ArticleInfectious Disease Modelling2021
On testing for infections during epidemics, with application to Covid-19 in Ontario, Canada.
Article in Infectious Disease Modelling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Multi-event dynamic capture-recapture model for big data: Estimating undetected COVID-19 cases in British Columbia, Canada.Infectious Disease Modelling · 2026Article
- Effect of testing criteria for infectious disease surveillance: The case of COVID-19 in Norway.PloS one · 2024Article
- Effectiveness of non-pharmaceutical interventions to reduce SARS-CoV-2 transmission in Canada and their association with COVID-19 hospitalization rates.Canada communicable disease report = Releve des maladies transmissibles au Canada · 2022Article
- Studying the mixed transmission in a community with age heterogeneity: COVID-19 as a case study.Infectious Disease Modelling · 2022Article
- Under-reporting of COVID-19 in the Northern Health Authority region of British Columbia.The Canadian journal of statistics = Revue canadienne de statistique · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During an epidemic, accurate estimation of the numbers of viral infections in different regions and groups is important for understanding transmission and guiding public health actions. This depends on effective testing strategies that identify a high proportion of infections (that is, provide high ascertainment rates). For the novel coronavirus SARS-CoV-2, ascertainment rates do not appear to be high in most jurisdictions, but quantitative analysis of testing has been limited. We provide statistical models for studying testing and ascertainment rates, and illustrate them on public data on testing and case counts in Ontario, Canada.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.