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So could you just start by saying your name and your title?

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So I'm Trudy Lange, and I'm the professor of global Health research at the University of Oxford.

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That's lovely. And going back to the very beginning. How did you first get interested in issues to do with health and global health?

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And can you give me the the sort of main milestones in your career so far?

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Oh, goodness, yes. Well, I did a degree in in biochemistry a long time ago,

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and then I got and went straight into the pharmaceutical industry, actually, and was working as a clinical trial monitor.

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And so for big pharmaceutical companies, SmithKline Beecham, as it was,

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and I then took some time out and I was working in the UK on antivirals and a clinical trial, which is being a clinical trial monitoring.

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So that was, that's where I really let my, my stripes really on how trials, how clinical trials operate.

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So the pharmaceutical industry, we're running big regulatory trials on sort of valacyclovir actually,

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and that was working on different antivirals and they were running the clinical trials across hospitals in the UK.

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So I'd go in and literally monitor the trials.

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So you just checking that they're recording everything correctly and the trials running according to the

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protocol and the data's all high quality so that they can submit it for their regulatory files and,

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but at that stage I'd gone straight from from school to polytechnic.

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As it was, I went to Kingston Poly, so from a straight into work and I hadn't had I haven't really been travelling or anything very much.

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So I'd got a volunteer post with a charity called Rally International,

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and I went to Africa and it just blew my mind and we were doing lots of conservation projects,

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but the bit that really got me was in the villages we're doing, and I was helping on a surgical expedition project.

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But that's where I first came across malaria and the impact of malaria and on communities.

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But was it the eyes? Is that trachoma is it was cataracts, trachoma, and there were all sorts of different eye complaints.

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And we it's this in a mobile surgical facility I was just helping on.

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It was incredible.

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But it was because we were in the villages, you saw the impact of all the everyday diseases poverty and malaria, total health issues, malnutrition,

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and it just totally blew my mind, having really not even the fear at that point,

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just completely that that was definitely my kind of watershed moment in my career.

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So I came back and running clinical trials and hospitals are doing it for me quite as much.

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But luckily it turned out that SmithKline Beecham had a tropical medicine department who knew?

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And so I just pitched up there sort of wide eyed and said, I want to work on malaria.

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And they it was just great timing because I was a product that they had that was they wanted to take through regulatory trials.

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And also there was a worm treatment. They had the children with worms and they needed somebody with the ability to run that regulatory standard,

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clinical trials, but in the context of Africa. So it was perfection for me.

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So then I spent the second half of my twenties still working this Long Beach and, and but working across Africa and Asia,

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actually trying to facilitate in the sort of international regulatory standards trials in these really challenging settings, which I just loved.

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And that's where I first got to work with Oxford actually, and, and,

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and working some of the overseas programs, the Oxford half in Kenya, Thailand and Vietnam and Yes, yes.

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And then I was going to ask about it because presumably in order to run those trials, you needed to have connections with local healthcare.

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Exactly. Exactly. So all of these trials were in local settings where they were all already doing research,

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and it was just getting them up to the standard that it was international standard research.

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And so training and the quality you need and things like how you use the laboratories and the and data and to that international standard.

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And so that was really great skills bringing that sort of skills that I learned in the UK environment.

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And and then I actually went in as a comment to the World Health Organisation for a year around this malaria product.

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And, and we were looking at how to implement it into the communities once it got approval.

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And during that time SmithKline Beecham merged and became GSK as it is now.

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And, and I, you know, I've, I've got a degree and you're not a clinician,

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which is something that's come through in my whole career and and and I had started doing a PhD part time, which was fantastic.

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SmithKline Beecham supported that and but there wasn't really anywhere to go.

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Now within the new organisation, they wanted me to move into oncology or different product or product management.

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And, and so Oxford persuaded me to leave industry and, and come over to.

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Academia, which I did happily. And and that's how I got to Oxford, is what it was.

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So that was 24 hours in my early thirties is when I had my two children come along at that point as well.

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So it's quite a nice juncture to move to what I thought might be a more flexible job unless I was.

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So yeah, I'm not sure that quite worked out as I thought it might, but it was, it was just excellent.

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And then I and I worked in Oxford. So while in the vaccine programmes and this is important for the clinical trial story because it was the reason I

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was useful in Oxford was because clinical trials have to work to something called good clinical practice standards.

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And so that's what you sort of. Absolutely.

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Then to the degree in industry, that's the whole, you know, goalpost you're only for is good clinical practice standard trials.

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That's how you get FDA approvals and things. And then what changed in 2004 was that a GCP became a legal requirement for every study sponsor.

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So academic trials all of a sudden had to apply to that as well.

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So the works but tropical medicine team here said, come on, come and join us because we really need you to help us with our studies.

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And at that point and then the what's now the DNA group were running phase one vaccine trials in the UK,

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but also vaccine trials overseas for malaria, for malaria and tuberculosis as well.

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So so I was coming in and helping look at how you could just make sure that the standard so, you know, absolutely at that level,

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which was brilliant and really quite challenging, especially some of the settings and then then from there and that the Oxford programme in Africa,

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in Kenya and the Can we welcome programme, that was at a point of beginning to do more HIV, TB,

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malaria vaccine trials and, and all of the local investigators too with welcome grants or gates funding.

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So it was the same story that everybody needed to get up to same standard.

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So I actually am swop from the vaccine group to the tropical medicine team and was placed

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out in Kilifi with my two small boys and and set up the clinical trial facility in Kilifi,

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which is, you know, one of the most fantastic opportunities that ever had and,

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and so spent two and half happy years in Kenya and setting up those systems there.

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So a lot of what you were doing was training at that point. Exactly. And that was the real switch.

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And that's where the Global Health Network came about. It was when I was based in Kenya.

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We were working across Africa actually with lots of organisations who were trying to work with

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really excellent research centres across Africa to try and really put those teams in place,

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because to run a good clinical trial you need and you need nurses, you need pharmacists,

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you need laboratory staff, you need research managers as well who could run grants and project knowledge.

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And so you need in that whole skill set across the whole team.

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And so that's what we put in place and kolisi And so then we were trying to replicate that in, in other centres.

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And I really look at that systematic approach. I talk about research systems in health systems quite a lot,

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and it was that's where I really began to shift from being a triallist myself to thinking about research methods

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and processes and how to implement really quite complex studies in vulnerable populations in difficult settings.

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And that's where the Global Health Network came about. So.

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Was that? Did that get funding from a particular source?

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And it took a while. Yes, it took a while.

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It took a long time, actually. And so the ideas began to come together in in when I was based in Kenya and working with

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lots of colleagues and we were working with and across different programs in Uganda,

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in Malawi and the Gambia, and we were all beginning to think this would be a good idea.

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And I came back to the UK and began the process of trying to get funding.

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Now the problem was, is that most health research funders are geared up for funding,

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you know, a vaccine trial or, you know, basic science or hypothesis driven study.

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And you go and say to them, look, you know, what we're wanting to do here is about research capacity building,

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training teams, putting the systems in place. So we set enabling and every funder was this is amazing.

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Of course we need it is desperately, you know, it's a huge gap, but we're just not set up to fund this sort of thing.

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So Oxfam were brilliant and did support me for a couple of years as I was going

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round with my begging bowl and pitch and and not really getting anywhere.

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And and then Gates and the Bill and Melinda Gates Foundation and really saw the value in it and quite flexible in how they can fund things.

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And so they fired me and we got our first award from the Bill Melinda Gates Foundation about 12 years ago now.

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And this fundraising has been the biggest challenge and remains the case still because

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it's the reason it works is because the things that make health research difficult.

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So if you are a hospital in Uganda or Nepal and you want to build your research infrastructure and capabilities,

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the barriers you have for doing that and won't vary whether you're working on malaria,

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HIV, TB, leishmaniasis, cancer, and that the same issues actually which are the skills you need to to scientifically design your study,

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you know, how do you set a question? How do you turn that question into a protocol?

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How do you work out what to measure?

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I always talk about measuring the the right.

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You ask the right question and then measure the right outcome. The answer to that question in the right place, on the right set of people,

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and and then turn those findings into something that could be taken into practice.

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So that whole ecosystem of how we set it,

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that they're really difficult skills that are not taught whether you're in a medical school or nursing college and it's a really particular skill set.

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And then on top of that, you also need the ability to win grants, manage budgets, hire people,

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do contracts, and also the laboratories or the and the pharmacists and all those pieces around it.

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And we also need things like social science. You need to do really good anthropology and social science to understand the context of the disease.

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And if people are going to trust you doing this research and you're not going to you know,

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you're working with them from the very beginning, right through the process to having it taken up.

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And you also need to do health economics and modelling. You know, we you know, we need that whole ecosystem of science to happen.

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And so to put all these pieces in place,

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what we've what we're doing with the Global Health Network and what we what we've learned

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is that it needs to almost be disease agnostic really is it's putting those skills in.

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And that's why it's called funders, because they totally get it,

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but they really are much more geared for where we want to fund a vaccine or, you know,

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to train a thousand nurses to do this and to the sort of more enabling and foundational piece everybody says is brilliant is exactly what we need.

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But it's not the turn to be grey.

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And utopias are trying to do with a member of the members of the network institutions such as hospitals are the individuals.

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So it's and it's, you know, now here we are 12 years later.

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And so the Global Health Network is definitely the community of people and organisations that use it.

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And so that's a really exciting piece.

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That is, it's all research,

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big research organisations who want to share their knowledge with the frontline health workers

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and research teams that have that that come to it to try and find that knowledge that they need.

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And so we have we work with organisations like the World Health Organisation, we're a W.H.O. collaborating centre, which is really nice.

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We work with and welcome and gates and organisations like Cepi, the Vaccine Group and some of the really big networks,

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they all have a knowledge hub on the global health network for their own work.

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And so it's like a kind of massive science park almost where each of these different organisations can have their

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own space on this platform where they can disseminate what they want to get across to research teams in Africa,

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Asia, Latin America. And then the other side of it is the frontline health care workers and research teams and have staff and pharmacists,

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community health workers come because they know that information is there and they can find it.

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So it's so that the global network is that community of research teams,

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health care workers, and also the research organisations that want to interact with them.

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And so it's we have a web platform, but that's just a vehicle, you know, it's very clever and thank you,

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the Gates Foundation for investing so much in it and it's really important, but it's really just a vehicle where groups can come.

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And then we're also really active in the region through many, many networks exist already across the Global South.

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We've got teams of people now embedded in those networks who can operate as research capacity building coordinators.

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So they'll actually do that, spreading knowledge from a centre of excellence of somewhere in a hospital.

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Maybe it's got really fantastic research expertise.

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They can go and run workshops at another hospital or connect up with the expertise might be HIV and they can go and talk to the group working on,

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I don't know, orthopaedic surgery and share their knowledge with how they do research.

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So there is the fantastic digital platform,

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but there's also a huge amount that happens face to face in hospitals and clinics to really try and impart that that knowledge.

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And you're the director of all this. Wow.

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What's really nice now is it's and we it it's really we describe it as a decentralised franchise so there's no leadership structure.

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There's this really nice it's a true network of collaborators.

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And so when grants come in as they might, we've got a really lovely new award from welcome around really expanding this in Latin America.

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And so that ground that the lead investigator is in Latin America and the accrues and we've had grants and come

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from the European Union for some of the work we do across Africa and we'll be a co applicant and support that.

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So it's quite an unusual sort of academic approach because I haven't really wanted to have it sort of sat and be led from Oxford ever,

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even from the beginning. It's always been this really open, collaborative community.

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And so, so the governance and the funding and the structures or the goals in that sort of concepts and, and is research part of the network's remit.

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I mean our papers coming out. Yeah, Yeah.

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And so the important contribution I think we're making from that perspective, which has been really exciting, is really understanding those gaps.

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So, you know, we know that those barriers are what stops groups leading their own research projects,

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but we only really need that anecdotally, and we didn't really know what makes things new, what the scale is or which particular problems.

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And so the Global Health Network community itself can be part of answering those questions.

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So we did a really big study, which we finished about a year ago, is just published as a W.H.O. report and where we said to the global community,

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okay, what makes research difficult for you as an individual in your institution and then in your country?

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And then from that we had 7000 responses, really comprehensive.

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And so we were really able to map, okay, these are the barriers for research at that individual institution on national level.

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And so from that, we've come up with a curriculum for health research.

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And so we know the component chunks that we need to tackle.

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So in a scientific thinking, research management, data science, community engagement,

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so we've got this really nice sort of chunked up curricula where we can then take solutions in there for all those component pieces.

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So and so that methodology of research pieces is really important.

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And the other really key side of that is that health research is changing really quickly.

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And, you know, we saw this with Ebola and see care in COVID, but it's it's around how,

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you know, when I began my career, we were all used to clinical trials.

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You'd have phase three, clinical work, phase one, face to face three.

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And now there's much more fluidity between this.

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So you'll have surveillance that can shift to running. A trial, can take through those phases almost seamlessly and then straight into implementation.

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Now that's fantastic.

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And it's been used really effectively in many therapeutic areas over the years, especially things like oncology or cardiology maybe.

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And what I am really determined to to pay whatever part we possibly can is that low income settings and diseases are left behind.

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In this evolution of methodology. And I think this really exciting things we can do with data science and

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technology and to to make the most of of those advances in how we analyse data.

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And so, you know, in Bangladesh there might be a big dataset on and I've been giving micronutrients to

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lots of children and not really understand whether it's working or if it's even safe.

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And so but they've been collecting the data, but there's not the expertise on even to begin to ask questions of those data.

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So can we can we look at how the Bangladesh team could collect that data in a in a way this you can then analyse it and then help formulate

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the questions and then ask the right questions and get the answers and then do something important with the evidence that you generate.

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And so even that process of understanding,

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even how you begin to work out what sort of data you've got and how you ask questions about that methodology,

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research is really key because if we if we follow how we do that with a Bangladesh dataset and micronutrients,

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then that same set of steps can be applied to a dataset on malaria and in Kenya or leishmaniasis in Nepal.

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00:19:55,290 --> 00:19:59,009
And it's because it always crosscutting, right? There's always the same barriers.

194
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And so that's we're constantly trying to do that and look at and tracking those pathways to to how you do research in the most challenging settings.

195
00:20:10,020 --> 00:20:15,270
And so I think if in my sort of absolute optimistic way,

196
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we could really look at that and and actually even be ahead of of some of these advances methodologies and use these really challenging settings.

197
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But there's an enormous amount of data, really important questions to be asked and really try and use the best available methodologies there

198
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are and and synthetic data analysis or and using real world data and asking quite novel questions.

199
00:20:39,690 --> 00:20:48,100
And that to me is a really exciting potential of the global community is to be part of that and

200
00:20:48,910 --> 00:20:55,530
a huge opportunity to not be left behind in the advances in health research that are here now.

201
00:20:55,690 --> 00:20:59,249
Mm hmm. That's a fantastic introduction to all of that.

202
00:20:59,250 --> 00:21:06,659
So let's get to COVID now. Can you remember how you first heard that there was a nasty respiratory outbreak

203
00:21:06,660 --> 00:21:10,980
going on in China and how soon you realised it was going to have a global impact?

204
00:21:11,220 --> 00:21:18,270
Yeah, I can remember. So there was the first indications through.

205
00:21:18,300 --> 00:21:25,830
So we work in these networks that came about through Ebola first and then we switched to Zika.

206
00:21:25,830 --> 00:21:30,570
And it's, you know, there's outbreaks all the time and we work in all of these networks that are looking at this.

207
00:21:31,350 --> 00:21:39,329
And there was the first discussions and emails coming through saying that something's happening in Wuhan and and, you know, is it real?

208
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What's what's happening? And then, of course, what follows really quickly behind that is in the press interest.

209
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And so you're suddenly, you know, it is beginning to scale up because suddenly you've got the BBC on the phone because, you know,

210
00:21:53,670 --> 00:21:55,010
you're really quite involved with them,

211
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with the press and Zika to focus and they sort of go back to those same groups and say, okay, this is really, really real.

212
00:22:03,810 --> 00:22:10,410
Yeah. And, you know, I think it was quite interesting being in some of these early discussions with the networks,

213
00:22:10,410 --> 00:22:22,050
as you saw, and with any disease, what we have ever known, especially in an outbreak, is an immediate set of unknowns.

214
00:22:23,280 --> 00:22:27,389
I think Zika is always a really interesting comparison because we knew much less around Zika

215
00:22:27,390 --> 00:22:33,150
than we did even around Ebola where So what you want to know in that immediate instance,

216
00:22:33,150 --> 00:22:37,790
and this is what we were all looking at with COVID immediately is and.

217
00:22:38,690 --> 00:22:44,390
What's what's causing the infection? What impact is that having on the patient?

218
00:22:44,690 --> 00:22:49,850
How can we stop the transmission? How how is it being transmitted?

219
00:22:49,910 --> 00:22:55,920
This always very immediate questions about who we understood most of those pieces, but we needed.

220
00:22:56,000 --> 00:22:58,250
But you never know if it's adapted.

221
00:22:58,310 --> 00:23:05,270
And if you're looking at something a bit different than you had before with previous Ebola outbreaks, with Zika, we knew hardly any of those.

222
00:23:05,630 --> 00:23:10,730
So that was a really interesting evolution that the science was had had it looking at.

223
00:23:10,970 --> 00:23:14,660
But how do we do that disease characterisation straightaway? How do we get these data out?

224
00:23:15,230 --> 00:23:23,930
And then as COVID was unfolding, obviously we knew a bit around that the class of virus, but we didn't know.

225
00:23:23,930 --> 00:23:28,760
Yeah, exactly. But what we didn't know is what this particular virus was doing.

226
00:23:29,210 --> 00:23:35,930
And, and obviously there's that information coming out of China was quite complex situation to be working in,

227
00:23:35,930 --> 00:23:40,730
but data was coming out and there was this increasing understanding.

228
00:23:41,120 --> 00:23:45,589
And then of course, we began to get the data from Italy, remember,

229
00:23:45,590 --> 00:23:52,940
and that was the next end to be the jigsaw began to come together on, you know, we're dealing with a respiratory infection.

230
00:23:52,940 --> 00:24:03,380
And it was always you was working with unknowns and it was just remarkable how it was

231
00:24:04,100 --> 00:24:09,200
mobilising those same sets of groups coming together to just write What do we know?

232
00:24:09,200 --> 00:24:10,070
What don't we know?

233
00:24:10,820 --> 00:24:21,470
And being in early meetings in London where and there was a recognition this is going to be intensive care beds and needing oxygen.

234
00:24:21,950 --> 00:24:22,459
But of course,

235
00:24:22,460 --> 00:24:34,340
my perspective on this was then talking to the global networks and and thinking about how to mobilise the research community straight away in

236
00:24:34,340 --> 00:24:44,959
those contexts so that everybody could respond and begin as soon as there were cases begin to to do to embed research in the emergency response,

237
00:24:44,960 --> 00:24:51,770
which is what and we were really keen to do after Ebola, is just make sure as soon as cases were happening,

238
00:24:52,070 --> 00:24:58,160
you were able to to act in a research environment with protocols, rolling consents in place,

239
00:24:58,160 --> 00:25:03,950
ethics approval, so you could capture the data and and use it to understand.

240
00:25:03,950 --> 00:25:07,909
And there's a really important definition around what's health research and what's an order.

241
00:25:07,910 --> 00:25:13,670
And that switch to being able to use the data as research.

242
00:25:13,670 --> 00:25:15,709
So you need a protocol, you need consent,

243
00:25:15,710 --> 00:25:24,890
you need to have all of those things in place so that you can associate your patient record data with prospectively,

244
00:25:25,250 --> 00:25:29,570
deliberately capturing data for the research purposes of research so that you could learn.

245
00:25:29,900 --> 00:25:35,840
And those data are shared, which is a really fundamental thing around learning across the globe as quickly as possible.

246
00:25:35,840 --> 00:25:45,169
So it was that very rapid connecting up across these networks that already exist following previous

247
00:25:45,170 --> 00:25:50,830
outbreaks and trying to mobilise everybody to be thinking about research from the get go.

248
00:25:50,910 --> 00:25:53,209
So fortuitous, but that's not quite fair really.

249
00:25:53,210 --> 00:26:01,400
It was probably providential the fact that you had the networks up and running and set up so that you could get this global perspective.

250
00:26:01,670 --> 00:26:09,170
And I think that's what I think from from my own perspective of how it how it

251
00:26:09,170 --> 00:26:15,890
began and then adjusted over time was it was it was very much a global issue.

252
00:26:16,190 --> 00:26:24,350
And at the beginning for everybody and and at the meetings, it was the same groups who had fought this expertise.

253
00:26:24,360 --> 00:26:33,600
And from our experience, and if you go back even further to the Ebola outbreak and you know,

254
00:26:33,980 --> 00:26:39,559
the people involved with that were all the groups of works in running research in challenging settings.

255
00:26:39,560 --> 00:26:46,490
So it was many of my colleagues from malaria or TB, HIV, they were aware because we knew how to run studies in these challenging settings.

256
00:26:46,490 --> 00:26:51,080
And so we, you know, we were galvanised to pivot and adapt and,

257
00:26:51,080 --> 00:27:04,010
and then here we were where again we'd been through Zika and here we are again at the beginning of COVID as it grew and became a huge issue in the UK.

258
00:27:04,010 --> 00:27:08,899
And, and for me personally, it was a really, it became really bizarre.

259
00:27:08,900 --> 00:27:14,420
I think that's the best word I can use because just like in Ebola and Zika,

260
00:27:14,420 --> 00:27:23,220
I spending an awful amount of time on Zoom, a huge amount of time and real crisis mode, really desperate work,

261
00:27:23,820 --> 00:27:27,260
talking to colleagues who were in awful situations,

262
00:27:27,260 --> 00:27:37,700
trying to set up studies and in really devastating circumstances to these families and communities that were impacted and working through with.

263
00:27:37,920 --> 00:27:42,950
Within big teams about how to design the best questions, how to gather data and all those practical pieces,

264
00:27:43,490 --> 00:27:50,330
and trying to just really make sure they had all the support they needed and all the information.

265
00:27:51,140 --> 00:27:58,490
And then I'd step back and I was still in Oxford and I picked my kids up from school and life is normal.

266
00:27:59,180 --> 00:28:08,690
The most bizarre thing for for me personally, for COVID was I was still having those conversations with my colleagues in Peru, Brazil or or Africa.

267
00:28:10,160 --> 00:28:14,090
And then I step back from my computer and my kids were at home,

268
00:28:14,090 --> 00:28:20,030
obviously doing their homework in lockdown, and it was absolutely catastrophic in the UK as well.

269
00:28:20,030 --> 00:28:25,550
And and and that was obviously an entirely different context.

270
00:28:26,960 --> 00:28:38,540
And some of the hardest bit, I think, is that sort of more it did shift from being global to being quite nationalistic, I think.

271
00:28:38,540 --> 00:28:43,040
And I think lots of the in terms of the the Western policy response.

272
00:28:43,070 --> 00:28:52,129
Yeah, Yeah. And I think the you know, the UK had a phenomenal response from the research perspective,

273
00:28:52,130 --> 00:28:58,920
obviously with the recovery trial and vaccine manufacture and an incredible process and.

274
00:29:00,560 --> 00:29:04,280
And I think the really huge positive through that.

275
00:29:04,280 --> 00:29:11,510
I think the the research system in the UK embedded with the NHS is is an envy to the world, quite rightly.

276
00:29:11,780 --> 00:29:16,309
There's really good research and managerial systems within hospitals.

277
00:29:16,310 --> 00:29:22,670
We could just switch straightaway off the recovery trial went and could be embedded across the NHS,

278
00:29:22,670 --> 00:29:27,470
which is something that's difficult to achieve in many places.

279
00:29:29,300 --> 00:29:38,120
However, I think it's when you look at where research happened and where the funding went,

280
00:29:38,900 --> 00:29:43,760
then we begin to it's not such a it's not such an attractive picture.

281
00:29:44,210 --> 00:29:55,790
And, and I really struggled with, with really trying that and keep a voice in creating this equity in where research was happening,

282
00:29:56,510 --> 00:29:59,930
who was leading the research, where the benefits of the research were going.

283
00:30:00,410 --> 00:30:05,990
So this is what the Global Health Network is all around equity in where research happens.

284
00:30:06,320 --> 00:30:11,870
And so that's, you know, the sort of underlying principles are.

285
00:30:11,870 --> 00:30:16,550
We still have 90% of the world's health research benefits, 10% of the population.

286
00:30:17,600 --> 00:30:21,049
And so even much of the research that led in the global is happens in the global

287
00:30:21,050 --> 00:30:24,680
South is still led from the global north and funds go to the global north.

288
00:30:25,100 --> 00:30:37,219
And there's this huge, huge disparity which is there and that's what the global network is entirely around tackling Covi comes along where it is.

289
00:30:37,220 --> 00:30:40,730
And if you. I did several pieces of work.

290
00:30:41,180 --> 00:30:51,649
Uh, our whole operational shift was to try and enable teams in the Global South to get

291
00:30:51,650 --> 00:30:56,209
research funding to put these protocols in place to do the disease characterisation work,

292
00:30:56,210 --> 00:31:03,050
to do it, engage and take part. And that was, you know, hours and hours on these Zoom calls.

293
00:31:03,620 --> 00:31:10,430
What I was also doing was trying to do some analysis on where the funding was going and what was happening.

294
00:31:10,640 --> 00:31:13,870
And many people did this and published on it. And you could, you know,

295
00:31:13,940 --> 00:31:19,730
there's lots of data out there and you can see that there was and at one point

296
00:31:20,150 --> 00:31:27,590
there was over 500 clinical trials funded asking the same question in hospitals.

297
00:31:28,310 --> 00:31:37,580
And I would say measuring and going back to the beginning of my career, working on antivirals, you know, back in the sort of late nineties, you know,

298
00:31:37,580 --> 00:31:48,320
the rule number one is when you develop an antiviral therapy, you want to try and hit that virus infection as early as possible when it's replicating.

299
00:31:49,100 --> 00:31:53,390
And so you try and, you know, day one three with infection.

300
00:31:53,870 --> 00:32:01,640
Excellent. Day seven, day 14, really, the horse has bolted a long gallop down the road if you're going to give antiviral treatment.

301
00:32:02,120 --> 00:32:13,580
And here we were with all these huge, huge clinical trial budgets, testing antiviral therapies on day 1014 of infection in hospital,

302
00:32:14,420 --> 00:32:22,280
which, you know, where were the community based trials, where was the primary health intervention, where were these happening globally?

303
00:32:22,550 --> 00:32:25,910
And and I was on funding committees.

304
00:32:25,910 --> 00:32:38,020
And what was really I found really quite distressing was that the funding was being awarded to the same criteria as was ever thus.

305
00:32:38,840 --> 00:32:51,620
And this is on track record and track record and track record really, you could argue rather than a, you know, an immediate need to get going.

306
00:32:51,620 --> 00:33:03,410
And there was and we've one of the activities that came out of COVID was to work with the UK government hosted and the,

307
00:33:03,610 --> 00:33:09,650
uh, which was the um, Boris Johnson hosted in Cornwall.

308
00:33:11,420 --> 00:33:17,800
My mind's gone blank on the. It was the big one of the G20.

309
00:33:18,440 --> 00:33:27,880
These are the governments hosting the G20 and the UK, quite rightly were, you know,

310
00:33:29,480 --> 00:33:34,220
just, you know, the vaccine success and the clinical trials really excellent.

311
00:33:34,730 --> 00:33:38,640
And and they wanted to really showcase around clinical trials.

312
00:33:40,190 --> 00:33:46,880
And so I was doing some work with the with Whitehall and the Cabinet Office to prepare for these G20 meetings.

313
00:33:47,420 --> 00:33:57,049
And so my, my input to this was, well, hang on a minute, actually, can we just look at this?

314
00:33:57,050 --> 00:34:06,740
Because if you draw a matrix and you back to what I said at the beginning around if you've got any disease, you need this whole ecosystem of research.

315
00:34:07,100 --> 00:34:10,220
You need a matrix that you can fill.

316
00:34:10,550 --> 00:34:14,810
You need to understand the impact of public health interventions.

317
00:34:15,050 --> 00:34:20,070
You need to understand what the community is thinking. You need to do modelling health economics.

318
00:34:20,090 --> 00:34:23,930
You need to do genomics. You know, we all understand around all these different types of research.

319
00:34:24,830 --> 00:34:32,120
And what would be great is if you had this big pot of money that you're going to give to research and then draw out a matrix.

320
00:34:32,120 --> 00:34:35,300
And when you have these funding applications that you're going to support,

321
00:34:35,690 --> 00:34:43,370
make sure they land across that whole matrix and not just in the bottom right hand corner, which they absolutely were.

322
00:34:43,810 --> 00:34:50,639
And I think it's we really, really needed better diagnostics really early on,

323
00:34:50,640 --> 00:34:53,600
and we needed affordable diagnostics that could be used across the globe.

324
00:34:54,020 --> 00:34:58,280
We really needed antivirals that could be used in a primary healthcare setting,

325
00:34:58,730 --> 00:35:06,610
and we really needed to understand the impact of public health interventions much, much better than we ever did.

326
00:35:06,620 --> 00:35:14,720
And that's true for the UK. But it's really true if you're in a slum in Nairobi and you it was lockdown and you couldn't earn

327
00:35:14,840 --> 00:35:21,950
money and the kids weren't at school and and we didn't really have any good data on on those,

328
00:35:23,060 --> 00:35:26,300
on the effectiveness of those. So, you know,

329
00:35:26,780 --> 00:35:33,259
and it's it's that whole spectrum that just really wasn't happening and and and there

330
00:35:33,260 --> 00:35:37,210
was this huge focus on clinical trials and I at this is a card carrying clinical trial.

331
00:35:37,250 --> 00:35:40,520
It is about my whole career from a clinical trial background.

332
00:35:40,520 --> 00:35:43,849
But what I think we is,

333
00:35:43,850 --> 00:35:48,500
is a bit of a shame to take forward that clinical trials are the most important thing

334
00:35:48,890 --> 00:35:53,510
in health research and they are really important that they don't happen in isolation.

335
00:35:53,930 --> 00:35:57,920
And you need surveillance data, you need the pathogen genomics, you need this whole piece.

336
00:35:58,340 --> 00:36:01,850
And the funding just was not going across that spectrum.

337
00:36:02,270 --> 00:36:06,290
And then you've got the global disparity, which was huge.

338
00:36:06,590 --> 00:36:11,209
And and it just if you if you there's lots of nice papers and graphs on this,

339
00:36:11,210 --> 00:36:19,100
but you can see how the funding immediately flowed to this, you know, the same old, same old, which is great.

340
00:36:19,100 --> 00:36:20,899
They got going and did some brilliant science.

341
00:36:20,900 --> 00:36:29,150
And that's, you know, we've we've got the vaccines and and we've got the big platform trials which which really helped us out for sure.

342
00:36:29,510 --> 00:36:38,719
But then we have this huge tail of information that we didn't get and and the how that followed so much later in the Global South,

343
00:36:38,720 --> 00:36:44,870
which is, you know, it's really quite a history.

344
00:36:44,870 --> 00:36:48,620
Oh, tell us what it is. You know, I think it was the right place to bring this up.

345
00:36:48,620 --> 00:36:52,120
But I had a quick look at the paper you wrote for a government select committee.

346
00:36:52,490 --> 00:36:58,280
Yeah. Where you talked about the the problem of competitiveness.

347
00:36:58,290 --> 00:37:02,600
Yeah. Competition. Yes. So do you think that's what's driving this?

348
00:37:02,640 --> 00:37:05,810
Yeah, it's private sector in the commercial sector and in universal.

349
00:37:05,820 --> 00:37:09,080
Well you've got people competing to be. Yes. Yes.

350
00:37:09,080 --> 00:37:13,399
I think, I think very much so. And I think let's talk about diagnostics,

351
00:37:13,400 --> 00:37:23,690
because I think that's a really interesting area to focus on that if you there was great collaboration in developing vaccines and drugs.

352
00:37:23,690 --> 00:37:27,259
And that's that's you know, W.H.O. showed great leadership.

353
00:37:27,260 --> 00:37:36,229
There was lots of push and public private partnerships across the industry, really great work that was just around sharing evidence,

354
00:37:36,230 --> 00:37:41,150
sharing data, using the platforms, organisations like Cepi that are driving that.

355
00:37:41,150 --> 00:37:44,810
And let's work together. We'll get there faster, really effective.

356
00:37:45,500 --> 00:37:47,389
Then let's look at diagnostics.

357
00:37:47,390 --> 00:37:57,020
And there was some really nice reports out that showed there was something like 25 different companies in the US completely in isolation,

358
00:37:57,500 --> 00:38:00,500
all trying to develop the same technology for some rapid tests.

359
00:38:01,340 --> 00:38:04,550
No incentive to work together. And,

360
00:38:04,720 --> 00:38:09,950
and we it's going to be there are some great advances but really nothing like

361
00:38:09,950 --> 00:38:14,030
the vaccine piece and and an awful lot were actually even purchased with.

362
00:38:14,420 --> 00:38:18,330
They even said that if they were if they were, you know, evidence led.

363
00:38:18,350 --> 00:38:28,790
Absolutely. And as you know, I think some of the huge benefits from, you know, societally from from COVID was the science education.

364
00:38:28,790 --> 00:38:34,940
And I just found the hilarious that my kids were happily talking about, you know, the R number and understood.

365
00:38:35,600 --> 00:38:39,110
And they get across to the BBC stats and go, Mom, isn't it all about the denominator?

366
00:38:39,170 --> 00:38:42,799
Yes, darling, it absolutely is. It's all about what they're not testing.

367
00:38:42,800 --> 00:38:49,160
Many are. They are so proud. And it was really it was just a great education with my children.

368
00:38:49,160 --> 00:39:01,280
I think it's amazing. And but I think that's, you know, if if we had to just the beginning test test test is absolutely right.

369
00:39:01,460 --> 00:39:05,630
Organisation. Yes, yes, yes. You know, absolutely right.

370
00:39:05,630 --> 00:39:10,970
But you need to get the test kits out and have that capability there, which just wasn't in place.

371
00:39:12,140 --> 00:39:19,250
So I think if you had that incentive to collaborate in diagnostics, we would be in a different place.

372
00:39:19,820 --> 00:39:30,740
So then that's academic funding. Well, you know, I, I really you can just see how the best science happens in collaborations and,

373
00:39:31,610 --> 00:39:38,090
you know, the the big EU Horizon grant systems are around working in these huge consortia.

374
00:39:38,300 --> 00:39:42,920
Zeta we the biggest funding came in from Horizon Project Zika.

375
00:39:43,430 --> 00:39:49,309
I was working in funding, yeah. And I was working with 56 partners, which yeah, there was lots of cuts to heard, no doubt.

376
00:39:49,310 --> 00:39:57,470
But the whole funding structure that you had to collaborate and have activities that we were working to to try and draw things from across them.

377
00:39:57,800 --> 00:40:09,410
And it was, you know, that's, that the system made that happen in a really good way and in academic support up to compete for funding for papers.

378
00:40:09,410 --> 00:40:12,260
You know I maybe it's because I came into academia late,

379
00:40:12,260 --> 00:40:18,319
but I've always found that slightly strange that you it's that's that's how you build your research

380
00:40:18,320 --> 00:40:23,840
career by being very pleasant but very sharp elbowed and you know and you have to sort of,

381
00:40:24,350 --> 00:40:31,090
you know, fight for your growth and funding and and so that makes sharing a bit of a difficult thing.

382
00:40:31,100 --> 00:40:39,290
And you've not been brought up to do that in your career. And so these systems, you know, these big lab towards are excellent for that.

383
00:40:39,530 --> 00:40:44,810
But the downside is still a lead applicant and they're still a lead institution,

384
00:40:45,500 --> 00:40:52,610
and especially for trying to get groups in less resourced areas, particularly in the global South, how can they compete?

385
00:40:52,610 --> 00:40:52,819
You know,

386
00:40:52,820 --> 00:41:02,300
how do they how do they get funding is is as quickly and equitably as somebody with that sort of track record from your Oxford and your Harvards,

387
00:41:02,600 --> 00:41:07,400
where they might have a huge need, massively important question,

388
00:41:07,880 --> 00:41:12,440
patient population as all those things I talked about what makes good research, right question Right.

389
00:41:12,470 --> 00:41:15,710
You know, all of that is they're very clever, smart people.

390
00:41:16,690 --> 00:41:18,380
Why do they need a track record?

391
00:41:18,650 --> 00:41:25,730
You know, we can bring in the collaborators to help them and why shouldn't they be the PI or why do we just not have a p I actually.

392
00:41:25,970 --> 00:41:30,530
And why don't we create these flat structures where nobody's the lead?

393
00:41:30,530 --> 00:41:35,480
Why do we need a lead? And why do you need a lead institution? Put the money across all of them.

394
00:41:35,750 --> 00:41:42,260
Set up this these governance system so everybody's accountable, but you create a sort of fair flat structure.

395
00:41:42,770 --> 00:41:45,409
And I think this you know, people talk about team science.

396
00:41:45,410 --> 00:41:52,340
You hear that, oh, we should work in team science, but we have to put the systems in from the funders to to really make that happen.

397
00:41:52,340 --> 00:41:56,209
And we're quite a long way from that, I think, still.

398
00:41:56,210 --> 00:41:59,570
Yes. So depressingly, your efforts despite working.

399
00:41:59,690 --> 00:42:02,899
Yeah whatever didn't steal my right on meetings.

400
00:42:02,900 --> 00:42:06,379
Yeah, yeah, yeah. And so did you feel like a lone voice or.

401
00:42:06,380 --> 00:42:14,330
Yeah. Okay. Yeah, I think yes, I think it's I think it's quite a.

402
00:42:17,530 --> 00:42:21,100
I think in in academia.

403
00:42:21,550 --> 00:42:28,270
And, you know, you need the universities need you to bring in the funds and they need the overheads and and to be.

404
00:42:29,860 --> 00:42:33,370
To be a successful academic it's often seems to be around.

405
00:42:34,450 --> 00:42:46,059
The more grants you when having a building, you know that whole traditional sort of successful academic is rewarded in that sense.

406
00:42:46,060 --> 00:42:52,959
It doesn't you know it's you've got to change it So and that that is the norm still, I think, isn't it?

407
00:42:52,960 --> 00:43:00,820
And I think the over time and, you know, maybe when we do really unpick what's happening with COVID,

408
00:43:00,830 --> 00:43:11,680
seeing if there's different ways to to award funds and have the reward fairly spread out, it needs to be saved papers as well.

409
00:43:11,680 --> 00:43:13,940
You know, having a lead author in the last also,

410
00:43:14,680 --> 00:43:18,610
because that doesn't help with it's really difficult when you've got these huge great collaborative papers.

411
00:43:18,910 --> 00:43:24,100
There have been many great examples. We have just listed everybody in the group, but that is still rare.

412
00:43:24,440 --> 00:43:27,580
And and that's that's the trouble that needs to be the norm.

413
00:43:27,910 --> 00:43:31,120
And now and everybody's equally recognised.

414
00:43:32,620 --> 00:43:40,750
So you, you published your paper on the methodology study across the network, as you said, just about a year ago.

415
00:43:40,990 --> 00:43:49,149
So were you still collecting responses while that was going on, or had you already got all the results by that time for that big paper?

416
00:43:49,150 --> 00:43:57,100
We had already got them, but we did do a big piece of work in the middle of COVID and with um Ukri,

417
00:43:57,100 --> 00:43:59,950
which is the collection of UK funders for global health.

418
00:44:00,520 --> 00:44:10,990
And so the World Health Organisation put their blueprint out for research in the June of the first year.

419
00:44:12,130 --> 00:44:15,940
So kind of six months into it, pretty much just saying this is what we need.

420
00:44:15,940 --> 00:44:20,080
Yes. And so they that and so their blueprint and, and they,

421
00:44:20,230 --> 00:44:27,880
they put out so we've done this obviously in an emergency situation and they would have liked to have had more engagement feed into that and no,

422
00:44:28,480 --> 00:44:33,250
no, no. A little bit through sort of wider groups but not particularly.

423
00:44:33,250 --> 00:44:37,810
And that was one of the things they talked about was that it was quite a rapid and.

424
00:44:39,430 --> 00:44:47,050
Development of this blueprint and the could out quite quickly. So it was, you know, not much input from the Global South and and quite rapidly.

425
00:44:47,080 --> 00:44:51,040
Of course it was an urgent situation and and it it wasn't difficult either.

426
00:44:51,040 --> 00:44:56,890
Of course, we needed vaccines, we needed drugs, we needed diagnostics, and it was probably all fair enough.

427
00:44:58,240 --> 00:45:06,430
But the UK, our friends said to us, Look, could we ask the Global Health Network community if the blueprint was relevant for them in their setting?

428
00:45:06,670 --> 00:45:11,840
And so this was then six months on from that. And so it was, you know,

429
00:45:11,860 --> 00:45:22,090
six months further into the pandemic and really asking really going for that opinion from the frontline researchers across the global South.

430
00:45:22,570 --> 00:45:28,600
Is this true for you now, and is this relevant to what your research priorities would be?

431
00:45:29,080 --> 00:45:34,720
And we had a four and a half thousand responses and we didn't run it really ran it for about a week.

432
00:45:34,730 --> 00:45:39,430
And and it was you know, we were getting saturation of the responses.

433
00:45:40,000 --> 00:45:49,450
And it was really interesting because and whilst of course, there was agreement that we needed drugs and vaccines and and diagnostics,

434
00:45:49,900 --> 00:45:56,680
what came out really clearly was this requirement for evidence around public health interventions.

435
00:45:57,070 --> 00:46:04,360
So be that lockdowns or wearing masks or travel restrictions, all of these public health measures,

436
00:46:04,750 --> 00:46:12,100
which are your first and your first line of defence in an outbreak and what you you know,

437
00:46:12,400 --> 00:46:19,629
if we look back again to Ebola and what we needed to do was educate people about hygiene practices,

438
00:46:19,630 --> 00:46:23,710
looking after the deceased and how communities behaved.

439
00:46:24,070 --> 00:46:30,550
Public health, when you don't know what's going on with you don't have a vaccine, you don't have any drugs, you don't have any diagnostics.

440
00:46:30,970 --> 00:46:34,180
Public health interventions is a first thing that you can roll out.

441
00:46:34,450 --> 00:46:40,540
And so absolutely, we have evidence for it and all, specially when it's going to really impact your community.

442
00:46:40,840 --> 00:46:50,290
Not in the blueprint. It was not really it not it was not anything like as strongly as it came out in in our response.

443
00:46:50,290 --> 00:46:53,079
So it is, you know, it wasn't massively different,

444
00:46:53,080 --> 00:47:01,209
but I think significantly in that voice that came through and and and also that it was about then doing

445
00:47:01,210 --> 00:47:07,090
research so that you absolutely need to do research on those sorts of interventions in that context.

446
00:47:07,480 --> 00:47:14,410
And so the ability to do research, to bring funding and all of those things that cycle around that, and it came through very clearly.

447
00:47:22,160 --> 00:47:28,830
Yes. I mean, are the obstacles to sharing data and experience among countries different than.

448
00:47:30,470 --> 00:47:39,300
No, I and I think. Well. So when you talk actually what I was thinking of was the tape you did about Lusophone countries.

449
00:47:39,870 --> 00:47:45,839
Portuguese. Yeah. Yeah. So I think I think there was well,

450
00:47:45,840 --> 00:47:50,430
there was absolutely a will to share everything as soon as possible and to get

451
00:47:50,430 --> 00:47:55,589
access to information and to and to really make sure everything was mobilised.

452
00:47:55,590 --> 00:47:59,129
So people talk a lot about data sharing and that's really key.

453
00:47:59,130 --> 00:48:04,110
And of course probably the best illustration of that, that is genomic data sharing.

454
00:48:04,110 --> 00:48:10,169
So as soon as people are sequenced, a virus is found, a new variant of concern.

455
00:48:10,170 --> 00:48:15,720
They shouted about it and we all remember what happened in South Africa and then it got called the Cape Town variation or whatever,

456
00:48:15,750 --> 00:48:20,910
you know, and then you get slammed for having been really good science who said and shared their findings.

457
00:48:21,640 --> 00:48:32,700
And, and, and data sharing is the sort of thorny end of that and is discussed in endless meetings and lots of investment and

458
00:48:33,120 --> 00:48:38,909
and and lots of problems still around where to put data repositories having data in a state that you can share it,

459
00:48:38,910 --> 00:48:40,740
the willingness to share permissions and all of that.

460
00:48:40,950 --> 00:48:48,030
Such is such a complicated area, but it's not just sharing the data, it's about sharing methods in the how to.

461
00:48:48,690 --> 00:48:52,230
And even so, it's access to knowledge, really.

462
00:48:52,500 --> 00:49:01,080
And so if you are in Brazil and you and you want to take part in research and you've got an immediate barrier,

463
00:49:01,440 --> 00:49:04,980
especially as as you go through this sort of community,

464
00:49:05,400 --> 00:49:11,910
our own language and some of our fantastic colleagues in Brazil were doing some amazing work in the favelas

465
00:49:12,720 --> 00:49:20,010
around vaccine hesitancy and the doing the vaccine trials and and doing disease characterisation studies.

466
00:49:20,010 --> 00:49:24,720
And, you know, Brazil had a terrible time, as we all remember,

467
00:49:25,500 --> 00:49:33,329
but trying to get the resources translated so that the nurses right out of favelas could use the disease,

468
00:49:33,330 --> 00:49:37,860
characterisation, protocols, the community engagement materials that some groups had developed elsewhere.

469
00:49:38,580 --> 00:49:43,710
Something as simple as translations is such an enabling factor that seems again back to fundraising.

470
00:49:44,430 --> 00:49:50,850
It's not very sexy to go to welcome and say, I need to, you know, we need $100,000 to translate all this materials.

471
00:49:51,360 --> 00:49:56,070
That's not, you know, anything like a sexy saying I need $100,000 to sequencing genome,

472
00:49:56,400 --> 00:50:05,490
but actually really transformational in terms of enabling those amazing community health care workers right on the front line to get data we all need.

473
00:50:05,790 --> 00:50:15,070
And what was going on in that context. Yeah.

474
00:50:16,030 --> 00:50:27,570
I think we've had quite a lot of this. Yes, I think I'm sort of into lessons learned now.

475
00:50:29,160 --> 00:50:33,950
Yeah. I mean, has. Your experience?

476
00:50:33,950 --> 00:50:40,910
I mean, it was it was a I suppose, in a way a kind of brilliant test of of the network and showed up.

477
00:50:41,150 --> 00:50:45,440
It showed up the kind of guy that you that you then helped to identify.

478
00:50:47,090 --> 00:50:51,050
How optimistic are you that that's going to make a difference going forward?

479
00:50:51,440 --> 00:50:57,770
Yeah, I think there's lots of things that worked really well as we were trying to mobilise researchers to work together,

480
00:50:58,490 --> 00:51:03,990
and there's lots of things that we did work really well. We will we are carrying forward even now and, and,

481
00:51:05,330 --> 00:51:14,380
and right now what we're busy doing is trying to work with teams to get their labs back on to being able to diagnose malaria again.

482
00:51:14,390 --> 00:51:17,750
They were, they were shifted on to COVID and now they need to come back.

483
00:51:17,760 --> 00:51:26,030
And so some of the big systems that we we really put in place with these abilities to design studies together, this is a really exciting area.

484
00:51:26,030 --> 00:51:34,669
And we're we're implementing this this right now. So we set up this process where an open protocol decided crowd protocol.

485
00:51:34,670 --> 00:51:40,969
So and groups came together and formed working groups around those different areas.

486
00:51:40,970 --> 00:51:48,230
So public health vaccine hesitancy, disease characterisation there for working groups was different sections.

487
00:51:48,680 --> 00:51:52,520
And then we we set up the system where they could design a protocol together.

488
00:51:52,520 --> 00:51:56,950
So nobody owned the protocol. I mean, that's ultimate team science and it was nobody's protocol.

489
00:51:57,770 --> 00:52:04,670
And they'd so they'd sort of bounce around the ideas for what questions needed and then what outcomes you'd measure,

490
00:52:04,730 --> 00:52:07,010
how you'd measure it, what a consent form would look like.

491
00:52:07,580 --> 00:52:13,549
And so we're taking that forward now because we really want to try and enable nurses, midwives,

492
00:52:13,550 --> 00:52:19,430
community health workers to be researchers, whatever they want to research, whatever is the disease in front of them.

493
00:52:19,460 --> 00:52:27,050
And our hypothesis is that many nurses and community health workers are doing some brilliant things already,

494
00:52:27,380 --> 00:52:30,980
but they haven't got they don't know if it works or not, but they'd like the evidence to know.

495
00:52:31,340 --> 00:52:35,389
So maybe they're working in mental health or that they're just working in the communities,

496
00:52:35,390 --> 00:52:42,290
but they're managing mental health issues and they're probably doing things like setting up little community groups, momentum teams or whatever.

497
00:52:42,290 --> 00:52:45,440
It might be counselling, informal counselling sessions.

498
00:52:46,430 --> 00:52:48,649
It would be brilliant to know if that works, wouldn't it?

499
00:52:48,650 --> 00:52:56,330
And so if if we set up a system where nurses could come together and say, Oh yeah, actually I'm doing this in my group a lot, we're doing this.

500
00:52:56,630 --> 00:52:59,870
Okay, well let's design something together that can test that.

501
00:52:59,870 --> 00:53:04,490
And so we've, we, we've coming out with this challenge for 1000 nurse led study challenge.

502
00:53:04,970 --> 00:53:08,360
And so we want a thousand nurses across the globe to run their own studies.

503
00:53:08,750 --> 00:53:14,810
And it could be this is one study that certain topics they develop and do in how many different countries.

504
00:53:15,200 --> 00:53:23,009
But we don't think it'll cost very much and we think we can just use that same system that we we developed through COVAX,

505
00:53:23,010 --> 00:53:28,270
the protocols for answering the questions that they could adapt to anything they want to.

506
00:53:28,790 --> 00:53:36,079
And so that crowd, one of the hardest things that we we know exists is they don't have access to experts.

507
00:53:36,080 --> 00:53:40,850
So normally when you develop a protocol, if you're sat in Oxford, developing a protocol, you talk to your colleagues,

508
00:53:40,850 --> 00:53:48,829
you have a meeting and you you'll get sort of and you take it to your institutional scientific board and you'll get it thrashed around.

509
00:53:48,830 --> 00:53:52,490
You'll get beaten up and told that's really you can't measure that or this won't work.

510
00:53:52,700 --> 00:53:54,440
And that's how you develop your protocol.

511
00:53:55,100 --> 00:54:01,490
If you're working in a place where there's no research experience, you just don't have access to that scientific refining of your idea.

512
00:54:01,910 --> 00:54:06,830
So why not make that in the community and say, this is what we're which we definitely did in COVID?

513
00:54:07,160 --> 00:54:09,139
And so we really wanted to take that forward.

514
00:54:09,140 --> 00:54:15,320
So we hoping we have a thousand nurses lead their own studies and using the tools we developed around the COVID.

515
00:54:15,470 --> 00:54:24,050
That sounds really inspiring. So let me turn now to a bit more about how you were impacted personally.

516
00:54:24,060 --> 00:54:29,210
And I'm asking everybody this about how to what extent did you feel threatened by the virus itself,

517
00:54:29,600 --> 00:54:35,030
which, you know, I'm in this record, I've never had a positive test, so that's one for me personally.

518
00:54:35,030 --> 00:54:43,310
But if I was going back to March, yes, at the beginning, did you feel about how did I feel and.

519
00:54:46,240 --> 00:54:57,400
I think as we learnt more quite early around it being dangerous for people with underlying health conditions and with respiratory background,

520
00:54:57,420 --> 00:55:09,070
I didn't feel personally worried for me or the family and, and having worked as soon as you were thinking in numbers.

521
00:55:09,310 --> 00:55:17,530
As a scientist, if that sort of, you know, COVID, you got 70% mortality and this is we're looking at, you know, we didn't really know.

522
00:55:18,370 --> 00:55:24,910
I mean, Ebola, you've got 70% mortality and it's horrific and it's young people.

523
00:55:25,270 --> 00:55:33,309
And we also did a lot of work on Disease X in W.H.O., which is our theoretical respiratory virus.

524
00:55:33,310 --> 00:55:39,460
This is what we were all working on before COVID came along, which I think there was a mortality of 60%.

525
00:55:39,700 --> 00:55:45,909
And it was young people, you know, So so straightaway in my mind, I was like, well, this isn't disease X, you know, it's the vulnerable, the elderly.

526
00:55:45,910 --> 00:55:51,110
So I was worried for my parents and my in-laws and and but I really want to end.

527
00:55:51,270 --> 00:55:58,720
And one of my personal things in the I live in a really small village and that and as we learned around how the disease was passed on,

528
00:55:59,200 --> 00:56:03,970
I really remember having a conversation with our vicar because my in-laws went to church every Sunday.

529
00:56:04,030 --> 00:56:11,709
I said, Please stop taking communion. You know, I really think passing that wine between the whole, you know,

530
00:56:11,710 --> 00:56:16,630
this is you have 30 elderly people in front of you every Sunday morning and this is a really bad idea.

531
00:56:17,110 --> 00:56:23,100
And we've all come back from skiing in Italy, by the way. Yes. And this is you know, so it was that was where it really personal.

532
00:56:23,110 --> 00:56:32,800
I was I was really I knew that that was a massive risk, not for me, but for my in-laws and the elderly in the village.

533
00:56:33,760 --> 00:56:42,550
And the vicar didn't want to know. So I was quite cross about that. But and then a week later, ten days later, he was came down from diocese.

534
00:56:44,230 --> 00:56:50,560
So I was quietly smug about that. But yeah, I didn't, I didn't feel personally threatened.

535
00:56:52,120 --> 00:57:00,909
And, and then it was but I was very worried about elderly relatives and trying to keep them safe.

536
00:57:00,910 --> 00:57:07,000
But and, and then it was a sort of practical, you know, trying to get the kids to test all the time.

537
00:57:07,720 --> 00:57:13,090
I was quite. Quite rigorous in making sure.

538
00:57:13,140 --> 00:57:19,100
And you've indicated you were flat out busy. Yeah, well, you were you were working from home by then.

539
00:57:19,110 --> 00:57:23,370
Yeah, absolutely. Yeah, they closed down. I mean, most of your work is there space isn't it.

540
00:57:23,640 --> 00:57:28,470
Yes, exactly. So all of the research we did, all the research report was all out to the regions.

541
00:57:28,740 --> 00:57:34,350
And so we used to travel a lot, obviously, but obviously also still do a lot on on Zoom calls.

542
00:57:34,350 --> 00:57:37,409
So it just moved to my desk.

543
00:57:37,410 --> 00:57:40,649
You were already very experienced at Zoom and calls. Yeah, all the Internet.

544
00:57:40,650 --> 00:57:51,120
Yeah. It was just a different level of intensity because it was, you know, I was I used to be in the office with my colleagues and so you would,

545
00:57:51,510 --> 00:57:55,530
you would have a few Zoom calls a week rather than now for like all of us.

546
00:57:55,530 --> 00:58:03,420
It was I was I absolutely could not do the family zoom calls in the evenings because I was fried.

547
00:58:03,720 --> 00:58:11,520
And I'm sure lots of people had a serious kind of eight in the morning. Yeah, well, and or early mornings or late evening and the meantime times.

548
00:58:11,610 --> 00:58:18,809
Yes. And also colleagues who were running studies in hospital were in hospitals, and then they wanted to speak in the evening or the mornings.

549
00:58:18,810 --> 00:58:28,049
And and I had many really heart rendering conversations and they'd had just the most awful time on the wards.

550
00:58:28,050 --> 00:58:33,930
And then they'd come back and they've since and still in all their care, you know, and, and they'd offload around their day.

551
00:58:33,930 --> 00:58:41,910
And it was just the strangest thing not to, you know, because often, you know, when I've worked on other diseases, you've been there,

552
00:58:42,060 --> 00:58:48,930
you know, I went to Brazil, I went to the the valleys, the up to Fortaleza, where the saw the Zika babies and the mums.

553
00:58:48,930 --> 00:58:52,169
And we walked through the wards and work to do the studies.

554
00:58:52,170 --> 00:59:02,880
And you know, you're, you're part of it and just to be feel so impotent and a distance and then, and then turn from my screen to, you know, my,

555
00:59:03,480 --> 00:59:07,980
I had two boys and they were going through GCSEs, A-levels, so I was,

556
00:59:08,700 --> 00:59:14,640
I was also doing A-level philosophy, English and law and all my and I'm rubbish at cricket apparently.

557
00:59:15,450 --> 00:59:19,740
So yeah. It was that was really, it was really, really intense.

558
00:59:20,400 --> 00:59:25,490
And I didn't want anybody to talk to me about Saturday Lives,

559
00:59:25,590 --> 00:59:32,610
which my friends who were not working seem to be occupying them and I really wasn't interested.

560
00:59:33,240 --> 00:59:36,899
Was there anything you were able to do to support your wellbeing?

561
00:59:36,900 --> 00:59:43,860
Have you talked about being in the village? Could do that, Yeah. Oh yeah. I've got my Labrador bramble and she was my running partner.

562
00:59:43,860 --> 00:59:51,299
So when we could, you know, I've and we've got a nice garden and so, you know, lucky as compared to our colleagues and it was a Yeah.

563
00:59:51,300 --> 00:59:57,570
So out for a run with a dog and throw a cricket ball for my son in the garden and it was a joy their having them, you know,

564
00:59:57,630 --> 01:00:04,709
I kept teasing them, you know, teenagers in the house who's supposed to be offered their mates say, you know, they were forced to go down.

565
01:00:04,710 --> 01:00:10,860
And so you know, there was upsides to it. But and, and the other side was I was I was really keen to look after.

566
01:00:11,130 --> 01:00:18,030
So in Oxford and quite a few students and Young's young team members and we had

567
01:00:18,030 --> 01:00:21,590
those periods where we were allowed to meet in groups of four or six white male.

568
01:00:21,630 --> 01:00:28,920
We could, we could meet outside. And and so we actually adopted one of the cafes in Oxford because they had a nice garden.

569
01:00:29,400 --> 01:00:34,710
And, and what I was really aware of on the Zoom calls, I mean, I have a nice house in the garden,

570
01:00:34,740 --> 01:00:38,400
I could sit and they were knees sitting on the end of their bed, you know, these rules.

571
01:00:38,850 --> 01:00:45,030
So we really set up the system if we would do walking meetings or I just used the

572
01:00:45,030 --> 01:00:50,549
cafe just and then we sat tab up in this cafe and used to just go there and,

573
01:00:50,550 --> 01:00:54,240
and, and meet, you know, students meet up together. We couldn't get out.

574
01:00:54,240 --> 01:01:02,010
And and so that was, you know, that was a really quite a significant worry for me was the sort of youngsters in my team.

575
01:01:02,190 --> 01:01:05,280
And it was really tough for them being by themselves.

576
01:01:05,310 --> 01:01:10,500
Mm hmm. Yeah. When you said students, I really I have a question I meant to ask you, which was about teaching.

577
01:01:10,500 --> 01:01:16,450
Are you involved in teaching as well? So I'm, I'm involved in Green Templeton College as an advisor to the students.

578
01:01:16,450 --> 01:01:22,770
So that was another federal graduate student. Yeah, but they, I mean, for those guys, you know, we had students arrive from,

579
01:01:23,250 --> 01:01:29,430
from China or India and they arrived and they had to do two weeks quarantine in their rooms.

580
01:01:29,700 --> 01:01:34,080
I mean, awful for them. And then on Zoom calls with their teachers.

581
01:01:34,530 --> 01:01:36,750
And I just felt you just couldn't do anything for them.

582
01:01:37,050 --> 01:01:41,670
You know, had this they had a food hamper if they were laughing, shame, You're like, what is this?

583
01:01:41,910 --> 01:01:45,870
It's like they're baked beans. What do I do with this?

584
01:01:46,200 --> 01:01:51,479
Oh, well, delicious. You know, it was just crazy, crazy times.

585
01:01:51,480 --> 01:01:54,719
It was poor souls, you know, had you know, it was their families.

586
01:01:54,720 --> 01:01:58,050
Our expectation they came from a small village in China and they'd got to Oxford

587
01:01:59,040 --> 01:02:02,190
to be just in a room with a tin of baked beans and a kettle and a cup of tea.

588
01:02:02,340 --> 01:02:05,489
It is awful. And, and we had to in reverse.

589
01:02:05,490 --> 01:02:09,920
I had a wonderful woman called Nicole, who worked for us, was from Argentina, and she was stuck here.

590
01:02:10,390 --> 01:02:16,540
And she was in this tiny bedsit in Italy or somewhere, and the landlord was awful.

591
01:02:16,540 --> 01:02:20,570
It was a terrible place. And I and I managed to get I used to take her in.

592
01:02:20,590 --> 01:02:25,840
I was really worried about her. And they had had quite a few students that was there and stuck there.

593
01:02:26,290 --> 01:02:28,089
So at least we got her there.

594
01:02:28,090 --> 01:02:34,270
And then they had this lovely community and they all looked after each other because, you know, people were stuck everywhere.

595
01:02:34,710 --> 01:02:37,300
So it was really, really important to look after them.

596
01:02:38,600 --> 01:02:44,830
And do you think the fact that you were working on something that was important, globally important,

597
01:02:45,340 --> 01:02:51,640
helped to give you a sense of purpose and in that sense, support your own wellbeing and.

598
01:02:54,230 --> 01:03:01,000
Yeah. It's interesting, isn't it? Because I think what to me was obviously the scale was huge.

599
01:03:03,640 --> 01:03:13,750
But I've spent my career working on devastating diseases, and so it was just really bizarre to have it here.

600
01:03:14,980 --> 01:03:24,700
And so it was it was extra busy and it was intense because I had the kids at home,

601
01:03:26,050 --> 01:03:32,500
but it didn't feel more catastrophic somehow than some of the disease areas I've worked in before.

602
01:03:32,500 --> 01:03:41,319
But it was just here. And obviously, when we were living in Kenya and I was it wasn't too different from that either.

603
01:03:41,320 --> 01:03:46,299
I was, you know, spending the day in villages where we were doing the vaccine trials,

604
01:03:46,300 --> 01:03:50,230
but we were bringing back kids every day from hospitals who've had these appalling burns or,

605
01:03:50,800 --> 01:03:55,780
you know, mothers dying in childbirth and just the daily grind of diseases of poverty.

606
01:03:56,800 --> 01:04:02,650
I maybe found that when I was coming home to my very healthy boys bouncing around, I maybe found that more.

607
01:04:04,850 --> 01:04:09,880
Too much. Yeah, maybe. And it was. And I always knew I could go.

608
01:04:11,090 --> 01:04:14,240
And I saw it living it in the UK. It was a very.

609
01:04:14,930 --> 01:04:17,990
It was like. It was. It was. It was all turned on its head.

610
01:04:18,920 --> 01:04:23,890
But I felt, I think, seeing in the UK how it is absolutely off.

611
01:04:23,900 --> 01:04:28,010
Of course it wasn't that and this you know the the old people's homes and the

612
01:04:28,010 --> 01:04:32,420
mistakes that are made and the difficulties on some of the scale of activities were,

613
01:04:32,900 --> 01:04:44,120
I mean, awful. But then I was on record colleagues in Peru and Delhi and and Brazil, and it was off the scale awful.

614
01:04:44,120 --> 01:04:47,749
So it was really humbling talking to them.

615
01:04:47,750 --> 01:04:53,059
And I found it quite hard to. You know, my friends are moaning.

616
01:04:53,060 --> 01:04:55,310
They come back for their 50th birthday. Oh, have you?

617
01:04:56,150 --> 01:05:03,790
You know, I would find that difficult coming off these calls to just sort of, you know, rant industry.

618
01:05:03,800 --> 01:05:06,980
That was I was awful, you know? Yeah, yeah, yeah, yeah.

619
01:05:06,980 --> 01:05:11,190
No, I, I can imagine. So I think I think we've more or less got to the end of it.

620
01:05:11,210 --> 01:05:16,460
Yes. Yes. That was good. So finally, has anything,

621
01:05:17,630 --> 01:05:27,740
had the have you changed anything about your attitude or your or the way you think about yourself and your future as a result of the experience?

622
01:05:27,980 --> 01:05:32,450
Of course I realise that. I mean, it's very interesting that, as you say, you've been working in.

623
01:05:32,900 --> 01:05:38,180
Yeah, the health and, and really serious outbreaks.

624
01:05:38,190 --> 01:05:48,349
Yeah. Long time. So yeah. And I would, so I, I do all the time if I'm giving talks to, to governments or,

625
01:05:48,350 --> 01:05:59,210
and academics wherever it is I group outbreaks and I talk about COVID Zika virus, you know, so I do group them.

626
01:06:00,380 --> 01:06:06,080
And what I really hope is that you know, I've got a slide as well that I used to take when I was doing the talk.

627
01:06:06,080 --> 01:06:10,940
If you go into W.H.O. website, there's a great it's just for Africa.

628
01:06:10,940 --> 01:06:16,340
There's a there's a tracker about how many outbreaks are there's usually about 136.

629
01:06:17,420 --> 01:06:19,909
And so it's just that's what they're dealing with every day.

630
01:06:19,910 --> 01:06:27,889
And what I think is an opportunity and a threat is that we and there's often these phrase about pandemic preparedness.

631
01:06:27,890 --> 01:06:38,209
Yes. Which does my head and and because to be prepared for the next pandemic, if we did a really good job at tackling everyday disease,

632
01:06:38,210 --> 01:06:44,390
poverty and put that research structure in where teens in low income countries

633
01:06:44,390 --> 01:06:48,020
are able to to really understand the diseases that threaten them every day,

634
01:06:48,500 --> 01:06:53,300
then they'll have the skills in place to spot whatever comes up.

635
01:06:53,450 --> 01:06:54,709
And this is what's missing.

636
01:06:54,710 --> 01:07:05,600
And so trying to talk about outbreaks in isolation is is the threat, I think, and is a really, really worries me because there are still diseases.

637
01:07:05,600 --> 01:07:16,940
You know, malaria has been around for a millennia and ten millennia, and we need the same things to tackle that a disease pops up.

638
01:07:16,940 --> 01:07:20,120
We need to do it all at once, sure. But it's exactly the same skills.

639
01:07:20,760 --> 01:07:28,520
And so if you take a rural town in Sierra Leone and we equip those nurses and this health co workers

640
01:07:28,520 --> 01:07:32,720
to think is research is a measure what they do and understand what's going on in their diseases,

641
01:07:33,110 --> 01:07:37,249
then they'll if something odd pops up, they'll go, oh, okay.

642
01:07:37,250 --> 01:07:42,530
And they'll measure it and they'll report it. And then we as a well, we'll find out and they'll be able to stop it.

643
01:07:43,130 --> 01:07:49,040
But if and that's how we'll all be protected in the future from other outbreaks, because we don't know where the next one is going to pop up.

644
01:07:49,490 --> 01:07:57,950
And so I think that is hopefully a really exciting opportunity to think about it in that more global context.

645
01:07:57,980 --> 01:08:02,450
I worry about the nationalistic side. We would just, oh, clinical trials would be brilliant.

646
01:08:02,560 --> 01:08:05,990
Said we should just build on that. Well, hang on a minute.

647
01:08:06,190 --> 01:08:15,440
That's taking it out of context of the real world. And so how it's affected how we're working is really focusing on that ecosystem of

648
01:08:15,440 --> 01:08:20,540
research and trying to train a thousand nurses to do studies and become researchers.

649
01:08:20,540 --> 01:08:28,309
And I, I think that's the best way to prevent the next pandemic is actually tackling all diseases.

650
01:08:28,310 --> 01:08:33,320
And then you can respond, but who knows whether that's going to trickle through or not.

651
01:08:34,310 --> 01:08:37,730
That's brilliant. Thank you very much. It's brilliant.

652
01:08:37,730 --> 01:08:37,970
Thank.