Abstract
The present work mainly focuses on the issue of risk model in spacial data analysis. Through the analysis on morbidity data of influenza A (H1N1) across China’s administrative regions from 2009 to 2012, a comparative study was carried out among four different estimators SMR, EBPG, EBLN and EBMarshall as risk model to explore and make improvements for the problems of risk model and pattern of survival distribution in spacial disease analysis. By using R programming language, the feasibility of the above analysis method was verified and the variability of the estimated value generated by each model was calculated. The research on spacial variability of disease morbidity is helpful in detecting epidemic area and forewarning the pathophoresis of prospective epidemic disease.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
Ying Q, Chen K (2012) Progress of spatial analysis techniques in tuberculosis research. Dis Surveill 27(4):330–334
Peng B, Zhang Y, Hu D, Luo K, Wang R (2007) Use of space Analysis technology to explore the spatial patterns of TB. Chin Health Stat 24(3):229–231
Li Z, Zhao W, Xie X (2013) PoissonLog_normal regression model evaluation. Kunming University (Nat Sci Ed) 38(4):102–108
Zhang G, Liu C, Ma X (2006) Bayes sequential estimation of lognormal population distribution parameters. Stat Decis 11:7–8
Bivand RS, Pebesma EJ, Gómez-Rubio V Applied spatial data analysis with R. ISBN: 978-1-4614-7617-7
Lv W, Wu Y, Ma H (2007) Lognormal distribution parameter estimation based on the EM algorithm. Stat Decis 21–23
Shi Y, Yang Z (1995) Linear regression coefficient EB consensus estimate of convergence rate. Pure Appl Math 11(2):15–20
Wang L (1999) An approximate method for three-parameter distribution parameter estimation log_normal. Stat Principle 18(2):40–43
Xie J (2008) RANDOM TESTING Poisson-gamma model coefficients based on longitudinal data. Series A Coll Univ Appl Math Newspaper 181–187
Li J (2006) The concept of spatial scales and Logic. Remote Sens 76–77
Chen H, Fang Y (2010) Network Traffic Gaussian mixture model-based clustering analysis. East Chin Unive Technol (Nat Sci) 255–260
Wang B, Wei Y, Sun C (2008) Poisson distribution and negative binomial distribution in the risk management. Tianshui Normal Univ Rep 28(5):23–24
Song H (2013) Management mechanism cloud user-oriented service requirements. University of Science and Technology of China
Shi H, Ji Y (2013) Multiple normal distribution parameter under semi-order restriction Bayes estimation and equivalence test. Jilin Univ Newspapers (Sci Ed) 51(1):1–8
Cuesta H (U.S) Practical data analysis
You Y (2013) EB estimates and convergence rate failure rate of exponential distribution. Luoyang Normal University Rep 32(5):9–12
Zhang M, Yue L (2014) Hybrid system reliability simulation Monte Kano and EB estimation. Shenyang Univ Technol 33(3):26–31
Zhao Z (2014) Estimation based on a small domain data binomial EB. Guizhou Sci 30–33
Chen F, Yang S (1999) Corresponding analysis and its application in a variety of diseases clustering analysis. Chin Health Stat 16(2):26–31
Yin F, Li X, Feng Z, Ma J (2009) Web-based reporting system and temporal clustering of infections detected simulated real-time monitoring and early warning. Mod Prev Med 36(12):2204–2207
Yang W, Li Z, Lan Y, Wang J, Ma J, Jin L, Sun Q, Lv W, Lai S, Laio Y, Hu W (2011) Chinese outbreak automatic detection and rapid response system is based on Internet. Monit Syst 67–71
Jiang W, Shen X, Zong F (2014) Multivariate normal spatial scan statistics model in detecting the strongest aggregation of endemic disease. Shanghai Univ Newspaper (Nat Sci) 20(3):274–280
Feng J, Wu X, Li S, Zhou X (2011) Statistical analysis of spatial and related software applications in infectious disease research. J Chin Schistosome Dis Prev 23(2):217–220
Liao Y, Wang J, Yang W, Li Z, Jin L, Lai S, Zheng X (2012) Infectious disease detection method of multi-dimensional clustering. Geogr Sci 67(4):135–443
Qi X, Zhou Y, Hu Y, Wang L, Ge H, Zhuang D, Yang GH (2010) Apply GIS to detect spatial clustering of gastrointestinal cancer mortality. Geography 29(1):181–187
Acknowledgments
(1) Funding Project of Science and Technology Research and Development in Hebei North University (Grant No. ZD201301). (2) Major Scientific Research Projects in Higher School in Hebei Province (Grant No. ZD20131085).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Zhao, Z., Zhang, X., Liu, Y., Liang, J., Wang, J., Liu, Y. (2016). A Comparative Study on Disease Risk Model in Exploratory Spatial Analysis. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_15
Download citation
DOI: https://doi.org/10.1007/978-981-10-0539-8_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0538-1
Online ISBN: 978-981-10-0539-8
eBook Packages: Computer ScienceComputer Science (R0)