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February 1, 2019Medicine28 citationsOpen Access

Application of a combined model with seasonal autoregressive integrated moving average and support vector regression in forecasting hand-foot-mouth disease incidence in Wuhan, China

JZJiaojiao ZouGJGaofeng JiangXXXiaoxu Xie

Key Result

The SARIMA-SVR hybrid model demonstrated superior forecasting performance for hand-foot-mouth disease incidence compared to the single SARIMA model, achieving a lower mean square error (22.707 vs 43.982).

Study Design

Type

Observational (n=107,636)

Structured PICO

P
Population
107,636 reported cases of hand-foot-mouth disease (HFMD) in Wuhan City, Hubei Province, China from January 2009 to December 2017.
I
Intervention
SARIMA-SVR hybrid model for forecasting disease incidence
C
Comparator
Single SARIMA model
O
Outcome
Forecasting accuracy assessed by mean square error (MSE) and mean absolute percentage error (MAPE) between actual and predicted values

A SARIMA-SVR hybrid model provides more accurate short-term forecasting of hand-foot-mouth disease incidence compared to a single SARIMA model.

Main Result

Absolute Event Rate: 22.707% vs 43.982%

Limitations

  • The model did not take into account natural environment and socioeconomic factors that influence HFMD incidence.
  • The hybrid model is merely suitable for short-term prediction.
  • The model must be constantly updated to maintain prediction performance.
  • Did not account for natural environment and socioeconomic factors that influence HFMD incidence.

Abstract

Hand-foot-mouth disease (HFMD) is a serious public health problem with increasing cases and substantial financial burden in China, especially in Wuhan city. Hence, there is an urgent need to construct a model to predict the incidence of HFMD that could make the prevention and control of this disease more effective.The incidence data of HFMD of Wuhan city from January 2009 to December 2016 were used to fit a combined model with seasonal autoregressive integrated moving average (SARIMA) model and support vector regression (SVR) model. Then, the SARIMA-SVR hybrid model was constructed. Subsequently, the fitted SARIMA-SVR hybrid model was applied to obtain the fitted HFMD incidence from 2009 to 2016. Finally, the fitted SARIMA-SVR hybrid model was used to forecast the incidence of HFMD of the year 2017. To assess the validity of the model, the mean square error (MSE) and mean absolute percentage error (MAPE) between the actual values and predicted values of HFMD incidence (2017) were calculated.From 2009 to 2017, a total of 107636 HFMD cases were reported in Wuhan City, Hubei Province, and the male-to-female ratio is 1.60:1. The age group of 0 to 5 years old accounts for 95.06% of all reported cases and scattered children made up the large proportion (accounted for 56.65%). There were 2 epidemic peaks, from April to July and September to December, respectively, with an emphasis on the former. High-prevalence areas mainly emerge in Dongxihu District, Jiangxia District, and Hongshan District. SARIMA (1,0,1)(0,0,2)12 is the optimal model given with a minimum Akaike information criterion (AIC) (700.71), then SVR model was constructed by using the optimum parameter (C = 100000, =0.00001, =0.01). The forecasted incidences of single SARIMA model and SARIMA-SVR hybrid model from January to December 2017 match the actual data well. The single SARIMA model shows poor performance with large MSE and MAPE values in comparison to SARIMA-SVR hybrid model.The SARIMA-SVR hybrid model in this study showed that accurate forecasting of the HFMD incidence is possible. It is a potential decision supportive tool for controlling HFMD in Wuhan, China.

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Cite This Study

Zou et al. (2019) conducted an observational in Hand-foot-mouth disease (HFMD) (n=107,636). SARIMA-SVR hybrid model vs. Single SARIMA model was evaluated on Mean square error (MSE) for forecasting 2017 incidence. The SARIMA-SVR hybrid model demonstrated superior forecasting performance for hand-foot-mouth disease incidence compared to the single SARIMA model, achieving a lower mean square error (22.707 vs 43.982).

synapsesocial.com/papers/6a080a4d0df715653be8a0echttps://doi.org/10.1097/md.0000000000014195
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