COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers

Sultana, Jabeen; Singha, Anjani Kumar; Siddiqui, Shams Tabrez; Nagalaxmi, Guthikonda; Sriram, Anil Kumar; Pathak, Nitish

doi:10.32604/iasc.2022.021507

Cited by 19 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The superiority of a deep learning model, including the Neural Prophet model, is confirmed by the study 12 conducted in 2022. Another research 13 illustrates the differences in applied statistical models depending on the location. The article examined multilayer perceptron, vector autoregression, and linear regression to predict the epidemic caused by the SARS-CoV-2 virus, utilizing data from Asian countries obtained from the Johns Hopkins University data repository.…”

Section: Related Workmentioning

confidence: 99%

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Yakovyna,

Shakhovska,

Szpakowska

2024

Sci Rep

View full text Add to dashboard Cite

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future.

show abstract

Section: Related Workmentioning

confidence: 99%

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

Yakovyna,

Shakhovska,

Szpakowska

2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Issues with these algorithms include overfitting, underfitting, multicollinearity, heteroscedasticity, outliers, missing data, non-linearity, autocorrelation, data scaling, and data transformation. These issues can be addressed through data preprocessing, feature engineering, model selection, and regularization [13][14][15].…”

Section: Regression Algorithmsmentioning

confidence: 99%

Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review

Moghimi,

Gulliver,

Thirumai Chelvan

2024

Energies

View full text Add to dashboard Cite

show abstract

“…(2020) evaluated the efficiency of the random forest and support vector regression models in forecasting the cumulative COVID-19 cases in Brazil. Sultana et al. (2022) employed linear regression, a multi-layer perceptron (MLP), and vector auto regression to predict various COVID-19 outbreaks in India.…”

Section: Literature Reviewmentioning

confidence: 99%