210 related articles for article (PubMed ID: 36913401)
1. Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.
Feng Y; Cui X; Lv J; Yan B; Meng X; Zhang L; Guo Y
PLoS One; 2023; 18(3):e0282928. PubMed ID: 36913401
[TBL] [Abstract][Full Text] [Related]
2. Prediction of hepatitis E using machine learning models.
Guo Y; Feng Y; Qu F; Zhang L; Yan B; Lv J
PLoS One; 2020; 15(9):e0237750. PubMed ID: 32941452
[TBL] [Abstract][Full Text] [Related]
3. Predicting incidence of hepatitis E using machine learning in Jiangsu Province, China.
Cheng X; Liu W; Zhang X; Wang M; Bao C; Wu T
Epidemiol Infect; 2022 Jul; 150():e149. PubMed ID: 35899849
[TBL] [Abstract][Full Text] [Related]
4. Deep learning time series prediction models in surveillance data of hepatitis incidence in China.
Xia Z; Qin L; Ning Z; Zhang X
PLoS One; 2022; 17(4):e0265660. PubMed ID: 35417459
[TBL] [Abstract][Full Text] [Related]
5. Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.
Zhu H; Chen S; Liang R; Feng Y; Joldosh A; Xie Z; Chen G; Li L; Chen K; Fang Y; Ou J
BMC Infect Dis; 2023 May; 23(1):299. PubMed ID: 37147566
[TBL] [Abstract][Full Text] [Related]
6. Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.
Wu T; Wang M; Cheng X; Liu W; Zhu S; Zhang X
Front Public Health; 2022; 10():942543. PubMed ID: 36262244
[TBL] [Abstract][Full Text] [Related]
7. Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.
Wang P; Zhang W; Wang H; Shi C; Li Z; Wang D; Luo L; Du Z; Hao Y
BMC Infect Dis; 2024 Feb; 24(1):265. PubMed ID: 38408967
[TBL] [Abstract][Full Text] [Related]
8. Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm.
Zhu H; Chen S; Lu W; Chen K; Feng Y; Xie Z; Zhang Z; Li L; Ou J; Chen G
BMC Public Health; 2022 Dec; 22(1):2335. PubMed ID: 36514013
[TBL] [Abstract][Full Text] [Related]
9. The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China.
Zhao D; Zhang H; Cao Q; Wang Z; He S; Zhou M; Zhang R
PLoS One; 2022; 17(2):e0262734. PubMed ID: 35196309
[TBL] [Abstract][Full Text] [Related]
10. Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China.
Zhang R; Guo Z; Meng Y; Wang S; Li S; Niu R; Wang Y; Guo Q; Li Y
Int J Environ Res Public Health; 2021 Jun; 18(11):. PubMed ID: 34200378
[TBL] [Abstract][Full Text] [Related]
11. Deep learning models for forecasting dengue fever based on climate data in Vietnam.
Nguyen VH; Tuyet-Hanh TT; Mulhall J; Minh HV; Duong TQ; Chien NV; Nhung NTT; Lan VH; Minh HB; Cuong D; Bich NN; Quyen NH; Linh TNQ; Tho NT; Nghia ND; Anh LVQ; Phan DTM; Hung NQV; Son MT
PLoS Negl Trop Dis; 2022 Jun; 16(6):e0010509. PubMed ID: 35696432
[TBL] [Abstract][Full Text] [Related]
12. Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.
Tang N; Yuan M; Chen Z; Ma J; Sun R; Yang Y; He Q; Guo X; Hu S; Zhou J
Int J Environ Res Public Health; 2023 Feb; 20(5):. PubMed ID: 36900920
[TBL] [Abstract][Full Text] [Related]
13. Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation.
Khullar S; Singh N
Environ Sci Pollut Res Int; 2022 Feb; 29(9):12875-12889. PubMed ID: 33988840
[TBL] [Abstract][Full Text] [Related]
14. A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.
Zhao R; Liu J; Zhao Z; Zhai M; Ren H; Wang X; Li Y; Cui Y; Qiao Y; Ren J; Chen L; Qiu L
BMC Infect Dis; 2023 Oct; 23(1):665. PubMed ID: 37805543
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
Lou HR; Wang X; Gao Y; Zeng Q
BMC Public Health; 2022 Nov; 22(1):2167. PubMed ID: 36434563
[TBL] [Abstract][Full Text] [Related]
17. A hybrid model for hand-foot-mouth disease prediction based on ARIMA-EEMD-LSTM.
Wan Y; Song P; Liu J; Xu X; Lei X
BMC Infect Dis; 2023 Dec; 23(1):879. PubMed ID: 38102558
[TBL] [Abstract][Full Text] [Related]
18. A multivariate multi-step LSTM forecasting model for tuberculosis incidence with model explanation in Liaoning Province, China.
Yang E; Zhang H; Guo X; Zang Z; Liu Z; Liu Y
BMC Infect Dis; 2022 May; 22(1):490. PubMed ID: 35606725
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.
Zhang R; Song H; Chen Q; Wang Y; Wang S; Li Y
PLoS One; 2022; 17(1):e0262009. PubMed ID: 35030203
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]