Forecasting Diurnal Covid-19 Cases for Top-5 Countries Using Various Time-series Forecasting Algorithms

Abstract

On January 30, 2020, the World Health Organisation classified the Covid-19 outbreak a Public Health Emergency of International Concern, and a pandemic was proclaimed on March 11, 2020. Two years after the Covid-19 outbreak, the virus has new transmutations plus is turning out to be more difficult for forecasting in terms of both its behaviour and severity. Various techniques for time series analysis of coronavirus (Covid-19) cases were examined in this study. The Deep Learning model chosen, Long Short-Term Memory (LSTM) is compared against Statistical approaches, such as Linear Regression, Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), based on a variety of performance metrics. Following the estimates of the superior algorithm, medical care professionals can act at the appropriate moment to supply Equipment to health care institutions and further help the public. According to our data, as the number of projected days grows, so does the model's error rate. Forecasted trends also suggest that statistical approaches are relatively better overall for predictions of fewer days, but Deep Learning methods are relatively better for forecasts of more days.