Estimating and analyzing the spread of Covid-19 in Turkey using Long Short-Term Memory

The COVID-19 virus that began in late December 2019 continues to spread rapidly in many countries around the world. Due to its contagious and fast-spreading nature, it causes great harm to countries economically, medically, socially and in all other areas. Therefore, it is imperative to predict the evolution and spread of the epidemic. By understanding the trend of developing confirmed cases in an area, governments can control the epidemic by launching appropriate plans and instructions.Many scientists have tried to predict the number of cases using traditional mathematical techniques; however, the common traditional mathematical differential equations have limitations in estimating cases numbers in time series data and even have major errors in estimation. To solve this problem, we propose an improved method for predicting validated states based on the LSTM (long-term memory) neural network.Since the traditional prediction models predict the number of cumulative cases only, so they expect that the rate of infections will always rise and they cannot predict when the spread of the virus will decrease or end, so our model is built on short-term memory that predicts the number of daily cases but not the number of cumulative cases (LSTM).