BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

140 related articles for article (PubMed ID: 35081217)

  • 1. Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil.
    Pimentel KBA; Oliveira RS; Aragão CF; Aquino Júnior J; Moura MES; Guimarães-E-Silva AS; Pinheiro VCS; Gonçalves EGR; Silva AR
    Braz J Biol; 2022; 84():e257402. PubMed ID: 35081217
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Forecasting the incidence of tuberculosis in China using the seasonal auto-regressive integrated moving average (SARIMA) model.
    Mao Q; Zhang K; Yan W; Cheng C
    J Infect Public Health; 2018; 11(5):707-712. PubMed ID: 29730253
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil.
    Martinez EZ; Silva EA; Fabbro AL
    Rev Soc Bras Med Trop; 2011; 44(4):436-40. PubMed ID: 21860888
    [TBL] [Abstract][Full Text] [Related]  

  • 4. SARFIMA model prediction for infectious diseases: application to hemorrhagic fever with renal syndrome and comparing with SARIMA.
    Qi C; Zhang D; Zhu Y; Liu L; Li C; Wang Z; Li X
    BMC Med Res Methodol; 2020 Sep; 20(1):243. PubMed ID: 32993517
    [TBL] [Abstract][Full Text] [Related]  

  • 5. [The epidemiological determinant aspects in the maintenance of visceral leishmaniasis in the state of Maranhão, Brazil].
    Nascimento Mdo D; Costa JM; Fiori BI; Viana GM; Filho MS; Alvim Ade C; Bastos OC; Nakatani M; Reed S; Badaró R; da Silva AR; Burattini MN
    Rev Soc Bras Med Trop; 1996; 29(3):233-40. PubMed ID: 8701042
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.
    Zenia S; L'Hadj M; Selmane S
    J Res Health Sci; 2023 Sep; 23(3):e00586. PubMed ID: 38315901
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018.
    Cong J; Ren M; Xie S; Wang P
    Int J Environ Res Public Health; 2019 Nov; 16(23):. PubMed ID: 31783697
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.
    Tohidinik HR; Mohebali M; Mansournia MA; Niakan Kalhori SR; Ali-Akbarpour M; Yazdani K
    Trop Med Int Health; 2018 Aug; 23(8):860-869. PubMed ID: 29790236
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting the number of cases of dengue infection in Ribeirão Preto, São Paulo State, Brazil, using a SARIMA model.
    Martinez EZ; Silva EA
    Cad Saude Publica; 2011 Sep; 27(9):1809-18. PubMed ID: 21986608
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Forecasting the incidence of mumps in Chongqing based on a SARIMA model.
    Qiu H; Zhao H; Xiang H; Ou R; Yi J; Hu L; Zhu H; Ye M
    BMC Public Health; 2021 Feb; 21(1):373. PubMed ID: 33596871
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Time-series analysis of tuberculosis from 2005 to 2017 in China.
    Wang H; Tian CW; Wang WM; Luo XM
    Epidemiol Infect; 2018 Jun; 146(8):935-939. PubMed ID: 29708082
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Forecasting mortality of road traffic injuries in China using seasonal autoregressive integrated moving average model.
    Zhang X; Pang Y; Cui M; Stallones L; Xiang H
    Ann Epidemiol; 2015 Feb; 25(2):101-6. PubMed ID: 25467006
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. Relationship between rainfall and temperature: observations on the cases of visceral leishmaniasis in São Luis Island, State of Maranhão, Brazil.
    Viana GM; Nascimento Mdo D; Rabelo ÉM; Diniz Neto JA; Binda Júnior JR; Galvão Cde S; Santos AC; Santos Júnior OM; Oliveira RA; Guimarães RS
    Rev Soc Bras Med Trop; 2011; 44(6):722-4. PubMed ID: 22231245
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Forecasting the Incidence of Mumps in Zibo City Based on a SARIMA Model.
    Xu Q; Li R; Liu Y; Luo C; Xu A; Xue F; Xu Q; Li X
    Int J Environ Res Public Health; 2017 Aug; 14(8):. PubMed ID: 28817101
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Time trends in gender-specific incidence rates of road traffic injuries in Iran.
    Delavary Foroutaghe M; Mohammadzadeh Moghaddam A; Fakoor V
    PLoS One; 2019; 14(5):e0216462. PubMed ID: 31071156
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq.
    Mohammed SH; Ahmed MM; Al-Mousawi AM; Azeez A
    Int J Mycobacteriol; 2018; 7(4):361-367. PubMed ID: 30531036
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.