Open AccessPrediction of the effective reproduction number of COVID-19 in Greece. A machine learning approach using Google mobility data
Author(s) -
Athanasios Arvanitis,
Irini Furxhi,
Thomas Tasioulis,
Konstantinos Karatzas
Publication year - 2021
Publication title -
journal of decision analytics and intelligent computing
Language(s) - English
Resource type - Journals
ISSN - 2787-2572
DOI - 10.31181/jdaic1001202201f
Subject(s) - random forest , covid-19 , computer science , machine learning , artificial intelligence , correlation , affect (linguistics) , predictive modelling , pearson product moment correlation coefficient , ensemble learning , spearman's rank correlation coefficient , statistics , data mining , mathematics , psychology , medicine , infectious disease (medical specialty) , geometry , disease , communication , pathology
This paper demonstrates how a short-term prediction of the effective reproduction number (Rt) of COVID-19 in regions of Greece is achieved based on online mobility data. Various machine learning methods are applied to predict Rt and attribute importance analysis is performed to reveal the most important variables that affect the accurate prediction of Rt. Work and Park categories are identified as the most important mobility features when compared to the other attributes, with values of 0.25 and 0.24, respectively. Our results are based on an ensemble of diverse Rt methodologies to provide non-precautious and non-indulgent predictions. Random Forest algorithm achieved the highest R2 (0.8 approximately), Pearson’s and Spearman’s correlation values close to 0.9, outperforming in all metrics the other models. The model demonstrates robust results and the methodology overall represents a promising approach towards COVID-19 outbreak prediction. This paper can help health-related authorities when deciding on non-nosocomial interventions to prevent the spread of COVID-19.