Observational studyBMC infectious diseases2022
Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics.
Observational study in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Estimating COVID-19 incidence and prevalence using lateral flow tests in England and Scotland, 2023-2024.Nature communications · 2026Article
- The real-time infection hospitalisation and fatality risk across the COVID-19 pandemic in England.Nature communications · 2024Article
- SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar.Scientific reports · 2021Article
- Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19.Journal of global health · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProspective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics.
methodsAn age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions.
resultsThe prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was twofold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R
conclusionsCaution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.
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