ArticleBMC medical research methodology2020
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.
Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 32 citations in OpenAlex.
- Undetected circulation of monkeypox virus in Portugal: Evidence for a 50-day gap before first detection.Global epidemiology · 2026Article
- A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.Biostatistics (Oxford, England) · 2024Article
- Article
- Extending the susceptible-exposed-infected-removed (SEIR) model to handle the false negative rate and symptom-based administration of COVID-19 diagnostic tests: SEIR-fansy.Statistics in medicine · 2022Article
- Time Series Analysis Using Different Forecast Methods and Case Fatality Rate for Covid-19 Pandemic.Regional science policy & practice · 2022Article
- Further Improving Analysis of Date-Based COVID-19 Surveillance Data.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
- Clinical Performance of the Standard Q COVID-19 Rapid Antigen Test and Simulation of its Real-World Application in Korea.Annals of laboratory medicine · 2021Article
- A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA.Spatial and spatio-temporal epidemiology · 2021Article
- Article
- On the importance of early testing even when imperfect in a pandemic such as COVID-19.Global epidemiology · 2020Article
- EXTENDING THE SUSCEPTIBLE-EXPOSED-INFECTED-REMOVED(SEIR) MODEL TO HANDLE THE HIGH FALSE NEGATIVE RATE AND SYMPTOM-BASED ADMINISTRATION OF COVID-19 DIAGNOSTIC TESTS: SEIR-fansy.medRxiv : the preprint server for health sciences · 2020Article
- It can be dangerous to take epidemic curves of COVID-19 at face value.Canadian journal of public health = Revue canadienne de sante publique · 2020Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDespite 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 using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.
methodsWe examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.
resultsOur analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.
conclusionThe best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves.
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