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
Prediction of the effects of environmental factors towards COVID-19 outbreak using AI-based models