Because SARS-CoV-2 testing often happens after symptoms appear, it's been difficult to estimate detection probability early in infection. So great to collaborate with team at @TheCrick & @ucl to tackle this question, with @HellewellJoel & @timwrussell – https://cmmid.github.io/topics/covid19/pcr-positivity-over-time.html 1/

We analysed data from London front-line healthcare workers who'd been regularly tested and reported whether they had symptoms at point of test ( https://www.medrxiv.org/content/10.1101/2020.06.08.20120584v1)... 2/

To estimate when people were likely infected and hence detection probability over time, we combined the HCW data with a model of unobserved infection times. 3/

We then used our estimates for PCR positivity over time to estimate the probability that different testing strategies would detect infections prior to symptoms or early in the infection: 4/

Given the current interest in scaled-up testing, we hope these estimates can be a useful benchmark for potential test performance, as well as highlighting the value of fast, frequent testing in higher risk populations. 5/

For more on the value of high frequency testing data, see this recent paper by @StephenKissler and co: https://www.medrxiv.org/content/10.1101/2020.10.21.20217042v1 6/6