Clustering Countries based on the Trend of Covid-19 Mortality Rates: An Application of Growth Mixture Models



Mohammadreza Balooch Hasankhani1, Yunes Jahani1, Hamid Sharifi2, Ali Jafari-Khounigh3, *, Zahra Khorrami4, *
1 Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
2 HIV/STI Surveillance Research Center, and WHO Collaborating Center for HIV Surveillance, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
3 Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
4 Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abstract

Background:

The pattern of death due to Covid-19 is not the same worldwide and requires special approaches and strategies to identify.

Objective:

This study aimed to investigate the pattern of Covid-19 mortality rates in different countries using the Growth Mixture Model (GMM).

Methods:

This longitudinal study examined mortality trends due to Covid-19 for 214 countries during 2020-2022. Data were extracted from the World Health Organization reports. Countries were classified using Latent Growth Models (LGM) and GMM based on reported death trends.

Results:

Countries worldwide were classified into four clusters with different mortality patterns due to Covid-19. The highest increase in the death rate was related to cluster 2, including three countries of Iran, Peru, and Spain. The lowest increase in the death rate in each period belonged to cluster 1, which included about 60% of the world's countries. In cluster 3, most European countries, the United States, and a few countries from South America and Southeast Asia were placed. Italy was the only country in the fourth cluster.

Conclusion:

Our findings showed which countries performed better or worse in dealing with the Covid-19 pandemic.

Keywords: Covid-19, Mortality rate, Trend, Clustering, Growth mixture model, Longitudinal study, WHO.


Abstract Information


Identifiers and Pagination:

Year: 2023
Volume: 16
DOI: 10.2174/18749445-v16-e230919-2023-120

Article History:

Electronic publication date: 19/09/2023
Collection year: 2023

© 2023 Hasankhani et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to these authors at the Road Traffic Injury Research Center, Tabriz University of Medical Sciences, and Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences Tabriz, Iran; Tel: +98 914 417 2787; +98 917 891 7363; E-mails: jafariali77@gmail.com, zahrakhorrame@ymail.com