Akademik Veri Yönetim Sistemi
Ahi Evran Medical Journal, cilt.7, sa.1, ss.41-48, 2023 (Hakemli Dergi)
Purpose: During the COVID-19 outbreak, governments, scientists, health workers, and numerous people worked on strategies or solutions for halting disease propagation. Unfortunately, the need for monitoring is steeply increasing, and restrictive actions are currently unavoidable. Due to the lack of epidemiological data and constantly changing numbers, constructing less error-prone predictive models and reliable mathematical models for the near future will help make better legal actions and prevention strategies. Materials and Methods: In this study, daily data from eleven countries between 21/01/2020-02/05/2020 and 21/01/2020-17/06/2020 were used to forecast the number of future COVID-19 events by using different forecasting models. Best fit models were chosen after analysis with ARIMA, Brown’s LES, and Holt’s LES models based on MAPE values. Results: The study showed the least error-prone best-fit models for short-term future predictions by analyzing two datasets and demonstrated that models changed after data updates among the selected countries. Investigation of the data from eleven countries, USA, Turkey, Brazil, and Russia analysis showed that updating data alters the model selection resulting in changes in the predictions. Conclusion: The results of this study indicate that using more than one statistical model has superiority over the current approaches, and fluctuations in the numbers should be considered when using the data to construct mathematical models and create future predictions for the management of the already complicated and exhausting COVID-19 pandemic. Thus, policies and restrictions against COVID-19 spread might be more successful after considering that adjusted predictions for providing more accurate results.