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In connection with the spread of COVID\u201019 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\u2010based 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\u201019 case reports and another data set is related to weather. Moreover, COVID\u201019 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\u201019 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. 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