Creation of a Spatiotemporal Algorithm and Application to COVID-19 Data
Author:
Bou Sakr Natalia12ORCID, Mansour Gihane2ORCID, Salhi Yahia1ORCID
Affiliation:
1. Laboratory of Actuarial and Financial Sciences, ISFA, University Claude Bernard Lyon 1, Univ Lyon, 50 Avenue Tony Garnier, F-69007 Lyon, France 2. Laboratory of Mathematics and Applications, Research Unit of Mathematics and Modeling, Faculty of Sciences, Saint Joseph University, Beirut 1104 2020, Lebanon
Abstract
This study offers an in-depth analysis of the COVID-19 pandemic’s trajectory in several member countries of the European Union (EU) in order to assess similarities in their crisis experiences. We also examine data from the United States to facilitate a larger comparison across continents. We introduce our new approach, which uses a spatiotemporal algorithm to identify five distinct and recurring phases that each country underwent at different times during the COVID-19 pandemic. These stages include: Comfort Period, characterized by minimal COVID-19 activity and limited impacts; Preventive Situation, demonstrating the implementation of proactive measures, with relatively low numbers of cases, deaths, and Intensive Care Unit (ICU) admissions; Worrying Situation, is defined by high levels of concern and preparation as deaths and cases begin to rise and reach substantial levels; Panic Situation, marked by a high number of deaths relative to the number of cases and a rise in ICU admissions, denoting a critical and alarming period of the pandemic; and finally, Epidemic Control Situation, distinguished by limited numbers of COVID-19 deaths despite a high number of new cases. By examining these phases, we identify the various waves of the pandemic, indicating periods where the health crisis had a significant impact. This comparative analysis highlights the time lags between countries as they transitioned through these different critical stages and navigated the waves of the COVID-19 pandemic.
Funder
AXA Research Fund as well as the CY Initiative of Excellence Project “EcoDep”
Reference38 articles.
1. Rios, R.A., Nogueira, T., Coimbra, D.B., Lopes, T.J.S., Abraham, A., and de Mello, R.F. (2021). Country Transition Index Based on Hierarchical Clustering to Predict Next COVID-19 Waves. Sci. Rep., 11. 2. Huang, Z. (2021). Spatiotemporal Evolution Patterns of the COVID-19 Pandemic Using Space-Time Aggregation and Spatial Statistics: A Global Perspective. ISPRS Int. J. Geo-Inf., 10. 3. Spassiani, I., Sebastiani, G., and Palù, G. (2021). Spatiotemporal Analysis of COVID-19 Incidence Data. Viruses, 13. 4. Yu, H., Li, J., Bardin, S., Gu, H., and Fan, C. (2021). Spatiotemporal Dynamic of COVID-19 Diffusion in China: A Dynamic Spatial Autoregressive Model Analysis. ISPRS Int. J. Geo-Inf., 10. 5. Signorelli, C., Odone, A., Gianfredi, V., Bossi, E., Bucci, D., Oradini-Alacreu, A., Frascella, B., Capraro, M., Chiappa, F., and Blandi, L. (2020). The Spread of COVID-19 in Six Western Metropolitan Regions: A False Myth on the Excess of Mortality in Lombardy and the Defense of the City of Milan. Acta Bio Med. Atenei Parm., 91.
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