Using mathematical modelling to predict virus evolution and inform pandemic response

Clare Taylor: 0:16

Welcome all to ESWI Airborne. This is your host, Clare Taylor speaking, and this is the place to be to meet the members of ESWI, the European Scientific Working Group on Influenza, and get all the latest news and insights on viruses, vaccines and, more directly, from ESWI's expert members of the scientific and medical communities working on these topics. Today, we'll be talking all about diagnostics and, in particular, on the relationships between diagnostics, surveillance and treatment here in the studio. To enlighten us, I'm very pleased to welcome Dr Colin Russell, professor of Applied Evolutionary Biology at the University of Amsterdam's Faculty of Medicine, and Colin is also an ESWI board member. Colin, welcome to ESWI Airborne.

Colin Russell: 1:10

Thank you so much, Clare.

As already mentioned, diagnostics is today's hot topic, but before we get into that, Colin, I'd like to know a little more about you. You spent 15 years at the University of Cambridge altogether, starting there in 2002 as a PhD student. What was your topic?

So, believe it or not, long before I ever got into flu, I studied the spread of rabies in wild animals. So there I really started off as a mathematical modeler trying to figure out how rabies spread through raccoons in the northeastern United States.

And how did your research work on virus evolution develop over time?

So in my PhD I was really looking at basic models of how diseases spread. But through that work what I increasingly realized was that the sort of frontier of mathematical modeling and epidemiological research was at the interface between virus evolution and epidemiology. And I was particularly attracted to flu, because flu is one of these interesting situations where evolution directly impacts the epidemiology of the virus itself. And so in that way, when the virus changes the way it appears to our immune system, it can infect more people and it can spread more rapidly through a population. And so in that way there's this dynamic interplay between virus evolution and epidemiology. And so when I first got into it it was really about looking at how influenza viruses spread around the world and the role that evolution played in that. But since then I've become interested in a wide range of topics around virus evolution, including sort of how we can evolve these viruses in the lab to see what are the limits of their capacity to evolve.

So in the lab you try and find out what would happen next in terms of virus evolution?

Well, that's one of the things that we do. Yeah, so we have, w e could call it in vitro or ex vivo, but we use human tissue that we grow in the lab and we use that for experimental infections, and then we infect those cultures with influenza viruses and we pathology them over and over and over again to see how the viruses themselves either want to change or can change.

Wow, how fascinating. For the past five years, you've been in Amsterdam, right, and you made the leap across the water in 2017 to head up the Laboratory of Applied Evolutionary Biology at the University of Amsterdam's Faculty of Medicine. Now this title Professor of Applied Evolutionary Biology. This is certainly a magnificent title, Colin, what do you like most about the role?

I'm glad that you appreciate the grandeur of the title. It feels pretty ostentatious to me too. So this is my second big leap, because I was born and raised in Atlanta, Georgia, so I started off far from home when I was doing my PhD. But after I'd been in Cambridge for about 15 years I decided that it was really time for me to have a bit of a change, and the Brexit referendum was definitely part of that decision-making process.

Very understandable.

Yeah, right, but so I was looking for opportunities both inside the UK and outside the UK, but I was particularly attracted to a position in Amsterdam. So at the Options for the Control of Influenza Conference in 2016, which was held in Chicago that year, I was having dinner with Minnow De Jong and I think Minnow will be known to many of the listeners here, but if not, Minnow is a clinical virologist at the University of Amsterdam. And we sat down and we clashed with each other at dinner and Minnow asked me he says what are you thinking about Brexit? And I said well, you know, I'm thinking about a personal Brexit, if I'm totally honest. And he said ah, would you like to come to Amsterdam? I said, okay, well, why? And he said well, I have funds from the board of directors of our hospital to set up a new section in our department that's focused on computational approaches to studying pathogen evolution, and you seem like a great candidate to lead that initiative. And so I came to Amsterdam and I visited and I love Amsterdam, so it's not hard from a city perspective to want to come here. But then there was also a lot of great colleagues here, and so I was given this for me incredible freedom to set up a new section of a department, and so when I came it was me and a postdoc, and so there were just the two of us, but now we have a team of nearly 25 people, including four faculty members, and so it's been something where we have created, we have sort of fulfilled the vision for having this new section in the department in a relatively short period of time, and the thing that I love is all of the different things that I get to work on. So I still love flu. It's sort of my first love and I imagine it'll be the last one that I give up. But you know we started doing projects on other respiratory viruses and as well as being involved in work on HIV and hepatitis C, and so just that breadth of opportunity to be involved in so many different projects has been tremendously exciting.

That is some spectacular growth in building out the department and the team on it and at this stage you've published extensively on virus evolution and you've also been an advisor to the World Health Organization Influenza Vaccine Strain Selection Committee. Your expertise must have been very much in demand during the coronavirus pandemic. How did your work change in late 2019, early 2020 with the onset of the pandemic?

You know, it was an interesting time to be in the field that I think of myself as being in, and in large part that was because suddenly everyone in the world was an expert in that field and we had a million armchair epidemiologists and evolutionary biologists who all had the right answer. So in some ways it sort of took away my thunder, because I used to be this expert with in-demand knowledge and now I'm just one of the masses. But it was interesting, particularly at the beginning of the pandemic, because literally every scientist who had even worked on whatever, worked on anything even vaguely related to infectious diseases, was suddenly trying to do coronavirus research. Now, I'm not a coronavirus researcher per se and there were lots of experts out there who were well positioned to do this work, but because so many people were all doing the same things all at the same time, there was just a ton of redundant research that was happening all over the world and it was all important, but it was just a question of who was going to find the answer first, not whether anyone had a particularly innovative angle on it. I saw this thing on Twitter the other day, related to monkeypox, where someone jokingly wrote in and said well, looks like we've got about two good months to do really obvious research on monkeypox and get it published in Nature. And that's honestly what the start of the pandemic felt like, which was everyone did all the really obvious things and it all needed to be done. But for me, I've never liked just seeing if I can do something the fastest, because usually the work that gets done the fastest is not the work that gets done best or is the most interesting. And so what I looked for for myself was where is a niche that I can actually affect a difference? Because everyone was looking for a vaccine and everyone was trying to study the spread of the virus using genetics and genomics and that sort of thing. Thanks to a colleague of mine named Brooke Nichols, I got interested in how we could use mathematical models to inform pandemic response in low and middle income countries. Because while there was lots of emphasis on well, we have to get this kind of testing here and that kind of stuff going on there, in high income settings where resources weren't really a question, in low and middle income settings, severe resource constraints meant that in many places the pandemic well, there just wasn't anything to do about it other than let it spread, and so my own research activities focused there, because it seemed like the place where there was the most opportunity for a change.

Well, and a very practical application as well, and I suppose, as one of the perhaps you know armchair specialists you referred to during the pandemic, we've all gained more awareness of virus evolution or the idea of it, with Omicron and Delta and so on, you know making the headlines. So, just to sort of unpack this a bit for non-experts like me, please explain to us how a new variant is identified in the first place. Is this what we mean by diagnostics?

Well yes and no. So, in order to identify a new variant, the first thing you have to do is you have to be able to accurately diagnose whether someone is infected or not. And then the question is well, how do we go about identifying a new variant? And it's something that's it's awkward because, if we look at the emergence of the first variant of the COVID pandemic, which was the alpha variant that appeared in England in late 2020, we had an interesting situation there There where I think history will tell us oh yes, we sequenced the virus and we saw that it was a new variant and we were concerned. But that wasn't actually how that particular variant was identified. It was actually a change in the epidemiological situation, which was suddenly lots of people who were getting infected at a rate that hadn't been seen previously. And then we went back and we sequenced the virus in hindsight and like, oh yes, well, the virus itself seems to have changed, which is often the case. So we see an epidemiological pattern and then we use genetics or genomics to go and figure out oh, has the virus changed or not? But the key thing that underpins all of that is actually testing in the first place. So we knew that the epidemiological situation in southeastern England had changed because there were a lot of people getting tested, and so we could reliably say ah, more people seem to be getting infected. What's happening there?

great. So you observe this effect and then you understand from the effect there's probably a new variant at work or at large. So thanks for explaining that Now Another term I need your help with is surveillance. I automatically think about spies with binoculars, sort of hunched down in a car on the dark side of the street, but when it comes to tracking the spread and evolution of a virus, this means something different. So can you talk about these terms for our listeners sentinel surveillance, genomic surveillance and what these mean?

I think that over the last couple of years, we've all gotten incredibly accustomed to what it means to get tested for a virus. Between the swabs that we've been sticking down our own throats or the ones that have been applied to us by doctors, we now know what it means to test positive or not, and so one of the key things about surveillance is that surveillance is our ability to monitor something. So it's kind of like being on the car on the dark side of the street, except it's more like your doctor is doing it from their office and you're willingly participating in this process. So, basically, if we want to understand if a disease is spreading or not, or if a disease is changing or not, we need people to get tested, and we need them to get tested in a way that the results get reported. So if you test yourself at home, that's fine. You know that you're infected and you can then change your behavior to help minimize spread. But that doesn't give us any capacity to monitor the situation from a sort of policy level or a public health level, and so there we really need people to go to doctors to get tested so that those cases can be recorded, and then we know if the number of cases is increasing or changing or what.

Now in the COVID pandemic, particularly in high income settings, I think it was pretty easy to get tested basically anywhere you wanted to, anytime, and so in that way we basically just had population level surveillance. But you don't really need that much testing to understand what's happening. If you can put testing places in the right places. Um, so here you can use what's called sentinel surveillance, and it's the way that we monitored flu for decades before the covid pandemic, which is, if you go to your doctor to get tested and well, if you go to your doctor with a respiratory tract infection particularly in the uk, some doctors you just get tested and yeah, well, whatever you, you're infected, you're not infected, it's fine. But some doctors actually report their test results to the national uh, to the national registry of infectious diseases, and the key thing there is just whether or not a doctor has opted to participate in the government's national sentinel surveillance scheme, so your doctor is a sentinel physician at that point. So they're keeping an eye out for the rest of everyone. But what that means is that you have a handful of doctors that are actually doing surveillance for public health purposes, and at the end of the day, it doesn't really matter to you. You're going to get tested one way or another, but the key thing is whether or not that data gets reported to a higher level, such that you can actually monitor trends.

Now in many parts of the world, for flu for example, the way that this works is that positive samples are sent to the National Influenza Center, and at the National Influenza Center, if you have a positive swab, they might characterize the virus to see how it appears to our immune system, or they might sequence it. And if they sequence it, this is where genomic surveillance comes into play, and the role, with either characterizing the virus in terms of how it appears to our immune system or sequencing it genetically, is to figure out if the virus is changing. And if it is changing, how is it changing? And so there, genomic surveillance allows for a couple of things. So when genomic surveillance is accompanied by the sort of testing that allows us to see if the virus has changed in terms of how our immune system would see it. That allows us to infer well, exactly how the virus is changing and why, and whether we might need to update the vaccine For genomic surveillance all by itself, genomic surveillance allows us to monitor the prevalence of different variants. So if we already know how the genetic changes manifest themselves in terms of immune escape or increased transmissibility, then we can just sequence the virus and then monitor those changes in prevalence to see if a variant is increasing or decreasing. But we can also use that information for other kinds of investigations, like seeing how viruses spread through a community or around the world and ultimately try to determine where viruses or virus variants originated from.

ll so, with the information coming up from the ground via testing, how did your understanding of how to administer testing I mean other than the sort of population level testing or population level surveillance that you described what can you tell us about how often, how many, and where people are getting tested, where the information is coming from? What did you learn about those relationships?

You know, the pandemic was a fascinating time on a number of fronts, in large part because people were so unbelievably willing to be tested and they were willing to stand in long lines to get tested, and so there were all sorts of trends that emerged from all of this. So here in the Netherlands, where I live, for example, test positivity rates so this is how many people who think they have COVID and went and got tested, what proportion of them actually had COVID? And so early in the pandemic, when widespread testing became commonly available, test positivity rates fluctuated between about 10 and 20%. So somewhere between one in 10 and one in five people who thought they might have COVID actually had COVID. But by sort of February, March of this year, test positivity rates were near 70%. And so there's an interesting thing there, all by itself, which is does that mean that everyone just had COVID all of a sudden? Or, you know, does it mean that the situation is getting worse? I think the answer to this is really just a matter of self-testing. So people were testing themselves at home. They realized, oh, I think I'm infected, I'm going to go and double check, I'm going to make sure, and so then they would go and get tested at the national level and then we would say, oh okay, well, it looks like you have COVID, but so that increased the positivity rate of healthcare provided testing, when in fact the epidemiological situation was probably not changing very much, and so in that way, the sort of public health statistics were being strongly influenced by people's capacity to test themselves at home.

So in high-income countries, throughout a lot of the pandemic, testing rates have been very, very high and tests have been widely available. You could get tested by the public health authorities or you could go and get a self-test. This contrasts really starkly with the situation in most low and middle-income settings, where in high-income settings, like here in the Netherlands, for example, at our peak, we were testing roughly 1600 people per 100,000 people per day, which is crazy. That's testing 1.6% of the population every day, and that's not self-test, that's actually healthcare provided tests. So testing rates are high because I think for myself I took self-tests almost every day, and so then there are lots of people who are doing that. So the overall testing rate is probably like 2%, 3%, maybe even 5% of the population testing every day.

In low and middle income settings, there are plenty of places where the average testing rate is less than 10 per 100,000 people per day, and self-tests aren't available at all. So there we have 0.001% of population getting tested every day, and the important difference there is that when you're testing at very high levels, testing can actually have an important impact on reducing the spread of the disease, but also your capacity to monitor how many people are getting infected. And then, if you have sequencing capacity as well, your ability to monitor not only the prevalence of variants but the emergence of new variants. But when your testing rate is that low, the information that you get from it is negligible and your capacity to help reduce transmission is also negligible because you have so few people who are actually getting tested.

So this is a huge range that we see between, I guess, the sample sizes that are coming up and then in your work as a modeler, how do you model these different scenarios?

Yeah, so one of the things that one would ideally like to do is you look back and you have all the data on what happened previously and how things worked out. But of course, that requires that you have data in the first place and that tests were ultimately done so you could look at the impact of different testing rates. There's a really bad negative feedback loop in all of that that makes it difficult to do anything with empirical information in the first place, and so we constructed a mathematical model and this is one of the beauties of mathematical models, because you can get insights into how things are happening based on things that you definitely do understand. So we knew what the transmission rate of the virus was, we knew what people's likelihood of getting tested was, based on how close they live to healthcare facilities and things of that nature, and so we made a mathematical model that had a million people in it, and we knew everything about every single one of those people in that model. So we knew how old they were, we knew where they lived, we knew if they went to school or to church or to work, and if they went to work, did they work in a place where you could test them regularly, or did they work in a different place every day? It was something where we really tried to capture what life looked like for the average person in a low and middle income country with respect to the spread of disease. And so what we can then do there is we can say, okay, if SARS-CoV-2 starts spreading through and you only have 10 tests per 100,000 people available, what's the outcome of that going to look like? If suddenly you doubled the amount of tests that were available, so now we're at 20 a day, would that make a difference or not? Or how much testing do you need to actually start having a difference or to start reliably being able to monitor the situation, such that you could do something about it if you wanted to? And so we use that model to figure out those numbers. And the key thing there is for low and middle income settings. The countries themselves, by and large, want tests to be available. Funders like the Gates Foundation and the Rockefeller Foundation want to help make those tools available, and then large international organizations like WHO want to help guide policy in that domain to make sure that all of these things happen in a good and coherent way. But the question is what should the number be? Do we need to test as much as we're testing in high-income countries? I mean, do we need to get to 1,500 people per 100,000 people per day, or is it more like 100? Where does that balance lie? And if you do invest more, what can you expect? And so the other aspect of all of this is really one of expectation management. You can say, oh yes, I want to monitor the emergence of new variants, but we're only going to test 10 people a day. Well, you got to set your expectations real low. On the other side, you have situations, like in England, where you try to sequence every virus and there's a question there is like well, what kind of information gain can you expect from that? And in the context of monitoring the spread of variants or detecting the emergence of a new variant, once you're sequencing more than 5% to 10% of viruses, there's no information gain whatsoever. It's just a bunch of data for the sake of data. So you know there's the other side of that equation as well, but particularly with a lens towards low and middle income settings, it's really a question about setting reasonable targets and expectation management.

This is an incredibly practical application and guarding against the sort of law of diminishing returns, especially well in settings where resources are constrained. And here on ESWI Airborne, we discuss often the different aspects of pandemic preparedness with the shared understanding that, unfortunately, there will be more pandemics to come, and I suppose one of the aspects that your research really brings home to me is something that Peter Openshaw also emphasized. In containing a pandemic, protecting people, early warning systems followed by very rapid, usually political, decision-making these are the necessary elements for containment. Were there any research findings that really surprised you or that can be used and applied in other contexts, for example with influenza?

Yeah, this issue of pandemic preparedness is one that always comes up in the wake of these sorts of outbreaks or the pandemic that we are currently living through, and then unfortunately wanes in in the public's mind in in the years that follow. Uh, because, well, I mean the pandemic's over now. Right, so we can go back to worrying about other things, but, of course, the threat of an influenza pandemic has remained largely unchanged over the last decade. We still confront the very real possibility that an influenza pandemic could start at any moment in time. Now, the probability that it starts today is relatively low, and the same for tomorrow, but the probability that we will have another pandemic in the next decade or two is very high, and so, in that way, it is a matter of maintaining vigilance against these sorts of threats. Now, I know your question was about research findings, but I think that the main thing that sticks out in my mind is that we still haven't come up with a good way to keep people's attention on pandemics after the current episode whatever that episode is has reached a sort of post-crisis phase, and the things that we really need are, well, really policy changes and funding changes that allow for the continuity of programs that have been built up over the last couple of years to continue into the future. Because you talk about early warning systems, and the key thing is that early warning systems fundamentally require access to diagnostics, so you have to have the capacity to test someone in a way that discriminates ordinary from extraordinary at the point that that person is right there. So I mean, if this person is in a village in West Africa and the doctor can't tell if they have something new and unusual or something very common, well then you have a situation where that disease is ripe for spread, and so the big changes that we really need are cheap and easy to access diagnostics that allow for the discrimination of common diseases versus uncommon diseases, and it's something that we're now comfortable with terms like rapid tests, for example, and PCR tests and that sort of thing. But these sorts of technologies, we need them for more diseases and we need to be able to roll them out to more parts of the world, and that's really the change that's coming, and so I think the thing that really stands out in my mind in terms of developments over the last few years, particularly in the testing front, is that we now all see the value of rapid tests and PCR testing, and now we need to figure out how to get it into the hands of more people.

So within a reinforced public health infrastructure. I'm hearing from you that ongoing testing and surveillance should be maintained, even though we're through the most acute phase of the virus pandemic.

Yeah. So I think the difficult bit there is that we need to make sure that in high-income countries we don't dismantle everything, because that poses its own risks. We definitely don't need to test as much as we were testing at the height of the pandemic, but we need to keep testing some. And my worry now for high-income settings is that we are entering this post-crisis phase and everyone thinks everything is over, including the politicians who say, oh well, we can just dismantle all of the infrastructure that we've built up, but we need to keep some of it. So there it's about scaling down to a good minimum rather than dismantling everything. On the other side of the coin, in lots of low and middle income settings, testing never reached levels that would be good for monitoring the public health situation or for helping to mitigate the spread of the disease, and so there there's still plenty of places where we need to scale up in testing and access to diagnostics. But then the other thing is that, while the pandemic might feel like it's over for me, here in the Netherlands, I had a party for one of my postdocs who was leaving yesterday, and there were 40 people in a very small coffee room and it felt very, very strange, in the wake of the pandemic, where we work in a hospital. I don't think that there should be so many people in here, but it's not against the rules anymore, so it's okay, right. So I'm still not convinced for myself that the pandemic is really over, even though I don't you know, I don't wear a mask when I go out anymore, but yesterday made me feel like I probably should but there are many parts of the world where the pandemic really isn't over and that's really the key point, which is especially for places like in most of Africa the level of vaccine coverage is less than 10%, and so the pandemic is very much alive and well there, and so we still need action that addresses that fact. And so, because we have this duality, where a lot of countries think it's over and here in the Netherlands, yeah, we're pretty convinced that the pandemic is over, but unfortunately there's still lots of parts of the world where it's not and so maintaining that focus and maintaining COVID research and access to vaccines and diagnostics as a priority is something that we really have to confront in the years ahead.

And indeed it is polarised when we compare the disparity, you know, in capacity for action between different parts of the world. And is there anywhere where you can get a sense of the political appetite for an understanding of the need for ongoing pandemic preparedness?

So for example, here in the Netherlands, there is a constant dialogue amongst the government, public health authorities and scientists about the need for pandemic preparedness because of the way that the COVID pandemic impacted the Netherlands as a country and really as a society, and so there the issue is obvious, and there is a strong appetite for making plans for the future. In other parts of the world there is a desire to make plans, but a lack of capacity, and that capacity can be driven from a variety of sources. One is that you simply have bigger day-to-day problems. So in a country with high infectious disease burden that exists outside of COVID, covid is just part of the problem, not the problem itself, and so in that way, there are other problems that need to be tackled In terms of a national priority. There are other problems that are better tackled first than worrying about managing the next pandemic or not, and that goes really back to the basic question of you know how do we go about maintaining the public health infrastructure that we've built up over the last couple of years to make sure that we're better capable of dealing with health emergencies in general in the future. So just one brief aside, because it's an example that I really like and I think it's a good metaphor for the world is a very Dutch-specific situation that happened in the 1950s. So in the early 1950s here in the Netherlands there was a historic flood that I mean it devastated the country completely, and it was a sufficiently big problem that the government decided that they needed a nationally coordinated effort to make sure that this kind of flooding never happened again in the Netherlands. And so they started an initiative called the Delta Works, which was one of the largest constructions of dams and slouses and everything else in the world as far as I understand. But basically it was all geared towards the problem of water management, basically because flooding was going to be a problem, and even if it was only going to be a problem every 50 years, we shouldn't allow that kind of destruction to ever hit us again. And so the Delta Works project really transformed the relationship between the Dutch and the sea. But we need something like that for pandemics, because there will be another pandemic and the maintenance of the Delta Works is expensive, but it's a lot less expensive than dealing with a catastrophic flood. And in that same way, a Delta works for pandemics would be expensive, yes, but it would be less expensive than dealing with the consequences of another severe pandemic, and so, in that way, we need both national approaches but also international approaches to pandemic preparedness that are mindful of the fact that preparedness is expensive, but it's cheaper and a lot fewer people die than if you have a big, catastrophic pandemic.

For sure, Colin. Is this your biggest priority in this area of work over the next couple of years? I mean, what would you most like to see happen next?

Goodness, this is one of those situations where, as a scientist, there's so many things that I'm interested in doing over the next few years, because part of me wants to get over the fact that the pandemic ever happened and go back to doing all the cool stuff that I was really excited about before the pandemic started, because we had all these projects on flu that are still I mean, at least to my mind very important, and yet the whole research agenda for many people has been overwhelmed and sort of just flipped right upside down by the whole COVID pandemic. So in some respects, my biggest priority is getting back to flu and finishing all of those projects that were started before the pandemic, before the pandemic happened. And yet, at the same time, I think the way in which the pandemic has increased my own perspective on the plight of inequality in the world in terms of access to vaccines and access to diagnostics, makes this particular issue of inequality and inequity a really key priority for me, going forwards as well. And so I got to do some stuff on flu because it's, as I mentioned earlier, still my first love. But I think that a lot of the work that we've been doing with modeling and this is, in particular, work that we've been doing with the Foundation for Innovative New Diagnostics, which is FIND, and with WHO, and particularly my sort of co-conspirator in all this modeling work, Brooke Nichols. The things that we've been doing are really an important part of my research agenda going forward, because I feel like what we're doing is really helping to increase the visibility of these issues but also providing information-based paths forwards such that we can set targets in reasonable ways that also funders and countries and policymakers can get behind.

It's really incredibly worthwhile work and, despite your great love for the flu, I think we'll have to keep you on the front lines there for the potential it has to help many, many people and protect them. I wish you all the best with this work ahead. Colin, Dr Russell, thanks so much for being with us on ESWI Airborne today.

Thank you so much, Clare. My pleasure.

Folks keep on tuning in to ESWI Airborne, the viral podcast series, for all the latest on diagnostics, pandemics, vaccination, influenza visualizing, viruses and more. Get your news direct from the members of ESWI, the European Scientific Working Group on Influenza. And until next time, dear listeners, stay safe.

Aida Bakri: 36:07

ESWI Airborne is brought to you by ESWI, the European Scientific Working Group on Influenza and other acute respiratory viruses. These episodes would not be possible without the team's efforts and we would like to extend special thanks to our ESWI secretariat, our technical and IT teams, our arts team and our host, Clare Taylor. The podcasts are recorded virtually and we thank our guests for their participation in this inspiring series. Talks are adapted to a global audience and are intended to be educational. For any specific medical questions you may have, these should be addressed to your local general practitioner. Many thanks to our sponsoring partners and thank you for listening.

Using mathematical modelling to predict virus evolution and inform pandemic response

ESWI Airborne: Meet the Members

More info?

ESWI Airborne: Meet the Members