2023
Sousa, R.; Oliveira, D.; Hak, F.; Machado, J.
The Impact of Contingency Measures on the COVID-19 Reproduction Rate Proceedings Article
Em: J.M., Peixoto H. Machado (Ed.): pp. 28-37, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18678211, (cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899).
Resumo | Links | BibTeX | Etiquetas: Big data analyse; Contigency; Contigency measure; Correlation; Covid-19; Gross domestic products; Powerbi; Preventive measures; Proliferation rate; Real- time, Big data; Cell proliferation; Data handling; Digital storage, COVID-19
@inproceedings{Sousa202328,
title = {The Impact of Contingency Measures on the COVID-19 Reproduction Rate},
author = {R. Sousa and D. Oliveira and F. Hak and J. Machado},
editor = {Peixoto H. Machado J.M.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172722736&doi=10.1007%2f978-3-031-38204-8_3&partnerID=40&md5=f2bd2a51fa043e2d5a2f974d3f33ef48},
doi = {10.1007/978-3-031-38204-8_3},
issn = {18678211},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
volume = {485 LNICST},
pages = {28-37},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The SARS-CoV-2 virus had a major impact on the health of the world’s population, causing governments to take progressively more cautious measures. All of these measures took into account the pandemic situation in the region in real time, with the aim of slowing down the spread of the infection as much as possible and reducing the associated mortality. This article aims to study the impact of preventive measures on the spread of COVID-19 and the consequent impact on excess deaths. In order to obtain the results presented, Big Data techniques were used for data storage and processing. As a result it can be concluded that Gross Domestic Product (GDP) is directly proportional to the Human Development Index (HDI), Higher GDP per capita are associated with a higher number of new cases of COVID-19 and R-index is inversely proportional to the severity of the contingency measures. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
note = {cited By 0; Conference of 3rd International Conference on AI-assisted Solutions for COVID-19 and Biometrical Applications in Smart Cities, AISCOVID-19 2022 ; Conference Date: 16 November 2022 Through 18 November 2022; Conference Code:298899},
keywords = {Big data analyse; Contigency; Contigency measure; Correlation; Covid-19; Gross domestic products; Powerbi; Preventive measures; Proliferation rate; Real- time, Big data; Cell proliferation; Data handling; Digital storage, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
The SARS-CoV-2 virus had a major impact on the health of the world’s population, causing governments to take progressively more cautious measures. All of these measures took into account the pandemic situation in the region in real time, with the aim of slowing down the spread of the infection as much as possible and reducing the associated mortality. This article aims to study the impact of preventive measures on the spread of COVID-19 and the consequent impact on excess deaths. In order to obtain the results presented, Big Data techniques were used for data storage and processing. As a result it can be concluded that Gross Domestic Product (GDP) is directly proportional to the Human Development Index (HDI), Higher GDP per capita are associated with a higher number of new cases of COVID-19 and R-index is inversely proportional to the severity of the contingency measures. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
2022
Trisuciana, F. M.; Witarsyah, D.; Sutoyo, E.; Machado, J. M.
Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using the K-Medoids Algorithm Proceedings Article
Em: Institute of Electrical and Electronics Engineers Inc., 2022, ISBN: 9781665463874, (cited By 1; Conference of 4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022 ; Conference Date: 23 November 2022 Through 24 November 2022; Conference Code:186654).
Resumo | Links | BibTeX | Etiquetas: Cluster analysis; Clustering algorithms; Data mining; Vaccines, Clustering methods; Clustering process; Clusterings; Condition; Jakarta; K-medoids; K-medoids algorithms; Pandemic; Social-economic; Vaccination, COVID-19
@inproceedings{Trisuciana2022,
title = {Clustering of COVID-19 Vaccination Recipients in DKI Jakarta Using the K-Medoids Algorithm},
author = {F. M. Trisuciana and D. Witarsyah and E. Sutoyo and J. M. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148897632&doi=10.1109%2fICADEIS56544.2022.10037509&partnerID=40&md5=661310bce5d9f9c229e0e64542e79a81},
doi = {10.1109/ICADEIS56544.2022.10037509},
isbn = {9781665463874},
year = {2022},
date = {2022-01-01},
journal = {Proceedings - International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained in the simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209. The clusters obtained are clusters 0 to cluster 5. The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters. © 2022 IEEE.},
note = {cited By 1; Conference of 4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022 ; Conference Date: 23 November 2022 Through 24 November 2022; Conference Code:186654},
keywords = {Cluster analysis; Clustering algorithms; Data mining; Vaccines, Clustering methods; Clustering process; Clusterings; Condition; Jakarta; K-medoids; K-medoids algorithms; Pandemic; Social-economic; Vaccination, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
The COVID-19 pandemic has become a serious problem that has attacked various aspects of life such as social, economic, religious, and others. The government has held a COVID-19 vaccination program as an effort to deal with the COVID-19 problem since January 13, 2021. Many problems occurred due to difficulties in dividing the vaccination recipient areas. This is due to the large number of regions with different conditions for each region. One of the efforts to assist the process of processing large vaccination data is data mining techniques and using the clustering method with the K-medoids algorithm. In this study, data on COVID-19 vaccination was grouped in the East Jakarta area using the K-medoids algorithm clustering method. The calculation is carried out using the Euclidean Distance equation and the value of S > 0. The grouped area categories are at the kelurahan level which will then be divided into several clusters. The clustering process was carried out with RapidMiner on 267 kelurahan data on four main attributes, namely the number of targets, the number of vaccine doses 1, the number of vaccine doses 2, and the number of vaccine doses 3. The clustering process was carried out in 6 simulations with variations of k medoids as much as 2 to 7. The results of clustering show the best number of clusters obtained in the simulation is cluster 6 with the smallest Davies Bouldin Index (DBI) value of 0.209. The clusters obtained are clusters 0 to cluster 5. The cluster that is prioritized in giving vaccinations is cluster 2 with 67 items because its members are areas in DKI Jakarta and give a high score in cases of COVID-19 compared to other clusters. © 2022 IEEE.
Sousa, R.; Oliveira, D.; Carneiro, A.; Pinto, L.; Pereira, A.; Peixoto, A.; Peixoto, H.; Machado, J.
The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture Proceedings Article
Em: H., Tino P. Camacho D. Yin (Ed.): pp. 199-210, Springer Science and Business Media Deutschland GmbH, 2022, ISSN: 03029743, (cited By 0; Conference of 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022 ; Conference Date: 24 November 2022 Through 26 November 2022; Conference Code:287419).
Resumo | Links | BibTeX | Etiquetas: Behavior analysis; Business-intelligence; Data architectures; Data tools; New case; Research focus; Stay at home, Big data, COVID-19
@inproceedings{Sousa2022199,
title = {The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture},
author = {R. Sousa and D. Oliveira and A. Carneiro and L. Pinto and A. Pereira and A. Peixoto and H. Peixoto and J. Machado},
editor = {Tino P. Camacho D. Yin H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144825704&doi=10.1007%2f978-3-031-21753-1_20&partnerID=40&md5=07933409c2e62457b481165cbb7438e0},
doi = {10.1007/978-3-031-21753-1_20},
issn = {03029743},
year = {2022},
date = {2022-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {13756 LNCS},
pages = {199-210},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic’s insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people’s desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
note = {cited By 0; Conference of 23rd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2022 ; Conference Date: 24 November 2022 Through 26 November 2022; Conference Code:287419},
keywords = {Behavior analysis; Business-intelligence; Data architectures; Data tools; New case; Research focus; Stay at home, Big data, COVID-19},
pubstate = {published},
tppubtype = {inproceedings}
}
The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic’s insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people’s desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.