Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease

Background According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness. Methods The foodborne disease monthly inc...

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Veröffentlicht in:BMC Infectious Diseases Jg. 23; H. 1; S. 1 - 9
Hauptverfasser: Xian, Xiaobing, Wang, Liang, Wu, Xiaohua, Tang, Xiaoqing, Zhai, Xingpeng, Yu, Rong, Qu, Linhan, Ye, Mengliang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer Science and Business Media LLC 16.11.2023
BioMed Central
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
China
Disease control
Epidemics
Epidemiology
Estimates
Exponential smoothing model
Food contamination & poisoning
Food safety
Foodborne and waterborne diseases
Foodborne Disease
Foodborne Diseases
Forecasting
Gastroenteritis
Holt-Winters model
Humans
Incidence
Infectious and parasitic diseases
Infectious Diseases
Internal Medicine
Medical Microbiology
Medicine
Medicine & Public Health
Model accuracy
Models, Statistical
Parameter estimation
Parasitology
Pathogens
Performance prediction
Predictions
Public health
Public policy
RC109-216
SARIMA model
Seasonal variations
Seasons
Time series
Trends
Tropical Medicine
China
SARIMA model
Foodborne Disease
Holt-Winters model
Exponential smoothing model
ISSN:1471-2334, 1471-2334
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Zusammenfassung:Background According to the World Health Organization, foodborne disease is a significant public health issue. We will choose the best model to predict foodborne disease by comparison, to provide evidence for government policies to prevent foodborne illness. Methods The foodborne disease monthly incidence data from June 2017 to April 2022 were obtained from the Chongqing Nan’an District Center for Disease Prevention and Control. Data from June 2017 to June 2021 were used to train the model, and the last 10 months of incidence were used for prediction and validation The incidence was fitted using the seasonal autoregressive integrated moving average (SARIMA) model, Holt-Winters model and Exponential Smoothing (ETS) model. Besides, we used MSE, MAE, RMSE to determine which model fits better. Results During June 2017 to April 2022, the incidence of foodborne disease showed seasonal changes, the months with the highest incidence are June to November. The optimal model of SARIMA is SARIMA (1,0,0) (1,1,0) 12 . The MSE, MAE, RMSE of the Holt-Winters model are 8.78, 2.33 and 2.96 respectively, which less than those of the SARIMA and ETS model, and its prediction curve is closer to the true value. The optimal model has good predictive performance. Conclusion Based on the results, Holt-Winters model produces better prediction accuracy of the model.
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SourceType-Scholarly Journals-1
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ISSN:1471-2334
1471-2334
DOI:10.1186/s12879-023-08799-4