Ambient temperature change Vs confirmed cases of COVID-19: A machine learning model

Abstract

Background The relation between ambient air temperature and prevalence of viral infection has been under investigation in recent years. The present study 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.Method The data of confirmed cases of COVID-19 as well as some climatic factors related to 31 provinces of Iran, during 04/03/2020 to 05/05/2020 were 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. Moreover, the statistical analysis were used to assess the trend of positive cases in comparison with the fluctuations of some climatic factors. Results The proposed ANN model showed the accuracy rate of 87.25% and 86.4% in the training and testing stage, respectively for classification of COVID-19 confirmed cases. Moreover, multiple linear regression analysis was obtained the R2 equal to 0.40 and 0.68 in two cities of Qom and Ahvaz, respectively. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. Conclusion 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 indoor environment of tropical cities, along with the impact of climatic factors in the frequency of positive cases of COVID-19 and the capacity of ANN classification in the surveys.