Impact of Weather Predictions on COVID-19 Infection Rate by Using Deep Learning Models - Yogesh Gupta; Ghanshyam Raghuwanshi; Abdullah Ali H. Ahmadini; Utkarsh Sharma; Amit Kumar Mishra; Wali Khan Mashwani; Pinar Goktas; Shokrya S. Alshqaq; Oluwafemi Samson Balogun (Complexity)
Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases (COVID-19). In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model.