ArticleHealthcare (Basel, Switzerland)2022
SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Potential Public Health Impact of Updated COVID-19 Vaccination Strategies in Thailand: Epidemiological Data Update.Pulmonary therapy · 2026Article
- Augmenting Circadian Biology Research With Data Science.Journal of biological rhythms · 2025Review
- Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023.BMC pulmonary medicine · 2024Article
- Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models.Epidemiology and infection · 2024Article
- The influence of the COVID-19 pandemic on identifying HIV/AIDS cases in China: an interrupted time series study.The Lancet regional health. Western Pacific · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Health Informatics: The Foundations of Public Health.Healthcare (Basel, Switzerland) · 2023Article
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4 authors.
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Abstract
This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung-Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance.
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