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

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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)

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.

Suggested Citation

  • Dunfrey Pires Aragão & Davi Henrique dos Santos & Adriano Mondini & Luiz Marcos Garcia Gonçalves, 2021. "National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil," IJERPH, MDPI, vol. 18(21), pages 1-24, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11595-:d:672086
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    References listed on IDEAS

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    1. Konstantinos Demertzis & Dimitrios Tsiotas & Lykourgos Magafas, 2020. "Modeling and Forecasting the COVID-19 Temporal Spread in Greece: An Exploratory Approach Based on Complex Network Defined Splines," IJERPH, MDPI, vol. 17(13), pages 1-17, June.
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    Cited by:

    1. Emerson Vilar de Oliveira & Dunfrey Pires Aragão & Luiz Marcos Garcia Gonçalves, 2024. "A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics," IJERPH, MDPI, vol. 21(4), pages 1-19, April.
    2. Dunfrey Pires Aragão & Andouglas Gonçalves da Silva Junior & Adriano Mondini & Cosimo Distante & Luiz Marcos Garcia Gonçalves, 2023. "COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis," IJERPH, MDPI, vol. 20(6), pages 1-21, March.

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