In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 3 ( 2021-3-11), p. e0248161-
Abstract:
The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0248161
DOI:
10.1371/journal.pone.0248161.g001
DOI:
10.1371/journal.pone.0248161.g002
DOI:
10.1371/journal.pone.0248161.g003
DOI:
10.1371/journal.pone.0248161.g004
DOI:
10.1371/journal.pone.0248161.g005
DOI:
10.1371/journal.pone.0248161.g006
DOI:
10.1371/journal.pone.0248161.g007
DOI:
10.1371/journal.pone.0248161.g008
DOI:
10.1371/journal.pone.0248161.g009
DOI:
10.1371/journal.pone.0248161.g010
DOI:
10.1371/journal.pone.0248161.g011
DOI:
10.1371/journal.pone.0248161.g012
DOI:
10.1371/journal.pone.0248161.g013
DOI:
10.1371/journal.pone.0248161.g014
DOI:
10.1371/journal.pone.0248161.g015
DOI:
10.1371/journal.pone.0248161.t001
DOI:
10.1371/journal.pone.0248161.t002
DOI:
10.1371/journal.pone.0248161.s001
DOI:
10.1371/journal.pone.0248161.s002
DOI:
10.1371/journal.pone.0248161.s003
DOI:
10.1371/journal.pone.0248161.s004
DOI:
10.1371/journal.pone.0248161.s005
DOI:
10.1371/journal.pone.0248161.s006
DOI:
10.1371/journal.pone.0248161.s007
DOI:
10.1371/journal.pone.0248161.s008
DOI:
10.1371/journal.pone.0248161.r001
DOI:
10.1371/journal.pone.0248161.r002
DOI:
10.1371/journal.pone.0248161.r003
DOI:
10.1371/journal.pone.0248161.r004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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