Clustering the countries for quantifying the status of Covid-19 through time series analysis
| DOI | https://doi.org/10.1108/IDD-03-2021-0034 |
| Published date | 20 September 2021 |
| Date | 20 September 2021 |
| Pages | 297-311 |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
| Author | Madurapperumage Erandathi,William Yu Chung Wang,Chih-Chia Hsieh |
Clustering the countries for quantifying the
status of Covid-19 through time series analysis
Madurapperumage Erandathi
Sabaragamuwa University of Sri Lanka, Belihuloya, Sri Lanka
William Yu Chung Wang
University of Waikato, Hamilton, New Zealand, and
Chih-Chia Hsieh
National Chen Kung University Affiliated Hospital, Tainan, Taiwan
Abstract
Purpose –This study aims to use financial stability and health facilities of countries, to cluster them for making a more consensus environment for
manifesting the status of Covid-19 in a justifiable manner. The scarcity of the categorisation of the countries of the world in a common platform, and
the requirement of manifesting the pandemic status such as Covid-19 in a justifiable manner create the demanding requirement. This study mainly
focusses on assisting to generate a liable manifesto to criticise the span of viral infection of the severe acute respiratory syndrome coronavirus-2
over the globe.
Design/methodology/approach –Data for this study has been gathered from official websites of the World Bank, and the world in data. The
Louvain clustering method has been used to cluster the countries based on their financial strength and health facilities. The resulted cluster s are
visualised using Silhouette plots. The anomalies of the clusters had been used to quantify the pandemic situation. The status of Covid-19 has been
manifested with the time series analysis through python programming.
Findings –The countries of the world have been clustered into seven, where developed countries divided into three clusters and the countries with
transition economies and developing clustered together into four clusters. The time series analysis of recognised anomalies of the clusters assist to
monitor the government responses and analyse the efficiency of used safety measures against the pandemic.
Originality/value –This study’s resulted clusters are highly valuable as a division of countries of the whole world for evaluating the health systems
and for the regional levels. Further, the results of time series analysis are beneficial in monitoring the government responses and analysing the
efficiency of used safety measures against the pandemic.
Keywords Clustering, Machine learning, SARS-COV-2, Time series analysis, Covid-19, Health systems evaluation, Infection prevention and control
Paper type Conceptual paper
1. Introduction
A Coronavirus causesthe existing Covid-19 pandemic, initially
reported from Wuhan, China, in December 2019. The WHO
named the pandemic severe acute respiratory syndrome
coronavirus-2 (SARS-COV-2) in January 2020 by making a
public health emergency at internationalconcern. Severe acute
respiratory syndrome (SARS) and Middle East respiratory
syndrome coronavirus (MERS-CoV) are similar pandemics
reported in the past. Even though the infectionhas been limited
to China due to its high prevalence rate, it has quickly spread
worldwide. It took 67 days for the first reported case to reach
the first 100,000 cases, 11days for the second 100,000, just 4
days for the third 100,000 and reached 61,964,890 instances
reported by the Worldometers on 23 November 2020
(Worldometer, 2020). Due to the high prevalence rate and
unavailability of a successful vaccine, the countries follow
various prevention mechanisms. WHO and US Centres for
Disease Control and Prevention recommended the viral
infection’s preventive actions as avoiding contact with
individuals with symptoms and travel to high-risk areas.
Further, necessary hygiene measures are advised to follow,
such as using personal protective equipments, sneeze to the
elbow, frequent hand washing, avoiding handshaking,
maintaining a safe distance amongst people and avoiding face
touching (Riaz et al.,2020). With the inequalities of the
pandemic status, such as the prevalence of the disease, deaths
and recovered cases worldwide, the preventive mechanisms
used in different countries have been criticised as there are
different ways of readiness in preparing the medical supplies
and implementing the prevention policies and suggestions.
Further, there are some environmental and social factors
dynamically influencing the prevalence of the pandemic. The
geographical location, accessibility to the borders of the
countries, the weather condition of the countries in the peak
The current issue and full text archiveof this journal is available on Emerald
Insight at: https://www.emerald.com/insight/2398-6247.htm
Information Discovery and Delivery
50/3 (2022) 297–311
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2021-0034]
Received 18 March 2021
Revised 29 June 2021
21 July 2021
10 August 2021
17 August 2021
Accepted 20 August 2021
297
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