National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil

In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate mode...

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Autores principales:	Dunfrey Pires Aragão, Davi Henrique dos Santos, Adriano Mondini, Luiz Marcos Garcia Gonçalves
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 epidemiological SEIRD model PCA LSTM time-series forecast Medicine R
Acceso en línea:	https://doaj.org/article/e19ac2700c5d4e878a65176a19cda2b1
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spelling	oai:doaj.org-article:e19ac2700c5d4e878a65176a19cda2b12021-11-11T16:42:07ZNational Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil10.3390/ijerph1821115951660-46011661-7827https://doaj.org/article/e19ac2700c5d4e878a65176a19cda2b12021-11-01T00:00:00Zhttps://www.mdpi.com/1660-4601/18/21/11595https://doaj.org/toc/1661-7827https://doaj.org/toc/1660-4601In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mn>0</mn></msub></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mi>e</mi></msub></semantics></math></inline-formula>) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.Dunfrey Pires AragãoDavi Henrique dos SantosAdriano MondiniLuiz Marcos Garcia GonçalvesMDPI AGarticleCOVID-19epidemiological SEIRD modelPCALSTMtime-series forecastMedicineRENInternational Journal of Environmental Research and Public Health, Vol 18, Iss 11595, p 11595 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 epidemiological SEIRD model PCA LSTM time-series forecast Medicine R
spellingShingle	COVID-19 epidemiological SEIRD model PCA LSTM time-series forecast Medicine R Dunfrey Pires Aragão Davi Henrique dos Santos Adriano Mondini Luiz Marcos Garcia Gonçalves National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
description	In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mn>0</mn></msub></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mi>e</mi></msub></semantics></math></inline-formula>) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model.
format	article
author	Dunfrey Pires Aragão Davi Henrique dos Santos Adriano Mondini Luiz Marcos Garcia Gonçalves
author_facet	Dunfrey Pires Aragão Davi Henrique dos Santos Adriano Mondini Luiz Marcos Garcia Gonçalves
author_sort	Dunfrey Pires Aragão
title	National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
title_short	National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
title_full	National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
title_fullStr	National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
title_full_unstemmed	National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil
title_sort	national holidays and social mobility behaviors: alternatives for forecasting covid-19 deaths in brazil
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e19ac2700c5d4e878a65176a19cda2b1
work_keys_str_mv	AT dunfreypiresaragao nationalholidaysandsocialmobilitybehaviorsalternativesforforecastingcovid19deathsinbrazil AT davihenriquedossantos nationalholidaysandsocialmobilitybehaviorsalternativesforforecastingcovid19deathsinbrazil AT adrianomondini nationalholidaysandsocialmobilitybehaviorsalternativesforforecastingcovid19deathsinbrazil AT luizmarcosgarciagoncalves nationalholidaysandsocialmobilitybehaviorsalternativesforforecastingcovid19deathsinbrazil
_version_	1718432271201992704

National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil

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