İstatistiksel ve makine öğrenme yöntemleri ile COVID-19 salgın tahmini üzerine yapılan güncel çalışmaların incelemesi

Year 2022, Volume: 11 Issue: 3, 484 - 495, 18.07.2022

Umut Ahmet Çetin , Fatih Abut

https://doi.org/10.28948/ngumuh.1025095

Cited By: 1

Abstract

COVID-19, ilk olarak Aralık 2019'da Çin'in Wuhan şehrinde ortaya çıkan bulaşıcı bir hastalıktır. O zaman beri COVID-19 dünya çapında 70 milyondan fazla insanı enfekte etmiştir ve 1 milyondan fazla ölüme neden olmuştur. Bu denli bulaşıcı ve ölümcül bir hastalıkla mücadele etmek için COVID-19 salgınını mümkün olduğunca doğru tahmin eden modellere ihtiyaç duyulmaktadır. Hükümetler tahmin modellerinin sonuçlarını kullanarak hastalıkla mücadele için bütçe ve tesis planlaması, ne kadar ilaç ve tıbbi ekipmanın üretileceğine veya ithal edileceği ve ne kadar tıbbi personele ihtiyaç duyulacağı hakkında daha iyi kararlar ve kontrol önlemleri alabilir. Sonuç olarak, çeşitli ülke ve kıtalarda COVID-19 salgınının zaman serileri veya denetimli tahmini için çeşitli regresyon ve sınıflandırma modelleri önerilmiştir. Bu makale, istatistiksel ve makine öğrenimi yöntemlerini kullanarak COVID-19 salgınını tahmin etmeye yönelik son çalışmalara genel bir bakış sunmayı amaçlamaktadır. Özellikle, her çalışma için, kullanılan veri kümesi özelliklerini, geliştirilen modellerin türünü, tahmin değişkenlerini, istatistiksel ve makine öğrenimi yöntemlerini, performans ölçümlerini ve son olarak ana sonucu ana hatlarıyla özetlenmiştir. Araştırma sonucu, makine öğrenme yöntemlerinin, COVID-19 salgını eğilimini tahmin etmek veya bir hastanın COVID-19 ile enfekte olup olmadığını tespit etmek gibi çeşitli ihtiyaçlar için tahminler yapmak için umut verici araçlar olduğunu ortaya koymaktadır.

Keywords

Makine öğrenimi, sınıflandırma, regresyon, COVID-19, koronavirüs

A survey of recent studies on COVID-19 outbreak prediction using statistical and machine learning methods

Abstract

COVID-19 is an infectious disease first discovered in Wuhan City, China, in December 2019. Ever since, COVID-19 has infected more than 70 million people and caused more than 1 million deaths worldwide. There is a need for models that predict the COVID-19 outbreak as accurately as possible to combat such an infectious and deadly disease. By using the results of the prediction models, governments can make better decisions and control measures about the disease, such as arranging budget and facility planning to combat the disease, deciding on how many medicines and medical equipment should be produced or imported, and how much medical staff is going to be needed. Consequently, various regression and classification models have been proposed for time series or supervised prediction of the COVID-19 outbreak in several countries and continents. This study aims to give an overview of recent studies on predicting the COVID-19 outbreak utilizing statistical and machine learning methods. Particularly, for each study, we outline the utilized ground-truth dataset characteristics, the type of the developed models, the predictor variables, the statistical and machine learning methods, the performance metrics, and finally, the major conclusion. The survey results reveal that machine learning methods are promising tools for making predictions for various needs, such as predicting whether a patient is infected with COVID-19 or not, predicting the trend of COVID-19 outbreaks, or predicting which age groups are most affected by COVID-19.

Keywords

Machine learning, classification, regression, COVID-19, coronavirus

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