Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques

Oyewola, David Opeoluwa; Dada, Emmanuel Gbenga; Al-Mustapha, K. A.; Fadele, Ayotunde Alaba; Bassi, Joseph Stephen; Ibrahim, Asabe

doi:10.1007/978-981-16-3783-4_3

Cited by 2 publications

(1 citation statement)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Empirical methods such as data mining and machine learning, for example, might help. Machine learning classification algorithms are data processing methods that make it easier to create closed-form mathematical models [8,9]. Machine learning classification algorithms are data processing techniques that make the building of analytical models easier.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

2022

Self Cite

View full text Add to dashboard Cite

Introduction Vaccines are the most important instrument for bringing the pandemic to a close and saving lives and helping to reduce the risks of infection. It is important that everyone has equal access to immunizations that are both safe and effective. There is no one who is safe until everyone gets vaccinated. COVID-19 vaccinations are a game-changer in the fight against diseases. In addition to examining attitudes toward these vaccines in Africa, Asia, Oceania, Europe, North America, and South America, the purpose of this paper is to predict the acceptability of COVID-19 vaccines and study their predictors. Materials and methods Kaggle datasets are used to estimate the prediction outcomes of the daily COVID-19 vaccination to prevent a pandemic. The Kaggle data sets are classified into training and testing datasets. The training dataset is comprised of COVID-19 daily data from the 13th of December 2020 to the 13th of June 2021, while the testing dataset is comprised of COVID-19 daily data from the 14th of June 2021 to the 14th of October 2021. For the prediction of daily COVID-19 vaccination, four well-known machine learning algorithms were described and used in this study: CUBIST, Gaussian Process (GAUSS), Elastic Net (ENET), Spikes, and Slab (SPIKES). Results Among the models considered in this paper, CUBIST has the best prediction accuracy in terms of Mean Absolute Scaled Error (MASE) of 9.7368 for Asia, 2.8901 for America, 13.2169 for Oceania, and 3.9510 for South America respectively. Conclusion This research shows that machine learning can be of great benefit for optimizing daily immunization of citizens across the globe. And if used properly, it can help decision makers and health administrators to comprehend immunization rates and create strategies to enhance them.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

2022

Self Cite

View full text Add to dashboard Cite

show abstract

Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting

et al. 2022

View full text Add to dashboard Cite

Public health is now in danger because of the current monkeypox outbreak, which has spread rapidly to more than 40 countries outside of Africa. The growing monkeypox epidemic has been classified as a “public health emergency of international concern” (PHEIC) by the World Health Organization (WHO). Infection outcomes, risk factors, clinical presentation, and transmission are all poorly understood. Computer- and machine-learning-assisted prediction and forecasting will be useful for controlling its spread. The objective of this research is to use the historical data of all reported human monkey pox cases to predict the transmission rate of the disease. This paper proposed stacking ensemble learning and machine learning techniques to forecast the rate of transmission of monkeypox. In this work, adaptive boosting regression (Adaboost), gradient boosting regression (GBOOST), random forest regression (RFR), ordinary least square regression (OLS), least absolute shrinkage selection operator regression (LASSO), and ridge regression (RIDGE) were applied for time series forecasting of monkeypox transmission. Performance metrics considered in this study are root mean square (RMSE), mean absolute error (MAE), and mean square error (MSE), which were used to evaluate the performance of the machine learning and the proposed Stacking Ensemble Learning (SEL) technique. Additionally, the monkey pox dataset was used as test data for this investigation. Experimental results revealed that SEL outperformed other machine learning approaches considered in this work with an RMSE of 33.1075; a MSE of 1096.1068; and a MAE of 22.4214. This is an indication that SEL is a better predictor than all the other models used in this study. It is hoped that this research will help government officials understand the threat of monkey pox and take the necessary mitigation actions.

show abstract

Predicting Transmission Rate of Coronavirus (COVID-19) Pandemic Using Machine Learning Techniques

Cited by 2 publications

References 33 publications

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

Machine learning for optimizing daily COVID-19 vaccine dissemination to combat the pandemic

Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting

Contact Info

Product

Resources

About