191
Views
10
CrossRef citations to date
0
Altmetric
Original Research

Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework

, , , ORCID Icon, , , & show all
Pages 733-747 | Published online: 05 Mar 2020

References

  • Singh R, Dwivedi SP, Gaharwar US, Meena R, Rajamani P, Prasad T. Recent updates on drug resistance in Mycobacterium tuberculosis. J Appl Microbiol. 2019;1–21. doi:10.1111/jam.14478
  • WHO. Global tuberculosis report. 2019 Available from: https://wwwwhoint/tb/publications/global_report/en/. Accessed 1111, 2019.
  • WHO. Global strategy and targets for tuberculosis prevention, care and control after 2015. Available from: https://wwwwhoint/tb/post2015_strategy/en/. Accessed 1111, 2019.
  • Zhou C, Long Q, Chen J, et al. Factors that determine catastrophic expenditure for tuberculosis care: a patient survey in China. Infect Dis Poverty. 2016;5:6. doi:10.1186/s40249-016-0100-626806552
  • Xu CH, Jeyashree K, Shewade HD, et al. Inequity in catastrophic costs among tuberculosis-affected households in China. Infect Dis Poverty. 2019;8(1):46. doi:10.1186/s40249-019-0564-231215476
  • WHO. The End TB Strategy. 2014 Available from: https://wwwwhoint/tb/End_TB_brochurepdf. Accessed 1111, 2019.
  • Ayelign B, Negash M. Immunological impacts of diabetes on the susceptibility of mycobacterium tuberculosis. J Immunol Res. 2019;2019:6196532. doi:10.1155/2019/619653231583258
  • Walaza S, Cohen C, Tempia S, et al. Influenza and tuberculosis co-infection: a systematic review. Influenza Other Respir Viruses. 2019. doi:10.1111/irv.12670
  • Zhou Q, Jiang H, Wang J, Zhou J. A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci Total Environ. 2014;496:264–274. doi:10.1016/j.scitotenv.2014.07.05125089688
  • Khan MT, Kaushik AC, Ji L, Malik SI, Ali S, Wei DQ. Artificial neural networks for prediction of tuberculosis disease. Front Microbiol. 2019;10:395. doi:10.3389/fmicb.2019.0039530886608
  • Golestani A, Gras R. Can we predict the unpredictable? Sci Rep. 2014;4:6834. doi:10.1038/srep0683425355427
  • Chatfield C, Koehler AB, Ord JK, Snyder RD. A new look at models for exponential smoothing. J R Stat Soc. 2001;50(2):147–159. doi:10.1111/1467-9884.00267
  • Ke G, Hu Y, Huang X, et al. Epidemiological analysis of hemorrhagic fever with renal syndrome in China with the seasonal-trend decomposition method and the exponential smoothing model. Sci Rep. 2016;6:39350. doi:10.1038/srep3935027976704
  • Hyndman RJ, Khandakar Y. Automatic time series forecasting: the forecast package for R. J Stat Softw. 2008;27(3):1–22. doi:10.18637/jss.v027.i03
  • Hyndman RJ, Koehler AB, Keith OJ, Snyder RD. Forecasting with Exponential Smoothing the State Space Approach. berlin: springer-verlag; 2008.
  • Zhang X, Pang Y, Cui M, Stallones L, Xiang H. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model. Ann Epidemiol. 2015;25(2):101–106. doi:10.1016/j.annepidem.2014.10.01525467006
  • Mao Q, Zhang K, Yan W, Cheng C. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health. 2018;11(5):707–712. doi:10.1016/j.jiph.2018.04.00929730253
  • Wei W, Jiang J, Liang H, et al. Application of a combined model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One. 2016;11(6):e0156768. doi:10.1371/journal.pone.015676827258555
  • Wang YW, Shen ZZ, Jiang Y. Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study. BMJ Open. 2019;9(6):e025773. doi:10.1136/bmjopen-2018-025773
  • 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
  • Hyndman RJ, Koehler AB, Snyder RD, Grose S. A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast. 2000;18(3):439–454. doi:10.1016/S0169-2070(01)00110-8
  • Wang Y, Xu C, Zhang S, Wang Z, Zhu Y, Yuan J. Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018. Sci Rep. 2018;8(1):15901. doi:10.1038/s41598-018-33165-930367079
  • Zhao Y, Lafta R, Hagopian A, Flaxman AD. The epidemiology of 32 selected communicable diseases in Iraq, 2004-2016. Int J Infect Dis. 2019;89:102–109. doi:10.1016/j.ijid.2019.09.01831560993
  • 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
  • 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
  • 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
  • Earnest A, Evans SM, Sampurno F, Millar J. Forecasting annual incidence and mortality rate for prostate cancer in Australia until 2022 using autoregressive integrated moving average (ARIMA) models. BMJ Open. 2019;9(8):e031331. doi:10.1136/bmjopen-2019-031331
  • Ku CC, Dodd PJ. Forecasting the impact of population ageing on tuberculosis incidence. PLoS One. 2019;14(9):e0222937. doi:10.1371/journal.pone.022293731550293
  • Cao S, Wang F, Tam W, et al. A hybrid seasonal prediction model for tuberculosis incidence in China. BMC Med Inform Decis Mak. 2013;13(1):56. doi:10.1186/1472-6947-13-5623638635
  • Kim EH, Bae JM. Seasonality of tuberculosis in the Republic of Korea, 2006-2016. Epi Heal. 2018;40:e2018051. doi:10.4178/epih.e2018051
  • Luquero FJ, Sanchez-padilla E, Simon-soria F, Eiros JM, Golub JE. Trend and seasonality of tuberculosis in Spain, 1996-2004. Int J Tuberc Lung Dis. 2008;12(2):221–224.18230258
  • Sumi A, Kobayashi N. Time-series analysis of geographically specific monthly number of newly registered cases of active tuberculosis in Japan. PLoS One. 2019;14(3):e0213856. doi:10.1371/journal.pone.021385630883581
  • Mohammed SH, Ahmed MM, Al-mousawi AM, Azeez A. Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq. Int J Mycobacteriol. 2018;7(4):361–367. doi:10.4103/ijmy.ijmy_109_1830531036
  • 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
  • Wang H, Tian C, Wang W, Luo X. Temporal cross-correlations between ambient air pollutants and seasonality of tuberculosis: a time-series analysis. Int J Environ Res Public Health. 2019;16(9). doi:10.3390/ijerph16091585
  • Blount RJ, Pascopella L, Catanzaro DG, et al. Traffic-related air pollution and all-cause mortality during tuberculosis treatment in California. Environ Health Perspect. 2017;125(9):097026. doi:10.1289/ehp169928963088
  • Nnoaham KE, Clarke A. Low serum vitamin D levels and tuberculosis: a systematic review and meta-analysis. Int J Epidemiol. 2008;37(1):113–119. doi:10.1093/ije/dym24718245055
  • Xiao Y, He L, Chen Y, et al. The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015. Sci Rep. 2018;8(1):10053. doi:10.1038/s41598-018-28426-629968800
  • Koh GC, Hawthorne G, Turner AM, Kunst H, Dedicoat M. Tuberculosis incidence correlates with sunshine: an ecological 28-year time series study. PLoS One. 2013;8(3):e57752. doi:10.1371/journal.pone.005775223483924
  • Xu G, Mao X, Wang J, Pan H. Clustering and recent transmission of Mycobacterium tuberculosis in a Chinese population. Infect Drug Resist. 2018;11:323–330. doi:10.2147/idr.s15653429563813