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

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National Holidays and Social Mobility Behaviors

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

International Journal of Environmental Research and Public Health

Project Language

Folder structure and Code

The main code is divided in three files (folder notebooks/):

  • Holiday - passo 1 - SEIRD Model.ipynb
  • Holiday - passo 2 - Data Analysis SEIRD and Mobility.ipynb
  • Holiday - passo 3 - Regression.ipynb

Also, all dataset that were used are in dbs/ and dbs/handled/:

  • dbs/handled/*
  • brasil_df.csv
  • cases-brazil-states.csv
  • mobility_report_brazil.csv

Results

Curves of selected settings: Conf.1: Cases, that is, our baseline case; considering which parameters can improve the baseline case, we select Conf.3: Cases + R0; without using Cases as an input feature, we select Conf.9: R0 + Re + Holiday flag, and; Conf.10: PC1 + PC2 as input data.

(a) COVID-19 daily deaths forecast using cases as input data.

(b) COVID-19 daily deaths forecasts using Cases + R0 as input data.

(c) COVID-19 daily deaths forecasts using R0 + Re + Holiday flag as input data.

(d) COVID-19 daily deaths forecasts using PC1 + PC2 as input data.

For these configurations, we draw the best curves (with the lowest RMSE) of each configuration over the test data.

alt text

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Citation

This work had many contributions and many authors.

If you are interested in using this code in your research, we would like to receive a quote from the original paper:

@Article{ijerph182111595,
      AUTHOR = {Aragão, Dunfrey Pires and dos Santos, Davi Henrique and Mondini, Adriano and Gonçalves, Luiz Marcos Garcia},
      TITLE = {National Holidays and Social Mobility Behaviors: Alternatives for Forecasting COVID-19 Deaths in Brazil},
      JOURNAL = {International Journal of Environmental Research and Public Health},
      VOLUME = {18},
      YEAR = {2021},
      NUMBER = {21},
      ARTICLE-NUMBER = {11595},
      URL = {https://www.mdpi.com/1660-4601/18/21/11595},
      PubMedID = {34770108},
      ISSN = {1660-4601},
      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.},
      DOI = {10.3390/ijerph182111595}
      }

Acknowledgments

  • CAPES Edital 12/2020 - Telemedicinas e Análise de Dados Médicos

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