COVID-19 Prevalence Forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey

Toğa, Gülhan; Atalay, Berrin; Toksari, M. Duran

A local outbreak of unknown pneumonia was detected in Wuhan (Hubei, China) in December 2019. It is determined to be caused by a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and called COVID-19 by scientists. The outbreak has since spread all over the world with a total of 120,815,512 cases and 2,673,308 deaths as of 16 March 2021. The health systems in the world collapsed in many countries due to the pandemic and many countries were negatively affected in the social life. In such situations, it is very important to predict the load that will occur in the health system of a country. In this study, the COVID-19 prevalence of Turkey is inspected. The infected cases, the number of deaths, and the recovered cases are predicted with Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) in Turkey. The techniques are compared in terms of correlation coefficient and mean square error (MSE). The results showed that the used techniques used are very successful in the estimation of prevalence in Turkey.