I’m back! Yay! Sorry about all that, but my workload was just ridiculous. Things should be a lot more slack for the next few months, so it’s time I got back blogging. This also means I can finally put into action something I’ve been sitting on for months.

Richard Carrier has been a sore spot for me. He was one of the reasons I got interested in Bayesian statistics, and for a while there I thought he was a cool progressive. Alas, when it was revealed he was instead a vindictive creepy asshole, it shook me a bit. I promised myself I’d help out somehow, but I’d already done the obsessive analysis thing and in hindsight I’m not convinced it did more good than harm. I was at a loss for what I could do, beyond sharing links to the fundraiser.

Now, I think I know. The lawsuits may be long over, thanks to Carrier coincidentally dropping them at roughly the same time he came under threat of a counter-suit, but the legal bill are still there and not going away anytime soon. Worse, with the removal of the threat people are starting to forget about those debts. There have been only five donations this month, and four in April. It’s time to bring a little attention back that way.

One nasty side-effect of Carrier’s lawsuits is that Bayesian statistics has become a punchline in the atheist/skeptic community. The reasoning is understandable, if flawed: Carrier is a crank, he promotes Bayesian statistics, ergo Bayesian statistics must be the tool of crackpots. This has been surreal for me to witness, as Bayes has become a critical tool in my kit over the last three years. I suppose I could survive without it, if I had to, but every alternative I’m aware of is worse. I’m not the only one in this camp, either.

Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries.

Flaxman, Seth, Swapnil Mishra, Axel Gandy, H Juliette T Unwin, Helen Coupland, Thomas A Mellan, Tresnia Berah, et al. “Estimating the Number of Infections and the Impact of Non- Pharmaceutical Interventions on COVID-19 in 11 European Countries,” 2020, 35.

In estimating time intervals between symptom onset and outcome, it was necessary to account for the fact that, during a growing epidemic, a higher proportion of the cases will have been infected recently (…). Therefore, we re-parameterised a gamma model to account for exponential growth using a growth rate of 0·14 per day, obtained from the early case onset data (…). Using Bayesian methods, we fitted gamma distributions to the data on time from onset to death and onset to recovery, conditional on having observed the final outcome.

Verity, Robert, Lucy C. Okell, Ilaria Dorigatti, Peter Winskill, Charles Whittaker, Natsuko Imai, Gina Cuomo-Dannenburg, et al. “Estimates of the Severity of Coronavirus Disease 2019: A Model-Based Analysis.” The Lancet Infectious Diseases 0, no. 0 (March 30, 2020). https://doi.org/10.1016/S1473-3099(20)30243-7.

… we used Bayesian methods to infer parameter estimates and obtain credible intervals.

Linton, Natalie M., Tetsuro Kobayashi, Yichi Yang, Katsuma Hayashi, Andrei R. Akhmetzhanov, Sung-mok Jung, Baoyin Yuan, Ryo Kinoshita, and Hiroshi Nishiura. “Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data.” Journal of Clinical Medicine 9, no. 2 (February 2020): 538. https://doi.org/10.3390/jcm9020538.

A significant chunk of our understanding of COVID-19 depends on Bayesian statistics. I’ll go further and argue that you cannot fully understand this pandemic without it. And yet thanks to Richard Carrier, the atheist/skeptic community is primed to dismiss Bayesian statistics.

So let’s catch two stones with one bird. If enough people donate to this fundraiser, I’ll start blogging a course on Bayesian statistics. I think I’ve got a novel angle on the subject, one that’s easier to slip into than my 201-level stuff and yet more rigorous. If y’all really start tossing in the funds, I’ll make it a video series. Yes yes, there’s a pandemic and potential global depression going on, but that just means I’ll work for cheap! I’ll release the milestones and course outline over the next few days, but there’s no harm in an early start.

Help me help the people Richard Carrier hurt. I’ll try to make it worth your while.