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

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  • Ali Arab

    (Department of Mathematics and Statistics, Georgetown University, 37th and O streets, Washington, DC 20057, USA)

Abstract

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.

Suggested Citation

  • Ali Arab, 2015. "Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros," IJERPH, MDPI, vol. 12(9), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:9:p:10536-10548:d:54917
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    References listed on IDEAS

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    Cited by:

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    2. Moniche-Bermejo, Ana, 2022. "Do collective energy switching campaigns engage vulnerable households? Evidence from The Big Switch," Energy Policy, Elsevier, vol. 167(C).
    3. Chao Song & Yaqian He & Yanchen Bo & Jinfeng Wang & Zhoupeng Ren & Huibin Yang, 2018. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models," IJERPH, MDPI, vol. 15(7), pages 1-16, July.
    4. Naeimehossadat Asmarian & Seyyed Mohammad Taghi Ayatollahi & Zahra Sharafi & Najaf Zare, 2019. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran," IJERPH, MDPI, vol. 16(22), pages 1-13, November.
    5. Dirk Douwes‐Schultz & Alexandra M. Schmidt, 2022. "Zero‐state coupled Markov switching count models for spatio‐temporal infectious disease spread," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 589-612, June.

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