COVID-19 Seasonal Effect on Infection Cases and Forecasting Using Deep Learning

The COVID-19 pandemic infected billions of people worldwide. The government has taken a number of steps to control infection cases, but due to frequent changes in variants, it is difficult to control the infection rate, and taking precautions over a long period of time is infelicitous. Any correlation between the virus’s ascendancy and changes in climate or temperature is difficult to detect. In this research, different countries, and seasons have been investigated to assess the relationship between temperature and the rate of infection. In predicting the infection rate of new cases, popular deep learning (DL) methods, long short-term memory (LSTM), and gated recurrent units (GRUs) have been applied here. Infection cases were visualized, including the time period from previous data. Individual countries have particular weather conditions that vary between countries; for this reason, the temperature has taken in a specified range in seasonal-based forecasting. To specify a particular temperature or season is challenging but combining season and temperature from past data generated a pattern. It shows that the highest number of infection cases reached at a certain time of the season and found a seasonal effect on COVID-19.