COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction

Abstract The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method n...

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Autores principales:	Siawpeng Er, Shihao Yang, Tuo Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ef3c8fbf1a62486bafddd5061807de62
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spelling	oai:doaj.org-article:ef3c8fbf1a62486bafddd5061807de622021-12-02T16:14:09ZCOUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction10.1038/s41598-021-93545-62045-2322https://doaj.org/article/ef3c8fbf1a62486bafddd5061807de622021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93545-6https://doaj.org/toc/2045-2322Abstract The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.Siawpeng ErShihao YangTuo ZhaoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Siawpeng Er Shihao Yang Tuo Zhao COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
description	Abstract The global spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2, has casted a significant threat to mankind. As the COVID-19 situation continues to evolve, predicting localized disease severity is crucial for advanced resource allocation. This paper proposes a method named COURAGE (COUnty aggRegation mixup AuGmEntation) to generate a short-term prediction of 2-week-ahead COVID-19 related deaths for each county in the United States, leveraging modern deep learning techniques. Specifically, our method adopts a self-attention model from Natural Language Processing, known as the transformer model, to capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model solely utilizes publicly available information for COVID-19 related confirmed cases, deaths, community mobility trends and demographic information, and can produce state-level predictions as an aggregation of the corresponding county-level predictions. Our numerical experiments demonstrate that our model achieves the state-of-the-art performance among the publicly available benchmark models.
format	article
author	Siawpeng Er Shihao Yang Tuo Zhao
author_facet	Siawpeng Er Shihao Yang Tuo Zhao
author_sort	Siawpeng Er
title	COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
title_short	COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
title_full	COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
title_fullStr	COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
title_full_unstemmed	COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction
title_sort	county aggregation mixup augmentation (courage) covid-19 prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ef3c8fbf1a62486bafddd5061807de62
work_keys_str_mv	AT siawpenger countyaggregationmixupaugmentationcouragecovid19prediction AT shihaoyang countyaggregationmixupaugmentationcouragecovid19prediction AT tuozhao countyaggregationmixupaugmentationcouragecovid19prediction
_version_	1718384384221904896

COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 prediction

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