BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

219 related articles for article (PubMed ID: 29708082)

  • 1. Time-series analysis of tuberculosis from 2005 to 2017 in China.
    Wang H; Tian CW; Wang WM; Luo XM
    Epidemiol Infect; 2018 Jun; 146(8):935-939. PubMed ID: 29708082
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model.
    Wang Y; Xu C; Zhang S; Wang Z; Yang L; Zhu Y; Yuan J
    BMJ Open; 2019 Jul; 9(7):e024409. PubMed ID: 31371283
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A hybrid seasonal prediction model for tuberculosis incidence in China.
    Cao S; Wang F; Tam W; Tse LA; Kim JH; Liu J; Lu Z
    BMC Med Inform Decis Mak; 2013 May; 13():56. PubMed ID: 23638635
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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.
    Wang YW; Shen ZZ; Jiang Y
    BMJ Open; 2019 Jun; 9(6):e025773. PubMed ID: 31209084
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system.
    Zuo Z; Wang M; Cui H; Wang Y; Wu J; Qi J; Pan K; Sui D; Liu P; Xu A
    BMC Public Health; 2020 Aug; 20(1):1284. PubMed ID: 32843011
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Seasonality and Trend Forecasting of Tuberculosis Incidence in Chongqing, China.
    Liao Z; Zhang X; Zhang Y; Peng D
    Interdiscip Sci; 2019 Mar; 11(1):77-85. PubMed ID: 30734907
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.
    Azeez A; Obaromi D; Odeyemi A; Ndege J; Muntabayi R
    Int J Environ Res Public Health; 2016 Jul; 13(8):. PubMed ID: 27472353
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Applying SARIMA, ETS, and hybrid models for prediction of tuberculosis incidence rate in Taiwan.
    Kuan MM
    PeerJ; 2022; 10():e13117. PubMed ID: 36164599
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.
    Mao Q; Zhang K; Yan W; Cheng C
    J Infect Public Health; 2018; 11(5):707-712. PubMed ID: 29730253
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Time series analysis of rubella incidence in Chongqing, China using SARIMA and BPNN mathematical models.
    Chen Q; Zhao H; Qiu H; Wang Q; Zeng D; Ye M
    J Infect Dev Ctries; 2022 Aug; 16(8):1343-1350. PubMed ID: 36099379
    [TBL] [Abstract][Full Text] [Related]  

  • 11. An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China.
    Wang Y; Xu C; Li Y; Wu W; Gui L; Ren J; Yao S
    Infect Drug Resist; 2020; 13():867-880. PubMed ID: 32273731
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018.
    Tian CW; Wang H; Luo XM
    Epidemiol Infect; 2019 Jan; 147():e82. PubMed ID: 30868999
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A hybrid model for short-term bacillary dysentery prediction in Yichang City, China.
    Yan W; Xu Y; Yang X; Zhou Y
    Jpn J Infect Dis; 2010 Jul; 63(4):264-70. PubMed ID: 20657066
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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.
    Wei W; Jiang J; Gao L; Liang B; Huang J; Zang N; Ning C; Liao Y; Lai J; Yu J; Qin F; Chen H; Su J; Ye L; Liang H
    Am J Trop Med Hyg; 2017 Sep; 97(3):799-805. PubMed ID: 28820678
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Application of a hybrid model in predicting the incidence of tuberculosis in a Chinese population.
    Li Z; Wang Z; Song H; Liu Q; He B; Shi P; Ji Y; Xu D; Wang J
    Infect Drug Resist; 2019; 12():1011-1020. PubMed ID: 31118707
    [No Abstract]   [Full Text] [Related]  

  • 16. Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq.
    Mohammed SH; Ahmed MM; Al-Mousawi AM; Azeez A
    Int J Mycobacteriol; 2018; 7(4):361-367. PubMed ID: 30531036
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China.
    Li J; Li Y; Ye M; Yao S; Yu C; Wang L; Wu W; Wang Y
    Infect Drug Resist; 2021; 14():1941-1955. PubMed ID: 34079304
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.
    Wang KW; Deng C; Li JP; Zhang YY; Li XY; Wu MC
    Epidemiol Infect; 2017 Apr; 145(6):1118-1129. PubMed ID: 28115032
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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.
    Wei W; Jiang J; Liang H; Gao L; Liang B; Huang J; Zang N; Liao Y; Yu J; Lai J; Qin F; Su J; Ye L; Chen H
    PLoS One; 2016; 11(6):e0156768. PubMed ID: 27258555
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A new hybrid model SARIMA-ETS-SVR for seasonal influenza incidence prediction in mainland China.
    Zhao D; Zhang R
    J Infect Dev Ctries; 2023 Nov; 17(11):1581-1590. PubMed ID: 38064398
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.