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Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China

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Abstract

Tuberculosis (TB) is a global infectious disease and one of the ten leading causes of death worldwide. As TB incidence is seasonal, a reliable forecasting model that incorporates both seasonal and trend effects would be useful to improve the prevention and control of TB. In this study, the X12 autoregressive integrated moving average (X12-ARIMA) model was constructed by dividing the sequence into season term and trend term to forecast the two terms, respectively. Data regarding the TB report rate from January 2004 to December 2015 were included in the model, and the TB report data from January 2016 to December 2016 were used to validate the results. The X12-ARIMA model was compared with the seasonal autoregressive integrated moving average (SARIMA) model. A total of 383,797 cases were reported from January 2004 to December 2016 in Chongqing, China. The report rate of TB was highest in 2005 (151.06 per 100,000 population) and lowest in 2016 (72.58 per 100,000 population). The final X12-ARIMA model included the ARIMA (3,1,3) model for the trend term and the ARIMA (2,1,3) model for the season term. The SARIMA (1,0,2) * (1,1,1)12 model was selected for the SARIMA model. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of fitting and predicting performance based on the X12-ARIMA model were less than the SARIMA model. In conclusion, the occurrence of TB in Chongqing is controlled, which may be attributed to socioeconomic developments and improved TB prevention and control services. Applying the X12-ARIMA model is an effective method to forecast and analyze the trend and seasonality of TB.

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References

  1. Khanna D, Rana PS (2018) Ensemble technique for prediction of T-cell mycobacterium tuberculosis epitopes. Interdiscip Sci Comput Life Sci 2018:1–17

    Google Scholar 

  2. Dheda K, Barry CE, Maartens G (2016) Tuberculosis. Lancet 387:1211–1226

    Article  PubMed  Google Scholar 

  3. World Health Organization (2016) Top ten cause of death globally 2016. http://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death. Accessed 30 Dec 2018

  4. Cause of Death Collaborators (2016) Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 390:1151–1210

    Google Scholar 

  5. Zhu S, Xia L, Yu SC, Chen SB, Zhang JY (2017) The burden and challenges of tuberculosis in China: findings from the Global Burden of Disease Study 2015. Sci Rep 7(1):1746

    Article  CAS  Google Scholar 

  6. World Health Organization (2018) Global tuberculosis report 2017. Geneva. World Health Organization. http://www.who.int/tb/publications/global_report/MainText_13Nov2017.pdf. Accessed 30 Dec 2018

  7. Chen W, Xia YY, Li T, Chen H (2016) Analysis for the global and China TB Epidemic situation in 2015. J Tuber Lung Health 5:32–36

    Google Scholar 

  8. Technical Guidance Group for the Fifth National TB Epidemiological Survey (2012) The fifth national tuberculosis epidemiological survey in 2010. Chin J Antituberc 34:485–508

    Google Scholar 

  9. Durairaj DR, Shanmughavel P (2017) In silico drug design of thiolactomycin derivatives against Mtb-KasA enzyme to inhibit multidrug resistance of mycobacterium tuberculosis. Interdiscip Sci Comput Life Sci 22:1–11

    Google Scholar 

  10. Zhang L, Zheng CQ, Li T et al (2017) Building up a robust risk mathematical platform to predict colorectal cancer. Complexity 8:1–14

    Google Scholar 

  11. Zhang L, Liu Y, Wang M et al (2017) EZH2-, CHD4-, and IDH-linked epigenetic perturbation and its association with survival in glioma patients. J Mol Cell Bio 9(6):477–488

    Article  CAS  Google Scholar 

  12. Wah W, Das S, Earnest A, Lim LK, Chee CB, Cook AR (2014) Time series analysis of demographic and temporal trends of tuberculosis in Singapore. BMC Public Health 14:1121

    Article  PubMed  PubMed Central  Google Scholar 

  13. Liao CM, Hsieh NH, Huang TL, Cheng YH, Lin YJ, Choo CP (2012) Assessing trends and predictors of tuberculosis in Taiwan. BMC Public Health 12:29

    Article  PubMed  PubMed Central  Google Scholar 

  14. Hu XY et al (2016) Comparative study on ARIMA model and GRNN model for predicting the incidencde of tuberculosis. Acad J Sec Mil Med Univ 37:115–119

    Google Scholar 

  15. Zhang G, Huang S, Duan Q, Shu W, Hou Y, Zhu S (2013) Application of a hybrid model for predicting the incidence of tuberculosis in Hubei, China. PloS One 8:e80969

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Azeez A, Obaromi D, Odeyemi A, Ndege J, Muntabayi R (2013) Seasonality and trend forecasting of tuberculosis prevalence data in Eastern Cape, South Africa, using a hybrid model. Int J Environ Res Public Health 13:8

    Google Scholar 

  17. Wei W, Jiang J, Gao L, Liang B, Huang J, Zang N (2017) A New hybrid model using an autoregressive integrated moving average and a generalized regression neural network for the incidence of tuberculosis in Heng County, China. Am J Trop Med Hyg 97:799–805

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wu W, Guo J, An S, Guan P, Ren Y, Xia L (2015) Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu Province, China. PloS One 10:e0135492

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S (2014) Application of a new hybrid model with seasonal auto-regressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NARNN) in forecasting incidence cases of HFMD in Shenzhen, China. PloS One 9:e98241

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Cortes F, Turchi C, Arraes D, Montarroyos UR, Siqueira JB, Goncalves C (2018) Time series analysis of dengue surveillance data in two Brazilian cities. Acta Trop 182:190–197

    Article  PubMed  Google Scholar 

  21. Wang KW, Deng C, Li JP, Zhang YY, Li XY, Wu MC (2017) Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol Infect 145:1118–1129

    Article  PubMed  CAS  Google Scholar 

  22. Thorpe LE, Frieden TR, Laserson KF, Wells C, Khatri GR (2014) Seasonality of tuberculosis in India: is it real and what does it tell us? Lancet 364:1613–1614

    Article  Google Scholar 

  23. Zhang ZB, Lu ZQ, Xie H, Duan QH (2016) Seasonal variation and related influencing factors for tuberculosis. Chin J Epedemiol 37:1183–1186

    CAS  Google Scholar 

  24. Ren ZH (2013) The epidemiological characteristics and trends of tuberculosis in China. Chin J Health Stat 2013:158–161

    Google Scholar 

  25. Wang L, Liu J, Chin DP (2007) Progress in tuberculosis control and the evolving public-health system in China. Lancet 369(9562):691–696

    Article  PubMed  Google Scholar 

  26. Fares A (2011) Seasonality of tuberculosis. J Glob Infect Dis 3:46–55

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bruce AG, Jurke SR (1996) Non-Gaussian seasonal adjustment: X-12-ARIMA versus robust structural models. J Forecast 15:305–327

    Article  Google Scholar 

  28. Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control, 3 edn. Wiley, Hoboken, pp 79–86

    Google Scholar 

  29. Song X, Xiao J, Deng J (2016) Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011. Medicine 95:e3929

    Article  PubMed  PubMed Central  Google Scholar 

  30. Tseng FM, Tzeng GH (2002) A fuzzy seasonal ARIMA model for forecasting. Fuzzy Sets Syst 3:367–376

    Article  Google Scholar 

  31. Moosazadeh M, Khanjani N, Nasehi M, Bahrampour A (2015) Predicting the Incidence of smear positive tuberculosis cases in Iran using time series analysis. Iran J Public Health 44:1526–1534

    PubMed  PubMed Central  Google Scholar 

  32. Burck L, Gubman Y (2018) Pre-adjustment in X12-ARIMA. In: working paper of the Israel Central Bureau of Statistics. http://www.fcsm.gov/03papers/Burck.pdf. Accessed 30 Dec 2018

  33. Wang H, Tian C, Wang W, Luo X (2018) Time-series analysis of tuberculosis from 2005 to 2017 in China. Epidemiol Infect 2018:1–5

    Google Scholar 

  34. Ortblad KF, Salomon JA, Bärnighausen T, Atun R (2015) Stopping tuberculosis: a biosocial model for sustainable development. Lancet 386:2354–2362

    Article  PubMed  PubMed Central  Google Scholar 

  35. National Bureau of Statistics (2017) The statistical report of gross domestic product in Chongqing. http://data.stats.gov.cn/ks.htm?cn=E0103&zb=A0101&reg=500000. Accessed 30 Dec 2018

  36. Que P et al (2017) A Spatio-Temporal analysis of tuberculosis incidence and regional per capita GDP in Chongqing, 2011–2014. J Chongqing Med Univ 2017:1328–1331

    Google Scholar 

  37. Hou WL, Song FJ, Zhang NX, Dong XX (2012) Implementation and community involvement in DOTS strategy: a systematic review of studies in China. Int J Tuberc Lung Dis 16(11):1433–1440

    Article  PubMed  Google Scholar 

  38. Wu B et al (2017) Epidemiology of tuberculosis in Chongqing, China: a secular trend from 1992 to 2015. Sci Rep 7(1):7832

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Wubuli A et al (2017) Seasonality of active tuberculosis notification from 2005 to 2014 in Xinjiang, China. PloS One 12:e0180226

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Xu J et al (2017) Difference of urban development in China from the perspective of passenger transport around Spring festival. Appl Geogr 87:85–96

    Article  Google Scholar 

  41. Shi YH, She Y, He H, Zeng QQ, Chang C (2018) Analysis of the medical intention of TB and its influencing factors among floating population in six provinces in China. Chin J Antituberc 2018:296–301

    Google Scholar 

  42. Sreeramareddy CT, Panduru KV, Menten J et al (2009) Time delays in diagnosis of pulmonary tuberculosis: a systematic review of literature. BMC Infect Dis 9:91

    Article  PubMed  PubMed Central  Google Scholar 

  43. Li XX, Wang LX, Zhang H et al (2013) Seasonal variations in notification of active tuberculosis cases in China, 2005–2012. PLoS One 8(7):e68102

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. de Alba E (1993) Constrained forecasting in autoregressive time series models: a Bayesian analysis. Int J Forecast 9:95–108

    Article  Google Scholar 

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Acknowledgements

This project was supported by Chongqing Tuberculosis Control Institute.

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Authors and Affiliations

  1. Department of Respiratory, Children’s Hospital of Chongqing Medical University, Chongqing, 40010, People’s Republic of China

    Zhaoying Liao & Donghong Peng

  2. College of Stomatology, Chongqing Medical University, 40016, Chongqing, People’s Republic of China

    Xiaonan Zhang

  3. Medicine Engineering Research Center, College of Pharmacy, Chongqing Medical University, Chongqing, 40016, People’s Republic of China

    Yonghong Zhang

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  1. Zhaoying Liao
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  2. Xiaonan Zhang
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  3. Yonghong Zhang
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Correspondence to Donghong Peng.

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Liao, Z., Zhang, X., Zhang, Y. et al. Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China. Interdiscip Sci Comput Life Sci 11, 77–85 (2019). https://doi.org/10.1007/s12539-019-00318-x

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  • DOI: https://doi.org/10.1007/s12539-019-00318-x

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