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First up, something I've been wanting to get around to for a long time – spurred on now by a particularly egregious example of handling criticism badly. At Absolutely Maybe, A Cartoon Guide to Criticism: Scientist Edition.

And soon after, I had to decide how to put this into practice, when I read a critique by Steven Salzberg, of my most recent post in The Atlantic. In this case, my response is a rebuttal.

 

 

Seen the narrative that efficacy against hospitalizations & deaths is 100% for all Covid vaccines? They're amazing, but we don't know how high that protection is. Exaggerating vaccine benefits is risky: we can do better than this! My 1st piece at The Atlantic: The Differences Between the Vaccines Matter.

I've got a data dive on hospitalizations & deaths for the 6 vaccines used to support that "100% efficacy" claim, and the data behind The Atlantic op-ed here at this website.

And a second piece at The Atlantic - this time about the serious blood disorders being investigated around the Oxford-AstraZeneca vaccine.

Over at Absolutely Maybe, another vaccine roundup – Community Impact Data, 3 New Covid Vaccines, and Trials in Children: A Month of Dilemmas and Good News.

Plus another thing Covid-19 has brought to our lives: The Pioneering Crossover Trials for Covid Vaccines and What We'll Find Out. Supported by a post at Statistically Funny, returning after a long hiatus: In clinical trials, you can have it both ways.

 

 

An interview with me on vaccines by Julia Belluz at Vox: The scientist who's been right about Covid-19 vaccines predicts what's next. In which Julia asks tough questions and excels at getting me to say what I really think! (No one expects an Australian to be diplomatic, right?? Right!)

I think it's the most poorly communicated part of vaccine evidence – adverse events. Here's how not to get caught in a bunch of common traps – like, "adverse events were mostly mild and moderate". Spoiler: severe adverse events never outnumber the others – it doesn't tell you anything. At Absolutely Maybe: A Reader's Guide to Safety & Adverse Events Data From Clinical Trials.

Even when I tried to write a non-Covid vaccine post, one crept in! This year's addition to my series on research about peer review in science journals: 5 Things We Learned About Peer Review in 2020.

 

 

 

It was a monster month in Covid-19 and vaccines – and my monthly vaccines roundup was a monster, too:

Variants, 3 New Covid Vaccines, and Contested Efficacy Claims: A Month of Seismic Shifts and Confusion.

And a debunking post: Unpacking Doshi's Take at BMJ on Covid Vaccine Trials.

Early in the month I talked about Covid vaccines with Alexander Heffner for an NPR podcast. And late in the month I was on BBC Newsnight, answering questions about the Oxford/AstraZeneca vaccine again.

 

 

 

 

With a deluge of phase 3 vaccine trial data coming, we need to guard against both hype and fear-mongering. At WIRED, New Vaccine Data is Coming: Watch Out for These 3 Claims. It contains the answer to the question, how many events* were there to show vaccine efficacy in the 1954 mega-trial on polio? (* Paralytic polio.)

Also at WIRED – is it really because they were mRNA-based that the vaccines arrived so fast? No: The Race for a Covid Vaccine Was More About Luck Than Tech - and virtuoso clinical trial management.

One thing we didn't see coming: the end of the line for the major Australian Covid vaccine candidate. At Absolutely Maybe, A Would-Be Covid Vaccine Crashed in Australia, Leaving Important Questions in Its Wake.

And my December vaccine race post at Absolutely Maybe was a whopper: Why Two Vaccines Passed the Finishing Line In a Year and Others Didn't, and a Month 12 Roundup.

 

 

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