It can be dangerous to take epidemic curves of COVID-19 at face value.
Can J Public Health
; 111(3): 397-400, 2020 06.
Article
in English
| MEDLINE | ID: covidwho-1005629
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Coronavirus Infections
/
Clinical Laboratory Techniques
/
Epidemics
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
/
Systematic review/Meta Analysis
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Journal:
Can J Public Health
Year:
2020
Document Type:
Article
Affiliation country:
S41997-020-00367-6
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