Interview with Samuel Scarpino, assistant professor in the Network Science Institute at Northeastern University in Boston, Massachusetts.
What impact does the news about fresh vaccines for COVID-19 have on the importance of backward tracing?
Samuel Scarpino: With the new vaccines, I think certainly the early signs are a lot more optimistic than most of us were planning for – which is great. If they hold up, it means we may be out of this years earlier than we could have been.
However, there are some challenges. For example, the first vaccine [the Pfizer and BioNTech one unveiled in early November] has to be stored at -70 °C, which means distribution is going to be complicated and likely to be heavily biased towards higher-income countries – at least initially.
In addition, the manufacturing process is going to take time. Let’s just pretend that the US took all the vaccine: it’s probably still not going to be enough, even at 90% efficacy, for us to get to herd immunity for a year. If you then imagine we’re going to spread that out globally, we’re not going to be at herd immunity with the vaccine alone for probably two years in most countries.
So if we took vaccines like these and layered that with case investigation, cluster-busting and mask-wearing, we may be able to get back to a kind of newer normal by the summer in a lot of places – including the Northern Hemisphere. But if we just rely on the vaccine, it will still be quite some time. And we’re going to need those measures even more if it turns out that the vaccine’s not quite as effective as we thought.
What we really need to do is be a lot more data-driven around our interventions: case investigation both helps us control the disease and tells us how we can intervene in such a way that we only disrupt the things that are going to maximally interrupt transmission.
Can the learnings from backwards tracing also be useful if infectious diseases with similar patterns emerge in the future too?
Samuel Scarpino: There’s almost certainly going to be another COVID in the future – another respiratory virus that finds itself in a crowded market – and we’re going to have to stop that from becoming a pandemic again. So we’re going to need all this stuff for the next one.
But setting that aside, there are also plenty of existing diseases in front of us right now that are causing huge amounts of death, pain, suffering and economic consequences – and we can use all of these things for those as well. For example, a lot of the same kinds of things we would do to intervene against tuberculosis we could also bring over to and from COVID.
Why haven’t we been as effective in the West as in countries such as some in Asia at dealing with COVID, even though the world has been aware of superspreader-type patterns in previous outbreaks such as Ebola and SARS?
Samuel Scarpino: Ebola, SARS and MERS are different for a couple of reasons, at least. One is that they’re a little bit more reliant on superspreading, which makes them easier to control than COVID because the sources of outbreak become evident more quickly. A disease is less controllable if it looks like influenza, which doesn’t cluster in the same way. It turns out that COVID is somewhere in the middle – so it’s controllable with a lot of work, but very hard to control once it gets rolling.
Another factor is that in the West, we mostly didn’t directly experience SARS – and there’s definitely a strong association between countries that dealt heavily with SARS and are doing better with COVID.
With Ebola and SARS, too, you’re not infectious until you have symptoms, which then come on hard and fast. But with COVID, you’re infectious before, and a lot of our screening and control of disease is around symptoms, especially in the US and Europe. So much of our pandemic response has also been focused on influenza, which is the wrong kind of model for COVID.
The other big piece is that the Centers for Disease Control and Prevention – both a leader in the US, but also internationally in coordinating efforts – was sidelined by President Trump. I really think that we can’t upweight enough the resulting lack of ability to coordinate across the health agencies between the US and Europe.
Are you starting to see more of this type of backwards-tracing approach, and combining R and k numbers, gain traction in the West?
Samuel Scarpino: I’m starting to see pieces of it, but not a coordinated understanding. I think it’s going to require that, especially once the vaccines come online, because we’re going to have to coordinate how those are distributed and layer those in with these other kinds of interventions. All of our countries are in this together.
We also now have an incoming administration that has stood up a COVID task force with real experts on it and is putting together a plan – so I think Europe, the UK and the rest of the world should be planning on a very different US response starting on January 21.
How complicated is this type of methodology to use, and do we have the tools to do it?
Samuel Scarpino: Yeah, we definitely do. For me, the R and k numbers help us classify different diseases in terms of what kinds of responses we’re going to need for them, but I think it’s not necessary that we can all conceptualise exactly what’s happening with the math. And even though we don’t have enough data overall, which is why we’re in this mess in the first place, we do have enough to put programmes in place and get things rolling.
The resources are there; we just have to look back in the other direction. We’ve also seen it working in South Korea and Japan, so we know it can be done; it’s just going to require an appreciation of the potential importance on the part of the political leaders, and then the will to implement it.
To me that means, let’s see the policy plan, and find out how much it would cost and what it would take. Then we can always decide that we just can’t afford it or we’re going to have to wait until there’s a vaccine to bring the viral load down. But let’s get those plans together and not do what we’ve been doing for the past 10 months – which is reacting after it’s too late.