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BIBTEX RIS

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

2021; Multidisciplinary Digital Publishing Institute; Volume: 18; Issue: 21 Linguagem: Inglês

10.3390/ijerph182111595

ISSN

1661-7827

Autores

Dunfrey Pires Aragão, Davi Henrique dos Santos, Adriano Mondini, Luiz M. G. Gonçalves,

Tópico(s)

COVID-19 diagnosis using AI

Resumo

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 (R0, Re) 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.

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