ArticlePeerJ2019
Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 34 citations in OpenAlex.
- Impact of policy shifts and festival holidays on tuberculosis surveillance: a time-series analysis in Southwest China (2014-2024).Frontiers in public health · 2026Article
- Hospitalisations in Brazil: an ecological time series analysis of the impact of medical decision support data as an exogenous variable.BMC public health · 2025Article
- Prediction of monthly occurrence number of scrub typhus in Ganzhou City, China, based on SARIMA and BPNN models.Infectious Disease Modelling · 2025Article
- Prediction analysis of human brucellosis cases in Ili Kazakh Autonomous Prefecture Xinjiang China based on time series.Scientific reports · 2025Article
- Analysis and forecasting of syphilis trends in mainland China based on hybrid time series models.Epidemiology and infection · 2024Article
- Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China.Infectious Disease Modelling · 2024Article
- Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models.Frontiers in public health · 2024Article
- Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.Journal of medical Internet research · 2023Article
- Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.BMC public health · 2023Article
- Comprehensive Vaccine-Preventable Disease Surveillance in the Western Pacific Region: A Literature Review on Integration of Surveillance Functions, 2000-2021.Global health, science and practice · 2022Review
- Prediction of global omicron pandemic using ARIMA, MLR, and Prophet models.Scientific reports · 2022Article
- Epidemiological trend in scarlet fever incidence in China during the COVID-19 pandemic: A time series analysis.Frontiers in public health · 2022Article
- Predicting COVID-19 Cases in South Korea Using Stringency and Niño Sea Surface Temperature Indices.Frontiers in public health · 2022Article
- Forecasting the incidence of mumps in Chongqing based on a SARIMA model.BMC public health · 2021Article
- Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China.Environmental science and pollution research international · 2021Article
- Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis.Epidemiology and infection · 2020Article
- Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India.Infection and drug resistance · 2020Article
- Epidemiological study of scarlet fever in Shenyang, China.BMC infectious diseases · 2019Article
- Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.BMJ open · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundScarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease.
methodsFour statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error.
resultsThe four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)
conclusionsThe SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.
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