So can you just start by saying your name and your title and where you are?
Okay. Yeah. My name's Lisa White and I'm a professor of modelling and epidemiology at the Big Data Institute,
the Nuffield Department of Medicine at Oxford University.
Lovely. Thanks very much. And without telling me your entire life history,
can you just go back to how you first got interested in health generally and what brought you to take me through?
What brought you to where you are now? Sure, yeah.
So my first love as a discipline was mathematics.
So my first degree was in mathematics, and I became interested in applied mathematics and particularly mathematical biology.
And that's what led me towards health, really, because I found.
There was something really special about the pleasure that comes from solving a mathematical problem,
but also the motivation that comes from the possibility that the solution is going to have an impact on people's lives.
And so that's how I found the two came together.
Where were you working and where are you studying? I did my first degree at Warwick University in Applied Mathematics,
and then I did I actually trying to be a teacher and did my pgce and then became a high school
teacher for a year in Birmingham and then did my master's and Ph.D. back at Warwick after that,
then the Biological Sciences Department. And since then I've been based in Lisbon and in Kenya, in Thailand, and then finally back here in the UK.
And when did you arrive back here? Actually, it was a few months before the pandemic hit, so it was 2019.
All right. Very interesting timing, but we're going to get to the pandemic a bit later.
But first, I want to talk a little bit about more about how you combine maths and and public health
and about your interest in in global health and how that modelling can could work on that.
So what what does modelling bring to or a computational approach bring to understanding public health?
Well, for me, in my experience, I would say it's it's a tool to help people make decisions.
So the way we try to think about it is, is it's a bit like a time machine.
So it allows you to consider potential futures and what decision you would make if those potential futures came to pass.
And that's what's different from mathematical modelling compared to statistical modelling,
which is more of a forecasting approach where we're trying to really look into the near future to say,
how many cases are we going to have if if things keep going the way they're going?
For example, for a disease where whereas mathematical modelling would be more along the lines of what would you do if there was a new variant?
We can't predict when the new variant would arrive, but what would you do if that happened?
What decision would you make? What intervention would you do? What would the impact be?
How much would it cost? And so it allows what we call a scenario analysis of a further look into a more uncertain future.
So you are including economics in that. So this is very much something that is going to be valuable in terms of future planning of health services.
Indeed, I think if we want models to be really useful for policymaking, we can't just predict impact.
We also have to predict.
Um, so, yes, I was saying that if, if we, if we as scientists really want to have an impact on policymaking and really support policy making,
then there are two sides of the coin to making an intervention.
One is the the possible impact that it might have.
But the other is the cost implications.
And that's the reality of of of our life, that if you you don't have unlimited resources.
So putting resources into one response will mean that there won't be those resources for other things.
And so the economic element of decision making is is very important, in my view, to incorporate.
So give me an example of a project that you worked on before you came here, before you fell into a job that turned out to be all about conflict.
Well, so I'm I'm a bit of a.
How can I say I have a lot of interests in terms of my research agenda.
So I will go through periods where I focus only on the the the mathematics of a problem and
become very interested in method development and the way that models are produced and analysed.
And so that pulls me sometimes into that world, and then other times I become more focussed on practical and pragmatic questions.
And that happened the most when I was living in Thailand.
So I lived in Thailand for 12 years and pretty much as soon as I arrived, the chairman of the Research Unit,
I was based the, which was the head on Oxford Research Unit and Nick White no relation.
And he, he walked into the newly created modelling office and said oh we've got might have a
problem with the new antimalarial drugs can you explore ways to eliminate resistance and,
and so that basically put me on a path,
a ten year path of research into dealing with methods for dealing with the emergence
and spread of anti-malarial drug resistance and other types of resistance,
which is it's a very fascinating field because, again, it's a balance,
but it's a different kind of balance compared to the sort of impact cost balances.
It's about the the the pressure, the selective pressure that you apply to these parasites.
And so these these parasites, your top priority for these parasites is to is to heal people.
And by doing that, to heal people, yes. So for us to deal with the parasite.
For the parasite. Yes. Sorry. Yes.
So by doing that, by applying the drugs where they're needed, you are also putting selective pressure on the parasites too, reinforcing what?
Actually forcing the parasites to evolve ways of getting around that way of dealing with them.
Exactly. But you can't stop treating people.
So then the question is,
how do you keep the drug alive for as long as possible and useful keep its
useful life for as long as possible under the condition that you must use it.
You can't withhold it from people who require treatment.
And so then you look at a combination of different combinations of drugs and how they work together and,
you know, how they can help each other to to delay resistance for longer.
So, for example, so you have a one in ten chance each year of resistance emerging for a particular drug,
if it's being used you to have one in ten chance of another drug, the resistance emerging from that one if that one was used on its own.
So by that logic, roughly in ten years time, if you use one drug on its own, you'll you'll have resistance.
Resistance will have arrived. Now, if you put the two drugs together, the probability of someone developing an infection,
developing resistance to both at the same time is a product of those two for probability.
So now it's one in 100 instead of one in ten.
So by using the two drugs together in combination, you don't just double the utility of the usable time, you multiply it, so you get 100 years.
So it's those concepts that can be deployed in more complex way and in mathematical modelling.
And this is very critical with malaria because I mean, artemisinin compounds are it really, aren't they?
It's for us treating the most concerned and in combination with others.
I mean, there's always other drugs in the pipeline. But malaria is an interesting disease in so many ways in terms of what you have available to you.
Yes, you have the drugs, but you also have bednets and other forms of prophylaxis and you have vector control and you have the vaccine as well.
And various different vaccine candidates are also in the pipeline.
And so the question becomes then, how do you combine none?
No, no. One of those is going to eliminate malaria.
But what if you use them in concert? And then you have again,
this is where modelling can really come into play because you can you can use models to optimise the combinations to form
integrated strategies and you can optimise those with different constraints like how much resources are there available,
what's the time span. So you can either have a time constraint and have unlimited resources and say, what will it cost?
Or you can have a fixed cost and say what you can you do with the money that you have.
And how do you save the most lives and how do you delay your resistance through as well as possible?
So malaria is you know,
people spend their whole lives just working on that one disease for that reason
because there is so many different elements of it that still need to be understood.
And it's still a massive challenge. No, I was going to ask you what the cause you talked about the opportunities,
modelling offers and I was going to ask you what some of the challenges were.
But I think you've you've described that very well in that in that one example.
And so. It's you've got you've got economics, you've got maths, you've got clinical science,
you've got infectious disease, viral evolution or other kinds of pathogen evolution going on.
How do you and you're boiling all that down to maths.
So you must need to build big collaborations with people who are collecting all that kind of data.
Yeah, yeah. It's it's a real collaborative, interdisciplinary life, being a modeller.
And so, yes, one element of my sort of networking side of my life is these collaborations with partners.
And the most the most satisfying and productive collaborations are the ones where both partners are still engaged in asking the questions.
So the model development and the the field research and the lab research happen in partnership.
And so as hypotheses form,
they can be elucidated using a mathematical structure which allows us to analyse those hypotheses and explore them leads to more questions.
Questions go back to the field in the lab, get more data, understand more, create a new model and continue the process.
And at some point, some of that gets adopted into practice, presumably.
Is that something you've seen in your in your research career,
being able to see effective implementation of some of the things that have emerged from the models?
Yes. And that that started with my malaria work.
Until then, I was more of an academic exercise.
And then, as I guess my track record improved and there was some you know,
there was some sort of weight behind the the evidence that I was providing then the the policy decisions and the changes started to happen.
And that was very exciting, but also quite unnerving initially.
Yeah. Mm hmm. So I think I just need to put a bit of perspective on the global burden of an infectious disease.
And leaving aside COVID, I think until COVID came along,
I think a lot of people in Western industrialised countries probably didn't realise the extent to which infectious disease is a global problem.
Can you put some kind of figure on it? Not off the top of my head.
I mean, there are different rankings. I had actually a.
An intern who was from Cote d'Ivoire who well, heritages Cote d'Ivoire.
She came over from London and she wanted to explore Cote d'Ivoire.
And so she showed me the rankings.
And in terms of cause of death and non-communicable diseases in in her area were were the highest.
But then next on the list was infectious disease as a cause of death.
So, of course, it depends where what setting you were exactly.
Yes. Yes. And as you mentioned yourself, you know, it varies over time.
Yeah. Mm hmm.
But that these I mean, for example, an awful lot of childhood illness in developing countries simply because of lack of sanitation, clean water.
And so. Yeah, I think that yeah.
I mean, I think a lot of the diseases it's.
It's surprising and an unpleasant thing to learn that there are a lot of preventable and curable diseases that prevail purely because of the finances,
aren't there? Yes, it is to address it. And, you know, we don't suffer from that so much in this country, but that's definitely a feature.
And that's that's why I think that there's another passion that I have, which is to train models in in other countries.
So we have centres of excellence in mostly well-resourced countries and there are a few that are emerging now in low and middle income settings.
And having people who can provide an evidence base and support efficient health policy decisions is
even more important in places where resources are limited than than it is in well-resourced countries.
And that's why I think that, you know, rather than doing the modelling myself training,
training people to to do this in their own countries would probably have an even bigger impact.
And that's something you've been engaged in yourself? Absolutely. Yes. Yes, yes.
Is there a I don't know. Is there a network that that helps to support that activity?
Or is this something you've been doing kind of as an individual initiative?
It's it's it started off for me as an individual mission.
And and then things really changed with the pandemic, actually.
And I was beginning to challenge and we'll talk about the pandemic in the world
circled back to talking about how that's had an impact on your work globally.
Yeah. So how did you first become aware that there was a pandemic in the offing?
Do you remember? You know what you were doing? I've asked everybody this question.
I would have just moved to Oxford from my 12 year time in Bangkok, which must have been a bit of a culture shock to start with.
Well, yeah. I mean, so sort of.
So circling back to what I was saying about training people and and nurturing talent in country, after ten years and in Thailand,
I worked with some really brilliant Thai mathematicians and modellers and and I led a group there.
And when I left that group, the,
the Thai deputy head of group took over as the head of group and is now that group is almost entirely Thai
nationals who are working on policy relevant modelling work for Thailand and I still collaborate with them a lot.
So I just left, I left my beloved group behind in Thailand and, and I was looking forward to Oxford life and,
you know, going to places and, and, you know, in the English language and all that sort of thing.
And, and, and then we started to hear these reports.
And so that would have been in late 2019 when we were hearing reports from from China of of this new this new virus.
And I remember thinking.
If they're finding it there, it's probably everywhere already.
That's that was my suspicion, because, I mean,
we sort of teach this to our students that the difference between a reported case and the underlying number of
cases that you would need to have and how long the epidemic could be going before you see that first case.
And so, yeah, that was quite a sobering and scary thought.
And yet, sadly, then reports started coming in from different countries.
And then it was here. And I remember saying to my husband, I'm not going to get involved.
I got I've got plenty of work to do. And in the UK, you know, it'd be different if I was still in Thailand, but in the UK, you know,
we have not just lots of modellers but lots of modelling groups and they, you know, I've just been doing malaria.
It's not, you know, I did a bit different thing.
It's a parasite, it's a virus. It's not. Yes, yeah.
It's people who have the flu models,
the best people to pivot or people who are working on the source pandemic previously, the best people to pivot across.
And that's who did. And so that was that was my original intention.
And I just couldn't resist writing my own model because there were news reports and
decisions that were affecting me personally and predictions that were being made.
And I felt really.
So I just wanted to check. Well, I would have predicted with my model.
So. So what were the key elements that you put into your models?
What were the kind of red flags that popped up that made you think, I need to investigate that?
So there are a few key things. That's the absolutely the most important thing to include in a COVID model is age structure.
Because of this, it's during a relationship between age and likelihood of severity of disease.
So with that age structure, I would argue the COVID model is not fit for purpose.
So that's the first step to create an age structured model.
The next step for me was I actually included loss of immunity and reinfection quite early on.
It wasn't found for months and I suspect that as well.
But that's because like me working on malaria. I see.
And that doesn't give immunity for very long. No, but you don't see reinfections for malaria if you don't look for them because they're asymptomatic.
Similarly for COVID. So. Just because we hadn't seen it.
I had a very strong feeling that it was coming.
So if you look at earlier versions of the models that my I and my team produced,
they all had this arrow back from R to S so being recovered to se being susceptible.
And unfortunately, we were right about that as well.
Um, I was hoping we wouldn't be because as soon as you have that flow back and you allow for reinfection,
then zero-covid is impossible, not impossible.
That would be. But it's extremely unlikely and would require a huge investment of resources to to achieve.
So you said you made your model essentially for your own satisfaction, but presumably published it having having done it.
Well, what happened was, um, I, I then contacted a few of my colleagues and said, you know, what have you been doing?
I made a model. Did you make a model?
And so my, my, some of my colleagues who have since joined my group that they've been writing models as well.
So one person, Ricardo Alquist, had had produced a very similar model with very similar ideas behind it.
As I said, we merged and he said, Yeah, I'm in.
And, um, and then I spoke with a colleague, Nathaniel, who put in New York and his specialism is looking at, um, resource management in hospitals.
And he'd written a model for, um, the hospital demand.
So we added to our structured transmission model hospital demand.
Um, and initially we, we used the output of our model as the input to his model.
So you were basically saying if this many people get COVID badly, this number of hospital beds is what we're going to need.
Yes. And then eventually, um, so we went through sort of several big sort of phase shifts in the model development sort of version 19 at the moment.
But that one was a big shift where instead of linking to, you know,
sort of using two separate models and taking out of one and adding and putting into the other, we we merged them together.
And then we had a model that looked at the epidemiology but also at the hospital demand and the mortality.
Um, and then we had one that was suitable for Western settings where, um,
so the first one was more focussed on, on low and middle income countries was, it was general ocean.
So to make it suitable for low and middle income settings, um, sadly,
you have to have a model for what happens when the hospital capacity is breached and the different death rates you get as a result.
And that's a very important element of the model that you have to include.
So this started to happen because I have colleagues in Thailand,
I have colleagues I have students from all over the world, former students, current students, collaborators.
Um, so the next step was essentially we said, look, we made a model so useful to you.
Um, and quite a few people said yes.
And so we created the International COVID Modelling Consortium, um, como.
And so my colleague Rima Shrestha was involved in that.
She's, she's an economics and policy and financing expert.
And so now we had a consortium with, um, my students involved.
I know remote Akhmatova from Kyrgyzstan, for example, and Caroline Franco from Brazil.
And we basically made this model.
Available to any anyone who would like to use it.
We created an interface so that they could run it without needing to use the code.
And we also shared the code. So if they wanted to take the code directly and modify, then they did.
So the Thai group did that and so did the Brazil group and other countries use the interface.
And yeah, that was the COMO Consortium was initiated and that basically became my whole life then for about 18 months.
And is it still running? It is, but it's very different now to how it was while the pandemic was raging.
So we've had groups come and go. I would say over the whole time we've probably had 40 to 50 different country teams and the idea of
a consortium was to empower national modellers to do their own modelling for their own country.
So there was a lot of training that we did on in real time as people needed it.
We we actually had also some stress management sessions.
We all needed that and especially some people who were, you know,
sort of watching while there was some catastrophic epidemiological, uh, fallout from this disease in their countries.
And you sort of feel powerless while this is happening.
Um, so we, you know, we tried to provide also, you know, not just an environment for, um,
academic collaboration, but also for support internationally sharing ideas, training.
Um, yeah, it was, it was an interesting time.
And did the consortium members have a hotline to their individual governments and all the health services?
Yes, they did. So we had a mixture of people who would join some people who had the modelling skills and background,
but they didn't have the policy links and we would help them to create those policy links and um, and support that process for them.
And on the other hand, we had people who had all of the policy,
they were already in an institution or linked to an institution, they didn't have the modelling background.
So that's where we used the interfaces.
And I mean this is why the model changed so many times because we would get requests coming in,
our partners would say, well, you know, we need this, we need this thing in the model.
Have you considered this thing? Have you considered you know, our country wants to explore this intervention, but it's not a new model.
So we have to, you know, include it. So so the model could do that as well as could look at things like mask wearing and yes.
Isolation and all the all the non-pharmaceutical interventions.
Yes. And we did them one by one as we discovered them because it was such a vibrant community.
We'd have a weekly meeting. And in that meeting people would bring things up and we'd we brainstormed together to try and understand it.
And then there would be a sort of burst of activity as we updated the model to include
all of these interventions that were either being used or were on the table to be used.
Um, you know, as soon as we could and we had a technical team who, who, who did this in, you know, very, very rapid, um, timelines.
I remember, you know, a couple of times where I just stayed up all night because I thought there was a bug in the code.
And when we were doing one of the updates, I thought I found a bug and we were already supporting with that model.
Um. Many countries decision making and one day of having a model that may be giving the wrong kinds of results was one day too many.
So that was a few times of an all nighter to try and find the bug.
And we also sort of made it into a game and I said I'd send her a voucher,
£10 voucher to anyone who found a bug in the code and the whole consortium after that to try and sort of hunt the bugs down, you know?
Yeah. Mm hmm. And have you been able to?
Is it. Have we got enough distance from it now to be able to evaluate how effective
the input from your your model was to the policies of the various countries?
I mean, I feel like we're in that phase now. The problem is that everyone's very tired.
Yes. So the people that would would and should evaluate it often.
You know, in a lot of ways we've had enough of it for a while. But having said that, there have been some activities where we've taken stock.
And so, for example, that that's the thing with scenario analysis.
If if one of the scenarios that we simulated or that one of our partners simulated came to pass, um, if, if the,
the decision makers went down that road or close to that road, then we can,
we can assess how accurate the model was in making that prediction under those assumptions.
And so we've done one or two of those exercises, and it's encouraging that we weren't that far away from from the reality.
Um, but, you know, the, the thing that we all have to accept,
and this was the main sort of support that we gave each other with that you've just got to do your best with the information you have at the time.
And so and that was flawed, presumably things like number of cases, number of deaths, even.
You're not necessarily getting the right numbers, are you? No, you're not.
And so the data are uncertain.
The future is uncertain. People's behaviour is uncertain.
So many things.
Um, and so the question I always ask myself is, is this going to help or, or, you know, is it, is it going to potentially damage something?
And if I think it's going to help, I'll go ahead.
Um, so the main thing is to, in my view, is to be very, very clear with the people who are using the models, uh, the model results,
the decision makers, what all the uncertainties are, because the temptation in such an extreme environment is to, is to hunt for certainty.
And so model output is very compelling because it looks like the real data.
And so to, to communicate that kind of uncertainty, um, is I think really the responsibility of the modeller.
And that's what we, we did, we did a lot of work in that, in that area,
both training ourselves and our partners and also engaging in that kind of discussion with the policymakers as well.
So that you mentioned that there are lots and lots of models in this country, in the US and places like that.
And to what extent did your work have an impact on what was going on in the UK?
Um, it's increasing. So actually interestingly Spi-m is led by my former supervisor.
All right. So this is a this is the modelling subgroup of sage.
Yes. Yes, exactly. So I called Graham.
A few times, will Graham Medley medley a few times through throughout this phase.
And the first time I called, I said, Well, can I help? I've made a model.
But it seems like you've got enough models. And he agreed.
And I agreed that it was, you know, at the time it was the best thing.
The best use of this particular model was to support partners like our partners in Afghanistan,
where they didn't have any models or didn't have a model. So that's where I thought the the focus should be for this particular work.
Having said that, we're the Oxford community is an interesting one where,
you know, you the the vaccine one of the main vaccines was being developed here.
A lot of the treatment with the treatments were being tested.
The studies. Yes, the principal trial and the recovery time.
Absolutely.
So we have collaborated with both the Oxford Vaccine Group and and the Recovery Trial Group to explore the potential impact of vaccine with,
um, with the vaccine group,
obviously, and, um, and the potential impact of dexamethasone, which was one of the main options for treatment at the time with the recovery group.
And but we actually used a different model for both of those.
Um, because, uh, the, the, the questions were more around.
Um, so for the vaccine one, we were interested in how the roll out could be done as efficiently as possible.
So while you were rolling out, how do you save as many lives during that roll out as you can?
Um, and would that be different in different countries?
And is that to do with who you give the vaccine to first and.
Yeah. Yeah. And, you know, how far do you go with the first dose before you go back round to give the second dose?
And that was interesting because the risk groups are so different in size.
You're looking at an order of magnitude between a low income setting and a high income
setting in terms of the proportion of the population that are in a risk group.
So although it's exactly the same disease, it's ten times as lethal per person in the UK than it is in Mozambique, for example.
So any other way round? No, it's ten times more lethal here.
More lethal here. Because it's ten times as many.
The proportion of the people. Yeah, mostly, obviously.
Yes. It's ten times higher. Right. Right. So this is so the because I know a lot of people are saying, oh,
we don't seem to be getting many cases in Africa, but that must be just because they can't count.
But it really meant it really was because they simply had a different age structure in their population.
Yeah, that's part of it. There's probably several reasons.
Uh, yes. Surveillance was an issue, but not in every country.
Some African countries had excellent surveillance systems and others didn't.
And probably similarly in in Europe.
But in terms of the spread of the surveillance system, um, efficiency, uh, but yeah, you only have to look at the age structure to see that the,
that the high income settings have ten times higher proportion of the population who will be at risk of this disease.
Um, and so that brings in a very interesting question about balance then, because.
If you have limited resources to take them away from malaria, TB and HIV and put them into COVID.
And so there was an enormous amount of pressure on every country and every administration to lock down the country.
But when you lock down, you pay and you pay in money and you pay in lives,
and even more so in low income settings where there isn't furlough and there isn't.
And when you when you lock down, you actually interrupt health programs that are saving babies lives like malaria.
So I think to understand that difference and to understand that violence and.
To put it into the context of each country is so important.
Same virus, different challenges. Mm hmm.
So you've said that you were extremely busy and you talked to you talked a little bit about the fact that your group had grown.
I mean, to what extent has COVID changed?
Um, I don't know what you envisaged yourself doing when you arrived here, but how, how have things changed?
And how has that I mean, it sounds awful to say,
but has there been a kind of silver lining in terms of extra resources, extra people, new problems to work on?
One thing that I was saying in 2018 and 2019 was.
It'd be really important to have people who know their own country doing modelling for their own country before there's a disaster.
And I was trying to get funding support for this,
and it was really difficult to to convince funders that this was something to invest in rather than the research,
because it didn't fall into a suitable category for research funding.
And then we have the COMO Consortium and we proved it.
So and did that need funding or was it that essentially a kind of all hands at the pump?
I was so lucky. It started off as purely voluntary and the level of commitment of the people involved was astounding.
And that was the silver lining in it. So just to see how much goodwill and how many different people there are out there in the world.
And so we started it's purely voluntary.
And some of us were lucky enough to sort of, you know, be able to pivot as part of our job.
It came under the purview of the job, in a way. And others who I collaborate with, who I have a huge amount of respect for, were consultants,
and they had a daily rate that they went for supporting us and working with us on a voluntary basis.
So that's how it started. And the one of my colleagues who she said, Arianna pointed out that there was a COVID response fund at Oxford.
So I just did not have time to make a formal application for funding to go to a normal funder,
because I just couldn't spare the time and it would have taken weeks and I didn't have that kind of time.
Where is the application for the Rapid Response Fund was was quick and low, low intensity.
So it went for it didn't get it the first time, but I got lots of good feedback.
So I went for it second time and we got it. And so then I was able to basically pay all the people that I could, sort of prioritising people who were,
you know, giving up and come to work with us first and and then working our way through the team.
And so that kept us going and it made it made it viable to keep to keep going throughout the the worst of the pandemic.
Yeah. And what about since you mentioned it's still going we're back to voluntary.
Oh, yeah. Yeah.
But do you not think you've got enough evidence now to go back to the gates or one of the other big international funders and I'm trying.
Yeah. So that's at that time. Yeah. It's, it's only it's in the plan.
One thing I did do with I did consulting work.
So because we had published in a lot about COVID and we tried to keep that bit of it up then,
then there were a few consultancy opportunities for me and my team, and so we did them and we used the money to support capacity building.
So that's one. This was the governments, companies that were advising governments of pharma companies.
So we were able to sort of refill the coffers a bit with with that.
And you're talking about evaluation earlier on now, you know, now would have been the time to apply for funding to support COMO.
And I've done a bit of that, but I've now become engaged in an evaluation project for the UK Health Security Health Security Agency to assess the,
the, the testing, the mass testing program that was deployed in, in England.
So. That's an evaluation that we're going to be using a range of methods, including modelling to, to, to perform.
And that's happening over the next six months. But that's very interesting.
And it will that feed into the inquiry into the effectiveness of the UK's COVID response.
This is separate from the inquiry. This is a this is an evaluation that so far do a lot of self evaluation.
And this is part of their evaluation of themselves, which I think is really exciting.
So that's something that is just starting for for us.
But and that will include the cost effectiveness, presumably.
Yes. And so I'm just thinking of but there have been there's been it's been a catalyst.
The a consortium and and partners have sort of experienced a sort of is yes, this catalysed a lot of research and research leadership.
She's really exciting. So my Kyrgyzstan colleagues are going for their own funding.
They're getting their own funding. They have got it. And they're getting more.
And they've it's given them it's opened doors for them that weren't open before.
So in that way, it's a good thing. And my partners in South Africa, they were not members of the Komen consortium.
They they they were members of their own national consortium in South Africa.
They've just been awarded some funding for on for their ongoing work.
So I think that's even better than, you know, I'd like some funding to.
But, you know, it's have to be funded.
It's better than me having funding if people are leading their own research in their own countries and that this was the catalyst for it.
That's that's that's an amazing thing. Yeah.
And as I say, you were relatively new to Oxford, although I'm sure you knew lots of people here already.
But do you think the fairly unprecedented degree of collaboration that went on across medical sciences,
division and beyond that has enabled you to sort of get to know what else is going on in
Oxford more quickly than you would have done in areas like epidemiology and public health,
the kinds of things you're interested in. Definitely.
I think, you know, the idea that, you know, we're interested in vaccines.
Well, why don't I just why don't we just call Andy Pollard, drop him a line on teams, you know, or, you know,
we're interested in dexamethasone or well, I'll lend our contacts and Peter Horby then, you know, we're interested in awareness.
So I'll. I'll call Sarah Walker. This is.
Yeah, I mean, the environment at Oxford was perfect for this.
For those of us in Oxford, for this internal funding, that meant that we could keep going was also incredible.
But also the Oxford brand internationally was extremely helpful for our modelling partners
who were part of Como to for their credibility when they were engaging with policymakers.
And sometimes they were, they were a little bit having to repel.
Some of the competition from other modelling groups from outside the country.
And it was a it was competition. It was not collaboration.
And in those cases, having the Oxford name at their back was with very valuable to them.
Hmm. That's very interesting. Um,
I noticed from your website that one of the things that you're interested in is integrating
strategies for multiple diseases so that you can model everything you've told me already.
Sounds horrendously complicated already. So to try and build models to deal with more than one disease at the time seems extraordinary.
Is that something you've had time to think about again or is it is that.
Well, the fact that it's been rumbling along.
This this actually came from the malaria work because you have this blanket term malaria, but actually ends at that term.
There's five different species and they're all different, different diseases.
And they have some interaction, but not not much.
So they they have some interaction. If you're doing vector control, they have some interaction.
Some of the treatments work on both diseases, but not all.
And so in order to write malaria models for especially for Southeast Asia or Asia in general or well,
basically as soon as you leave Africa, malaria everywhere else is a multi species problem.
And even in some parts of Africa it is.
So, um, we would, we were a while ago before the pandemic, myself and my colleague Shehu Cee-Lo from South Africa,
who's a former student of mine, and Raymond Shweta, who I mentioned earlier, who's the financing expert.
We worked together on a project where, uh, we, we were looking at the.
The simulation of potential elimination of malaria from the Asia Pacific region.
My goodness. Yeah. Yeah. I think that's a big task.
And so we that was only a year as well to do that work.
And so we had to manage expectations about what you can actually do in that time.
But what we did to make the model not a year to eliminate malaria.
No, exactly. Yeah. Just to make the model. Yes. Yes.
And do the economics and understand the data. And so in that time, because it was the Asia Pacific, you cannot ignore at least vivax malaria.
So we have falciparum malaria and vivax malaria in Asia as well as in fact you have all five species,
but those are the two most prevalent with falciparum being the most lethal.
Um, so we had to make a multi species model for that project.
So we made a multi species, multi patch economic epidemiological model for the Asia Pacific for malaria.
And patches being small areas.
So part. So we find that in there it was countries with the patch.
But the patch model can also be applied. You just have to choose what the patches and what the region is.
And you can use this structure to, to explore spatial heterogeneity at any level.
So this was the approach we took and the the development of the multi species model was so challenging
that we came up with a mathematical shortcut and this approximation of the full model meant that in,
say you have one species and you have five equations, you have another species and you have seven equations.
Then the full model for both species, then you need 35 equations.
So then if you bring in a third species, you can imagine how many equations you end up with.
You can't even write them down without making errors.
What we really want is we want to be able to have five plus 712 equations and then it's manageable.
You can write each model separately and then you wipe them out.
And so the approximation we came up with, with, uh, a way to wire these species models up together.
And so then the work, uh, so we did it, and then we proved it.
So the work that we're doing now is a paper will come out soon proving y proving that that
approximation is appropriate and under what conditions you can use an approximation like that.
And once we've proved it, we can then use it for any infectious diseases.
So putting any species together, as long as they conform to these rules that allow us to use that approximation.
Mm hmm. So, yeah, the next thing.
The things that interest me, uh, uh, the same diseases that share the same mosquito, the same vector.
Um, so dengue, chikungunya, yellow fever.
These all share the same vector. So if you think of independently, interventions against them independently may not be cost effective,
but if the intervention has added value against other diseases, then you start to see the possibility that things become cost effective.
Um, and similarly, other interactions, the things where you could have multiple vaccines or concurrent vaccines for respiratory diseases.
And so that's another area where we can use this technique to integrate different
diseases together and look at integrated strategy that's more efficient.
Yeah. Mm hmm. Yes.
I think I'll just ask this, but how far do you think the pandemic has raised awareness of the potential of data driven approaches to public health?
I think it's raised awareness, yes, a lot.
I think if you before the pandemic, if someone said, what do you do for a living?
And I said, I'm a mathematical model, they just wouldn't know what that was in general.
And so it always has to come with a description of what modern mathematical modelling is, but of, well,
the now from now on, if you say you're a modeller, people know what you are, whether they think you are useful.
But another issue. I think there's there's been some there's been some good and bad for all of us.
Well, a model is only as good as whatever you put into it is also the way you end with it.
I think where you see the problem, in my view, is where you have a huge amount of uncertainty.
The models, none of them can accurately predict into the future that far.
All you can do is say this might happen under these conditions, but we don't know if those conditions are going to actually happen.
And so you can have these different scenarios. Go back to the scenario analysis.
It's all about the scenario analysis. And I think where things have gone wrong is that you may have several different scenarios.
You try to span the uncertainty of what might happen in the future, and you or a partner or the two of you together decide which scenario you prefer.
And that's the one that you focus on.
And then when things go wrong, that's the problem.
And so I think that a bit of that was happening,
this kind of cherry picking of this across this huge range of possibilities, the few that suit a particular agenda.
And, you know, it's very hard to recognise sometimes when it's happening to you because you're you're so busy.
So it I think it can happen to the best of us and I think it's not okay to abdicate the responsibility of that.
So I think we as modellers, we have to take that responsibility on and be aware of that, that risk and push back against it.
And so I think that was, you know, sort of a negative end of the exposure to the field.
But there is, of course, lots of positive lots and lots of positive ends of the exposure as well.
I mean, one of my partners, he was this is Kerala, Steve, from Egypt.
He, he was working at the Eastern Mediterranean office at the show and he was in the technical team, very interested in modelling.
Soon as the pandemic hit. So does anyone know about modelling?
And he raised his hand and he suddenly got propelled into this position where he was basically having to use modelling to support 19 countries.
And so he joined the Como Consortium and was an amazing contributor as well as a beneficiary of it.
And you know, he would tell me some of his experiences where they would be in a room with heads of
state and they would get a standing ovation for the support they've been giving.
So, you know, that profile was definitely raised.
I just got a few more questions about how the pandemic impacted on you personally.
From lockdown. Presumably we're working from home like everybody else.
What difference does that make to how you were able to work and what were any of those changes positive her?
Yeah, I mean, so I just moved to Oxford and I couldn't get a mortgage for three months because
I've been living abroad for so long because I wanted to move into a house.
So I was my husband and I were both working at home, staying in this tiny little flat, waiting to move.
And then we were about to move and so we bought a load of stuff.
So then we were in a tiny little flat with like teetering piles of crockery that we bought for our food creeping around them.
And so, you know, in the early stages of the model development and the work and sort of forming the COMO Consortium,
we were I was doing it in this little flat with with my little personal computer.
And then we we did actually manage to move just before they the rule came in to not allow anyone to move.
We managed to get in just before that happened.
And then I discovered that the wi fi wasn't good enough in the house and my head nearly exploded because at that point, you know,
there was a constant stream of of requests for support and people needing this, needing that, wanting to talk all of this going on.
And, you know, all I had to stand in the back garden to to for the phone reception for the wi fi.
So we solved that in a couple of days with a with a wi fi device.
So it sounds like a silly minor thing, but it was instant.
It was my lifeline. And, you know, I didn't much remote working previously.
Obviously with all these international colleagues, remote working was presumably not a new thing for you.
For me, it was really easy actually, because I've done it a lot.
I've actually supervised two PhD students completely remotely.
So one one student was based,
he was from Lahore and he was based in Laval University in Canada and I co supervised his Ph.D. huge quantity of his PhD and that was remote.
And she thought [INAUDIBLE] she'll see Lal from South Africa.
I will also co supervise her completely remotely so yeah I was very used to it and that you do just have to have a decent broadband to do so.
Yeah, I mean I actually I don't mind working from home at all.
I quite like it actually.
And the only problem is that you do I did start to feel like a bit of a battery hen because I was in the same four walls for 12 hours,
12, 16 hours a day sometimes. And so if you're working normally, I think it's fine.
But if you're if you're working with that level of intensity, the only way to do it.
But it's also really stressful. Did you do anything to support your wellbeing or was the job too intense to be able to do that?
I bought a cross trainer and I would do some of my meetings on the cross trainer with my laptop balanced on to just to like move.
And I was actually I mean the the area of Oxford where I live is really beautiful.
And so I did a lot of walking. I took a week off during lockdown and dug a wildlife pond in my garden.
So that was an adventure into what people buried in the garden over the years.
And so, yeah, I think we all had our own coping mechanisms.
I had a few online drinks with friends and chats and that sort of thing.
And yeah, in terms of like my working environment, we,
we had a lot of support from a friend of mine who's a leadership trainer, and so he did some sort of more.
Social elements of working as a community online and, you know, sort of communication training and that sort of thing.
And we had help from the Oxford behavioural, the behavioural group who offer behavioural therapy, normally one of their main clients, the new doctors.
And so they also gave us some sessions as well on time management, stress management, that sort of thing.
So yeah, that was quite good. Yeah, so that sounds very good.
Yeah. So we did too. We did our best, all of us to, uh, to sort of keep, keep well and keep healthy throughout the period.
Yeah. And did you feel that the fact that you were working on something important helped to support you through that difficult time?
Yeah, I mean. It was it was a huge adrenaline buzz.
To be honest, we you know, it was terrible.
It was terrible thing that was happening.
But the things that we were doing, the work that we were doing, the the the intensity of the work and the the drive was exhilarating.
And so, you know, a lot of us miss it a bit.
We certainly miss the the closeness that we had with each other.
And that has continued. Well, yes, it has. But it was never going to have the same intensity as it did then, because,
you know, people have to go back to their normal work and their normal lives.
But at one point, we were having online meetings where everyone had to shut their cameras down.
Bandwidth was overwhelmed. And so I knew pretty much everybody.
I could recognise nearly everyone in the consortium just from their voice.
And, you know, we would sometimes have, you know, 30, 40, 50, even 100 people on a call every week.
And, you know, as I said, the work it was, you know, because you were in that sort of zone, you had to innovate,
didn't have time to you didn't have the time and resources to to sort of kick an idea around.
You had to innovate. And you had to do things. You had to find solutions to problems really quickly.
Working with other people is yeah, it was, you know, in that way it was it's unsustainable because of the intensity.
But it was fascinating and exciting.
Mm hmm. And did you personally feel threatened by the infection itself?
No, I think because of it's a bit of a reality check, working with people from different countries.
And so whenever I started feeling sorry for myself, I would sort of think, well, hang on a minute, it's not that bad.
I have a chronic kidney disease, stage three, which is really mild.
It's this world is it doesn't sound good. It's it's stable.
So I am in a mild risk group. I'm not in a high risk group.
So I was lucky I got the vaccine, you know, quite early on.
And, you know, so I took reasonable precautions.
But, you know, I didn't feel threatened.
You know, we live with risk all the time in our lives. And, you know, I felt that I wasn't under any extreme risk.
You can, you know, who is who is under the extreme risk.
You know, and and actually, there were some really, you know, the.
Bad things that were happening to people at that time in other countries.
And, you know, compared to what I was likely to face is really different kettle of fish.
You know, so for example,
people starving because of lockdown or people dying because they can't get to know that there wasn't any space at the hospital,
you know, that that wasn't going to happen to me. So, yeah.
So it gives you perspective. Yeah. So last question.
How has the experience of working through the pandemic changed your attitude or your approach to your work?
And is there anything you'd like to see changed in the future? I think it's.
It's given me hope that there's going to be some preparation in the future, both sort of within country,
within our country and institutionally, but also, you know, in a global sense.
As it changed my attitude. It's made me more humble.
It's made me less sure of myself. But it's given me more confidence to sort of pursue some of the.
The ambitions I had. Yeah.
And to work with others to that to that end, it sounds like. Absolutely, yeah.
Yeah. And it's also made me feel like I could relax a little bit.
And because there's some incredible people out there to do this work, and I think I can relax and collaborate with them.
Yeah.