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A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.

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.

SUBMITTER: Goldstein ND

PROVIDER: S-EPMC7833121 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.

Goldstein Neal D ND Wheeler David C DC Gustafson Paul P Burstyn Igor I

Spatial and spatio-temporal epidemiology 20210108

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 ...[more]

PMID: 33509436

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Project description:The aim of the study was to propose and demonstrate an approach to allow additional nonsampling uncertainty about HIV prevalence measured at antenatal clinic sentinel surveillance (ANC-SS) in model-based inferences about trends in HIV incidence and prevalence.Mathematical model fitted to surveillance data with Bayesian inference.We introduce a variance inflation parameter (Equation is included in full-text article.)that accounts for the uncertainty of nonsampling errors in ANC-SS prevalence. It is additive to the sampling error variance. Three approaches are tested for estimating (Equation is included in full-text article.)using ANC-SS and household survey data from 40 subnational regions in nine countries in sub-Saharan, as defined in UNAIDS 2016 estimates. Methods were compared using in-sample fit and out-of-sample prediction of ANC-SS data, fit to household survey prevalence data, and the computational implications.Introducing the additional variance parameter (Equation is included in full-text article.)increased the error variance around ANC-SS prevalence observations by a median of 2.7 times (interquartile range 1.9-3.8). Using only sampling error in ANC-SS prevalence (Equation is included in full-text article.), coverage of 95% prediction intervals was 69% in out-of-sample prediction tests. This increased to 90% after introducing the additional variance parameter (Equation is included in full-text article.). The revised probabilistic model improved model fit to household survey prevalence and increased epidemic uncertainty intervals most during the early epidemic period before 2005. Estimating (Equation is included in full-text article.)did not increase the computational cost of model fitting.We recommend estimating nonsampling error in ANC-SS as an additional parameter in Bayesian inference using the Estimation and Projection Package model. This approach may prove useful for incorporating other data sources such as routine prevalence from Prevention of mother-to-child transmission testing into future epidemic estimates.

Dataset Information

A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.

Publications

A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.

Similar Datasets

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