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Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros.

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  • المؤلفون: Arab A;Arab A
  • المصدر:
    International journal of environmental research and public health [Int J Environ Res Public Health] 2015 Aug 28; Vol. 12 (9), pp. 10536-48. Date of Electronic Publication: 2015 Aug 28.
  • نوع النشر :
    Journal Article; Review
  • اللغة:
    English
  • معلومة اضافية
    • المصدر:
      Publisher: MDPI Country of Publication: Switzerland NLM ID: 101238455 Publication Model: Electronic Cited Medium: Internet ISSN: 1660-4601 (Electronic) Linking ISSN: 16604601 NLM ISO Abbreviation: Int J Environ Res Public Health Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: Basel : MDPI, c2004-
    • الموضوع:
      Epidemiologic Methods* ; Models, Theoretical*; Lyme Disease/*epidemiology; Bayes Theorem ; Humans ; Illinois/epidemiology ; Lyme Disease/microbiology ; Spatial Analysis ; Time Factors
    • نبذة مختصرة :
      Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level.
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    • Contributed Indexing:
      Keywords: Bayesian analysis; Integrated Nested Laplace Approximation (INLA); hierarchical modeling; hurdle models; spatial models; spatio-temporal models; zero-inflated models
    • الموضوع:
      Date Created: 20150908 Date Completed: 20160408 Latest Revision: 20181202
    • الموضوع:
      20221213
    • الرقم المعرف:
      PMC4586626
    • الرقم المعرف:
      10.3390/ijerph120910536
    • الرقم المعرف:
      26343696