Debiasing Covid-19 prevalence estimates
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Abstract
Timely, accurate epidemic figures are necessary for informed policy. In the Covid-19 pandemic, mismeasurement can lead to tremendous waste, in health or economic output. “Random” testing is commonly used to estimate virus prevalence, reporting daily positivity rates. However, since testing is necessarily voluntary, all “random” tests done in the field suffer from selection bias . This bias, unlike standard polling biases, goes beyond demographical representativeness and cannot be corrected by oversampling (i.e. selecting people without symptoms to test). Using controlled, incentivized experiments on a sample of all ages, we show that people who feel symptoms are up to 33 times more likely to seek testing. The bias in testing propensities leads to sizable prevalence bias: test positivity is inflated by up to five times, even if testing is costless. This effect varies greatly across time and age groups, making comparisons over time and across countries misleading. We validate our results using the REACT study in the UK and find that positivity figures have indeed a very large and time varying bias. We present calculations to debias positivity rates, but importantly, suggest a parsimonious way to sample the population bypassing the bias altogether. Our estimation is both real-time and consistently close to true values. These results are relevant for all epidemics, besides covid-19, when carriers have informative beliefs about their own status.
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SciScore for 10.1101/2021.01.10.21249298: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Upon signing a consent form, the participant was first asked about general and Covid-19-related health. Randomization After completing the compulsory part of the study, the participants were offered an optional task for which they were randomly allocated to one of the two prize treatments. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations …SciScore for 10.1101/2021.01.10.21249298: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: Upon signing a consent form, the participant was first asked about general and Covid-19-related health. Randomization After completing the compulsory part of the study, the participants were offered an optional task for which they were randomly allocated to one of the two prize treatments. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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