ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
A statistical model of COVID-19 testing in populations: effects of sampling bias andtesting errors.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Potential for Bias in Prevalence Estimates when Not Accounting for Test Sensitivity and Specificity: A Systematic Review of COVID-19 Seroprevalence Studies.International journal of public health · 2025Pooled it
- Prevalence Estimation Methods for Time-Dependent Antibody Kinetics of Infected and Vaccinated Individuals: A Markov Chain Approach.Bulletin of mathematical biology · 2025Article
- Aggregating multiple test results to improve medical decision-making.PLoS computational biology · 2025Article
- Article
- Bayesian sequential approach to monitor COVID-19 variants through test positivity rate from wastewater.mSystems · 2023Article
- Bayesian sequential approach to monitor COVID-19 variants through positivity rate from wastewater.medRxiv : the preprint server for health sciences · 2023Article
- Article
- Operational analysis for COVID-19 testing: Determining the risk from asymptomatic infections.PloS one · 2023Article
- Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays.Mathematical biosciences · 2022Article
- Article
- Article
- Data science approaches to infectious disease surveillance.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Review
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3 authors.
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Abstract
We develop a statistical model for the testing of disease prevalence in a population. The model assumes a binary test result, positive or negative, but allows for biases in sample selection and both type I (false positive) and type II (false negative) testing errors. Our model also incorporates multiple test types and is able to distinguish between retesting and exclusion after testing. Our quantitative framework allows us to directly interpret testing results as a function of errors and biases. By applying our testing model to COVID-19 testing data and actual case data from specific jurisdictions, we are able to estimate and provide uncertainty quantification of indices that are crucial in a pandemic, such as disease prevalence and fatality ratios. This article is part of the theme issue 'Data science approach to infectious disease surveillance'.
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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.