A multivariate data analysis approach for investigating daily statistics of countries affected with COVID-19 pandemic

Ramadan, Ahmed; Kamel, Ahmed H.; Taha, Alaa; Elshabrawy, Abdelhamid; Abdel-Fatah, Noura Anwar

doi:10.1016/j.heliyon.2020.e05575

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…A more interesting approach that uses statistical dimension reduction techniques along with clustering algorithms was proposed by Ramadan et al . 18 Their analysis focused on 30 March, 15 April and 25 April data points, including 56, 82 and 91 countries, respectively. They first applied principal component analysis on several COVID-19 related measures, then performed clustering using the Partition around Medoid algorithm.…”

Section: Discussionmentioning

confidence: 99%

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

Sadeghi¹,

Cheung

Hanbury³

2021

BMJ Open

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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.

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Section: Discussionmentioning

confidence: 99%

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

Sadeghi¹,

Cheung

Hanbury³

2021

BMJ Open

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“…The utilization of graphical highlights, like the Scatterplot Matrix and Color Maps, to identify dependencies, outliers, and clusters among the variables [9]. There are extra multivariate investigation strategies to further look at the connection between variables, including head segments examination, anomaly examination, and thing unwavering quality [10]. These methods are accessible through the Multivariate report.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

Aashmi¹,

Jaya²

2023

Intelligent Automation &Amp; Soft Computing

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Cloud computing is the technology that is currently used to provide users with infrastructure, platform, and software services effectively. Under this system, Platform as a Service (PaaS) offers a medium headed for a web development platform that uniformly distributes the requests and resources. Hackers using Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks abruptly interrupt these requests. Even though several existing methods like signature-based, statistical anomaly-based, and stateful protocol analysis are available, they are not sufficient enough to get rid of Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks and hence there is a great need for a definite algorithm. Concerning this issue, we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis, Spearman coefficient, and mitigation technique. It can easily differentiate common traffic and attack traffic. Not only that, it greatly helps the network to distribute the resources only for authenticated requests. The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99% for the numerous DoS attack as well as DDoS attacks.Keywords: Hybrid algorithm (HA); distributed denial of service (DDoS); denial of service (DoS); platform as a service (PaaS); infrastructure as a service (IaaS); software as a service (SaaS)

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“…Among them are the studies of Mohammad Reza Mahmoudi et al, [45], Ye W et al, [46], S.M. Didar-Ul Islam et al, [47], Konishi [48] (2020), Ahmed Ramadan et al, [49], Ilan et al, [40] and Ceyhun Elgin et al, [50] however these studies mostly use only PCA analysis and do not integrate PCA with GIS.…”

Section: Geographic Information System (Gis) and Principal Component ...mentioning

confidence: 99%

Mapping Of Population Behaviour During The Early Phase Of COVID19 Disease Spread In Kota Kinabalu, Sabah Using PCA-GIS

Kalang

Eboy

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Coronavirus 2019 (COVID19) has now become a pandemic. In the early stages of the pandemic, Kota Kinabalu was one of the districts in Sabah with the greatest number of COVID19 positive cases. Despite the government’s announcement of the Movement Control Order (MCO), some residents were unable to follow the rules. As a result, the number of COVID19 positive cases in Sabah has increased, particularly in the Kota Kinabalu district. The increase of COVID 19 cases is indeed influenced by the behavior of the population in a place. The behavior of the population during previous pandemics has already shown how human attitudes can affect the spread of disease in the area. In Sabah, it is also seen to occur, such as violating the movement control order. Due to a shortage of manpower, it is difficult to identify and monitor residents who violate this MCO. Geographical factors in Sabah are also among the reasons why authorities find it difficult to keep control over all areas. In addition, the lack of technology, such as Geographic Information System (GIS), has made it difficult for authorities to monitor all locations. As a result, using Principal Component Analysis (PCA), this study was undertaken to identify the primary determinants of population behaviour that cause the spread of COVID19, which was then mapped using Geographic Information System (GIS). Only zones that registered positive cases of COVID19 from March to August 2020 were included in this study, which included a total of 100 respondents in the Kota Kinabalu area. Population behaviour, factor location, and responder location are among the data sources. To investigate the pattern of population behaviour in Kota Kinabalu, this study used factor analysis using PCA and the classification method using GIS. The study’s findings include a behaviour pattern map for the Kota Kinabalu district, which influences COVID19 distribution in the early phases of the pandemic. This study can assist various parties in identifying a potential area in Kota Kinabalu that has a high risk of COVID19 infection.

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A multivariate data analysis approach for investigating daily statistics of countries affected with COVID-19 pandemic

Cited by 8 publications

References 19 publications

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

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

Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

Mapping Of Population Behaviour During The Early Phase Of COVID19 Disease Spread In Kota Kinabalu, Sabah Using PCA-GIS

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