Journal article
It can be dangerous to take epidemic curves of COVID-19 at face value
Canadian journal of public health, v 111(3), pp 397-400
01 Jun 2020
PMID: 32578184
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
During an epidemic with a new virus, we depend on modelling to plan the response: but how good are the data? The aim of our work was to better understand the impact of misclassification errors in identification of true cases of COVID-19 on epidemic curves. Data originated from Alberta, Canada (available on 28 May 2020). There is presently no information of sensitivity (Sn) and specificity (Sp) of laboratory tests used in Canada for the causal agent for COVID-19. Therefore, we examined best attainable performance in other jurisdictions and similar viruses. This suggested perfect Sp and Sn 60-95%. We used these values to re-calculate epidemic curves to visualize the potential bias due to imperfect testing. If the sensitivity improved, the observed and adjusted epidemic curves likely fall within 95% confidence intervals of the observed counts. However, bias in shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. These issues are minor early in the epidemic, but hundreds of undiagnosed cases are likely later on. It is therefore hazardous to judge progress of the epidemic based on observed epidemic curves unless quality of testing is better understood.
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Details
- Title
- It can be dangerous to take epidemic curves of COVID-19 at face value
- Creators
- Igor Burstyn - Drexel UniversityNeal D. Goldstein - Drexel UniversityPaul Gustafson - University of British Columbia
- Publication Details
- Canadian journal of public health, v 111(3), pp 397-400
- Publisher
- SPRINGER INT PUBL AG
- Number of pages
- 4
- Grant note
- K01AI143356 / National Institute of Allergy and Infectious Diseases of the National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics; Environmental and Occupational Health
- Web of Science ID
- WOS:000542545700003
- Scopus ID
- 2-s2.0-85086782019
- Other Identifier
- 991019169707004721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health