Acta Scientific Computer Sciences

Research Article Volume 5 Issue 1

Predicting the Ending Time of COVID-19 Pandemic in Turkey

Ekin Kara, Önder Ertan*, Furkan Yalçinkaya and Meltem Eryilmaz

Atilim University, Ankara, Turkey

*Corresponding Author: Önder Ertan, Atilim University, Ankara, Turkey.

Received: September 01, 2022; Published: December 07, 2022

Abstract

Coronavirus pandemic has been going on since late 2019, millions of people died worldwide, vaccination has recently started in many countries and new strategies are sought by countries since they are still struggling to defeat the virus. So, this research is made to predict the possible ending time of the coronavirus pandemic in Turkey using data mining and statistical studies. Data mining is a computer science study that processes large amounts of data and produces new useful information. It is especially used to support decision making in companies today. So, this project could support the decision making of authorities in developing an effective strategy against the on-going pandemic. During the research we have practiced on Turkey’s coronavirus and vaccination data between 13 January 2021 and 28 May 2021. We used Rapidminer and the Random Forest method for data mining. After all the simulations we have applied and observed during our research, it was clearly seen that vaccination parameters were decreasing the new cases. Also, the stringency index did not affect the new cases. As a conclusion of our research and observations, we think that the government should vaccinate as many people as it can in order to relax restrictions for the last time.

Keywords: Coronavirus; COVID-19; Turkey

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Citation

Citation: Önder Ertan., et al. “Predicting the Ending Time of COVID-19 Pandemic in Turkey". Acta Scientific Computer Sciences 5.1 (2023): 13-19.

Copyright

Copyright: © 2022 Önder Ertan., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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