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A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States

Author

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  • Jinjie Chen

    (Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA)

  • Joon Jin Song

    (Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA)

  • James D. Stamey

    (Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA)

Abstract

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people’s lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.

Suggested Citation

  • Jinjie Chen & Joon Jin Song & James D. Stamey, 2022. "A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States," IJERPH, MDPI, vol. 19(6), pages 1-15, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3327-:d:769222
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
    3. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    4. Ali Hortaçsu & Jiarui Liu & Timothy Schwieg, 2020. "Estimating the Fraction of Unreported Infections in Epidemics with a Known Epicenter: An Application to COVID-19," Working Papers 2020-37, Becker Friedman Institute for Research In Economics.
    5. Oliver Stoner & Theo Economou & Gabriela Drummond Marques da Silva, 2019. "A Hierarchical Framework for Correcting Under-Reporting in Count Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(528), pages 1481-1492, October.
    6. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    7. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    8. Leonardo Costa Ribeiro & Américo Tristão Bernardes, 2020. "Estimate of underreporting of COVID-19 in Brazil by Acute Respiratory Syndrome hospitalization reports," Notas Técnicas Cedeplar-UFMG 010, Cedeplar, Universidade Federal de Minas Gerais.
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