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Cited by 58Bacterial Infections and VaccinesData-Driven Disease SurveillanceForecasting Techniques and Applications

ARIMA and ARIMA-ERNN Models for Prediction of Pertussis Incidence in Mainland China From 2004 to 2021

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Wang, Meng, Pan, Jinhua, Li, Xinghui, Li, Mengying, Liu, Zhixi, Zhao, Qi, Luo, Linyun, Chen, Haiping +5 more·2022·BMC Public Health
DOI 10.1186/s12889-022-13872-9PMC PMC9338508
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Abstract

Objective: To compare an autoregressive integrated moving average (ARIMA) model with a model that combines ARIMA with the Elman recurrent neural network (ARIMA-ERNN) in predicting the incidence of pertussis in mainland China. Background: The incidence of pertussis has increased rapidly in mainland China since 2016, making the disease an increasing public health threat. There is a pressing need for models capable of accurately predicting the incidence of pertussis in order to guide prevention and control measures. We developed and compared two models for predicting pertussis incidence in mainland China. Methods: Data on the incidence of pertussis in mainland China from 2004 to 2019 were obtained from the official website of the Chinese Center for Disease Control and Prevention. An ARIMA model was established using SAS (ver. 9.4) software and an ARIMA-ERNN model was established using MATLAB (ver. R2019a) software. The performances of these models were compared. Results: From 2004 to 2019, there were 104,837 reported cases of pertussis in mainland China, with an increasing incidence over time. The incidence of pertussis showed obvious seasonal characteristics, with the peak lasting from March to September every year. Compared with the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the ARIMA model, those of the ARIMA-ERNN model were 81.43%, 95.97% and 80.86% lower, respectively, in fitting performance. In terms of prediction performance, the MAE, MSE and MAPE were 37.75%, 56.88% and 43.75% lower, respectively. Conclusion: The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA model. This provides theoretical support for the prediction of infectious diseases and should be beneficial to public health decision making.

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  • Previous studies have successfully applied ARIMA models to forecast disease incidence trends, consistently achieving robust fitting and predictive performance.

    “previous studies have successfully applied ARIMA models to forecast disease incidence trends, consistently achieving robust fitting and predictive performance [28]”

    From Longitudinal Analysis and Predictive Modeling of Nursing‐Sensitive Quality Indicators in Hemodialysis (2026) · cites this paper as [28]

    The Introduction (page not available in our copy) reports two previous ARIMA studies of pertussis incidence: one predicted trends with acceptably low errors, and another found its ARIMA model performed best among those tested. That supports successful applications, but not the broader claim of consistently robust fitting and prediction across disease incidence trends. The Results (Model Prediction; page not available) also report that this paper’s ARIMA model performed worse than its combined ARIMA-ERNN model. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Wang, M., Pan, J., Li, X., Li, M., Liu, Z., Zhao, Q., Luo, L., Chen, H., Chen, S., Jiang, F., Zhang, L., Wang, W., & Wang, Y.
Affiliations
  • Fudan University (CN)
  • First Affiliated Hospital Zhejiang University (CN)
  • China National Biotechnology (China) (CN)
  • Hunan Normal University (CN)
  • Guizhou Center for Disease Control and Prevention (CN)
Corresponding author
  • Wang, Weibing
  • Wang, Ying
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2022-07-29
Volume
22
Issue
1
Pages
1447-1447
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/s12889-022-13872-9
PMC
PMC9338508
OpenAlex
W4288704567
Cited by
58
References
0
Citations by year
2026: 15 · 2025: 15 · 2024: 11 · 2023: 14 · 2022: 3
FWCI
6.72
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Life Sciences › Immunology and Microbiology › Microbiology › Bacterial Infections and Vaccines
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
pertussis, incidence forecasting, ARIMA model, Elman neural network, infectious disease forecasting, seasonal variation, mainland China, public health decision-making
MeSH terms
China, Forecasting, Humans, Software, Whooping Cough, Models, Statistical, Incidence, Neural Networks, Computer

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