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Comparative study of SARIMA and NARX models in predicting the incidence of schistosomiasis in China

School of Mathematical Sciences, Anhui University, Hefei, 230601, P.R.China

In this paper, based on the data of the incidence of schistosomiasis in China from January 2011 to May 2018 we establish SARIMA model and NARX model. These two models are used to predict the incidence of schistosomiasis in China from June 2018 to September 2018. By comparing the mean square error and the mean absolute error of two sets of predicted values, the results show that the NARX model is better and it has an e ective forecasting precision to incidence of schistosomiasis. Then according to the results, a mixed model called NARX-SARIMA model is used to predict the incidence future trends and make a comparison with the two model. The mixed model has a better application based on its good fitting capability.
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References

1. L.X. Qi, M. Xue and J.A. Cui, et al., Schistosomiasis Transmission Model and its Control in Anhui Province, B. Math Biol., 80 (2018), 2435–2451.

2. L.X. Qi, Y.W. Tang and S.J. Tian, Parameter estimation of modeling schistosomiasis transmission for four provinces in China, . Biosci. Eng., 16 (2019), 1005–1020.

3. L.X Qi, S.J Tian and J.A Cui, et al., Multiple infection leads to backward bifurcation for a schistosomiasis model, Math. Biosci. Eng., 16 (2019), 701–712.

4. L.G. Song, X.Y. Wu and M. Sacko, et al., History of schistosomiasis epidemiology, current status, and challenges in China: on the road to schistosomiasis elimination, Parasitol. Res., 115 (2016), 4071–4081.

5. X.N. Zhou, L.Y. Wang and M.G Chen, et al., The public health significance and control of schistosomiasis in China–then and now, Acta. Trop., 96 (2005), 97–105.

6. Q.Y. Liu, X.D. Liu and B.F Jiang, et al., Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model, BMC. Infect. Dis., 11 (2011), 1–7.

7. S.M. Awan, M.U. Riaz and A.G. Khan, Prediction of heart disease using artificial neural network, VFAST., 13 (2018), 102–112.

8. M.E. Banihabib, A. Ahmadian and F.S Jamali, Hybrid DARIMA-NARX model for forecasting long-term daily inflow to Dez reservoir using the North Atlantic Oscillation (NAO) and rainfall data, GeoResJ., 13 (2017), 9–16.

9. G.E.P. Box, G.M. Jenkins and G.C. Reinsel, Time series analysis: Forecasting and control, Prentice Hall., 1994.

10. M.Y. Anwar, J.A. Lewnard and S. Parikh, et al., Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence, Malaria. J., 15 (2016), 566–576.

11. A. Earnest, M.I. Chen and D. Ng, et al., Using autoregressive integrated moving average(ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore, Bmc. Health. Serv. Res., 5 (2005), 36–44.

12. F. Cortes, C.M.T. Martelli and R.A.A. Ximenes, et al., Time series analysis of dengue surveillance data in two Brazilian cities, Acta. Trop., 182 (2018), 190–197.

13. F.F. Nobre, A.B.S. Monteiro and P.B. Telles, et al., Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology, Stat. Med., 20 (2001), 3051–3069.

14. R. Allard, Use of time-series analysis in infectious disease surveillance, B. World. Health. Organ., 76 (1998), 327–333.

15. J.M.P.M. J´unior and G.A. Barreto, Long-term time series prediction with the NARX network: An empirical evaluation, Neurocomputing., 71 (2008), 3335–3343.

16. E. Diaconescu, The use of NARX Neural Networks to predict Chaotic Time Series, WSEAS., 3 (2008), 182–191.

17. M. Valipour, M.E. Banihabib and S.M.R Behbahani, Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir, J. Hydrol., 476 (2013), 433–441.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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