BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

277 related articles for article (PubMed ID: 30586416)

  • 1. Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model.
    Wang Y; Xu C; Wang Z; Zhang S; Zhu Y; Yuan J
    PLoS One; 2018; 13(12):e0208404. PubMed ID: 30586416
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model.
    Wang Y; Xu C; Wang Z; Yuan J
    PeerJ; 2019; 7():e6165. PubMed ID: 30671295
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Forecasting hand-foot-and-mouth disease cases using wavelet-based SARIMA-NNAR hybrid model.
    Yu G; Feng H; Feng S; Zhao J; Xu J
    PLoS One; 2021; 16(2):e0246673. PubMed ID: 33544752
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Application of a combined model with seasonal autoregressive integrated moving average and support vector regression in forecasting hand-foot-mouth disease incidence in Wuhan, China.
    Zou JJ; Jiang GF; Xie XX; Huang J; Yang XB
    Medicine (Baltimore); 2019 Feb; 98(6):e14195. PubMed ID: 30732135
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China.
    Wang Y; Xu C; Zhang S; Yang L; Wang Z; Zhu Y; Yuan J
    Sci Rep; 2019 May; 9(1):8046. PubMed ID: 31142826
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model.
    Wang Z; Wang Y; Zhang S; Wang S; Xu Z; Feng Z
    BMC Infect Dis; 2024 Jan; 24(1):113. PubMed ID: 38253998
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Time series analysis-based seasonal autoregressive fractionally integrated moving average to estimate hepatitis B and C epidemics in China.
    Wang YB; Qing SY; Liang ZY; Ma C; Bai YC; Xu CJ
    World J Gastroenterol; 2023 Nov; 29(42):5716-5727. PubMed ID: 38075851
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China.
    Zhao D; Zhang H; Zhang R; He S
    BMC Public Health; 2023 Mar; 23(1):619. PubMed ID: 37003988
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China.
    Xiao Y; Li Y; Li Y; Yu C; Bai Y; Wang L; Wang Y
    Infect Drug Resist; 2021; 14():3849-3862. PubMed ID: 34584428
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Time series model for forecasting the number of new admission inpatients.
    Zhou L; Zhao P; Wu D; Cheng C; Huang H
    BMC Med Inform Decis Mak; 2018 Jun; 18(1):39. PubMed ID: 29907102
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.
    Zhao Z; Zhai M; Li G; Gao X; Song W; Wang X; Ren H; Cui Y; Qiao Y; Ren J; Chen L; Qiu L
    BMC Infect Dis; 2023 Feb; 23(1):71. PubMed ID: 36747126
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.