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Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model.
PeerJ. 2019; 7:e6165.P

Abstract

Background

Scarlet 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.

Methods

Four 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.

Results

The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year.

Conclusions

The 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.

Authors+Show Affiliations

School of Public Health, North China University of Science and Technology, Tangshan, China.School of Public Health, Capital Medical University, Beijing, China.School of Public Health, North China University of Science and Technology, Tangshan, China.School of Public Health, North China University of Science and Technology, Tangshan, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30671295

Citation

Wang, Yongbin, et al. "Seasonality and Trend Prediction of Scarlet Fever Incidence in Mainland China From 2004 to 2018 Using a Hybrid SARIMA-NARX Model." PeerJ, vol. 7, 2019, pp. e6165.
Wang Y, Xu C, Wang Z, et al. Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model. PeerJ. 2019;7:e6165.
Wang, Y., Xu, C., Wang, Z., & Yuan, J. (2019). Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model. PeerJ, 7, e6165. https://doi.org/10.7717/peerj.6165
Wang Y, et al. Seasonality and Trend Prediction of Scarlet Fever Incidence in Mainland China From 2004 to 2018 Using a Hybrid SARIMA-NARX Model. PeerJ. 2019;7:e6165. PubMed PMID: 30671295.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model. AU - Wang,Yongbin, AU - Xu,Chunjie, AU - Wang,Zhende, AU - Yuan,Juxiang, Y1 - 2019/01/17/ PY - 2018/09/19/received PY - 2018/11/27/accepted PY - 2019/1/24/entrez PY - 2019/1/24/pubmed PY - 2019/1/24/medline KW - Forecasting KW - Hybrid model KW - In mainland China KW - Incidence cases KW - NAR model KW - NARX model KW - SARIMA model KW - Scarlet fever KW - Seasonality KW - Trend SP - e6165 EP - e6165 JF - PeerJ VL - 7 N2 - Background: Scarlet 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. Methods: Four 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. Results: The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions: The 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. SN - 2167-8359 UR - https://www.unboundmedicine.com/medline/citation/30671295/Seasonality_and_trend_prediction_of_scarlet_fever_incidence_in_mainland_China_from_2004_to_2018_using_a_hybrid_SARIMA_NARX_model_ L2 - https://doi.org/10.7717/peerj.6165 DB - PRIME DP - Unbound Medicine ER -
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