Series of observations of HFMD in Shenzhen.

<p>Series 1 shows the observations of the training set (from January 2008 to August 2012). Series 2 shows the observations of training set without the abnormal observation (AO). Series 3 shows the series 2 achieving stationary after one regular differencing and one seasonal differencing (d = 1, s = 12). Series 4 shows the validation set (from September 2012 to November 2012).</p

Series of predictions of all the observations.

<p>Series 1 shows the observations of the training set without the abnormal observations and the validation set. Series 2 shows the predictions of series 1 and the expected cases of forecasting set (from December 2012 to May 2013) obtained by ARIMA and NARNN with 15 hidden units and 5 delays. There is a significantly increasing trend in first half of 2013.</p

Expected incidence cases and observations in the corresponding period from 2010 to 2013 in Shenzhen.

<p>*December in the previous year.</p><p>**We made the assumption that the expected cases in January and February were zero.</p

Series of observations of HFMD in Shenzhen.

<p>Series 1 shows the observations of the training set (from January 2008 to August 2012). Series 2 shows the observations of training set without the abnormal observation (AO). Series 3 shows the series 2 achieving stationary after one regular differencing and one seasonal differencing (d = 1, s = 12). Series 4 shows the validation set (from September 2012 to November 2012).</p

Case distribution and demographic characteristics of HFMD in Shenzhen from January 2008 to November 2012.

<p>Case distribution and demographic characteristics of HFMD in Shenzhen from January 2008 to November 2012.</p

Series of the predictions of the validation set.

<p>Series 1 shows the observations of training set without the abnormal observation. Series 3 shows the validation set. Series 2 shows the predictions of training set obtained by hybrid model combined with ARIMA and NARNN with 12 hidden units and 4 delays, and Series 4 shows the predictions of validation set obtained by the same model. Each prediction is very close to each observation.</p

Autocorrelation function (ACF) and partial autocorrelation function (PACF) plotted against time lags.

<p>A and B show ACF and PACF of the training set. C and D show ACF and PACF of the training set after one order of regular differencing and one order of seasonal differencing (d = 1, s = 12). After differencing, Most of the correlations fall around zero within their 95% confidence intervals (95%CI, U95: upper limit of 95%CI, L95: lower limit of 95%CI) except the one at the first lag, which is indicated the series would achieve stationary.</p

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

<div><p>Backgrounds/Objective</p><p>Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas.</p><p>Methods</p><p>A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model.</p><p>Results</p><p>The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10⁻⁴, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10⁻⁴, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend.</p><p>Conclusion</p><p>The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases.</p></div

Autocorrelation function (ACF) and partial autocorrelation function (PACF) plots of original prevalence series.

<p>A and B. ACF and PACF plots of original schistosomisis prevalence (1956–2008); C and D. ACF and PACF plots after one order of regular differencing (1956–2008); E and F. ACF and PACF plots of original schistosomisis prevalence (1956–2012); G and H. ACF and PACF plots after one order of regular differencing (1956–2012). Dotted lines indicate 95% confidence intervals. Most of the correlations fall around zero within their 95% confidence intervals except for the one at zero lag, which indicate the series achieved stationary.</p

The optimum networks configuration of different target series.

<p>Note: OS = original prevalence series, RS = residual series, NRS = new residual series</p><p>All MSE values should be multiplied by 10⁻⁴.</p