Evidence map›Paper›PMID 35365101›Full record

ArticleBMC public health2022

Clustering of countries according to the COVID-19 incidence and mortality rates.

Kimiya Gohari, Anoshirvan Kazemnejad, Ali Sheidaei, Sarah Hajari

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Article in BMC public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

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6citing papers in PubMed
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3 · Its place in the literature

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6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Kimiya GohariDepartment of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, P.O. BOX 14115-111, Tehran, Iran.
Anoshirvan KazemnejadDepartment of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, P.O. BOX 14115-111, Tehran, Iran. kazem_an@modares.ac.ir.
Ali SheidaeiDepartment of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Sarah HajariDepartment of Computer Science, University of the Sunshine Coast, Sippy Downs, Queensland, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTwo years after the beginning of the COVID-19 pandemic on December 29, 2021, there have been 281,808,270 confirmed cases of COVID-19, including 5,411,759 deaths. This information belongs to almost 216 Countries, areas, or territories facing COVID-19. The disease trend was not homogeneous across these locations, and studying this variation is a crucial source of information for policymakers and researchers. Therefore, we address different patterns in mortality and incidence of COVID-19 across countries using a clustering approach.

methodsThe daily records of new cases and deaths of 216 countries were available on the WHO online COVID-19 dashboard. We used a three-step approach for identifying longitudinal patterns of change in quantitative COVID-19 incidence and mortality rates. At the first, we calculated 27 summary measurements for each trajectory. Then we used factor analysis as a dimension reduction method to capture the correlation between measurements. Finally, we applied a K-means algorithm on the factor scores and clustered the trajectories.

resultsWe determined three different patterns for the trajectories of COVID-19 incidence and the three different ones for mortality rates. According to incidence rates, among 206 countries the 133 (64.56) countries belong to the second cluster, and 15 (7.28%) and 58 (28.16%) belong to the first and 3rd clusters, respectively. All clusters seem to show an increased rate in the study period, but there are several different patterns. The first one exhibited a mild increasing trend; however, the 3rd and the second clusters followed the severe and moderate increasing trend. According to mortality clusters, the frequency of sets is 37 (18.22%) for the first cluster with moderate increases, 157 (77.34%) for the second one with a mild rise, and 9 (4.34%) for the 3rd one with severe increase.

conclusionsWe determined that besides all variations within the countries, the pattern of a contagious disease follows three different trajectories. This variation looks to be a function of the government's health policies more than geographical distribution. Comparing this trajectory to others declares that death is highly related to the nature of epidemy.

Indexed as

COVID-19Cluster AnalysisFactor Analysis, StatisticalHumansIncidencePandemicsClusteringCOVID-19Incidence rateMortality rateTrajectory

Identifiers

PMID35365101
PMCPMC8972710

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.