International journal of Neural Networks and Advanced Applications, cilt.8, ss.6-11, 2021 (Hakemli Dergi)
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.