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

Autor: Souza GN Jr; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil., Mendes AGB; Universidade Federal Rural da Amazônia, Capitão Poço Campus, Parauapebas, Pará, Brazil., Costa JDS; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil., Oliveira MDS; Universidade Federal Rural da Amazônia, Parauapebas Campus, Parauapebas, Pará, Brazil., Lima PVC; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil., de Moraes VN; Cyberspace Institute, Universidade Federal Rural da Amazônia, Belém, Pará, Brazil., Silva DCC; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil., Rocha JECD; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil., Botelho MDN; Socio-Environmental Institute of Water Resources, Universidade Federal Rural da Amazônia, Belém, Pará, Brazil., Araujo FA; Computer Science Department, Universidade da Amazônia, Belém, Pará, Brazil., Fernandes RDS; Universidade Federal Rural da Amazônia, Parauapebas Campus, Parauapebas, Pará, Brazil., Souza DL; Computer Science Institute, Centro Universitário do Estado do Pará, Belém, Pará, Brazil., Braga MB; Universidade Federal Rural da Amazônia, Paragominas Campus, Paragominas, Pará, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 17; Vol. 18 (11), pp. e0291138. Date of Electronic Publication: 2023 Nov 17 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0291138
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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