351 related articles for article (PubMed ID: 35162523)
41. Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province.
Yang W; Su A; Ding L
BMC Public Health; 2023 Nov; 23(1):2309. PubMed ID: 37993836
[TBL] [Abstract][Full Text] [Related]
42. Epidemiology and time series analysis of human brucellosis in Tebessa province, Algeria, from 2000 to 2020.
Akermi SE; L'Hadj M; Selmane S
J Res Health Sci; 2022 Mar; 22(1):e00544. PubMed ID: 36511254
[TBL] [Abstract][Full Text] [Related]
43. Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models.
Punyapornwithaya V; Jampachaisri K; Klaharn K; Sansamur C
Front Vet Sci; 2021; 8():775114. PubMed ID: 34917670
[TBL] [Abstract][Full Text] [Related]
44. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011-2019 using the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Exponential Smoothing (ETS) models.
Liu H; Li C; Shao Y; Zhang X; Zhai Z; Wang X; Qi X; Wang J; Hao Y; Wu Q; Jiao M
J Infect Public Health; 2020 Feb; 13(2):287-294. PubMed ID: 31953020
[TBL] [Abstract][Full Text] [Related]
45. The development of a combined mathematical model to forecast the incidence of hepatitis E in Shanghai, China.
Ren H; Li J; Yuan ZA; Hu JY; Yu Y; Lu YH
BMC Infect Dis; 2013 Sep; 13():421. PubMed ID: 24010871
[TBL] [Abstract][Full Text] [Related]
46. 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]
47. Developing forecasting model for future pandemic applications based on COVID-19 data 2020-2022.
Wan Mohamad Nawi WIA; K Abdul Hamid AA; Lola MS; Zakaria S; Aruchunan E; Gobithaasan RU; Zainuddin NH; Mustafa WA; Abdullah ML; Mokhtar NA; Abdullah MT
PLoS One; 2023; 18(5):e0285407. PubMed ID: 37172040
[TBL] [Abstract][Full Text] [Related]
48. Forecasting the effects of vaccination on the COVID-19 pandemic in Malaysia using SEIRV compartmental models.
Lim MC; Singh S; Lai CH; Gill BS; Kamarudin MK; Md Zamri ASS; Tan CV; Zulkifli AA; Nadzri MNM; Mohd Ghazali N; Mohd Ghazali S; Md Iderus NH; Ahmad NARB; Suppiah J; Tee KK; Aris T; Ahmad LCRQ
Epidemiol Health; 2023; 45():e2023093. PubMed ID: 37905314
[TBL] [Abstract][Full Text] [Related]
49. Comparison of Predictive Models and Impact Assessment of Lockdown for COVID-19 over the United States.
Makinde OS; Adeola AM; Abiodun GJ; Olusola-Makinde OO; Alejandro A
J Epidemiol Glob Health; 2021 Jun; 11(2):200-207. PubMed ID: 33876598
[TBL] [Abstract][Full Text] [Related]
50. Effectiveness of the movement control measures during the third wave of COVID-19 in Malaysia.
Zamri ASSM; Singh S; Ghazali SM; Herng LC; Dass SC; Aris T; Ibrahim HM; Gill BS
Epidemiol Health; 2021; 43():e2021073. PubMed ID: 34607399
[TBL] [Abstract][Full Text] [Related]
51. Time Series Modeling of Tuberculosis Cases in India from 2017 to 2022 Based on the SARIMA-NNAR Hybrid Model.
Yadav BK; Srivastava SK; Arasu PT; Singh P
Can J Infect Dis Med Microbiol; 2023; 2023():5934552. PubMed ID: 38144388
[TBL] [Abstract][Full Text] [Related]
52. Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.
Nguyen HM; Turk PJ; McWilliams AD
JMIR Public Health Surveill; 2021 Aug; 7(8):e28195. PubMed ID: 34346897
[TBL] [Abstract][Full Text] [Related]
53. 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]
54. Wastewater inflow time series forecasting at low temporal resolution using SARIMA model: a case study in South Australia.
Do P; Chow CWK; Rameezdeen R; Gorjian N
Environ Sci Pollut Res Int; 2022 Oct; 29(47):70984-70999. PubMed ID: 35595895
[TBL] [Abstract][Full Text] [Related]
55. An Advanced Data-Driven Hybrid Model of SARIMA-NNNAR for Tuberculosis Incidence Time Series Forecasting in Qinghai Province, China.
Wang Y; Xu C; Li Y; Wu W; Gui L; Ren J; Yao S
Infect Drug Resist; 2020; 13():867-880. PubMed ID: 32273731
[TBL] [Abstract][Full Text] [Related]
56. Short-term forecasting of the COVID-19 outbreak in India.
Mangla S; Pathak AK; Arshad M; Haque U
Int Health; 2021 Sep; 13(5):410-420. PubMed ID: 34091670
[TBL] [Abstract][Full Text] [Related]
57. [A comparative study of time series models in predicting COVID-19 cases].
Li ZQ; Tao B; Zhan M; Wu Z; Wu J; Wang J
Zhonghua Liu Xing Bing Xue Za Zhi; 2021 Mar; 42(3):421-426. PubMed ID: 34814408
[No Abstract] [Full Text] [Related]
58. Forecasting New Tuberculosis Cases in Malaysia: A Time-Series Study Using the Autoregressive Integrated Moving Average (ARIMA) Model.
Ab Rashid MA; Ahmad Zaki R; Wan Mahiyuddin WR; Yahya A
Cureus; 2023 Sep; 15(9):e44676. PubMed ID: 37809275
[TBL] [Abstract][Full Text] [Related]
59. 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]
60. Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced
Wang Y; Xu C; Yao S; Zhao Y
Epidemiol Infect; 2020 Oct; 148():e236. PubMed ID: 33012300
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
