Predicting COVID-19 fatality rate based on age group using LSTM

Objective: To predict the daily incidence and fatality rates based on long short-term memory (LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran. Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM...

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Published in:Asian Pacific Journal of Tropical Medicine
Main Authors: Zahra Ramezani, Seyed Abbas Mousavi, Ghasem Oveis, Mohammad Reza Parsai, Fatemeh Abdollahi, Jamshid Yazdani Charati
Format: Article in Journal/Newspaper
Language:English
Published: Wolters Kluwer Medknow Publications 2021
Subjects:
Online Access:https://doi.org/10.4103/1995-7645.332809
https://doaj.org/article/cc875f70431d49f3ba668e8a63da2f7f
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spelling ftdoajarticles:oai:doaj.org/article:cc875f70431d49f3ba668e8a63da2f7f 2023-05-15T15:18:14+02:00 Predicting COVID-19 fatality rate based on age group using LSTM Zahra Ramezani Seyed Abbas Mousavi Ghasem Oveis Mohammad Reza Parsai Fatemeh Abdollahi Jamshid Yazdani Charati 2021-01-01T00:00:00Z https://doi.org/10.4103/1995-7645.332809 https://doaj.org/article/cc875f70431d49f3ba668e8a63da2f7f EN eng Wolters Kluwer Medknow Publications http://www.apjtm.org/article.asp?issn=1995-7645;year=2021;volume=14;issue=12;spage=564;epage=574;aulast=Ramezani https://doaj.org/toc/2352-4146 2352-4146 doi:10.4103/1995-7645.332809 https://doaj.org/article/cc875f70431d49f3ba668e8a63da2f7f Asian Pacific Journal of Tropical Medicine, Vol 14, Iss 12, Pp 564-574 (2021) covid-19 long short-term memory model incidence rate fatality rate prediction age classification Arctic medicine. Tropical medicine RC955-962 article 2021 ftdoajarticles https://doi.org/10.4103/1995-7645.332809 2022-12-30T20:39:31Z Objective: To predict the daily incidence and fatality rates based on long short-term memory (LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran. Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other. Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females (49.7%), and 25 586 were males (50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively; for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Asian Pacific Journal of Tropical Medicine 14 12 564
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic covid-19
long short-term memory model
incidence rate
fatality rate
prediction
age classification
Arctic medicine. Tropical medicine
RC955-962
spellingShingle covid-19
long short-term memory model
incidence rate
fatality rate
prediction
age classification
Arctic medicine. Tropical medicine
RC955-962
Zahra Ramezani
Seyed Abbas Mousavi
Ghasem Oveis
Mohammad Reza Parsai
Fatemeh Abdollahi
Jamshid Yazdani Charati
Predicting COVID-19 fatality rate based on age group using LSTM
topic_facet covid-19
long short-term memory model
incidence rate
fatality rate
prediction
age classification
Arctic medicine. Tropical medicine
RC955-962
description Objective: To predict the daily incidence and fatality rates based on long short-term memory (LSTM) in 4 age groups of COVID-19 patients in Mazandaran Province, Iran. Methods: To predict the daily incidence and fatality rates by age groups, this epidemiological study was conducted based on the LSTM model. All data of COVID-19 disease were collected daily for training the LSTM model from February 22, 2020 to April 10, 2021 in the Mazandaran University of Medical Sciences. We defined 4 age groups, i.e., patients under 29, between 30 and 49, between 50 and 59, and over 60 years old. Then, LSTM models were applied to predict the trend of daily incidence and fatality rates from 14 to 40 days in different age groups. The results of different methods were compared with each other. Results: This study evaluated 5 0826 patients and 5 109 deaths with COVID-19 daily in 20 cities of Mazandaran Province. Among the patients, 25 240 were females (49.7%), and 25 586 were males (50.3%). The predicted daily incidence rates on April 11, 2021 were 91.76, 155.84, 150.03, and 325.99 per 100 000 people, respectively; for the fourteenth day April 24, 2021, the predicted daily incidence rates were 35.91, 92.90, 83.74, and 225.68 in each group per 100 000 people. Furthermore, the predicted average daily incidence rates in 40 days for the 4 age groups were 34.25, 95.68, 76.43, and 210.80 per 100 000 people, and the daily fatality rates were 8.38, 4.18, 3.40, 22.53 per 100 000 people according to the established LSTM model. The findings demonstrated the daily incidence and fatality rates of 417.16 and 38.49 per 100 000 people for all age groups over the next 40 days. Conclusions: The results highlighted the proper performance of the LSTM model for predicting the daily incidence and fatality rates. It can clarify the path of spread or decline of the COVID-19 outbreak and the priority of vaccination in age groups.
format Article in Journal/Newspaper
author Zahra Ramezani
Seyed Abbas Mousavi
Ghasem Oveis
Mohammad Reza Parsai
Fatemeh Abdollahi
Jamshid Yazdani Charati
author_facet Zahra Ramezani
Seyed Abbas Mousavi
Ghasem Oveis
Mohammad Reza Parsai
Fatemeh Abdollahi
Jamshid Yazdani Charati
author_sort Zahra Ramezani
title Predicting COVID-19 fatality rate based on age group using LSTM
title_short Predicting COVID-19 fatality rate based on age group using LSTM
title_full Predicting COVID-19 fatality rate based on age group using LSTM
title_fullStr Predicting COVID-19 fatality rate based on age group using LSTM
title_full_unstemmed Predicting COVID-19 fatality rate based on age group using LSTM
title_sort predicting covid-19 fatality rate based on age group using lstm
publisher Wolters Kluwer Medknow Publications
publishDate 2021
url https://doi.org/10.4103/1995-7645.332809
https://doaj.org/article/cc875f70431d49f3ba668e8a63da2f7f
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source Asian Pacific Journal of Tropical Medicine, Vol 14, Iss 12, Pp 564-574 (2021)
op_relation http://www.apjtm.org/article.asp?issn=1995-7645;year=2021;volume=14;issue=12;spage=564;epage=574;aulast=Ramezani
https://doaj.org/toc/2352-4146
2352-4146
doi:10.4103/1995-7645.332809
https://doaj.org/article/cc875f70431d49f3ba668e8a63da2f7f
op_doi https://doi.org/10.4103/1995-7645.332809
container_title Asian Pacific Journal of Tropical Medicine
container_volume 14
container_issue 12
container_start_page 564
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