{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:34:05Z","timestamp":1783942445608,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,3,23]],"date-time":"2016-03-23T00:00:00Z","timestamp":1458691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis.<\/jats:p>","DOI":"10.3390\/ijerph13040355","type":"journal-article","created":{"date-parts":[[2016,3,23]],"date-time":"2016-03-23T11:40:54Z","timestamp":1458733254000},"page":"355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans"],"prefix":"10.3390","volume":"13","author":[{"given":"Lingling","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China"},{"name":"Department of Information, Research Institute of Field Surgery, Daping Hospital of Third Military Medical University, Chongqing 400042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xia","sequence":"additional","affiliation":[{"name":"Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijing","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunxiang","family":"Cai","sequence":"additional","affiliation":[{"name":"Institute of Parasitic Disease Control, Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaofa","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,23]]},"reference":[{"key":"ref_1","unstructured":"10 Facts about Schistosomiasis. Available online: http:\/\/www.who.int\/features\/factfiles\/schistosomiasis\/en\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.3201\/eid1310.061423","article-title":"Epidemiology of schistosomiasis in the People\u2019s Republic of China, 2004","volume":"13","author":"Zhou","year":"2007","journal-title":"Emerg. Infect. Dis."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1007\/s00436-013-3295-5","article-title":"The new national integrated strategy emphasizing infection sources control for schistosomiasis control in China has made remarkable achievements","volume":"112","author":"Liu","year":"2013","journal-title":"Parasitol. Res."},{"key":"ref_4","first-page":"591","article-title":"Endemic status of schistosomiasis in People\u2019s Republic of China in 2013","volume":"26","author":"Lei","year":"2014","journal-title":"Chin. J. Schistosomiasis Control"},{"key":"ref_5","first-page":"521","article-title":"Schistosomiasis situation in People\u2019s Republic of China in 2009","volume":"22","author":"Hao","year":"2010","journal-title":"Chin. J. Schistosomiasis Control"},{"key":"ref_6","unstructured":"China MohotPsRo (2007). GB15976\u20132006 Criteria for Control and Elimination of Schistosomiasis, Beijing People\u2019s Medical Publishing House."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"264","DOI":"10.7883\/yoken.63.264","article-title":"A hybrid model for short-term bacillary dysentery prediction in Yichang City, China","volume":"63","author":"Yan","year":"2010","journal-title":"Jpn. J. Infect. Dis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ren, H., Li, J., Yuan, Z.A., Hu, J.Y., Yu, Y., and Lu, Y.H. (2013). The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China. BMC Infect. Dis., 13.","DOI":"10.1186\/1471-2334-13-421"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, G., Huang, S., Duan, Q., Shu, W., Hou, Y., Zhu, S., Miao, X., Nie, S., Wei, S., and Guo, N. (2013). Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0080969"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yu, L.J., Zhou, L.L., Tan, L., Jiang, H.B., Wang, Y., Wei, S., and Nie, S.F. (2014). Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (ANRNN) in forecasting incidence cases of HFMD in Shenzen, China. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0098241"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3278","DOI":"10.1002\/sim.5343","article-title":"A Bayesian analysis of the 2009 decline in tuberculosis morbidity in the United States","volume":"31","author":"Chen","year":"2012","journal-title":"Stat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kumar, V., Mangal, A., Panesar, S., Yadav, G., Talwar, R., Raut, D., and Singh, S. (2014). Forecasting malaria cases using climatic factors in Delhi, India: A time series analysis. Malar. Res. Treat., 2014.","DOI":"10.1155\/2014\/482851"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.3748\/wjg.15.1607","article-title":"An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan","volume":"15","author":"Akhtar","year":"2009","journal-title":"World J. Gastroenterol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"364","DOI":"10.4269\/ajtmh.2012.11-0472","article-title":"Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome","volume":"87","author":"Li","year":"2012","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gharbi, M., Quenel, P., Gustave, J., Cassadou, S., La Ruche, G., Girdary, L., and Marrama, L. (2011). Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors. BMC Infect. Dis., 11.","DOI":"10.1186\/1471-2334-11-166"},{"key":"ref_16","first-page":"1108","article-title":"The application of time series analysis in predicting the influenza incidence and early warning","volume":"45","author":"Zhu","year":"2011","journal-title":"Chin. J. Prevent. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.asoc.2014.05.028","article-title":"A moving-average-filter-based hybrid ARIMA\u2013ANN model for forecasting time series data","volume":"23","author":"Babu","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3579","DOI":"10.3748\/wjg.v10.i24.3579","article-title":"Forecasting model for the incidence of hepatitis A based on artificial neural network","volume":"10","author":"Guan","year":"2004","journal-title":"World J. Gastroenterol."},{"key":"ref_19","first-page":"322","article-title":"Artificial neural networks applied to forecasting time series","volume":"23","year":"2011","journal-title":"Psicothema"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1007\/s11356-010-0375-2","article-title":"Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management","volume":"18","author":"Paschalidou","year":"2011","journal-title":"Environ. Sci. Pollut. Res. Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1121\/1.4807779","article-title":"Comment on \u201cShort-term acoustic forecasting via artificial neural networks for neonatal intensive care units\u201d","volume":"134","author":"Park","year":"2013","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/S0140-6736(95)91746-2","article-title":"Introduction to neural networks","volume":"346","author":"Cross","year":"1995","journal-title":"Lancet"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, L.L., Yu, L.J., Wang, Y., Lu, Z., Tian, L., Tan, L., Shi, Y., Nie, S.F., and Liu, L. (2014). A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0104875"},{"key":"ref_25","unstructured":"National Surveillance Scheme of Schistosomiasis. Available online: http:\/\/www.chinacdc.cn\/gwswxx\/jkyjb\/201107\/t20110719_49203.htm."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (2008). Time Series Analysis, Forecasting and Control, Wiley. [4th ed.].","DOI":"10.1002\/9781118619193"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/72.279188","article-title":"Recurrent neural networks and robust time series prediction","volume":"5","author":"Connor","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_28","first-page":"43","article-title":"Schistosomiasis control in China: The impact of a 10-year World Bank Loan Project (1992\u20132001)","volume":"83","author":"Xianyi","year":"2005","journal-title":"Bull. World Health Organ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.actatropica.2005.07.005","article-title":"The public health significance and control of schistosomiasis in China\u2014Then and now","volume":"96","author":"Zhou","year":"2005","journal-title":"Acta Trop."},{"key":"ref_30","first-page":"127","article-title":"A potential impact of climate change and water resource development on the transmission of Schistosoma japonicum in China","volume":"47","author":"Yang","year":"2005","journal-title":"Parassitologia"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1128\/CMR.00044-09","article-title":"Schistosomiasis in the People\u2019s Republic of China: The era of the Three Gorges Dam","volume":"23","author":"McManus","year":"2010","journal-title":"Clin. Microbiol. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2049-9957-1-8","article-title":"Schistosomiasis control and the health system in P.R. China","volume":"1","author":"Collins","year":"2012","journal-title":"Infect. Dis. Poverty"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.omega.2004.07.024","article-title":"A hybrid ARIMA and support vector machines model in stock price forecasting","volume":"33","author":"Pai","year":"2005","journal-title":"Omega"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neunet.2013.10.008","article-title":"Correcting and combining time series forecasters","volume":"50","author":"Firmino","year":"2014","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"De Mattos Neto, P.S.G., Cavalcanti, G.D., Madeiro, F., and Ferreira, T.A.E. (2015). An approach to improve the performance of PM forecasters. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0138507"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.neucom.2014.11.030","article-title":"Error modeling approach to improve time series forecasters","volume":"153","author":"Firmino","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/72.641453","article-title":"A neural-network learning theory and a polynomial time RBF algorithm","volume":"8","author":"Roy","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_39","first-page":"73","article-title":"Early warning on measles through the neural networks","volume":"32","author":"Yu","year":"2011","journal-title":"Chin. J. Epidemiol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1039\/c1em10127g","article-title":"Forecasting of cyanobacterial density in Torrao reservoir using artificial neural networks","volume":"13","author":"Torres","year":"2011","journal-title":"J. Environ. Monit."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1756-3305-5-275","article-title":"Factors impacting on progress towards elimination of transmission of schistosomiasis japonica in China","volume":"5","author":"Zhou","year":"2012","journal-title":"Parasit. Vectors"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, W., Dai, J.R., and Liang, Y.S. (2014). Apropos: Factors impacting on progress towards elimination of transmission of schistosomiasis japonica in China. Parasit. Vectors, 7.","DOI":"10.1186\/1756-3305-7-408"}],"container-title":["International Journal of Environmental Research and Public Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1660-4601\/13\/4\/355\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:21:09Z","timestamp":1760210469000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1660-4601\/13\/4\/355"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,23]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,4]]}},"alternative-id":["ijerph13040355"],"URL":"https:\/\/doi.org\/10.3390\/ijerph13040355","relation":{},"ISSN":["1660-4601"],"issn-type":[{"value":"1660-4601","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,23]]}}}