Forecasting the Spread of COVID-19 and ICU Requirements

Podder, Prajoy; Khamparia, Aditya; Mondal, M. Rubaiyat Hossain; Rahman, Md. Ashiqur; Bharati, Subrato

doi:10.3991/ijoe.v17i05.20009

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Experimentsperformed by [46,51,53] to predict the vaccination ratesand infected/ death casesfor variouscountries using ARIMA and SARIMA models.…”

Section: Arimamentioning

confidence: 99%

Time Series Analysis with Systematic Survey on Covid-19 Based Predictive Studies During Pandemic Period using Enhanced Machine Learning Techniques

K. Rajeswari,

Sushma Vispute,

Amulya Maitre

et al. 2023

Int. J. Onl. Eng.

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Coronavirus 2 virus is responsible for the spread of the infectious disease COVID-19 (also known as Coronavirus disease). People around the globe who got infected with the virus experienced a respiratory illness that could become as serious as leading someone to lose their life. However, the upside of the pandemic is that it has led to numerous types of research and explorations, majorly in the medical science field. Since a systematic survey of previous research activities and bibliometric analysis gives a brief idea about such contributions and acts as a reference to future research, this study aims to cover the research related to COVID-19 in the computer technology domain. It is limited to the works accepted and accessible with the keywords - Covid-19, prediction, and pandemic, in the Scopus search engine to justify the scope of this survey. Further, the paper highlights a few prior works used for predictive analysis and presents a quantitative angle on their algorithms. Earlier works showcase Time Series Analysis using ARIMA/SARIMA models for predicting the vaccination rates, and Extreme Gradient Boosting (XGBoost), Xtremely Boosted Network (XBNet) Regression, and Recurrent Neural Network (RNN) for Confirmed, Cured, and Death cases. Amongst the algorithms used in the latter use case, XBNet regression performed better than XGBoost regressor.

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“…Experimentsperformed by [46,51,53] to predict the vaccination ratesand infected/ death casesfor variouscountries using ARIMA and SARIMA models.…”

Section: Arimamentioning

confidence: 99%

Time Series Analysis with Systematic Survey on Covid-19 Based Predictive Studies During Pandemic Period using Enhanced Machine Learning Techniques

K. Rajeswari,

Sushma Vispute,

Amulya Maitre

et al. 2023

Int. J. Onl. Eng.

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“…This study uses an FS technique called the Extratrees Classifier which is a classifier with an ensemble approach and is used for classification and regression problems [40][41][42]. Several studies have found performance improvements when using the Extratrees Classifier [43,44] and obtaining high accuracy values even without parameter tuning [45]. In this study, the FS process was carried out by processing the pre-processed data from the TF-IDF weighting, then processing using the ExtraTreesClassifier module in scikit-learn [46].…”

Section: Figure 3 Google Translation Of Several Examples From the Hea...mentioning

confidence: 99%

Multi-class SVM Classification Comparison for Health Service Satisfaction Survey Data in Bahasa

Indrawan

Setiawan

Gunadi

2022

HighTech. Innov. J.

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This study aimed to compare the Multi-class Support Vector Machine (MSVM) classification with the One-versus-One (OvO) and One-versus-Rest (OvR) approaches using unigram and bigram features. The study used the service satisfaction survey report of Denpasar public health centers by the Center for Public Health Innovation (CPHI), Medical School, Udayana University. As Bali is known as the world's main tourism destination, it is important to know about its supporting public health service through its representative capital city, Denpasar. Moreover, this study laid the foundation for the classification process using the available methods to fit in Indonesian health service satisfaction survey data, which assists in making decisions to improve health services. Since Bali is one of the provinces in Indonesia and all of those provinces refer to the same national regulation, health service satisfaction survey data that is in the Indonesian language (Bahasa) should have the same aspects, like category, priority, word-related matters (including abbreviations, acronyms, terminology), etc. that overall make it unique and need specific processing. That work was considered a contribution since there is no such study to the best of the author's knowledge and the foundation would be useful as a part of the future vision for the integrated system of Indonesian health big data. Since in reality, satisfaction survey data tends to be unbalanced, this study also compares the developed models using unigram and bigram features without and with feature selection (FS). Those features were then processed using the OvO MSVM and OvR MSVM models. k-fold cross-validation was used to divide training data and testing data and, at the same time, validate the models. Through experiments without and with FS, the OvO MSVM and OvR MSVM models with unigram features had better performance in general than the same models with bigram features. Without FS and with unigram features, comparable differences were found where the OvO MSVM model was slightly better on accuracy and precision, while the OvR MSVM model was slightly better on recall and the F1score. Without FS and with bigram features, comparable differences were also found, where the OvR MSVM model had slightly better performance than the OvO MSVM model. With FS and with unigram and bigram features, the OvR MSVM model had better performance in general than the OvO MSVM model. Doi: 10.28991/HIJ-2022-03-04-05 Full Text: PDF

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“…COVID-19 causes symptoms of fever, lung issues, dyspnea [9], etc. In severe cases, patients need admission to intensive care units [10]. In many countries, COVID-19 has created a massive burden on the healthcare system as the number of ICUs is limited.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning and Federated Learning for Screening COVID-19: A Review

Mondal,

Bharati,

Podder

et al. 2023

BioMedInformatics

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Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts a thorough study of the use of deep learning (DL) and federated learning (FL) approaches to COVID-19 screening. To begin, an evaluation of research articles published between 1 January 2020 and 28 June 2023 is presented, considering the preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, and ultrasound images, in terms of the number of images, COVID-19 samples, and classes in the datasets. Following that, a description of existing DL algorithms applied to various datasets is offered. Additionally, a summary of recent work on FL for COVID-19 screening is provided. Efforts to improve the quality of FL models are comprehensively reviewed and objectively evaluated.

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Forecasting the Spread of COVID-19 and ICU Requirements

Cited by 14 publications

References 38 publications

Time Series Analysis with Systematic Survey on Covid-19 Based Predictive Studies During Pandemic Period using Enhanced Machine Learning Techniques

Time Series Analysis with Systematic Survey on Covid-19 Based Predictive Studies During Pandemic Period using Enhanced Machine Learning Techniques

Multi-class SVM Classification Comparison for Health Service Satisfaction Survey Data in Bahasa

Deep Learning and Federated Learning for Screening COVID-19: A Review

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