Analysis of the Distribution of COVID-19 in Italy Using Clustering Algorithms

Doroshenko, Anastasiya

doi:10.1109/dsmp47368.2020.9204202

Cited by 13 publications

(9 citation statements)

References 2 publications

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“…Our purpose is to group countries that showed a comparable discussion about COVID-19 vaccination. We have used the Hierarchical Clustering [50] method that groups data objects into hierarchies or “trees” of clusters. More specifically, we have employed Agglomerative Hierarchical Clustering [51] that starts by letting each object form its own cluster and iteratively merges the cluster into larger ones.…”

Section: Data Exploration By Hierarchical Clusteringmentioning

confidence: 99%

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Canaparo¹,

Ronchieri²,

Scarso³

2023

Healthcare Analytics

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Section: Data Exploration By Hierarchical Clusteringmentioning

confidence: 99%

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Canaparo¹,

Ronchieri²,

Scarso³

2023

Healthcare Analytics

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“…Lai et al (2020) used a clustering technique to classify geographic, demographic and socioeconomic features to understand the coronavirus at the county level. Doroshenko (2020) provided different clustering algorithms, including k-means and hierarchical clustering, in analyzing COVID-19 in Italy. Yang et al (2023) used clustering methods, including k-means, hierarchical clustering, the k-means autoencoder and the k-means self-organizing map, to analyze medical data to build a smart health-care system in the context of the 2022 epidemic trends in Taiwan.…”

Section: Related Workmentioning

confidence: 99%

“…(2020) used a clustering technique to classify geographic, demographic and socioeconomic features to understand the coronavirus at the county level. Doroshenko (2020) provided different clustering algorithms, including k-means and hierarchical clustering, in analyzing COVID-19 in Italy. Yang et al.…”

Section: Related Workmentioning

confidence: 99%

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

Hettiarachchi,

Sun,

et al. 2024

JSIT

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Purpose The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively. Design/methodology/approach In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment. Findings The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge. Originality/value To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

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“…Niu ve arkadaşları adalardaki elektrik enerjisi problemini çözmek için hiyerarşik kümeleme ve K-means kümeleme algoritmasından yararlanmıştır [10]. Doroshenko İtalyada'daki Covid-19 vakalarının sınıflandırılmasında hiyerarşik kümeleme ve K-means algoritmasını kullanmıştır [11]. Li ve Xing canlı hayvanları insansız hava aracı ile izlemek için K-means algoritmasından yararlanmıştır [12].…”

Section: Introductionunclassified

İmar uygulamalarında dağıtımın veri madenciliği yöntemi kullanılarak yapılması

KARAASLAN¹,

Azgınoglu²,

Taşyürek³

2022

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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Şehirlerin plansız büyümeleri zamanla çarpık şehirleşme sorununu da beraberinde getirmiştir. Düzenli bir şehir modeli oluşturmak için belediyelerce, mevcut haliyle düzensiz kadastro parselleri bulunan alanların düzenli parseller haline getirilmesi gerekmekte, bu ise imar düzenlemeleriyle mümkün olmaktadır. Şehirleşmelerde insanlar daha büyük topluluklar haline gelmeye başladığında ortaya çıkan sosyal yaşantı alanlarının oluşturulması problemi için kullanılan yöntemlerden bir tanesi 3194 Sayılı İmar Kanunu'nun 18. Maddesi uygulamasıdır. Bu uygulama daha düzenli şehirleşmeye geçmek için belediyeler tarafından resen yapılmakta olup, dağıtım işleminde kadastro parselinin hangi imar parselinden verileceği manuel olarak yapılmaktadır. Bu durum hem uygulamanın yapılma süresini uzatmakta hem de yeni verilen parsellerde ortak sayısının optimumdan uzak gerçekleşmesine yol açmaktadır. Çalışma kapsamında ele alınan ve karakteristik olarak bir kümeleme problemi olan dağıtım problemine mekânsal veri madenciliği kapsamında kümeleme yöntemlerinden olan K-means ve Adaptif K-means algoritmalarıyla çözüm aranmış, 12 adet kadastro parsel alanının 6 adet imar parseline dağıtımı gerçekleştirilmiştir. Deneysel değerlendirmeler sonucunda Klasik K-means yönteminde parsellerde dağıtılamayan minimum alan 435 km2 iken Adaptik K-means yönteminde bu oran 23 km2'ye kadar düşmüş olup, genel değerlendirmede de yine Adaptif K-means yöntemi öne çıkmıştır.The unplanned growth of cities has brought about unplanned urbanization over time. To generate a regular city model, municipalities need to transform the areas with irregular cadastral parcels into standard parcels, which is possible with zoning regulations. One of the methods used to generate social life areas that arise when people start to become larger communities in urbanization is the application of Article 18 of the Zoning Law No. 3194. The municipalities make this application ex officio to switch to more regular urbanization. The zoning parcels the cadastral parcel will be given in the distribution process is done manually. This situation prolongs the implementation period and causes the number of partners to be far from the optimum in the newly assigned parcels. A solution has been sought for the distribution problem, clustering problem characteristically, which is considered within the scope of the study, with K-means and Adaptive K-means Algorithms, which are clustering method within the scope of spatial data mining. Twelve cadastral parcel areas were distributed to 6 zoning parcels. As a result of the experimental evaluations, it was seen that the Adaptive Kmeans algorithm was more successful. As a result of the experimental assessment, while the minimum area that cannot be distributed in the parcels was 435 km2 in the Classical K-means method, this ratio decreased to 23 km2 in the Adaptive K-means method, and the Adaptive Kmeans method came to the fore in the general evaluation.

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Analysis of the Distribution of COVID-19 in Italy Using Clustering Algorithms

Abstract: This article describes the using of various clustering methods for in-depth analysis and further researches of the spread of the virus COVID-19 in Italy during February-April 2020.

Cited by 13 publications

References 2 publications

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

County-level prioritization for managing the Covid-19 pandemic: a systematic unsupervised learning approach

İmar uygulamalarında dağıtımın veri madenciliği yöntemi kullanılarak yapılması

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