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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
M. B. Jibril
Kano University of Science and Technology
Computer Science & IT Engineering

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Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models Khalid Mahmoud; Hatice Bebiş; A. G. Usman; A. N. Salihu; M. S. Gaya; Umar Farouk Dalhat; R. A. Abdulkadir; M. B. Jibril; S. I. Abba
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp35-42

Abstract

The need for elucidating the effects of environmental factors in the determination of the novel corona virus (COVID-19) is very vital. This study is a methodological study to compare three different test models (1. Artificial neural networks (ANN), 2. Adaptive neuro fuzzy inference system (ANFIS), 3. A linear classical model (MLR)) used to determine the relationship between COVID-19 spread and environmental factors (temperature, humidity and wind). These data were obtained from the studies (Pirouz, Haghshenas, Haghshenas, & Piro, 2020) with confirmed COVID-19 patients in Wuhan, China, using temperature, humidity and wind as the independent variables. The measured and the predicted results were checked based on three different performance indices; Root mean square error (RMSE), determination coefficient (R2) and correlation coefficient (R). The results showed that ANFIS and ANN are more promising over the classical MLR models having an average R-values of 0.90 in both calibration and verification stages. The findings indicated that ANFIS outperformed MLR and ANN. In addition, their performance skills boosted up to 25% and 9% respectively based on the determination coefficient for the prediction of confirmed COVID-19 cases in Wuhan city of China. Overall, the results depict the reliability and ability of AI-based models (ANFIS and ANN) for the simulation of COVID-19 using the effects of various environmental variables. 
Co-Authors A. G. Usman A. N. Salihu Hatice Bebiş Khalid Mahmoud M. S. Gaya R. A. Abdulkadir S. I. Abba Umar Farouk Dalhat