Bayesian penalized spline models for the analysis of spatio-temporal count data.
In recent years, the availability of infectious disease counts in time and space has increased, and consequently, there has been renewed interest in model formulation for such data. In this paper, we describe a model that was motivated by the need to analyze hand, foot, and mouth disease surveillanc...
| Publicado en: | Statistics in Medicine Vol. 35; no. 11; pp. 1848 - 1866 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=114436684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 114436684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: May2016 vid: 35 iid: 11 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 114436684 114436684 NLM26530705 114436684 10.1002/sim.6785 NLM26530705 PMC4959802 114436684 ppf: 1848 ppct: 18 formats: tig: atl: Bayesian penalized spline models for the analysis of spatio-temporal count data. aug: au: Bauer, Cici Wakefield, Jon Rue, Håvard Self, Steve Feng, Zijian Wang, Yu affil: Department of Biostatistics, Brown University, Providence RI, U.S.A. sug: subj: Probability Hand, Foot, and Mouth Disease Epidemiology Poisson Distribution Risk Factors Male Disease Outbreaks Computer Simulation Female Population Surveillance Child China Funding Source Human Child: 6-12 years Male Female ab: In recent years, the availability of infectious disease counts in time and space has increased, and consequently, there has been renewed interest in model formulation for such data. In this paper, we describe a model that was motivated by the need to analyze hand, foot, and mouth disease surveillance data in China. The data are aggregated by geographical areas and by week, with the aims of the analysis being to gain insight into the space-time dynamics and to make short-term predictions, which will aid in the implementation of public health campaigns in those areas with a large predicted disease burden. The model we develop decomposes disease-risk into marginal spatial and temporal components and a space-time interaction piece. The latter is the crucial element, and we use a tensor product spline model with a Markov random field prior on the coefficients of the basis functions. The model can be formulated as a Gaussian Markov random field and so fast computation can be carried out using the integrated nested Laplace approximation approach. A simulation study shows that the model can pick up complex space-time structure and our analysis of hand, foot, and mouth disease data in the central north region of China provides new insights into the dynamics of the disease. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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