ArticleEpidemiology (Cambridge, Mass.)2020
Estimating the Size of a COVID-19 Epidemic from Surveillance Systems.
Article in Epidemiology (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Gaining further insights into the COVID-19 pandemic in Australia: Evidence using capture-recapture methods.Heliyon · 2024Article
- Estimating the infection burden of COVID-19 in Malaysia.PLoS neglected tropical diseases · 2022Article
- Statistical Analysis of the COVID-19 Mortality Rates with Probability Distributions: The Case of Pakistan and Afghanistan.Computational and mathematical methods in medicine · 2022Article
- Correcting pandemic data analysis through environmental fluid dynamics.Physics of fluids (Woodbury, N.Y. : 1994) · 2021Article
- A versatile web app for identifying the drivers of COVID-19 epidemics.Journal of translational medicine · 2021Article
- An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian regions.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Kenya's response to the COVID-19 pandemic: a balance between minimising morbidity and adverse economic impact.AAS open research · 2021Article
- A Survey-based Estimate of COVID-19 Incidence and Outcomes among Patients with Pulmonary Arterial Hypertension or Chronic Thromboembolic Pulmonary Hypertension and Impact on the Process of Care.Annals of the American Thoracic Society · 2020Article
- Substantial underestimation of SARS-CoV-2 infection in the United States.Nature communications · 2020Article
- Information technology in emergency management of COVID-19 outbreak.Informatics in medicine unlocked · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Public health policy makers in countries with Coronavirus Disease 2019 (COVID-19) outbreaks face the decision of when to switch from measures that seek to contain and eliminate the outbreak to those designed to mitigate its effects. Estimates of epidemic size are complicated by surveillance systems that cannot capture all cases, and by the need for timely estimates as the epidemic is ongoing. This article provides a Bayesian methodology to estimate outbreak size from one or more surveillance systems such as virologic testing of pneumonia cases or samples from a network of general practitioners.
Indexed as
Identifiers
What OpenQuestion holds
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.