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

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  • 1Department of Mathematics and Statistics, Georgetown University, 37th and O streets, Washington, DC 20057, USA. ali.arab@georgetown.edu.

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Summary

This study reviews statistical models for epidemiological count data with excess zeros, like hurdle and zero-inflated models. It explores spatial and spatio-temporal extensions using Bayesian methods, demonstrated with Lyme disease data.

Keywords:
Bayesian analysisIntegrated Nested Laplace Approximation (INLA)hierarchical modelinghurdle modelsspatial modelsspatio-temporal modelszero-inflated models

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Spatial Statistics

Background:

  • Epidemiological datasets frequently exhibit an excess of zero counts, particularly for rare, geographically localized, or emerging diseases.
  • Standard statistical models may not adequately capture the complexities of zero-inflated count data in epidemiological contexts.

Purpose of the Study:

  • To review and discuss statistical methodologies for modeling epidemiological count data with excess zeros.
  • To explore extensions of these models to incorporate spatial and spatio-temporal dependencies.
  • To provide a practical demonstration using a case study of Lyme disease incidence.

Main Methods:

  • Review of zero-inflated and hurdle models for count data.
  • Application of Bayesian hierarchical frameworks for spatial and spatio-temporal modeling.
  • Examination of current computational tools and implementation strategies.

Main Results:

  • Hurdle and zero-inflated models offer robust approaches for excess zero count data.
  • Bayesian spatial and spatio-temporal models effectively handle complex dependencies.
  • The case study illustrates the practical application and utility of these advanced modeling techniques.

Conclusions:

  • Appropriate statistical modeling is crucial for accurately analyzing epidemiological data with excess zeros.
  • Spatial and spatio-temporal extensions enhance the understanding of disease distribution and risk.
  • The reviewed methods and case study provide valuable insights for researchers in epidemiology and biostatistics.