National holidays and social mobility behaviors: Alternatives for forecasting covid-19 deaths in brazil

• Autor(a) principal: Aragão, Dunfrey Pires
• Data de Publicação: 2021
• Outros Autores: Dos Santos, Davi Henrique, Mondini, Adriano [UNESP], Gonçalves, Luiz Marcos Garcia
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.3390/ijerph182111595; http://hdl.handle.net/11449/222777
• topic: COVID-19; Epidemiological SEIRD model; LSTM; PCA; Time-series forecast

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.