These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

141 related articles for article (PubMed ID: 32615923)

  • 1. Effects of meteorological factors on the incidence of mumps and models for prediction, China.
    Zha WT; Li WT; Zhou N; Zhu JJ; Feng R; Li T; Du YB; Liu Y; Hong XQ; Lv Y
    BMC Infect Dis; 2020 Jul; 20(1):468. PubMed ID: 32615923
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Association between Meteorological Factors and Mumps and Models for Prediction in Chongqing, China.
    Zhang H; Su K; Zhong X
    Int J Environ Res Public Health; 2022 May; 19(11):. PubMed ID: 35682208
    [TBL] [Abstract][Full Text] [Related]  

  • 3. The role of meteorological factors on mumps incidence among children in Guangzhou, Southern China.
    Lu J; Yang Z; Ma X; Ma M; Zhang Z
    PLoS One; 2020; 15(4):e0232273. PubMed ID: 32348370
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Impact of meteorological factors on mumps and potential effect modifiers: An analysis of 10 cities in Guangxi, Southern China.
    Yu G; Yang R; Yu D; Cai J; Tang J; Zhai W; Wei Y; Chen S; Chen Q; Zhong G; Qin J
    Environ Res; 2018 Oct; 166():577-587. PubMed ID: 29966878
    [TBL] [Abstract][Full Text] [Related]  

  • 5. The short-term association between meteorological factors and mumps in Jining, China.
    Li R; Lin H; Liang Y; Zhang T; Luo C; Jiang Z; Xu Q; Xue F; Liu Y; Li X
    Sci Total Environ; 2016 Oct; 568():1069-1075. PubMed ID: 27353959
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. [Application of multiple seasonal autoregressive integrated moving average model in predicting the mumps incidence].
    Hui S; Chen L; Liu F; Ouyang Y
    Zhonghua Yu Fang Yi Xue Za Zhi; 2015 Dec; 49(12):1042-6. PubMed ID: 26887296
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Meteorological factors and the incidence of mumps in Fujian Province, China, 2005-2013: Non-linear effects.
    Hu W; Li Y; Han W; Xue L; Zhang W; Ma W; Bi P
    Sci Total Environ; 2018 Apr; 619-620():1286-1298. PubMed ID: 29734606
    [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. 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]  

  • 11. The relationship between meteorological factors and mumps incidence in Guangzhou, China, 2005-2012:
    Yang Q; Yang Z; Ding H; Zhang X; Dong Z; Hu W; Liu X; Wang M; Hu G; Fu C
    Hum Vaccin Immunother; 2014; 10(8):2421-32. PubMed ID: 25424950
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. [Application of autoregressive integrated moving average model to predict and analyze the incidence trend of mumps in Jiangxi Province].
    Zhao YQ; Shi JH; Xu F; Guo SC
    Zhonghua Liu Xing Bing Xue Za Zhi; 2023 Dec; 44(12):1911-1915. PubMed ID: 38129147
    [No Abstract]   [Full Text] [Related]  

  • 14. Time series analysis of mumps and meteorological factors in Beijing, China.
    Hao Y; Wang RR; Han L; Wang H; Zhang X; Tang QL; Yan L; He J
    BMC Infect Dis; 2019 May; 19(1):435. PubMed ID: 31101079
    [TBL] [Abstract][Full Text] [Related]  

  • 15. [Epidemic profile of mumps in China during 2004-2013].
    Su QR; Liu J; Ma C; Fan CX; Wen N; Luo HM; Wang HQ; Li L; Hao LX
    Zhonghua Yu Fang Yi Xue Za Zhi; 2016 Jul; 50(7):611-4. PubMed ID: 27412837
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Non-linear relationships and interactions of meteorological factors on mumps in Jinan, China.
    Lin S; Ruan S; Geng X; Song K; Cui L; Liu X; Zhang Y; Cao M; Zhang Y
    Int J Biometeorol; 2021 Apr; 65(4):555-563. PubMed ID: 33180186
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Effects of extreme meteorological factors on daily mumps cases in Hefei, China, during 2011-2016.
    Wu H; You E; Jiang C; Yang Y; Wang L; Niu Q; Lu X; Huang F
    Environ Sci Pollut Res Int; 2020 Feb; 27(4):4489-4501. PubMed ID: 31832956
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Analysis epidemiological and pathogen characters of mumps in China from 2004 to 2006].
    Bo F; Cui AL; Guo XB
    Zhongguo Yi Miao He Mian Yi; 2009 Apr; 15(2):115-8. PubMed ID: 20077654
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Development of temporal modelling for forecasting and prediction of malaria infections using time-series and ARIMAX analyses: a case study in endemic districts of Bhutan.
    Wangdi K; Singhasivanon P; Silawan T; Lawpoolsri S; White NJ; Kaewkungwal J
    Malar J; 2010 Sep; 9():251. PubMed ID: 20813066
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.