518 related articles for article (PubMed ID: 31590636)
1. 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]
2. 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]
3. Forecasting incidence of infectious diarrhea using random forest in Jiangsu Province, China.
Fang X; Liu W; Ai J; He M; Wu Y; Shi Y; Shen W; Bao C
BMC Infect Dis; 2020 Mar; 20(1):222. PubMed ID: 32171261
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. 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]
8. Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province.
Zhao D; Wang L; Cheng J; Xu J; Xu Z; Xie M; Yang H; Li K; Wen L; Wang X; Zhang H; Wang S; Su H
Int J Biometeorol; 2017 Mar; 61(3):453-461. PubMed ID: 27557791
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. Forecast and early warning of hand, foot, and mouth disease based on meteorological factors: Evidence from a multicity study of 11 meteorological geographical divisions in mainland China.
Gao Q; Liu Z; Xiang J; Tong M; Zhang Y; Wang S; Zhang Y; Lu L; Jiang B; Bi P
Environ Res; 2021 Jan; 192():110301. PubMed ID: 33069698
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Different effects of meteorological factors on hand, foot and mouth disease in various climates: a spatial panel data model analysis.
Wang C; Cao K; Zhang Y; Fang L; Li X; Xu Q; Huang F; Tao L; Guo J; Gao Q; Guo X
BMC Infect Dis; 2016 May; 16():233. PubMed ID: 27230283
[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. [Application of R-based multiple seasonal ARIMA model, in predicting the incidence of hand, foot and mouth disease in Shaanxi province].
Liu F; Zhu N; Qiu L; Wang JJ; Wang WH
Zhonghua Liu Xing Bing Xue Za Zhi; 2016 Aug; 37(8):1117-20. PubMed ID: 27539344
[TBL] [Abstract][Full Text] [Related]
18. 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]
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]
