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…I think it remains to be seen whether we in the United States can do better than just letting everybody get it gradually … If it’s about 5 or 10% of the population now, I can’t envision a scenario where we have a vaccine or a really good treatment before it’s about twice that. … Clearly we need a lot of creative thinking about alternative ways to make life go on.

Marc Lipsitch

In March Professor Marc Lipsitch — director of Harvard’s Center for Communicable Disease Dynamics — abruptly found himself a global celebrity, his social media following growing 40-fold and journalists knocking down his door, as everyone turned to him for information they could trust.

Here he lays out where the fight against COVID-19 stands today, why he’s open to deliberately giving people COVID-19 to speed up vaccine development, and how we could do better next time.

As Marc tells us, island nations like Taiwan and New Zealand are successfully suppressing SARS-COV-2. But everyone else is struggling.

Even Singapore, with plenty of warning and one of the best test and trace systems in the world, lost control of the virus in mid-April after successfully holding back the tide for 2 months.

This doesn’t bode well for how the US or Europe will cope as they ease their lockdowns. It also suggests it would have been exceedingly hard for China to stop the virus before it spread overseas.

But sadly, there’s no easy way out.

The original estimates of COVID-19’s infection fatality rate, of 0.5-1%, have turned out to be basically right. And the latest serology surveys indicate only 5-10% of people in countries like the US, UK and Spain have been infected so far, leaving us far short of herd immunity. To get there, even these worst affected countries would need to endure something like ten times the number of deaths they have so far.

Marc has one good piece of news: research suggests that most of those who get infected do indeed develop immunity, for a while at least.

To escape the COVID-19 trap sooner rather than later, Marc recommends we go hard on all the familiar options — vaccines, antivirals, and mass testing — but also open our minds to creative options we’ve so far left on the shelf.

Despite the importance of his work, even now the training and grant programs that produced the community of experts Marc is a part of, are shrinking. We look at a new article he’s written about how to instead build and improve the field of epidemiology, so humanity can respond faster and smarter next time we face a disease that could kill millions and cost tens of trillions of dollars.

We also cover:

  • How listeners might contribute as future contagious disease experts, or donors to current projects
  • How we can learn from cross-country comparisons
  • Modelling that has gone wrong in an instructive way
  • What governments should stop doing
  • How people can figure out who to trust, and who has been most on the mark this time
  • Why Marc supports infecting people with COVID-19 to speed up the development of a vaccines
  • How we can ensure there’s population-level surveillance early during the next pandemic
  • Whether people from other fields trying to help with COVID-19 has done more good than harm
  • Whether it’s experts in diseases, or experts in forecasting, who produce better disease forecasts

Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type 80,000 Hours into your podcasting app. Or read the transcript below.

Producer: Keiran Harris.
Audio mastering: Ben Cordell.
Transcriptions: Zakee Ulhaq.

Highlights

The true infection fatality rate in the US

I think it’s clearly under, say, one and a half percent, and it’s very likely under 1%. It’s clearly above 0.2% and probably above 0.4%. So I think if I really had to cover all my bases, I would say between 0.2 and 1.5, but I would put most of my money in the intermediate range.

I think one of the challenges is that it’s so variable by your risk factors, that a little bit of unrepresentativeness in the sampling of cases can translate into a really wrong inference about the death rate.

Immunity

I think there’s some slightly encouraging news that was published, I think, just yesterday in Cell by a large group of people, I think, mostly American researchers, that seems to show that the large majority of people create all three types of immunities through types of T-cells and antibodies and that essentially everybody that they studied creates an antibody response if you wait long enough.

So that’s a good sign. Of course, what we don’t know exactly is the degree of protection it offers, but we don’t know that for any new virus. We assume, in most cases, that there’s pretty good immunity. The only reason everyone’s hesitant with this one is that it’s a coronavirus and a lot of coronaviruses have sort of weak immunity, but I think all indications are that there will be some level of immunity in virtually everyone.

Human challenge trials

I think the resistance to it is that there is a very long tradition, unfortunately, of research that was done either with unwilling, non-volunteers or people who were under compulsion or in other unethical fashions. So research has been seen as an area where ethics have to be vastly more restrictive than in other activities that we do. I think in some ways that’s for very good reason, especially the reluctance of many physicians to be involved in deliberately giving someone an infection is an admirable trait. I think that on the other hand, in a situation like this where there is, as you say, massive social value to a trial, in expectation, of course a trial could fail. And some people have suggested that there’s no social value to finding out that a vaccine doesn’t work, but I disagree with that because we need to know which ones do and don’t work.

But I think in a situation like this with a large social value to doing such a study and the possibility that there really are thousands of people who have already stated their willingness to take on, actually in some cases, quite large risks… Some of them are not young and healthy, and they understand that that means that it is a risk. We should think of this as an activity like other altruistic activities that people engage in. It’s the nature of altruistic activities and of risky activities especially, that you don’t always know what the risk is. Someone doesn’t join the military on the grounds that they’ve been told that over history, 7% of military recruits have suffered some kind of bad outcome. I just made that number up. You join the military and, to the extent that that’s an altruistic decision, you decide, “I’m willing to take this risk because I want to serve a purpose that I believe in”.

Present uncertainties, and speak clearly

I’m a real fan of people who are very explicit about their sources of uncertainty and what their findings do and don’t imply for decision making. And I think that is characteristic of the people that I’ve just described. And it’s sometimes in competition with trying to get a splashy publication, but I think the best groups in our field have figured out a way to balance that and to say what they found but not to overclaim it. And just applying that filter of “Is there a good presentation of the uncertainties and the limitations” gets rid of a lot of garbage in the field. It’s not a perfect filter, but it’s a pretty good heuristic.

And I also think my doctoral advisor, Bob May, who died very recently, a couple of weeks ago, used to say, he was Australian so he said it with a little bit more color than this. He said, “If the freaking guy can’t explain that he doesn’t understand what he’s talking about, it’s not your fault”. And I also do see a very strong correlation between people who explain things clearly and people who are reliable. Because it’s harder to hide what’s really going on… I think a lot of people who do low-quality work don’t understand why they got the results that they got. They just got them and thought, “Oh, that’s exciting”. And the people who do high-quality work and can explain what they did I find much more credible. Because ultimately, all of this boils down to multiplication, division, and a little bit of calculus.

It’s not quantum physics. There are no truly counterintuitive things happening in our field. There are some things that are surprising when you first hear about them. We just worked through an exercise like that in my class right before I spoke to you. So there are things that are surprising, but there are no things that are so surprising you just say, “Well, the model says this and I don’t understand why”. So if you can’t explain it, you really don’t understand it. And those two heuristics together I think actually are a pretty good filter on work that’s likely to be meaningful.

Global catastrophic biological risks

I think that the most severe possible pandemics are either going to be addressable by the types of approaches that we develop for a pandemic like this one, or dealing with them is going to be pointless, and we’re just going to have a catastrophe. I don’t think that there’s a set of approaches that is distinctive to the utterly catastrophic scenario. I mean, this is in some ways close to catastrophic, but it’s not going to erase humanity. It will do a lot of damage economically and to health, but it’s not going to erase humanity or even a large fraction of humanity. But I think from a methodological perspective, this one is bad enough that we’re essentially throwing everything we have at it. There’s no extra thing that we’re saying, “Oh well we’re not going to do that, because this isn’t bad enough”.

We’re pretty much doing everything we know how to do. And moreover, less severe pandemics are more common because there’s been no pandemic to date that has wiped out the human race, by definition. And from a human resource perspective, again, the people who are going to deal with the problem in a catastrophic scenario, are the people who are dealing with this problem. They’re exactly the same people, and their successors. So the idea that I found hard to understand in some effective altruism discussions, is this idea that we know that all the minor cases are taken care of and we have to focus on the edge cases. But I don’t see any activity that would be helpful for the edge cases that doesn’t also involve preparing for the less edge cases like this one. I literally can’t think of one thing that is like that.

Articles, books, and other media discussed in the show

Marc’s work

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About the show

The 80,000 Hours Podcast features unusually in-depth conversations about the world's most pressing problems and how you can use your career to solve them. We invite guests pursuing a wide range of career paths — from academics and activists to entrepreneurs and policymakers — to analyse the case for and against working on different issues and which approaches are best for solving them.

The 80,000 Hours Podcast is produced and edited by Keiran Harris. Get in touch with feedback or guest suggestions by emailing [email protected].

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