518 related articles for article (PubMed ID: 29907102)
1. 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]
2. Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans.
Zhou L; Xia J; Yu L; Wang Y; Shi Y; Cai S; Nie S
Int J Environ Res Public Health; 2016 Mar; 13(4):355. PubMed ID: 27023573
[TBL] [Abstract][Full Text] [Related]
3. [Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].
Ke-Wei W; Yu W; Jin-Ping L; Yu-Yu J
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2016 Jul; 28(6):630-634. PubMed ID: 29469251
[TBL] [Abstract][Full Text] [Related]
4. A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China.
Zhou L; Yu L; Wang Y; Lu Z; Tian L; Tan L; Shi Y; Nie S; Liu L
PLoS One; 2014; 9(8):e104875. PubMed ID: 25119882
[TBL] [Abstract][Full Text] [Related]
5. Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.
Wu W; Guo J; An S; Guan P; Ren Y; Xia L; Zhou B
PLoS One; 2015; 10(8):e0135492. PubMed ID: 26270814
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.
Wu W; An SY; Guan P; Huang DS; Zhou BS
BMC Infect Dis; 2019 May; 19(1):414. PubMed ID: 31088391
[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. 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]
12. 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]
13. 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]
14. 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]
15. 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]
16. Forecasting deaths of road traffic injuries in China using an artificial neural network.
Qian Y; Zhang X; Fei G; Sun Q; Li X; Stallones L; Xiang H
Traffic Inj Prev; 2020; 21(6):407-412. PubMed ID: 32500738
[No Abstract] [Full Text] [Related]
17. A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.
Zenia S; L'Hadj M; Selmane S
J Res Health Sci; 2023 Sep; 23(3):e00586. PubMed ID: 38315901
[TBL] [Abstract][Full Text] [Related]
18. Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia.
Tan CV; Singh S; Lai CH; Zamri ASSM; Dass SC; Aris TB; Ibrahim HM; Gill BS
Int J Environ Res Public Health; 2022 Jan; 19(3):. PubMed ID: 35162523
[TBL] [Abstract][Full Text] [Related]
19. Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021-2022.
Davidescu AA; Apostu SA; Paul A
Entropy (Basel); 2021 Mar; 23(3):. PubMed ID: 33803384
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]