ArticleInternational journal of environmental research and public health2021
National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics.International journal of environmental research and public health · 2024Article
- Public holidays increased the transmission of COVID-19 in Japan, 2020-2021: a mathematical modelling study.Epidemiology and health · 2024Article
- Unravelling the effect of New Year's Eve celebrations on SARS-CoV-2 transmission.Scientific reports · 2023Article
- Epidemiological Surveillance Reveals the Rise and Establishment of the Omicron SARS-CoV-2 Variant in Brazil.Viruses · 2023Article
- COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.International journal of environmental research and public health · 2023Article
- Human behavior in the time of COVID-19: Learning from big data.Frontiers in big data · 2023Review
- Investigating the effects of absolute humidity and movement on COVID-19 seasonality in the United States.Scientific reports · 2022Article
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Authors and funding
4 authors.
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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 (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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