234 related articles for article (PubMed ID: 37003988)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. Time-series modelling and forecasting of hand, foot and mouth disease cases in China from 2008 to 2018.
Tian CW; Wang H; Luo XM
Epidemiol Infect; 2019 Jan; 147():e82. PubMed ID: 30868999
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. 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]
10. 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]
11. Forecasting the monthly incidence of scarlet fever in Chongqing, China using the SARIMA model.
Wu WW; Li Q; Tian DC; Zhao H; Xia Y; Xiong Y; Su K; Tang WG; Chen X; Wang J; Qi L
Epidemiol Infect; 2022 Apr; 150():e90. PubMed ID: 35543101
[TBL] [Abstract][Full Text] [Related]
12. Predicting the outbreak of hand, foot, and mouth disease in Nanjing, China: a time-series model based on weather variability.
Liu S; Chen J; Wang J; Wu Z; Wu W; Xu Z; Hu W; Xu F; Tong S; Shen H
Int J Biometeorol; 2018 Apr; 62(4):565-574. PubMed ID: 29086082
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. 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]
19. 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]
20. Statistical methods for predicting tuberculosis incidence based on data from Guangxi, China.
Zheng Y; Zhang L; Wang L; Rifhat R
BMC Infect Dis; 2020 Apr; 20(1):300. PubMed ID: 32321419
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]