Covid-19 and exponential growth: Actual cases = 22 × Reported cases

Covid-19 is growing exponentially. Exponential growth is funny (counterintuitive). For a while, nothing seems to be happening. Then very quickly “everything explodes.” (Also strange: this visual behavior repeats at different scales.) For example, here is a chart of reported cases in Italy. (Source: dev_thetromboneguy )

Exponentially growing data looks small at first; then it appears to zoom upwards

As an applied mathematician (among other things), I’ve worked with lots of exponential growth. I fear that reporters and others are not understanding the implications.)

  • Covid-19 infections are growing exponentially, with a doubling time of 5 days. This is based on Italy, and on China before they took drastic measures to limit spread. (On the Italian chart above, it’s even faster.)
  • The incubation period between getting sick and knowing you are sick averages 14 days.
  • In the US, it is taking multiple days to get tested when you ask for it. Optimistically, a 4 day delay on average.
  • The test results are apparently taking days to come back. Approximately 3 days
  • So total lag between being infected, and being reported in the statistics as a “confirmed new case” is 14 + 4 + 3 = 21 days.
  • This is 4.2 doubling intervals = 21/5
  • 2^4.2 = 22.6 ;2 raised to the 4.2 power is 22
  • So for every confirmed case, there are about 21 more that are sick but not yet reported.
  • This assumes everyone with the disease is eventually tested. If half of people who are sick are never tested, double this number.

This is why we each need to take personal defensive action before there are many reported cases in our city. By the time we know there are many cases near us, it is 3 weeks later and the real number is 22 times higher.

Of course there are many approximations in this model. If you know anywhere that has done similar analysis more rigorously, please post it in the comments or tweet it to me, @RogerBohn

Update March 17: The 20:1 calculation is driven by the 21 days assumed between being infected, and getting positive test results. The average may be lower, and it will fall as more tests become available. On the other hand Italy reports a case growth rate of 35% per day! So the ratio could be as low as 8.

John IOANNIDIS calls for random testing of entire population, which would give an unbiased estimate. That’s not yet feasible for the U.S. due to CDC’s inexplicable foot dragging on test development and procurement. But Korea could do it. The required sample size is large due to uneven incidence in different parts of a country. But a running sample e.g. 1000 per day would give very useful numbers.
If the test is sensitive enough, random sampling would also show how many people are sick with few or no symptoms.

Scrivener 3 for Academic Writing: An In-Depth Review

I use a variety of specialized software for note taking, managing academic papers, etc. Rather than write my own review of Scrivener, I link to someone else’s here. I added a comment to her post, about using it with bibliography software.

Feel free to add links to your own favorite Scrivener reviews, in the comments. There is a fair amount of overhead in learning Scrivener, but for longer projects (eg > 10,000 words) it saves writers from “multiple version hell.”

In this in-depth Scrivener 3 review, I show you why Scrivener is the best word processor for academic writing. Unlike Word, Scrivener 3 keeps all your research and writing in one place. Its best features? The ability to drag and drop to reorganize your draft, split screen mode, word targets, and linguistic focus.

Source: Scrivener 3 for Academic Writing: An In-Depth Review

Science policy fellowships @ UCSD

For STEM doctoral students at UCSD who have policy interests but are not in social science fields. I have advised several students in this program, and it has been useful for all of them.

Drawing applicants from UC San Diego’s STEM related programs, each year the School of Global Policy and Strategy (GPS) selects three doctoral students from across campus and pairs them with a GPS faculty advisor to explore the policy implications of their dissertation research.

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Drone crashes: EEs always underestimate the difficulty of precision manufacturing!

In documents filed in federal court this week, the San Francisco company Lily Robotics blames its demise on excessive product demand and a funding drought.  Source: Lily details failure, refund plans in bankruptcy filing

After getting burned by one Kickstarter project that died, I realized that very smart EEs think that if they can make 3 prototypes, everything else is just “details.” But high-volume, tight-tolerance manufacturing is its own field, and competition from excellent companies is stiff. So even if it eventually succeeds, by the time a Kickstarter hardware project has filled its initial orders,  conventional companies will have equivalent products in the stores.