COVID 19 Peak Time Prediction via a Gradient Boosting Method

COVID 19 Peak Time Prediction via a Gradient Boosting Method

The outbreak of COVID-19 has caught humanity off guard. Peak-times differ in countries based on their characteristics and precautions taken by governments. In this study, we aimed to determine relative importance of indicators on the spread and to assist non-peaked countries to estimate their peak-t...

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Bibliographic Details
Main Authors: Basak Cetinguc, Eyup Calik
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Proceedings
Subjects:
COVID-19
peak times
gradient boosting method
government
restrictions
Online Access:https://www.mdpi.com/2504-3900/74/1/8
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spelling doaj-20307e1fef71446596bfd3c16e7de2c62021-03-05T00:00:40ZengMDPI AGProceedings2504-39002021-03-01748810.3390/proceedings2021074008COVID 19 Peak Time Prediction via a Gradient Boosting MethodBasak Cetinguc0Eyup Calik1Industrial Engineering Department, Engineering Faculty, Yalova University, Central Campus, Yalova 77200, TurkeyIndustrial Engineering Department, Engineering Faculty, Yalova University, Central Campus, Yalova 77200, TurkeyThe outbreak of COVID-19 has caught humanity off guard. Peak-times differ in countries based on their characteristics and precautions taken by governments. In this study, we aimed to determine relative importance of indicators on the spread and to assist non-peaked countries to estimate their peak-times. Gradient Boosting Method was employed on 82 countries which reached peak-times. The findings indicate that hospital beds per thousand is the main predictor of peak-time estimation. Restrictions on gatherings and closing public transportation have the highest relative importance among governmental precautions. This model can be utilized and employed with various indices and alternative machine-learning algorithms.https://www.mdpi.com/2504-3900/74/1/8COVID-19peak timesgradient boosting methodgovernmentrestrictions
collection DOAJ
language English
format Article
sources DOAJ
author Basak Cetinguc
Eyup Calik
spellingShingle Basak Cetinguc
Eyup Calik
COVID 19 Peak Time Prediction via a Gradient Boosting Method
Proceedings
COVID-19
peak times
gradient boosting method
government
restrictions
author_facet Basak Cetinguc
Eyup Calik
author_sort Basak Cetinguc
title COVID 19 Peak Time Prediction via a Gradient Boosting Method
title_short COVID 19 Peak Time Prediction via a Gradient Boosting Method
title_full COVID 19 Peak Time Prediction via a Gradient Boosting Method
title_fullStr COVID 19 Peak Time Prediction via a Gradient Boosting Method
title_full_unstemmed COVID 19 Peak Time Prediction via a Gradient Boosting Method
title_sort covid 19 peak time prediction via a gradient boosting method
publisher MDPI AG
series Proceedings
issn 2504-3900
publishDate 2021-03-01
description The outbreak of COVID-19 has caught humanity off guard. Peak-times differ in countries based on their characteristics and precautions taken by governments. In this study, we aimed to determine relative importance of indicators on the spread and to assist non-peaked countries to estimate their peak-times. Gradient Boosting Method was employed on 82 countries which reached peak-times. The findings indicate that hospital beds per thousand is the main predictor of peak-time estimation. Restrictions on gatherings and closing public transportation have the highest relative importance among governmental precautions. This model can be utilized and employed with various indices and alternative machine-learning algorithms.
topic COVID-19
peak times
gradient boosting method
government
restrictions
url https://www.mdpi.com/2504-3900/74/1/8
work_keys_str_mv AT basakcetinguc covid19peaktimepredictionviaagradientboostingmethod
AT eyupcalik covid19peaktimepredictionviaagradientboostingmethod
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