation Artificial Neural Network. In Part 2, the CPRT index is introduced, the framework setup around the Artificial Neural Network is presented and the algorithm of ensemble modeling is discussed, which improves the accuracy of the predictions. In the simulations, 4 European countries with similar population numbers were considered. The capabilities of the COVID-LIBERTY framework for accurate predictions for periods up to 19 days will be demonstrated.