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Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil

Author

Listed:
  • Gilberto Nerino de Souza Jr
  • Alícia Graziella Balbino Mendes
  • Joaquim dos Santos Costa
  • Mikeias dos Santos Oliveira
  • Paulo Victor Cunha Lima
  • Vitor Nunes de Moraes
  • David Costa Correia Silva
  • Jonas Elias Castro da Rocha
  • Marcel do Nascimento Botelho
  • Fabricio Almeida Araujo
  • Rafael da Silva Fernandes
  • Daniel Leal Souza
  • Marcus de Barros Braga

Abstract

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

Suggested Citation

  • Gilberto Nerino de Souza Jr & Alícia Graziella Balbino Mendes & Joaquim dos Santos Costa & Mikeias dos Santos Oliveira & Paulo Victor Cunha Lima & Vitor Nunes de Moraes & David Costa Correia Silva & J, 2023. "Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-31, November.
    • Handle: RePEc:plo:pone00:0291138
    DOI: 10.1371/journal.pone.0291138
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    References listed on IDEAS

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    1. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
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