Predicting COVID-19 future trends for different European countries using Pearson correlation

Jihan Muhaidat; Aiman Albatayneh; Ramez Abdallah; Iliana Papamichael; Georgia Chatziparaskeva

doi:10.1007/s41207-022-00307-5

Predicting COVID-19 future trends for different European countries using Pearson correlation

EuroMediterr J Environ Integr. 2022;7(2):157-170. doi: 10.1007/s41207-022-00307-5. Epub 2022 May 12.

Authors

Jihan Muhaidat¹, Aiman Albatayneh², Ramez Abdallah³, Iliana Papamichael⁴, Georgia Chatziparaskeva⁴

Affiliations

¹ Department of Dermatology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110 Jordan.
² Energy Engineering Department, School of Natural Resources Engineering and Management, German Jordanian University, P.O. Box 35247, Amman, 11180 Jordan.
³ Mechanical and Mechatronics Engineering Department, Faculty of Engineering and Information Technology, An-Najah National University, P.O. Box 7, Nablus, Palestine.
⁴ Lab of Chemical Engineering and Engineering Sustainability, Faculty of Pure and Applied Sciences, Open University of Cyprus, Giannou Kranidioti 33, 2220 Nicosia, Cyprus.

Abstract

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases. During the past 18 months, many nations have watched countries whose COVID-19 cases have risen sharply, in anticipation of handling the situation themselves. However, this did not provide accurate results, as no research was conducted that compared countries to determine if their COVID-19 case trends were correlated. As official data on COVID-19 cases has become increasingly available, using the Pearson correlation technique to pinpoint the countries that should be closely monitored will help governments plan and prepare for the number of infections that are expected in the future at an early stage. In this study, a simple and real-time prediction of COVID-19 cases incorporating existing variables of coronavirus variants was used to explore the correlation among different European countries in terms of the number of COVID-19 cases officially recorded on a daily basis. Data from selected countries over the past 76 weeks were analysed using a Pearson correlation technique to determine if there were correlations between case trends and geographical position. The correlation coefficient (r) was employed for identifying whether the different countries in Europe were interrelated, with r > 0.85 indicating they were very strongly correlated, 0.85 > r > 0.8 indicating that they were strongly correlated, 0.8 > r > 0.7 indicating that they were moderately correlated, and r < 0.7 indicating that the examined countries were either weakly correlated or that a correlation did not exist. The results showed that although some neighbouring countries are strongly correlated, other countries that are not geographically close are also correlated. In addition, some countries on opposite sides of Europe (Belgium and Armenia) are also correlated. Other countries (France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden and Turkey) were either weakly correlated or had no relationship at all.

Keywords: ARIMA models; COVID-19; Coronavirus cases prediction; Correlated country; European countries; Pearson correlation.