Litcius/Paper detail

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

Neal D. Goldstein, David C. Wheeler, Paul Gustafson, Igor Burstyn

2021Spatial and Spatio-temporal Epidemiology20 citationsDOIOpen Access PDF

Topics & Concepts

Coronavirus disease 2019 (COVID-19)MedicineBayesian probability2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)StatisticsZip codeEnvironmental healthDemographyGeographyCartographyInternal medicineMathematicsOutbreakVirologyDiseaseInfectious disease (medical specialty)SociologyCOVID-19 epidemiological studiesData-Driven Disease SurveillanceHealth disparities and outcomes
A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA | Litcius