The Role of Ambient parameters on Transmission Rates of the COVID-19 Outbreak: A machine learning model

Abstract

The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran during 04/03/2020 to 05/05/2020 was gathered from the official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. The proposed ANN model showed accuracy rates of 87.25% and 86.4% in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable; thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.