COVID Transmissions for 11-19-2020

Nov 19, 2020

Good morning! It has been 368 days since the first documented human case of COVID-19.

Today I want to run through some reader comments, and also do some headlines. One headline is a submission from a reader, so I gave that one a little extra attention. I’ll be revisiting the duration of immunity paper tomorrow.

As usual, bolded terms are linked to the running newsletter glossary.

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Now, let’s talk COVID.

Pfizer adds further elucidation of their vaccine data

Today, Pfizer released more information about their vaccine. The story hasn’t changed much. Previously they had said that they have efficacy over 90%; today they indicated that it was 94.5% precisely in their interim analysis. That is, in fact, over 90%.

Safety signals also looked good, but let’s get real. There was no paper published yet, we don’t have all the details. I have a feeling that this press release came because Moderna released an exact number (95%) and Pfizer wanted to release an exact number too.

The numbers are very close. Efficacy over 90% is amazing. Let’s get that research paper with real data, though! I’m going to hold off on further speculating about this vaccine until we have that.

Modeling the spread of COVID-19: gyms, restaurants, and coffee shops?

Earlier this month, a paper was published in Nature that attempted to use mobile device location data combined with epidemiological modeling to make predictions about the locations that might be most serious for risk of transmission. A reader sent it to me, and I want to cover it here. This is the paper: https://www.nature.com/articles/s41586-020-2923-3

This paper was reported on in The Washington Post: https://www.washingtonpost.com/health/2020/11/10/coronavirus-restaurants-gyms-hotels-risk/

The Post article suggested that “restaurants, gyms, and coffee shops” were the highest-risk locations based on the results of the Nature paper. However, when you actually look at the model, the picture is slightly different. But let’s walk through the model.

Essentially, these researchers had access to a lot of data on how people move around and how long they stay in specific places, allowing an understanding of potential exposures and also what particular “points of interest” might lead to high exposure events. They could then overlay an epidemiological model onto that data to predict case growth. They validated their model by noting that it predicted case growth in the localities studied accurately compared to the actual data observed. This is probably the first issue with the model, to be quite honest—it could not possibly have been based on data that was truly representative of the infected population in the localities studied. The US, where the study was conducted, does not have a national testing network that could make that possible, and it certainly didn’t during the time studied.

However, I want to reiterate an adage that I learned during the pandemic: “All models are wrong. However, some models are useful.” This model is wrong, for obvious reasons (we’ll get to more of those later). On the other hand, I believe it is useful.

Returning to the modeling work, the authors then examine how traffic at specific locations might predict future cases. In other words, they were trying to assess how reopening a specific type of business might impact resurgence of infections after a shutdown. The riskiest business by far was a full-service restaurant, according to the model. It is followed by fitness centers, cafes/snack bars, hotels and motels, low-service restaurants, and houses of religious worship.

This is interesting, but it brings us to another limitation of the study—there is no assessment for the potential impact of disease control measures. This is intentional, however, because this isn’t a paper that’s really about keeping businesses closed. Instead, this paper is more about modeling ways to keep businesses safely open, based on things we already know about COVID-19. The authors here wanted to see what interventions they could apply to their model to allow businesses to safely open.

Now, information on masks, distancing, and measures of that type would not be readily adaptable to this model. The authors did not have clear data to use to account for the effect of these measures. Instead, what they had was a model about foot traffic, so they examined what their model predicted in the event that foot traffic at these locations was decreased.

They demonstrated that in their model, a 20% capacity restriction could have substantial impacts on spread of infection, and I think this makes the case effectively that opening high-risk businesses at 20-25% capacity would bring them into a much better margin of safety than reopening fully. In New York, this type of capacity restriction is precisely how reopening worked, and management of the pandemic in New York has long been more effective than management in other parts of the world.

Another interesting aspect of this paper is that it looks at disparities in transmission predicted across different income groups; unsurprisingly, the places that pose the greatest risk to high income-earners vs people in lower income segments are different. One thing that jumped out at me, though, was that full-service restaurants appeared to pose high risks for both groups. This only adds to the evidence, to my mind, that indoor dining at restaurants should be closed, and appropriate government support given to the businesses affected.

Now, let’s keep in mind that there is an additional margin of safety offered by things like distancing and masking that could impact this, but I found the model useful in its validation of the idea that capacity restrictions are likely to have an impact on disease while allowing a safe reopening.

What am I doing to cope with the pandemic? This:

Online games with friends

Every Wednesday, my wife and I connect with some friends to play simple party games over the Internet. It’s really a highlight to the week to have this venue for social connection.

Tonight, we played Codenames through the following online venue: https://codenames.game/

If you haven’t played Codenames, it’s a fun mixture of secret information, word games, and hint interpretation. Easily played over the Internet, and benefits from the addition of a Zoom or similar web call.

I have a few responses to reader comments today!

Reader Leyan left the following comment:

I liked this video about RNA vaccines. Dunno if you have seen it already:

I thought you’d all be interested in seeing this video, but I did have a reply on some of its finer points that I felt needed to be clarified:

This is a pretty good video. She makes some statements that I wouldn't personally make, though. The ones that jump out at me are:
-"dead" virus vaccines mentioned. I don't think this is an adequate way to describe inactivated virus vaccines. Virus particles are more like seeds, and I don't think it's apt to describe popcorn as "dead" corn, for example. It's not like it was alive before you popped it. It was replication-competent, and popping inactivated it. Similar thing with inactivated virus in a vaccine.
-mRNA being "unstable" as the explanation for why the Pfizer vaccine has a cold ship temperature. The Moderna vaccine can survive for weeks above freezing, at refrigerator temperatures, and it is also an mRNA vaccine. Lab people get this misimpression that RNA is a super unstable molecule, and it's a common myth among scientists. RNA is very unstable *in lab environments* and we have to be very careful to pretreat surfaces to remove enzymes, called RNAses, that can digest it. This is because lab environments are full of such enzymes. A lot of lab protocols call for digestion of RNA before you work with proteins or DNA, so labs tend to be full of RNAses, and RNAses are very stable. Thus when you work with RNA in a lab you need to clean any residual RNAses off your surfaces.
RNAs are also unstable in cells and tissue samples because cells and tissue samples contain RNAses, and those RNAses can be activated by cell stress.
They're not very unstable out in the world on a random surface, though--we've learned this from a lot of studies where virus RNA has been retrievable from things like surfaces in cruise ships for weeks after the actual virus structure has become inactivated.
Instead, I think the reason for the ship temperature of the Pfizer vaccine has to do with its formulation. As the presenter here mentions, these vaccines use lipids and other types of "vehicle" preparations to help get the RNA into your cells to produce the spike protein. Those vehicles can be finicky sometimes, and maybe that's what's going on with the Pfizer vaccine.
-She skips over the fact that these trials will continue not just to monitor efficacy but also safety. The possibility remains that there will be safety events 6 months or a year out, and we will need to watch for those.
With the exception of these items I think this is a nice video that explains the vaccine well.

Next up, Robert Berger asked the following question:

If someone developed natural immunity, and, as the Times article suggests, that immunity is long-lasting, is there any benefit or need for that person to be vaccinated?

I really liked this one. Here’s my answer:

Yes; I cannot add it here because of the limitations of the comment box, but in the original vaccine Phase 2 trials, there was a comparison published showing antibody levels in response to vaccination as compared with antibody levels in plasma from people who recovered from infection. As I recall, the vaccine-induced antibody levels were all quite high and all quite consistent. The convalescent plasma antibody levels were not. They were all over the map. If antibodies turn out to be a correlate of protection, and I suspect they are, then it would indicate that you will get more consistent and strong immunity from a vaccination than from natural, infection-induced immunity. In that case, I would feel that the vaccine would be a useful way to at least boost immunity in a person who was previously infected.
I imagine that medical guidelines will explore this topic and that the American Committee on Immunization Practices (ACIP) will provide recommendations on the use of these vaccines in people who have recovered from COVID-19. I look forward to seeing what they have to say, and I'll note that it's their advice and the advice of your physician(s) that you should follow--I'm not a licensed practitioner.

Here’s the figure that I was talking about from the Moderna Phase 2 paper, by the way:

`Image is a graph showing antibody titers (levels) in patients given vaccine at various times after vaccination; these are compared with antibody titers in serum from recovered COVID-19 patients. The response to most vaccine dose conditions shows a high, sustained level of antibodies. Recovered COVID-19 patients show much greater variation than any of the vaccine conditions. From The New England Journal of Medicine.`

The thing to look at in that figure is how spread out the dots for the “convalescent” bar are compared with the dots in the vaccine arms (those are the ones that are followed across a time course; the labels like “25 μg” indicate the vaccine dosage). The vaccine appears to induce a consistently high level of antibodies in vaccinated patients. There is much more variation in antibody levels in convalescent patients.

Now, of course, this could mean nothing. The antibody levels could be irrelevant to protection, or all of the levels seen in convalescent patients could be sufficient for protection. I just don’t know. But since I don’t know, this suggests to me that there could be a reason that a cautious person who has recovered from COVID-19 would still want to get the vaccine.

Anyway, this was a great set of comments to reply to and share.

You might have some questions too! Send them in.

Join the conversation, and what you say will impact what I talk about in the next issue.

Also, let me know any other thoughts you might have about the newsletter. I’d like to make sure you’re getting what you want out of this.

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