Starting now! I’ll be live tweeting this @DataSciJedi-sponsored session on Delivering Data Differently at #JSM2022. #JEDIatJSM

https://twitter.com/DataSciJedi/status/1554512848520417287

Our first speaker is @ajrgodfrey. He speaks from his experiences as a blind person. He emphasizes the importance of independence and dignity for the visually impaired. #JEDIatJSM

If a new SARS-CoV-2 variant is spreading 50% faster than an old one, does the new variant have a 50% higher R0? With each new variant more transmissible than the last, does that mean the latest variants have an R0 of 18? 😳

A short explainer…
1/5 A new variant may spread faster for a few main reasons (a non-exhaustive list):

- An increase in inherent transmissibility (e.g. higher viral load, better binding to cells)

- An increased ability to infect people with some baseline immunity (“immune evasion”)

2/5

I enjoyed participating in yesterday's #EEID2022 panel on scientific communication - what has worked, what hasn't, and what I've learned. For these types of panels, I have made a conscious decision to be very honest, including the good and the bad experiences. 1/ Yesterday, that included me telling the audience how much I angsted over questions like "Is it safe to do X? Our viewers want to know!" Or pressure to stay up to date on everything, or say yes to all requests. Worry that I'm saying the wrong thing or don't belong. 2/

Tracking down primary sources for the estimated 85% effectiveness of smallpox vaccines against #monkeypox is harder than I thought.

From what I can gather, ACAM2000 effectiveness was estimated from observational data of outbreaks in Africa. (Which studies are these?) ... And then the JYNNEOS vaccine is cited as having "up to 85% effectiveness."

Per CDC "The effectiveness of JYNNEOS against monkeypox was concluded from a clinical study on the immunogenicity of JYNNEOS and efficacy data from animal studies." ...

Recently I learned the term “hidden curriculum,” and it felt like an aha moment. A very short story about one of my hidden curriculum moments. 1/7
edglossary.org/hidden-curricu… I interviewed for biostats PhD programs during my senior year of college. I really wasn’t sure what to expect at my interviews. I remember being so thrilled to be flown anywhere. I showed up enthusiastic and ready to learn about the field. 2/7

The end of the pandemic? Let's talk about modeling assumptions and future uncertainty. Are the IHME projections discussed in this @TheLancet comment assuming ~90% of Omicron infections are asymptomatic and thus likely to be missed? 1/5
thelancet.com/journals/lance… The Omicron waves have certainly been large and many infections have been missed, but the evidence that the asymptomatic fraction is *so high* is thin. A model with this assumption would seem to attribute the rapid turnaround in a wave entirely to running out of susceptibles. 2/5

A sketch to explain how a new variant may appear milder even with no change in underlying virulence. This can occur because, when calculating the fraction of cases that are severe, the denominator now includes many re-infections that had previously been averted. A thread. 1/8 Imagine a variant with little capacity for re-infection. The susceptible population is exposed to enough virus to infect. The infections include severe, moderate, mild disease, and asymptomatic infections. (Here I point out that these sketches are not to scale.) 2/8

Reasons for Optimism and Pessimism
November 2021 Edition

Optimism: Boosters for all adults in the US and vaccines for 5+. Both will make a chunk of our population less likely to be infected (and so less likely to transmit).

Pessimism: Holdouts who haven't received a first dose. Optimism: Models projected a steady decline in cases, which reflects the large numbers vaccinated and previously infected. There is a lot of population immunity to dampen previous waves.

Pessimism: Nonetheless, we are seeing increases in regions of the country.

I've been enjoying @BreneBrown's Dare to Lead podcast, and I wanted to share two insightful points from her episode with @AdamMGrant. The first on polarization, and the second on leadership. On polarization, one might think that the way to bring someone away from an extreme position is to help them understand the opposing side. But in fact that can increase polarization by painting an issue as two-sided. With only two choices, which camp are you in?

Ah, to be as confidently wrong as a Silicon Valley software engineer. The author of this viral thread is amazed that a vaccine study found similar rates of COVID-like illness in vax & unvax. Almost like having COVID symptoms is a prerequisite for entry into a TEST NEGATIVE STUDY.

https://twitter.com/USMortality/status/1443431592249094146

In a test negative study, we only recruit people who develop symptoms. PCR is then used to distinguish positive cases from negative controls (people with symptoms, but due to another cause). COVID vaccines are not expected to prevent other causes of symptoms, like a cold.

I appreciate the lively discussion on Twitter about age confounding for vaccines. Recognizing the patterns helps us interpret raw data. But worth reminding people that adjustments for age appear in all but the crudest analyses. Unmeasured (unmeasurable) confounders are harder... Looking within age bands is one common approach, as shown in this figure below. Trickier problems include prior infection (missed or not included in the analysis), care-seeking behavior, risk-taking behaviors, location, etc. etc.

https://twitter.com/nataliexdean/status/1427703094062706691?s=20

What if we could measure your antibody levels shortly after vaccination and know how well you are protected? Would future vaccines eliciting the same antibody levels provide equally good protection?
That is the idea behind an immune correlates analysis. Thread on a new preprint. Using data from the Moderna trial, scientists studied the relationship between post-vaccination antibody levels and protection against disease. This is a unique study design to link the antibody response to efficacy directly.
medrxiv.org/content/10.110…

We know vaccines reduce your risk of infection, but they aren't *perfect.* Then what fraction of transmission in the population is from the vaccinated? How might this change with new variants?

In the thread below, I share a mental model with worked examples. 1/11 First, some definitions. We are familiar with vaccine efficacy (VE) against disease. This was estimated from trials. But many studies have helped us estimate VE against all infection (aka VE_S). For mRNA vaccines against early strains, this has hovered around 80-90%. 2/11

The discussion about COVID vaccine boosters is really many sub-discussions at once. The lack of a transparent decision framework makes it feel more confusing.

A thread below to start to break out the points… Question 1) Are boosters safe and effective? This includes boosters of the original strain and based on new variants. This is addressed by new trials of these new combinations, including immune response data.

The return of vaccine Monday, but maybe one of the last placebo-controlled trial results. In their large US/Mexico trial, Novavax confirms the excellent result seen in an earlier UK trial. This includes efficacy against the circulating alpha (UK) variant.

https://twitter.com/novavax/status/1404379473085091843

The two dose protein subunit vaccine can be stored at refrigerated temperatures and is cheaper to produce. The company will apply for an EUA in the third quarter of 2021, as they need more time to validate the manufacturing process.

A text from a colleague: "Ok how on earth has Twitter decided that we know VE against variants?" 🤔

Twitter does love certainty! But better to think about VE against variants in terms of likely ranges rather than precise estimates. So how do make these assessments? A thread. Sometimes we have randomized clinical trial data, like J&J Ensemble trial in South Africa and South America. But often we are assembling data from disparate (and imperfect) sources...
nejm.org/doi/full/10.10…

I hope people are aware of the enormous added value of HIV clinical trial networks. Groups who have been working on vaccine & treatment trials for decades leapt into action to work on COVID. OWS vaccine trials directly benefitted from the expertise of these groups. A big success!

https://twitter.com/JenniferNuzzo/status/1400546130279211013

There's a lot we can learn from this experience about:
- Leveraging existing networks
- Coordination between companies and researchers to standardize protocols across trials
- Using a centralized DSMB for oversight
- Pooling data, as planned for immune correlates analysis

How will we know if we need COVID vaccine boosters?
If we observe that:
(1) Vaccine protection wanes over time.
(2) New immune escape variants emerge, resulting in a vaccine-strain mismatch.
These are distinct reasons. We can imagine how we would distinguish these in the data. (1) Immunity wanes over time.
We would see that:
- Breakthrough cases occur most frequently in people who were vaccinated longest ago.
- These breakthrough cases include all types of strains.

In the US, we're rapidly building immunity thanks to highly effective vaccines. Cases have been quickly dropping. What might we expect in the coming months?

A few tweets on how I think about our patchwork of outbreaks... 1/6 Immunity will not be spread evenly throughout the population. While we track vaccination coverage at the national or state level, infectious disease dynamics are inherently local. Pockets of unvaccinated people who have not previously been infected will exist. 2/6

Separating out two common questions:

1) Do vaccines reduce transmission?

Yes. A vaccinated person is less likely to transmit because they are less likely to ever be infected. At a population-level, this translates to reduced transmission.
cdc.gov/mmwr/volumes/7… 2) But if a vaccinated person gets infected, are they less infectious?

This is hazier. Maybe they have lower viral load, shorter duration of infection. Maybe virus is contained to the nose only. But less infectious does not equal non-infectious.

https://twitter.com/ErinBromage/status/1394692831575486470?s=20

“Everyone believes in coordination, but no one wants to be coordinated.”

In today’s @WHO forum, Sir Michael Jacobs (@RoyalFreeNHS) with a call to action to improve collaboration for therapeutics research. 1/4 He provides a successful example of three large-scale platform trials collaborating to harmonize protocols for antithrombotics. Data are more valuable when they can be combined and compared. 2/4