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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582864/ https://www.ncbi.nlm.nih.gov/pubmed/34770108 http://dx.doi.org/10.3390/ijerph182111595 |
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author | Aragão, Dunfrey Pires dos Santos, Davi Henrique Mondini, Adriano Gonçalves, Luiz Marcos Garcia |
author_facet | Aragão, Dunfrey Pires dos Santos, Davi Henrique Mondini, Adriano Gonçalves, Luiz Marcos Garcia |
author_sort | Aragão, Dunfrey Pires |
collection | PubMed |
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 ([Formula: see text] , [Formula: see text]) 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 | Online Article Text |
id | pubmed-8582864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85828642021-11-12 National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil Aragão, Dunfrey Pires dos Santos, Davi Henrique Mondini, Adriano Gonçalves, Luiz Marcos Garcia Int J Environ Res Public Health Article 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 ([Formula: see text] , [Formula: see text]) 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. MDPI 2021-11-04 /pmc/articles/PMC8582864/ /pubmed/34770108 http://dx.doi.org/10.3390/ijerph182111595 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aragão, Dunfrey Pires dos Santos, Davi Henrique Mondini, Adriano Gonçalves, Luiz Marcos Garcia National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582864/ https://www.ncbi.nlm.nih.gov/pubmed/34770108 http://dx.doi.org/10.3390/ijerph182111595 |
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