An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China

References

• WHO. Global tuberculosis report 2018. Available from: https://wwwwhoint/tb/publications/global_report/en/. Accessed 918, 2019.
   Google Scholar
• WHO. Global strategy and targets for tuberculosis prevention, care and control after 2015. Available from: https://wwwwhoint/tb/post2015_strategy/en/. Accessed 918, 2019.
   Google Scholar
• WHO. The end TB strategy. 2014 Available from: https://wwwwhoint/tb/End_TB_brochurepdf. Accessed 918, 2019.
   Google Scholar
• Rao H, Shi X, Zhang X. Using the Kulldorff’s scan statistical analysis to detect spatio-temporal clusters of tuberculosis in Qinghai Province, China, 2009-2016. BMC Infect Dis. 2017;17(1):578. doi:10.1186/s12879-017-2643-y28826399
   PubMed Web of Science ®Google Scholar
• National Health Commission of the People’s Republic of China. National data of notifiable communicable disease in 2018. 2019 Available from: http://wwwnhcgovcn/jkj/s3578/201904/050427ff32704a5db64f4ae1f6d57c6cshtml. Accessed 309, 2020.
   Google Scholar
• Wang Y, Xu C, Zhang S, et al. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open. 2019;9(7):e024409. doi:10.1136/bmjopen-2018-024409
   PubMed Web of Science ®Google Scholar
• Liu Q, Li Z, Ji Y, et al. Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infect Drug Resist. 2019;12:2311–2322. doi:10.2147/idr.s20780931440067
   PubMed Web of Science ®Google Scholar
• Duan Y, Huang XL, Wang YJ, et al. Impact of meteorological changes on the incidence of scarlet fever in Hefei City, China. Int J Biometeorol. 2016;60(10):1543–1550. doi:10.1007/s00484-016-1145-826932715
   PubMed Web of Science ®Google Scholar
• Wu W, An SY, Guan P, Huang DS, Zhou BS. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks. BMC Infect Dis. 2019;19(1):414. doi:10.1186/s12879-019-4028-x31088391
   PubMed Web of Science ®Google Scholar
• Zhang Y, Bambrick H, Mengersen K, et al. Using big data to predict pertussis infections in Jinan city, China: a time series analysis. Int J Biometeorol. 2019. doi:10.1007/s00484-019-01796-w
   Web of Science ®Google Scholar
• Li Z, Wang Z, Song H, et al. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infect Drug Resist. 2019;12:1011–1020. doi:10.2147/idr.s19041831118707
   PubMed Web of Science ®Google Scholar
• Adeboye A, Davies O, Akinwumi O, James N, Ruffin M. Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, Using a Hybrid Model. Int J Environ Res Public Health. 2016;13(8):757. doi:10.3390/ijerph13080757
   Web of Science ®Google Scholar
• Fanoodi B, Malmir B, Jahantigh FF. Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Comput Biol Med. 2019;113:103415. doi:10.1016/j.compbiomed.2019.10341531536834
   PubMed Web of Science ®Google Scholar
• Ren H, Li J, Yuan ZA, Hu JY, Yu Y, Lu YH. The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China. BMC Infect Dis. 2013;13:421. doi:10.1186/1471-2334-13-42124010871
   PubMed Web of Science ®Google Scholar
• Mini KG, Kuriakose S, Sathianandan TV. Modeling CPUE series for the fishery along northeast coast of India: A comparison between the HoltWinters, ARIMA and NNAR models. J MarBiol Associa of India. 2015;2(57):75–82. doi:10.6024/jmbai.2015.57.2.1884-11
   Google Scholar
• Wang Y, Xu C, Zhang S, et al. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep. 2019;9(1):8046. doi:10.1038/s41598-019-44469-931142826
   PubMed Web of Science ®Google Scholar
• Akhtar S, Rozi S. An autoregressive integrated moving average model for short-term prediction of hepatitis C virus seropositivity among male volunteer blood donors in Karachi, Pakistan. World J Gastroenterol. 2009;15(13):1607–1612. doi:10.3748/wjg.15.160719340903
   PubMed Web of Science ®Google Scholar
• Nury AH, Hasan K, Alam MJB. Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh. J King Saud Univ Sci. 2017;29(1):47–61. doi:10.1016/j.jksus.2015.12.002
   Web of Science ®Google Scholar
• Lam KC, Oshodi OS. Forecasting construction output: a comparison of artificial neural network and Box-Jenkins model. Engin Cons Archi Manage. 2016;23(3):302–322. doi:10.1108/ECAM-05-2015-0080
   Web of Science ®Google Scholar
• Maleki A, Nasseri S, Aminabad MS, Hadi M. Comparison of ARIMA and NNAR models for forecasting water treatment plant’s influent characteristics. KSCE J Civil Eng. 2018;22(6):1–13.
   Google Scholar
• Thoplan R. Simple v/s sophisticated methods of forecasting for mauritius monthly tourist arrival data. Int J Stat Applic. 2014;4(5):217–223. doi:10.5923/j.statistics.20140405.01
   Google Scholar
• Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ. Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. PLoS One. 2015;10(3):e0116832. doi:10.1371/journal.pone.011683225760345
   PubMed Web of Science ®Google Scholar
• Pötscher BM. The behaviour of the Lagrangian multiplier test in testing the orders of an ARMA-model. Metrika. 1985;32(1):129–150. doi:10.1007/bf1897808
   Google Scholar
• Li X, Peng L, Yao X, et al. Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut. 2017;231(Pt 1):997–1004. doi:10.1016/j.envpol.2017.08.11428898956
   PubMed Web of Science ®Google Scholar
• Yuan W, Wang K, Bo X, Tang L, Wu J. A novel multi-factor & multi-scale method for PM2.5 concentration forecasting. Environ Pollut. 2019;255(1):113187. doi:10.1016/j.envpol.2019.11318731522003
   PubMed Web of Science ®Google Scholar
• Held L, Paul M. Modeling seasonality in space-time infectious disease surveillance data. Biom J. 2012;54(6):824–843. doi:10.1002/bimj.20120003723034894
   PubMed Web of Science ®Google Scholar
• Wang H, Tian CW, Wang WM, Luo XM. Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect. 2018;146(8):935–939. doi:10.1017/S095026881800111529708082
   PubMed Web of Science ®Google Scholar
• Wubuli A, Li Y, Xue F, Yao X, Upur H, Wushouer Q. Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China. PLoS One. 2017;12(7):e0180226. doi:10.1371/journal.pone.018022628678873
   PubMed Web of Science ®Google Scholar
• Nagayama N, Ohmori M. Seasonality in various forms of tuberculosis. Int J Tuberc Lung Dis. 2006;10(10):1117–1122.17044204
   PubMed Web of Science ®Google Scholar
• Rios M, Garcia JM, Sanchez JA, Perez D. A statistical analysis of the seasonality in pulmonary tuberculosis. Eur J Epidemiol. 2000;16(5):483–488. doi:10.1023/a:100765332997210997837
   PubMed Web of Science ®Google Scholar
• Thorpe LE, Frieden TR, Laserson KF, Wells C, Khatri GR. Seasonality of tuberculosis in India: is it real and what does it tell us? Lancet. 2004;364(9445):1613–1614. doi:10.1016/s0140-6736(04)17316-915519633
   PubMed Web of Science ®Google Scholar
• Qinghai Provincial Bureau Of Statistics. The latest statistics. Available from: http://tjjqinghaigovcn/tjData/newData/. Accessed 918, 2019.
   Google Scholar
• Rao HX, Zhang X, Zhao L, et al. Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis. Infect Dis Poverty. 2016;5(1):45. doi:10.1186/s40249-016-0139-427251154
   PubMed Web of Science ®Google Scholar
• Li XX, Wang LX, Zhang H, et al. Seasonal variations in notification of active tuberculosis cases in China, 2005-2012. PLoS One. 2013;8(7):e68102. doi:10.1371/journal.pone.006810223874512
   PubMed Web of Science ®Google Scholar
• Wang L, Zhang H, Ruan Y, et al. Tuberculosis prevalence in China, 1990-2010; a longitudinal analysis of national survey data. Lancet. 2014;383(9934):2057–2064. doi:10.1016/s0140-6736(13)62639-224650955
   PubMed Web of Science ®Google Scholar
• National Statistical Bureau. The latest statistics. Available from: http://data.stats.gov.cn/search.htm?s=GDP:615. Accessed 918, 2019.
   Google Scholar