Comparison of SARIMA model, Holt-winters model and ETS model in predicting the incidence of foodborne disease.
Exponential smoothing model
Foodborne Disease
Holt-Winters model
SARIMA model
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
16 Nov 2023
16 Nov 2023
Historique:
received:
17
08
2023
accepted:
07
11
2023
medline:
27
11
2023
pubmed:
17
11
2023
entrez:
17
11
2023
Statut:
epublish
Résumé
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. 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. 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) Based on the results, Holt-Winters model produces better prediction accuracy of the model.
Sections du résumé
BACKGROUND
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
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
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)
CONCLUSION
CONCLUSIONS
Based on the results, Holt-Winters model produces better prediction accuracy of the model.
Identifiants
pubmed: 37974072
doi: 10.1186/s12879-023-08799-4
pii: 10.1186/s12879-023-08799-4
pmc: PMC10652449
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
803Subventions
Organisme : Nan 'an District Center for Disease Prevention and Control for Study on the Application of Foodborne illness Surveillance and Early Warning System in Nan 'an District
ID : 2021MSXM264
Organisme : Agreement on Completing the Application Research of the Monitoring and Early Warning System for Foodborne Diseases in Nan'an District
ID : cqmuhx20210010
Informations de copyright
© 2023. The Author(s).
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