1. As you may know, I'm a strong advocate of COVID testing. For the past few months, I've been collaborating with the people at @Color Genomics to understand how workplace testing could reduce outbreak sizes.

Out today, a blog post about what we found. color.com/modeling-sars-…
2. Our results are consistent with other work on proactive testing, such as the paper by @DanLarremore, @MichaelMina, and colleagues

Frequent proactive testing helps, and the turnaround time of test results is critical.
3. One of the things that a stochastic network model highlights is the bimodal distribution of outbreak sizes from a single introduction. Even with R0=2.0 or higher, many fizzle immediately.
4. With @Color, we've developed an interactive web app that lets you explore our simulation results for a wide range of parameters. Spend a bit of time with it and you'll start to get a sense of why I think ubiquitous proactive testing is essential.

5. People have asked about my COIs. I'm consulting for @Color, as mentioned above. I've given one-off seminars for pharma companies about the COVID landscape. I otherwise have no financial interests in companies that manufacture or provide COVID testing or treatment.
6. I do have a vested interest in (1) seeing people, (2) going places, (3) working at the office, (4) drinking at the bar, (5) hearing live music, (6) letting my son go to school and my daughter to college, (7) getting on airplanes, (8) photographing birds not endemic to Seattle.
7. All of the code, parameter choices, etc. are available on github as described in the blog post. @RS_McGee is developing a wiki for the SEIRS+ project (github.com/ryansmcgee/sei…) more generally, where we will be providing updates, results, discussion, etc. We will post that soon.
8. And a bit more about @Color and their LAMP-based testing protocols. Nothing in our model is specific to those methods, but the model does emphasize the need for fast turn-around.


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