459 related articles for article (PubMed ID: 34200378)
1. 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]
2. Forecasting incidence of hand, foot and mouth disease using BP neural networks in Jiangsu province, China.
Liu W; Bao C; Zhou Y; Ji H; Wu Y; Shi Y; Shen W; Bao J; Li J; Hu J; Huo X
BMC Infect Dis; 2019 Oct; 19(1):828. PubMed ID: 31590636
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
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. A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China.
Gu J; Liang L; Song H; Kong Y; Ma R; Hou Y; Zhao J; Liu J; He N; Zhang Y
Sci Rep; 2019 Nov; 9(1):17928. PubMed ID: 31784625
[TBL] [Abstract][Full Text] [Related]
7. Application of seasonal auto-regressive integrated moving average model in forecasting the incidence of hand-foot-mouth disease in Wuhan, China.
Peng Y; Yu B; Wang P; Kong DG; Chen BH; Yang XB
J Huazhong Univ Sci Technolog Med Sci; 2017 Dec; 37(6):842-848. PubMed ID: 29270741
[TBL] [Abstract][Full Text] [Related]
8. Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China.
Li ZQ; Pan HQ; Liu Q; Song H; Wang JM
Infect Dis Poverty; 2020 Nov; 9(1):151. PubMed ID: 33148337
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Study on the National Monthly Reported Severe Cases of Hand-foot-mouth Disease Forecasted by Autoregressive Integrated Moving Average Model.
Zhang S; Qiu Q; Wang Y
Bing Du Xue Bao; 2017 Jan; 33(1):77-81. PubMed ID: 30702825
[TBL] [Abstract][Full Text] [Related]
12. Modeling and Predicting Pulmonary Tuberculosis Incidence and Its Association with Air Pollution and Meteorological Factors Using an ARIMAX Model: An Ecological Study in Ningbo of China.
Chen YP; Liu LF; Che Y; Huang J; Li GX; Sang GX; Xuan ZQ; He TF
Int J Environ Res Public Health; 2022 Apr; 19(9):. PubMed ID: 35564780
[TBL] [Abstract][Full Text] [Related]
13. Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model.
Liu L; Luan RS; Yin F; Zhu XP; Lü Q
Epidemiol Infect; 2016 Jan; 144(1):144-51. PubMed ID: 26027606
[TBL] [Abstract][Full Text] [Related]
14. 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.
Yu L; Zhou L; Tan L; Jiang H; Wang Y; Wei S; Nie S
PLoS One; 2014; 9(6):e98241. PubMed ID: 24893000
[TBL] [Abstract][Full Text] [Related]
15. Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China.
Du Z; Xu L; Zhang W; Zhang D; Yu S; Hao Y
BMJ Open; 2017 Oct; 7(10):e016263. PubMed ID: 28988169
[TBL] [Abstract][Full Text] [Related]
16. Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model.
Yang C; An S; Qiao B; Guan P; Huang D; Wu W
Environ Sci Pollut Res Int; 2023 Feb; 30(8):20369-20385. PubMed ID: 36255582
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Epidemiological characteristics, spatial clusters and monthly incidence prediction of hand, foot and mouth disease from 2017 to 2022 in Shanxi Province, China.
Ma Y; Xu S; Dong A; An J; Qin Y; Yang H; Yu H
Epidemiol Infect; 2023 Mar; 151():e54. PubMed ID: 37039461
[TBL] [Abstract][Full Text] [Related]
20. Development and comparison of forecast models of hand-foot-mouth disease with meteorological factors.
Fu T; Chen T; Dong ZB; Luo SY; Miao Z; Song XP; Huang RT; Sun JM
Sci Rep; 2019 Oct; 9(1):15691. PubMed ID: 31666565
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]