Forecasting Analysis of Covid-19 Cases with Wavelet Neural Network and Time Series Approach

Abstract

COVID-19 is a respiratory disease caused by a novel coronavirus first detected in December 2019. As the number of new cases increases rapidly, pandemic fatigue and public disinterest in different response strategies are creating new challenges for government officials in tackling the pandemic. Therefore, government officials need to fully understand the future dynamics of COVID-19 to develop strategic preparedness and flexible response planning. In the light of the above-mentioned conditions, in this study, autoregressive integrated moving average (ARIMA) time series model and Wavelet Neural Networks (WNN) methods are used to predict the number of new cases and new deaths to draw possible future epidemic scenarios. These two methods were applied to publicly available data of the COVID-19 pandemic for Turkey, Italy, and the United Kingdom. In our analysis, excluding Turkey data, the WNN algorithm outperformed the ARIMA model in terms of forecasting consistency. Our work highlighted the promising validation of using wavelet neural networks when making predictions with very few features and a smaller amount of historical data.