Ensemble Machine Learning for Monkeypox Transmission Time Series Forecasting

• Published in: Applied sciences Vol. 12; no. 23; p. 12128
• Main Authors: Dada, Emmanuel Gbenga, Oyewola, David Opeoluwa, Joseph, Stephen Bassi, Emebo, Onyeka, Oluwagbemi, Olugbenga Oluseun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Animals; Bioinformatics; Coronaviruses; COVID-19 vaccines; Disease transmission; Ebola virus; Epidemics; Forecasting; Health risks; Infections; Infectious diseases; Learning algorithms; Lymphatic system; Machine learning; Malaria; Mitigation; Monkeypox; Monkeys; Pandemics; Public health; Risk analysis; Risk factors; Root-mean-square errors; Severe acute respiratory syndrome coronavirus 2; Smallpox; stacking ensemble learning; Time series; Tuberculosis; Viruses; Congo Basin; United Kingdom > UK; Belgium; United States > US; Africa; Spain
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app122312128

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.