Last week, Philippe Lemoine gave an invited lecture at Arizona State University’s School of Mathematical and Statistical Sciences. Drawing on his CSPI research, Philippe presented an overview of what has gone wrong with epidemic modeling during the pandemic. It’s now available to watch on our YouTube channel.
The presentation provides an accessible overview of Philippe’s work detailing the failure of epidemiologists to accurately model transmission, the ineffectiveness of non-pharmaceutical interventions (NIPs) like masks and lockdowns, and how including network community structure as a relevant variable could improve pandemic modeling.
CSPI Essay Contest
On a different note, those of you who are new to CSPI should know that we’re currently sponsoring an essay contest, with the theme of “Policy Reform for Progress.” We’re looking for 1000-2000 word submissions that propose a plan for a reform that can facilitate or remove a barrier to the development or application of an important new technology, such as self-driving cars, commercial space travel, or anti-aging treatments.
We’ve received several submissions so far, but there’s still plenty of time left before the March 31st deadline for you to submit your own essay. Whether you’re a student, academic, or industry professional, this is a great opportunity to get people thinking about an important issue in a new light. Feel free to reach out if you have any questions.
1st prize is $5000, 2nd prize is $2500, and 3rd prize is $1000. A to-be-determined number of essays will get $500 each. Submissions will be judged by CSPI’s fellows and members of the progress studies community, and prize-winning essays will be published on our website and brought to the attention of policymakers.
To learn more, check out our post where we outline the rules of the contest:
Nice talk. Unfortunate that they stopped recording before questions, this is often the most interesting part!
My question is, do you think a reasonable estimate of lives saved by NPIs is even possible? (you say you don't believe your 200,000 number is accurate either) How broad is the problem? Is it so bad that we can dismiss any epidemiological model offhand, or are there instances of people using epidemiological models well and producing useful numbers?