Abstract:

COVID-19 was a pandemic that began in Wuhan, China and rapidly spread to over 200 nations across the world. All infected countries have begun to take the appropriate safeguards to limit the outbreak, offer the best medical care to sick individuals, and avoid future outbreaks. to limit breakouts Because diseases spread exponentially, disease transmission must be modeled to identify the number of individuals each calculation. Local governments must evaluate these patients in order to combat expansion, regulate hospital loads, and manage allocation of resources. So, LSTM is used to predict the number of COVID-19 patient population count. The LSTM is a type of Recurrent Neural Network (RNN) that can be used for classification, prediction, and regression. The RNN model was trained using Covid19 data from several nations in order to forecast the proportion of Covid19 positive cases in June 2020. Finally, the absolute percentage error (MAPE) must be calculated to determine the model's prediction performance on various LSTMs. The dataset is deconvoluted after the following process is completed. It is anticipated whether the patients have been affected by viral or anti- viral illness or covid-19 utilizing mucus tests and gene data. It is simple to forecast the covid-19 patient count using the deconvoluteddata.

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