Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA

Towards reduction in bias in epidemic curves due to outcome misclassification through Bayesian analysis of time-series of laboratory test results: case study of COVID-19 in Alberta, Canada and Philadelphia, USA

Abstract Background Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves us...

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Bibliographic Details
Main Authors: Igor Burstyn, Neal D. Goldstein, Paul Gustafson
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Medical Research Methodology
Subjects:
Epidemic
Bias analysis
Uncertainty
Simulation
Under-diagnosis
Sensitivity
Online Access:http://link.springer.com/article/10.1186/s12874-020-01037-4

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http://link.springer.com/article/10.1186/s12874-020-01037-4

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