Tag Archives: SARS-Cov-2

Jordana Cepelewicz on “The Hard Lessons of Modeling the Coronavirus Pandemic”

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Here’s a long and thoughtful article on issues that have come up with Covid modeling.

Jordana’s a staff writer for Quanta, a popular science magazine funded by the Simons Foundation, which also funds the Flatiron Institute, where I now work. She’s a science reporter, not a statistician or machine learning specialist. A lot of Simons Foundation funding goes to community outreach and math and science education. Quanta aims to be more like the old Scientific American than the new Scientific American; but it also has the more personal angle of a New Yorker article on science (my favorite source of articles on science because the writing is so darn good).

There’s also a film that goes along with Jordana’s article:

I found the comments on YouTube fascinating. Not as off the wall as replies to newspaper articles, but not the informed stats readership of this (Andrew’s) blog, either.

How many infectious people are likely to show up at an event?

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Stephen Kissler and Yonatan Grad launched a Shiny app,

Effective SARS-CoV-2 test sensitivity,

to help you answer the question,

How many infectious people are likely to show up to an event, given a screening test administered n days prior to the event?

Here’s a screenshot.



The app is based on some modeling they did with Stan followed by simulation-based predictions. Here’s the medRxiv paper.

Stephen M. Kissler et al. 2020. SARS-CoV-2 viral dynamics in acute infections. medRxiv.

Users input characteristics of the test taken, the event size, the time the tests are taken before the event, the duration of the event itself, etc. The tool then produces plots of expected number of infectious people at your event and even the projected viral load at your event with uncertainties.

This obviously isn’t a tool for amateurs. I don’t even understand the units Ct for the very first input slider; the authors describe it as “the RT-qPCR cycle threshold”. They said they’d welcome feedback in making this more usable.