ArticleSpatial and spatio-temporal epidemiology2021
A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.
Article in Spatial and spatio-temporal epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spatio-temporal distributions of COVID-19 vaccine doses uptake in the Netherlands: a Bayesian ecological modelling analysis.Epidemiology and infection · 2024Article
- Article
- Disparities of COVID-19 and HIV Occurrence Based on Neighborhood Infection Incidence in Philadelphia, Pennsylvania.American journal of public health · 2022Article
- Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes.Harvard data science review · 2022Article
- Effect of Adjustment for Case Misclassification and Infection Date Uncertainty on Estimates of COVID-19 Effective Reproduction Number.Epidemiology (Cambridge, Mass.) · 2021Article
- Symptoms of Anxiety and Depression in Relation to Work Patterns During the First Wave of the COVID-19 Epidemic in Philadelphia PA: A Cross-Sectional Survey.Journal of occupational and environmental medicine · 2021Article
- Article
- Antibody Responses in COVID-19: A Review.Frontiers in immunology · 2021Review
- On the importance of early testing even when imperfect in a pandemic such as COVID-19.Global epidemiology · 2020Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate due to undercounting and misdiagnosing. Using a Bayesian approach, we sought to reduce bias in the estimates of prevalence of COVID-19 in Philadelphia, PA at the ZIP code level. After evaluating various modeling approaches in a simulation study, we estimated true prevalence by ZIP code with and without conditioning on an area deprivation index (ADI). As of June 10, 2020, in Philadelphia, the observed citywide period prevalence was 1.5%. After accounting for bias in the surveillance data, the median posterior citywide true prevalence was 2.3% when accounting for ADI and 2.1% when not. Overall the median posterior surveillance sensitivity and specificity from the models were similar, about 60% and more than 99%, respectively. Surveillance of COVID-19 in Philadelphia tends to understate discrepancies in burden for the more affected areas, potentially misinforming mitigation priorities.
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