Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020

Abstract

ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.SettingOne hundred and eighty countries’ patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.DesignUsing the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries’ final scores for each measure.Primary outcomeThe primary outcomes are the COVID-19 cases and fatality grades in 2020.ResultsThe United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries’ preparedness.ConclusionCOVID-19 fatality can be a good proxy for countries’ resources and system’s resilience in managing the pandemic. These findings suggest that countries’ economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.

Understanding the COVID-19 pandemic prevalence in Africa through optimal feature selection and clustering: evidence from a statistical perspective

“…However, Freed et al ( 2020 ) discusses the clear distinction to be made between the level of socioeconomic achievement for a country on the one side, and on the other side, the preparedness of healthcare systems as well as the willingness of populations to cope with restrictive measures promoted by authorities. These features are decisive to achieve a swift and effective response to the ongoing health crisis (Sadeghi et al, 2021 ; Zhang et al, 2020 ).…”

“…For example, authorities should focus on good governance regarding health directives and open communication, to foster willingness of population to adopt mitigation measures, and also encourage them to get vaccinated. Providing financial support to vulnerable sectors of activities and populations might help in this regard, considering the limited resources of many African countries (James et al, 2018 ; Sadeghi et al, 2021 ).…”

“…Imtyaz et al ( 2020 ) assessed the effectiveness of measures taken by countries to limit the spread of COVID-19 based on 5 clusters identified and concluded the positive association between the mortality rate and the proportion of people over 65 years of age. Sadeghi et al ( 2021 ) used hierarchical clustering to rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare existing pandemic vulnerability prediction models and standard epidemiological scoring techniques. In Africa, the African Center for Strategic Studies (ACSS) identified 3 clusters of African countries by assessing their level of vulnerability through the rating of the 9 following socioeconomic factors: international exposure, healthcare system, urban density, urban population, population age, governmental transparency, press freedom, conflict and displacement (ACSS, 2020a ).…”

Real-Time Analysis of Predictors of COVID-19 Infection Spread in Countries in the European Union Through a New Tool

“…To find the typical groups of countries with similar country characteristics, we perform a hierarchical cluster analysis, which is a common method to form country groups (for example [ 56 , 57 ]). The variables included in the cluster analysis are time-constant, therefore this analysis is conducted only once.…”

Investigating the COVID19 Characteristics of the Countries Based on Time Series Clustering

“…They have used three clustering algorithms, including K-Means, agglomerative and BIRCH. Sadeghi et al [10] used hierarchical clustering algorithm to rank and score 180 countries in terms of COVID19 cases and fatality in 2020. Putra ve Kadyanan [11] have clustered 9 provinces in Bali by using K-Means clustering algorithm.…”