Sensitivity & specificity affect the inferences that we can draw from seroprevalence studies & inform the number of samples we need for statistical confidence. To help, we built two calculators. Calculator 1: survey data, se, sp → prevalence posterior. https://larremorelab.github.io/covid-calculator1 https://twitter.com/yhgrad/status/1250858246002348034

But let's also remember: sensitivity & specificity are *estimated from data*. That means that they, too, need statistical treatment. So for Calculator 2: survey data AND raw assay calibration data → posteriors for prevalence, sensitivity, and specificity. https://larremorelab.github.io/covid-calculator2

Calculator 2 is important because it shows that the way we calibrate our tests is as important as the survey data we collect. You can incorporate all sources of uncertainty together, learning about prevalence, but also about uncertainty in sensitivity & specificity.

Also, I've finished a draft of our paper's github, with scripts, plots, and recipes for reproducing what we've done in the paper. https://github.com/LarremoreLab/covid_serological_sampling Feedback is always welcomed; there's no point in this stuff if it doesn't work for others.

Finally, my coauthors and I are hoping to gather a community of those who are working on prevalence studies, design, analysis, and related issues. If you're interested, follow @covid19testgrp and please contribute tips or preprints on the page https://larremorelab.github.io/covid19testgroup.