Abstract
Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005–2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β = − 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β = − 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β = − 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.
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References
Azeez A, Obaromi D, Odeyemi A, Ndege J, Muntabayi R (2016) 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). https://doi.org/10.3390/ijerph13080757
Beiranvand R, Karimi A, Delpisheh A, Sayehmiri K, Soleimani S, Ghalavandi S (2016) Correlation assessment of climate and geographic distribution of tuberculosis using geographical information system (GIS). Iran J Public Health 45(1):86–93
Cao LT, Liu HH, Li J, Yin XD, Duan Y, Wang J (2020) Relationship of meteorological factors and human brucellosis in Hebei province, China. Sci Total Environ 703:135491. https://doi.org/10.1016/j.scitotenv.2019.135491
Goto K, Kumarendran B, Mettananda S, Gunasekara D, Fujii Y, Kaneko S (2013) Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data. PLoS ONE 8(5):e63717. https://doi.org/10.1371/journal.pone.0063717
Grassly NC, Fraser C (2006) Seasonal infectious disease epidemiology. Proc Biol Sci 273(1600):2541–2550. https://doi.org/10.1098/rspb.2006.3604
Guo C, Du Y, Shen SQ, Lao XQ, Qian J, Ou CQ (2017) Spatiotemporal analysis of tuberculosis incidence and its associated factors in mainland China. Epidemiol Infect 145(12):2510–2519. https://doi.org/10.1017/S0950268817001133
Gutierrez A, Carranza A, Carrasco A (2019) Detecting nonlinear dynamics using BDS test and surrogate data in financial time series. J Math Syst Sci
Hong JY, Kim SY, Chung KS, Kim EY, Jung JY, Park MS ,... Kang YA (2014) Association between vitamin D deficiency and tuberculosis in a Korean population. Int J Tuberc Lung Dis 18(1):73–78. https://doi.org/10.5588/ijtld.13.0536
Jian L, Zhao Y, Zhu YP, Zhang MB, Bertolatti D (2012) An application of ARIMA model to predict submicron particle concentrations from meteorological factors at a busy roadside in Hangzhou, China. Sci Total Environ 426:336–345. https://doi.org/10.1016/j.scitotenv.2012.03.025
Lau L, Wong NS, Leung CC, Chan CK, Lau A, Tian L, Lee SS (2021) Seasonality of tuberculosis in intermediate endemicity setting dominated by reactivation diseases in Hong Kong. Sci Rep 11(1):20259. https://doi.org/10.1038/s41598-021-99651-9
Lau SY, Chen E, Wang M, Cheng W, Zee BC, Han X,... Wang X (2019) Association between meteorological factors, spatiotemporal effects, and prevalence of influenza A subtype H7 in environmental samples in Zhejiang province, China. Sci Total Environ 663:793–803. https://doi.org/10.1016/j.scitotenv.2019.01.403
Li ZQ, Pan HQ, Liu Q, Song H, Wang JM (2020) Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China. Infect Dis Poverty 9(1):151. https://doi.org/10.1186/s40249-020-00771-7
Li Z, Wang Z, Song H, Liu Q, He B, Shi P,... Wang J (2019) Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population. Infect Drug Resist 12:1011–1020. https://doi.org/10.2147/IDR.S190418
Liao Z, Zhang X, Zhang Y, Peng D (2019) Seasonality and trend forecasting of tuberculosis incidence in Chongqing, China. Interdiscip Sci Comput Life Sci 11(1):77–85. https://doi.org/10.1007/s12539-019-00318-x
Liu L, Luan RS, Yin F, Zhu XP, Lu Q (2016) Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiol Infect 144(1):144–151. https://doi.org/10.1017/S0950268815001144
Liu Q, Li Z, Ji Y, Martinez L, Zia UH, Javaid A,... Wang J (2019) Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses. Infect Drug Resist 12:311–2322. https://doi.org/10.2147/IDR.S207809
Lu JY, Chen ZQ, Liu YH, Liu WH, Ma Y, Li TG,... Yang ZC (2019) Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017. Sci Total Environ 663:227–235. https://doi.org/10.1016/j.scitotenv.2019.01.318
Mao Q, Zhang K, Yan W, Cheng C (2018) Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model. J Infect Public Health 11(5):707–712. https://doi.org/10.1016/j.jiph.2018.04.009
Martinez EZ, Silva EA, Fabbro AL (2011) A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of Sao Paulo, Brazil. Rev Soc Bras Med Trop 44(4):436–440. https://doi.org/10.1590/s0037-86822011000400007
Nardell EA (2004) Catching droplet nuclei: toward a better understanding of tuberculosis transmission. Am J Respir Crit Care Med 169(5):553–554. https://doi.org/10.1164/rccm.2401003
Narula P, Sihota P, Azad S, Lio P (2015) Analyzing seasonality of tuberculosis across Indian states and union territories. J Epidemiol Glob Health 5(4):337–346. https://doi.org/10.1016/j.jegh.2015.02.004
Peccia J, Hernandez M (2004) UV-induced inactivation rates for airborne Mycobacterium bovis BCG. J Occup Environ Hyg 1(7):430–435. https://doi.org/10.1080/15459620490458495
Rao HX, Zhang X, Zhao L, Yu J, Ren W, Zhang XL,... Qiu LX (2016) Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis. Infect Dis Poverty 5(1):45. https://doi.org/10.1186/s40249-016-0139-4
Selvaraj P, Harishankar M, Afsal K (2015) Vitamin D: immuno-modulation and tuberculosis treatment. Can J Physiol Pharmacol 93(5):377–384. https://doi.org/10.1139/cjpp-2014-0386
Sharma A, Bloss E, Heilig CM, Click ES (2016) Tuberculosis caused by Mycobacterium africanum, United States, 2004–2013. Emerg Infect Dis 22(3):396–403. https://doi.org/10.3201/eid2203.151505
Song X, Xiao J, Deng J, Kang Q, Zhang Y, Xu J (2016) Time series analysis of influenza incidence in Chinese provinces from 2004 to 2011. Medicine (baltimore) 95(26):e3929. https://doi.org/10.1097/MD.0000000000003929
Squire SB, Tang S (2004) How much of China’s success in tuberculosis control is really due to DOTS? Lancet 364(9432):391–392. https://doi.org/10.1016/S0140-6736(04)16777-9
Wang Y, Xu C, Wang Z, Yuan J (2019a) Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model. PeerJ 7:e6165. https://doi.org/10.7717/peerj.6165
Wang Y, Xu C, Zhang S, Wang Z, Yang L, Zhu Y, Yuan J (2019b) Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model. BMJ Open 9(7):e24409. https://doi.org/10.1136/bmjopen-2018-024409
Wang Y, Xu C, Zhang S, Yang L, Wang Z, Zhu Y, Yuan J (2019c) Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep 9(1):8046. https://doi.org/10.1038/s41598-019-44469-9
Wang Y, Xu C, Li Y, Wu W, Gui L, Ren J, Yao S (2020) An advanced data-driven hybrid model of SARIMA-NNNAR for tuberculosis incidence time series forecasting in Qinghai Province, China. Infect Drug Resist 13:867–880. https://doi.org/10.2147/IDR.S232854
Wang Y, Xu C, Ren J, Li Y, Wu W, Yao S (2021) Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China. Environ Sci Pollut Res Int 28(6):7281–7294. https://doi.org/10.1007/s11356-020-11072-9
World Health Organization (2020) Global Tuberculosis Report 2020. Retrieved from https://www.who.int/teams/global-tuberculosis-programme/data. Accessed 20 October 2022
Xiao Y, He L, Chen Y, Wang Q, Meng Q, Chang W,... Yu Z (2018) The influence of meteorological factors on tuberculosis incidence in Southwest China from 2006 to 2015. Sci Rep 8(1):10053. https://doi.org/10.1038/s41598-018-28426-6
Yang Q, Yang Z, Ding H, Zhang X, Dong Z, Hu W,... Fu C (2014) The relationship between meteorological factors and mumps incidence in Guangzhou, China, 2005-2012. Hum Vaccin Immunother 10(8):2421–2432. https://doi.org/10.4161/hv.29286
Yu L, Zhou L, Tan L, Jiang H, Wang Y, Wei S, Nie 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(6):e98241. https://doi.org/10.1371/journal.pone.0098241
Zhang WY, Guo WD, Fang LQ, Li CP, Bi P, Glass GE,... Cao WC (2010) Climate variability and hemorrhagic fever with renal syndrome transmission in Northeastern China. Environ Health Perspect 118(7):915–920. https://doi.org/10.1289/ehp.0901504
Zhang Y, Bi P, Jiang B (2011) Climate variability and population health in China: updated Knowledge, Challenges and Opportunities. Chapters
Zhang Y, Liu M, Wu SS, Jiang H, Zhang J, Wang S, ... Guo X (2019) Spatial distribution of tuberculosis and its association with meteorological factors in mainland China. BMC Infect Dis 19(1):379. https://doi.org/10.1186/s12879-019-4008-1
Zheng YL, Zhang LP, Zhang XL, Wang K, Zheng YJ (2015) Forecast model analysis for the morbidity of tuberculosis in Xinjiang, China. Plos One 10(3):e116832. https://doi.org/10.1371/journal.pone.0116832
Acknowledgements
We are grateful for all people who diagnosed and submitted the TB cases to the National Notifiable Infectious Disease Surveillance System.
Funding
This project was supported by the Innovation Project for College Students of Xinxiang Medical University (XYXSKYZ201932), the Key Scientific Research Project of Universities in Henan (21A330004), and the PhD Research Project of Xinxiang Medical University (XYBSKYZZ201805).
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Liang, W., Hu, A., Hu, P. et al. Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China. Int J Biometeorol 67, 55–65 (2023). https://doi.org/10.1007/s00484-022-02385-0
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DOI: https://doi.org/10.1007/s00484-022-02385-0