A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.
Spat Spatiotemporal Epidemiol
; 36: 100401, 2021 02.
Article
in English
| MEDLINE | ID: covidwho-1014822
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Population Surveillance
/
Bayes Theorem
/
Spatial Analysis
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Systematic review/Meta Analysis
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Spat Spatiotemporal Epidemiol
Year:
2021
Document Type:
Article
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