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

Dunfrey Pires Aragão, Davi Henrique dos Santos, Adriano Mondini and Luiz Marcos Garcia Gonçalves
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Dunfrey Pires Aragão: Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil
Davi Henrique dos Santos: Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil
Adriano Mondini: Faculdade de Ciências Farmacêuticas, Universidade Estadual Paulista “Júlio Mesquita Filho”, Rodovia Araraquara-Jaú, Km 1, Campus Ville, Araraquara 14800-903, Brazil
Luiz Marcos Garcia Gonçalves: Pós-Graduação em Engenharia Elétrica e de Computação, Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova, Natal 59078-970, Brazil

IJERPH, 2021, vol. 18, issue 21, 1-24

Abstract: 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 ( R 0 , R e ) 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.

Keywords: COVID-19; epidemiological SEIRD model; PCA; LSTM; time-series forecast (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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