Prediction of the effects of environmental factors towards novel corona virus (COVID-19) outbreak using AI-based models

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 COVID19 spread and environmental factors (temperature, humidity, wind etc.). Three different models were used including two artificial intelligence (AI) based models (artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)) and a linear regression model (multilinear regression analysis (MLR). 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 (R 2 ) 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.