In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 2 ( 2021-2-5), p. e0246673-
Abstract:
Hand-foot-and-mouth disease_(HFMD) is one of the most typical diseases in children that is associated with high morbidity. Reliable forecasting is crucial for prevention and control. Recently, hybrid models have become popular, and wavelet analysis has been widely performed. Better prediction accuracy may be achieved using wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models. Materials and methods We fitted a wavelet-based seasonal autoregressive integrated moving average (SARIMA)–neural network nonlinear autoregressive (NNAR) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. Additionally, a single SARIMA model, simplex NNAR model, and pure SARIMA–NNAR hybrid model were established for comparison and estimation. Results The wavelet-based SARIMA–NNAR hybrid model demonstrates excellent performance whether in fitting or forecasting compared with other models. Its fitted and forecasting time series are similar to the actual observed time series. Conclusions The wavelet-based SARIMA–NNAR hybrid model fitted in this study is suitable for forecasting the number of HFMD cases. Hence, it will facilitate the prevention and control of HFMD.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0246673
DOI:
10.1371/journal.pone.0246673.g001
DOI:
10.1371/journal.pone.0246673.g002
DOI:
10.1371/journal.pone.0246673.g003
DOI:
10.1371/journal.pone.0246673.g004
DOI:
10.1371/journal.pone.0246673.g005
DOI:
10.1371/journal.pone.0246673.g006
DOI:
10.1371/journal.pone.0246673.t001
DOI:
10.1371/journal.pone.0246673.t002
DOI:
10.1371/journal.pone.0246673.t003
DOI:
10.1371/journal.pone.0246673.t004
DOI:
10.1371/journal.pone.0246673.s001
DOI:
10.1371/journal.pone.0246673.s002
DOI:
10.1371/journal.pone.0246673.s003
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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