Clustering analysis of countries using the COVID-19 cases dataset

Zarikas, Vasilios; Poulopoulos, Stavros G.; Gareiou, Zoe; Zervas, Efthimios

doi:10.1016/j.dib.2020.105787

Cited by 86 publications

(60 citation statements)

References 4 publications

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“…They considered a data-driven cluster analysis based on six variables from 8980 patients of the Swedish All New Diabetes in Scania cohort ( Ahlqvist et al., 2018 ). Another recent application of this procedure on Covid-19 was done by Zarikas et al. (2020) , observing the clustering of 30 countries with respect to active cases, active cases per population and active cases per population and per area.…”

Section: Resultsmentioning

confidence: 99%

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

Nascimento¹

2020

Infectious Disease Modelling

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This data-driven work aims to analyze and classify the spatiotemporal distribution of all Brazilian states considering data so diverse as the number of Covid-19 cases, deaths, confirmed cases per 100 k inhabitants, mortality per 100 k inhabitants and case fatality rates as health indicators. We also considered population, area and population density as geographic indicators. Finally, GDP and HDI were taken into account as economic and social criteria. For this task data were collected from April 3rd until August 8th, 2020, corresponding to epidemiological weeks 14–32, reaching three million cases and a hundred thousand deaths. With this data it was possible to classify Brazilian states using multivariate methods into possible groups by means of non-hierarchical ( k -means) cluster as well as factor analysis. It was possible to group all states plus the Federal District into five clusters, taking into account these 10 variables over the first five months of the epidemic. Group changes between states were observed over time and clusters, and between three and four factors were found. However, even with great difference on health indicators during days, the number of clusters remains fixed. Also, São Paulo and Rio de Janeiro states were ranked at top list taking into account all epidemiological weeks. Correlations were observed between variables, such as the number of Covid cases and deaths with GDP for most of epidemiological weeks. Some clusters were more critical due to specific variables, including cities that are main hotspots. These multivariate findings would provide a comprehensive description of the ongoing Covid-19 epidemic and may help to guide subsequent studies to understand and control virus transmission.

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Section: Resultsmentioning

confidence: 99%

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

Nascimento¹

2020

Infectious Disease Modelling

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“…The contribution of cluster detections and analysis in COVID-19 pandemic is becoming useful nowadays as it detects active and emerging clusters of COVID-19 and notify epidemiologist, decision makers, and public health care officials, which can help in eradicating infections from affected sites, and improving interventions, quarantine, and isolation measures. The significant applications of clustering with respect to infectious diseases modelling are demonstrated across the world (Zarikas et al 2020 ), for example, India (Bhosale and Shinde 2020 ), USA (Desjardins et al 2020 ; Hohl et al 2020 ), Brazil (Martines et al 2020 ), Italy (Cereda et al 2020 ), China (Ji et al 2020 ; Liu et al 2020a , b ; Qiu et al 2020 ; Zhang et al 2020 ), Singapore (Bhosale and Shinde 2020 ; Pung et al 2020 ), South Korea (Shim et al 2020 ), French Alps (Danis et al 2020 ), Germany (Pfefferle et al 2020 ), Sergipe (Andrade et al 2020 ), etc.…”

Section: Infectious Diseases Modellingmentioning

confidence: 99%

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

Saran

Singh

Kumar

et al. 2020

J Indian Soc Remote Sens

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This paper discusses on the increasing relevancy of geospatial technologies such as geographic information system (GIS) in the public health domain, particularly for the infectious disease surveillance and modelling strategies. Traditionally, the disease mapping tasks have faced many challenges-(1) authors rarely documented the evidence that were used to create map, (2) before evolution of GIS, many errors aroused in mapping tasks which were expanded extremely at global scales, and (3) there were no fidelity assessment of maps which resulted in inaccurate precision. This study on infectious diseases geo-surveillance is divided into four broad sections with emphasis on handling geographical and temporal issues to help in public health decision-making and planning policies: (1) geospatial mapping of diseases using its spatial and temporal information to understand their behaviour across geography; (2) the citizen's involvement as volunteers in giving health and disease data to assess the critical situation for disease's spread and prevention in neighbourhood effect; (3) scientific analysis of health-related behaviour using mathematical epidemiological and geo-statistical approaches with (4) capacity building program. To illustrate each theme, recent case studies are cited and case studies are performed on COVID-19 to demonstrate selected models.

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“…This dataset stores information about the first confirmed case and first confirmed the death of COVID-19 in 186 countries up to May 16, 2020. Vasilios [1] presents a dataset containing information related to the cluster-based active case, active case per population, and area for every country. Kabir and Peter [2] , [3] show the online forecasting mechanism for every 24 h and survey about risk perception and health behavior among Nigerian using Nigerian Government Data.…”

Section: Data Descriptionmentioning

confidence: 99%

Covid-19 Dataset: Worldwide spread log including countries first case and first death

et al. 2020

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The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.

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Clustering analysis of countries using the COVID-19 cases dataset

Cited by 86 publications

References 4 publications

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

A multivariate analysis on spatiotemporal evolution of Covid-19 in Brazil

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

Covid-19 Dataset: Worldwide spread log including countries first case and first death

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