Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model

Liu, Lei; Lu, Xin; Yin, Fei; Zhu, Xun; Lu, Q

doi:10.1017/s0950268815001144

Cited by 87 publications

(75 citation statements)

References 17 publications

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“…A seasonal ARIMA model would be designed as ARIMA ( p , d , q )( P , D , Q ) s , ( p = non-seasonal AR order, d = non-seasonal differencing, q = non-seasonal MA order, P = seasonal AR order, D = seasonal differencing, Q = seasonal MA order), and s = time span of repeating seasonal pattern. In R software, the seasonal ARIMA model automatically selected the parameters for the best performing model according to either the minimum of Akaike information criterion (AIC), the corrected Akaike information criterion (AICc) or the Bayesian information criterion (BIC)1516. In the second step, the simulating and forecasting results are given by the chosen model.…”

Section: Methodsmentioning

confidence: 99%

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Zeng

Huang

et al. 2016

Sci Rep

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Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2016. The ARIMA (0,1,0)(1,1,1)12 model (AICc = 1342.2 BIC = 1350.3) was selected as the best performing ARIMA model and the ETS (M,N,M) model (AICc = 1678.6, BIC = 1715.4) was selected as the best performing ETS model, and the ETS (M,N,M) model with the minimum RMSE was finally selected for in-sample-simulation and out-of-sample forecasting. Descriptive statistics showed that the reported number of pertussis cases by China CDC increased by 66.20% from 2005 (4058 cases) to 2015 (6744 cases). According to Hodrick-Prescott filter, there was an apparent cyclicity and seasonality in the pertussis reports. In out of sample forecasting, the model forecasted a relatively high incidence cases in 2016, which predicates an increasing risk of ongoing pertussis resurgence in the near future. In this regard, the ETS model would be a useful tool in simulating and forecasting the incidence of pertussis, and helping decision makers to take efficient decisions based on the advanced warning of disease incidence.

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Section: Methodsmentioning

confidence: 99%

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Zeng

Huang

et al. 2016

Sci Rep

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“…Time-series analysis is a method to extrapolate predictions, in which a mathematical model is established according to the regularity and trend of the observed historical values with time (L. Liu, Luan, Yin, Zhu, & Lü, 2016;Q. Liu, Liu, Jiang, & Yang, 2011) AND has been widely used in predicting the spread of infectious diseases in recent years.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure

Ibrahim

Oladipo

2020

Preprint

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Objective: This study is focused on the analysis of the spread of in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection. Methodology: Daily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria. Results: The ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box-Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame. Conclusion: Federal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of

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“…Many scholars used all kinds of models to forecast the incidence of HFMD. Among these models, the traditional ARIMA model is utilized widely [7][8][9]. Linearity is the necessary condition of its application.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

Feng

Shuang

et al. 2020

Preprint

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Background: Hand-foot-and-mouth disease(HFMD) is one of the most common diseases in children, which has high morbidity. Reliable forecasting is significant for prevention and control. Recently, hybrid models have been becoming popular and wavelet analysis has been widely used. Better prediction accuracy may be achieved with wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.Materials and methods: We fitted a wavelet-based SARIMA(seasonal autoregressive integrated moving average)-NNAR(neural network nonlinear autoregressive) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. At the same time, single SARIMA model, simplex NNAR model and pure SARIMA-NNAR hybrid model were established as well for comparison and estimation.Results: The wavelet-based SARIMA-NNAR hybrid model had an excellent performance whether in fitting or in forecasting compared to other models. Its fitted and forecasting time series were approximate to the actual observed time series.Conclusions: This wavelet-based SARIMA-NNAR hybrid model that we fitted is suitable for forecasting number of HFMD cases. It will facilitate prevention and control of HFMD.

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Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model

Cited by 87 publications

References 17 publications

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Time series analysis of temporal trends in the pertussis incidence in Mainland China from 2005 to 2016

Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure

Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

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