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A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19
Jinjie Chen1, Joon Jin Song1, James D Stamey1
1Department of Statistical Science, Baylor University, Waco, TX 76798-7140, USA.
Summary
Early COVID-19 spread was underestimated in the US, impacting interventions. This study uses a Bayesian model to quantify under-reporting and over-reporting of COVID-19 cases at the state level.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- The COVID-19 pandemic caused significant global health and economic disruption.
- Underestimation of early COVID-19 spread hindered timely public health interventions in the United States.
- Accurate incidence data is crucial for effective pandemic response.
Purpose of the Study:
- To investigate COVID-19 under-reporting and over-reporting at the US state level using a Bayesian hierarchical model.
- To analyze the influence of covariates on reported and actual COVID-19 incidence rates.
- To incorporate spatial dependencies and address misclassification errors in early pandemic data.
Main Methods:
- Bayesian hierarchical modeling to estimate true COVID-19 incidence.
- Inclusion of covariates to explain variations in under-reporting and over-reporting.
- Prior elicitation techniques for informative priors to handle unobserved data.
Main Results:
- Significant under-reporting of COVID-19 cases was identified at the state level as of April 2020.
- The model accounted for both false negatives (under-reporting) and false positives (over-reporting).
- Covariates were found to influence the accuracy of reported COVID-19 case numbers.
Conclusions:
- Adjusting for misclassification is essential for understanding the true burden of COVID-19.
- Bayesian modeling provides a robust framework for estimating disease incidence with imperfect data.
- Accurate data are vital for informing public health policy and pandemic control strategies.
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