1
00:00:00,630 --> 00:00:04,590
So can you just start by saying your name and your title and where you are?

2
00:00:04,980 --> 00:00:13,379
Okay. Yeah. My name's Lisa White and I'm a professor of modelling and epidemiology at the Big Data Institute,

3
00:00:13,380 --> 00:00:16,710
the Nuffield Department of Medicine at Oxford University.

4
00:00:16,920 --> 00:00:20,610
Lovely. Thanks very much. And without telling me your entire life history,

5
00:00:21,180 --> 00:00:28,350
can you just go back to how you first got interested in health generally and what brought you to take me through?

6
00:00:28,380 --> 00:00:31,590
What brought you to where you are now? Sure, yeah.

7
00:00:31,980 --> 00:00:36,629
So my first love as a discipline was mathematics.

8
00:00:36,630 --> 00:00:46,290
So my first degree was in mathematics, and I became interested in applied mathematics and particularly mathematical biology.

9
00:00:46,860 --> 00:00:53,460
And that's what led me towards health, really, because I found.

10
00:00:56,560 --> 00:01:03,100
There was something really special about the pleasure that comes from solving a mathematical problem,

11
00:01:03,730 --> 00:01:12,730
but also the motivation that comes from the possibility that the solution is going to have an impact on people's lives.

12
00:01:13,430 --> 00:01:17,800
And so that's how I found the two came together.

13
00:01:18,370 --> 00:01:27,100
Where were you working and where are you studying? I did my first degree at Warwick University in Applied Mathematics,

14
00:01:27,580 --> 00:01:38,350
and then I did I actually trying to be a teacher and did my pgce and then became a high school

15
00:01:38,350 --> 00:01:49,180
teacher for a year in Birmingham and then did my master's and Ph.D. back at Warwick after that,

16
00:01:49,280 --> 00:02:07,000
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.

17
00:02:07,570 --> 00:02:18,370
And when did you arrive back here? Actually, it was a few months before the pandemic hit, so it was 2019.

18
00:02:19,230 --> 00:02:23,139
All right. Very interesting timing, but we're going to get to the pandemic a bit later.

19
00:02:23,140 --> 00:02:29,860
But first, I want to talk a little bit about more about how you combine maths and and public health

20
00:02:30,130 --> 00:02:36,100
and about your interest in in global health and how that modelling can could work on that.

21
00:02:36,100 --> 00:02:45,100
So what what does modelling bring to or a computational approach bring to understanding public health?

22
00:02:47,670 --> 00:02:56,610
Well, for me, in my experience, I would say it's it's a tool to help people make decisions.

23
00:02:57,660 --> 00:03:05,440
So the way we try to think about it is, is it's a bit like a time machine.

24
00:03:05,910 --> 00:03:23,250
So it allows you to consider potential futures and what decision you would make if those potential futures came to pass.

25
00:03:23,970 --> 00:03:29,220
And that's what's different from mathematical modelling compared to statistical modelling,

26
00:03:29,220 --> 00:03:38,070
which is more of a forecasting approach where we're trying to really look into the near future to say,

27
00:03:38,070 --> 00:03:43,590
how many cases are we going to have if if things keep going the way they're going?

28
00:03:44,700 --> 00:03:53,070
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?

29
00:03:53,430 --> 00:03:59,010
We can't predict when the new variant would arrive, but what would you do if that happened?

30
00:03:59,340 --> 00:04:03,540
What decision would you make? What intervention would you do? What would the impact be?

31
00:04:03,570 --> 00:04:14,850
How much would it cost? And so it allows what we call a scenario analysis of a further look into a more uncertain future.

32
00:04:15,520 --> 00:04:23,760
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.

33
00:04:23,980 --> 00:04:37,620
Indeed, I think if we want models to be really useful for policymaking, we can't just predict impact.

34
00:04:37,620 --> 00:04:38,910
We also have to predict.

35
00:04:42,320 --> 00:04:58,080
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,

36
00:04:58,080 --> 00:05:03,180
then there are two sides of the coin to making an intervention.

37
00:05:04,050 --> 00:05:07,770
One is the the possible impact that it might have.

38
00:05:08,490 --> 00:05:13,200
But the other is the cost implications.

39
00:05:14,430 --> 00:05:21,360
And that's the reality of of of our life, that if you you don't have unlimited resources.

40
00:05:22,170 --> 00:05:30,690
So putting resources into one response will mean that there won't be those resources for other things.

41
00:05:31,350 --> 00:05:39,030
And so the economic element of decision making is is very important, in my view, to incorporate.

42
00:05:40,710 --> 00:05:49,980
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.

43
00:05:52,950 --> 00:05:59,580
Well, so I'm I'm a bit of a.

44
00:06:03,660 --> 00:06:11,580
How can I say I have a lot of interests in terms of my research agenda.

45
00:06:12,960 --> 00:06:22,410
So I will go through periods where I focus only on the the the mathematics of a problem and

46
00:06:23,640 --> 00:06:30,840
become very interested in method development and the way that models are produced and analysed.

47
00:06:31,440 --> 00:06:45,540
And so that pulls me sometimes into that world, and then other times I become more focussed on practical and pragmatic questions.

48
00:06:46,140 --> 00:06:51,299
And that happened the most when I was living in Thailand.

49
00:06:51,300 --> 00:07:02,850
So I lived in Thailand for 12 years and pretty much as soon as I arrived, the chairman of the Research Unit,

50
00:07:02,850 --> 00:07:12,870
I was based the, which was the head on Oxford Research Unit and Nick White no relation.

51
00:07:13,290 --> 00:07:26,309
And he, he walked into the newly created modelling office and said oh we've got might have a

52
00:07:26,310 --> 00:07:34,620
problem with the new antimalarial drugs can you explore ways to eliminate resistance and,

53
00:07:35,820 --> 00:07:39,360
and so that basically put me on a path,

54
00:07:39,360 --> 00:07:49,229
a ten year path of research into dealing with methods for dealing with the emergence

55
00:07:49,230 --> 00:07:55,710
and spread of anti-malarial drug resistance and other types of resistance,

56
00:07:56,520 --> 00:08:01,620
which is it's a very fascinating field because, again, it's a balance,

57
00:08:01,620 --> 00:08:07,260
but it's a different kind of balance compared to the sort of impact cost balances.

58
00:08:07,680 --> 00:08:17,790
It's about the the the pressure, the selective pressure that you apply to these parasites.

59
00:08:18,240 --> 00:08:25,650
And so these these parasites, your top priority for these parasites is to is to heal people.

60
00:08:26,970 --> 00:08:32,310
And by doing that, to heal people, yes. So for us to deal with the parasite.

61
00:08:32,550 --> 00:08:33,930
For the parasite. Yes. Sorry. Yes.

62
00:08:35,370 --> 00:08:48,870
So by doing that, by applying the drugs where they're needed, you are also putting selective pressure on the parasites too, reinforcing what?

63
00:08:48,870 --> 00:08:53,490
Actually forcing the parasites to evolve ways of getting around that way of dealing with them.

64
00:08:53,520 --> 00:08:57,720
Exactly. But you can't stop treating people.

65
00:08:58,470 --> 00:09:02,490
So then the question is,

66
00:09:02,790 --> 00:09:08,040
how do you keep the drug alive for as long as possible and useful keep its

67
00:09:08,040 --> 00:09:13,040
useful life for as long as possible under the condition that you must use it.

68
00:09:13,060 --> 00:09:15,750
You can't withhold it from people who require treatment.

69
00:09:16,260 --> 00:09:26,280
And so then you look at a combination of different combinations of drugs and how they work together and,

70
00:09:27,730 --> 00:09:34,050
you know, how they can help each other to to delay resistance for longer.

71
00:09:34,500 --> 00:09:44,309
So, for example, so you have a one in ten chance each year of resistance emerging for a particular drug,

72
00:09:44,310 --> 00:09:54,180
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.

73
00:09:55,680 --> 00:10:03,749
So by that logic, roughly in ten years time, if you use one drug on its own, you'll you'll have resistance.

74
00:10:03,750 --> 00:10:12,629
Resistance will have arrived. Now, if you put the two drugs together, the probability of someone developing an infection,

75
00:10:12,630 --> 00:10:18,810
developing resistance to both at the same time is a product of those two for probability.

76
00:10:18,830 --> 00:10:21,930
So now it's one in 100 instead of one in ten.

77
00:10:22,320 --> 00:10:37,020
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.

78
00:10:37,410 --> 00:10:46,050
So it's those concepts that can be deployed in more complex way and in mathematical modelling.

79
00:10:46,620 --> 00:10:53,579
And this is very critical with malaria because I mean, artemisinin compounds are it really, aren't they?

80
00:10:53,580 --> 00:10:58,410
It's for us treating the most concerned and in combination with others.

81
00:10:58,650 --> 00:11:11,270
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.

82
00:11:12,290 --> 00:11:28,639
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.

83
00:11:28,640 --> 00:11:31,910
And various different vaccine candidates are also in the pipeline.

84
00:11:32,390 --> 00:11:38,629
And so the question becomes then, how do you combine none?

85
00:11:38,630 --> 00:11:42,950
No, no. One of those is going to eliminate malaria.

86
00:11:43,520 --> 00:11:48,170
But what if you use them in concert? And then you have again,

87
00:11:48,380 --> 00:11:59,090
this is where modelling can really come into play because you can you can use models to optimise the combinations to form

88
00:11:59,090 --> 00:12:08,390
integrated strategies and you can optimise those with different constraints like how much resources are there available,

89
00:12:09,170 --> 00:12:16,280
what's the time span. So you can either have a time constraint and have unlimited resources and say, what will it cost?

90
00:12:16,730 --> 00:12:21,830
Or you can have a fixed cost and say what you can you do with the money that you have.

91
00:12:23,030 --> 00:12:26,960
And how do you save the most lives and how do you delay your resistance through as well as possible?

92
00:12:27,440 --> 00:12:30,889
So malaria is you know,

93
00:12:30,890 --> 00:12:35,090
people spend their whole lives just working on that one disease for that reason

94
00:12:35,090 --> 00:12:40,130
because there is so many different elements of it that still need to be understood.

95
00:12:40,550 --> 00:12:48,290
And it's still a massive challenge. No, I was going to ask you what the cause you talked about the opportunities,

96
00:12:48,290 --> 00:12:50,749
modelling offers and I was going to ask you what some of the challenges were.

97
00:12:50,750 --> 00:12:55,730
But I think you've you've described that very well in that in that one example.

98
00:12:56,330 --> 00:13:05,830
And so. It's you've got you've got economics, you've got maths, you've got clinical science,

99
00:13:05,830 --> 00:13:14,170
you've got infectious disease, viral evolution or other kinds of pathogen evolution going on.

100
00:13:15,160 --> 00:13:20,050
How do you and you're boiling all that down to maths.

101
00:13:20,380 --> 00:13:24,880
So you must need to build big collaborations with people who are collecting all that kind of data.

102
00:13:25,360 --> 00:13:34,910
Yeah, yeah. It's it's a real collaborative, interdisciplinary life, being a modeller.

103
00:13:36,280 --> 00:13:49,420
And so, yes, one element of my sort of networking side of my life is these collaborations with partners.

104
00:13:49,900 --> 00:14:03,310
And the most the most satisfying and productive collaborations are the ones where both partners are still engaged in asking the questions.

105
00:14:03,700 --> 00:14:11,860
So the model development and the the field research and the lab research happen in partnership.

106
00:14:12,430 --> 00:14:15,250
And so as hypotheses form,

107
00:14:15,640 --> 00:14:28,570
they can be elucidated using a mathematical structure which allows us to analyse those hypotheses and explore them leads to more questions.

108
00:14:28,990 --> 00:14:39,760
Questions go back to the field in the lab, get more data, understand more, create a new model and continue the process.

109
00:14:40,480 --> 00:14:44,660
And at some point, some of that gets adopted into practice, presumably.

110
00:14:44,680 --> 00:14:47,250
Is that something you've seen in your in your research career,

111
00:14:47,320 --> 00:14:52,750
being able to see effective implementation of some of the things that have emerged from the models?

112
00:14:53,710 --> 00:14:58,750
Yes. And that that started with my malaria work.

113
00:15:00,460 --> 00:15:05,530
Until then, I was more of an academic exercise.

114
00:15:05,530 --> 00:15:16,350
And then, as I guess my track record improved and there was some you know,

115
00:15:16,390 --> 00:15:25,900
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.

116
00:15:26,320 --> 00:15:32,110
And that was very exciting, but also quite unnerving initially.

117
00:15:32,500 --> 00:15:44,360
Yeah. Mm hmm. So I think I just need to put a bit of perspective on the global burden of an infectious disease.

118
00:15:44,530 --> 00:15:50,109
And leaving aside COVID, I think until COVID came along,

119
00:15:50,110 --> 00:15:58,420
I think a lot of people in Western industrialised countries probably didn't realise the extent to which infectious disease is a global problem.

120
00:15:59,230 --> 00:16:06,220
Can you put some kind of figure on it? Not off the top of my head.

121
00:16:08,440 --> 00:16:13,210
I mean, there are different rankings. I had actually a.

122
00:16:19,100 --> 00:16:28,050
An intern who was from Cote d'Ivoire who well, heritages Cote d'Ivoire.

123
00:16:28,070 --> 00:16:32,870
She came over from London and she wanted to explore Cote d'Ivoire.

124
00:16:33,290 --> 00:16:37,129
And so she showed me the rankings.

125
00:16:37,130 --> 00:16:48,920
And in terms of cause of death and non-communicable diseases in in her area were were the highest.

126
00:16:48,920 --> 00:16:54,210
But then next on the list was infectious disease as a cause of death.

127
00:16:54,230 --> 00:17:00,930
So, of course, it depends where what setting you were exactly.

128
00:17:00,950 --> 00:17:07,280
Yes. Yes. And as you mentioned yourself, you know, it varies over time.

129
00:17:07,640 --> 00:17:09,000
Yeah. Mm hmm.

130
00:17:10,010 --> 00:17:17,120
But that these I mean, for example, an awful lot of childhood illness in developing countries simply because of lack of sanitation, clean water.

131
00:17:17,300 --> 00:17:21,470
And so. Yeah, I think that yeah.

132
00:17:21,470 --> 00:17:27,050
I mean, I think a lot of the diseases it's.

133
00:17:28,890 --> 00:17:45,930
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,

134
00:17:45,930 --> 00:17:57,050
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.

135
00:17:57,060 --> 00:18:13,260
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.

136
00:18:13,860 --> 00:18:25,050
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.

137
00:18:25,560 --> 00:18:43,080
And having people who can provide an evidence base and support efficient health policy decisions is

138
00:18:43,860 --> 00:18:50,880
even more important in places where resources are limited than than it is in well-resourced countries.

139
00:18:51,390 --> 00:19:00,120
And that's why I think that, you know, rather than doing the modelling myself training,

140
00:19:00,840 --> 00:19:06,930
training people to to do this in their own countries would probably have an even bigger impact.

141
00:19:07,290 --> 00:19:11,010
And that's something you've been engaged in yourself? Absolutely. Yes. Yes, yes.

142
00:19:11,490 --> 00:19:17,520
Is there a I don't know. Is there a network that that helps to support that activity?

143
00:19:17,820 --> 00:19:21,090
Or is this something you've been doing kind of as an individual initiative?

144
00:19:22,260 --> 00:19:27,960
It's it's it started off for me as an individual mission.

145
00:19:29,640 --> 00:19:34,620
And and then things really changed with the pandemic, actually.

146
00:19:35,030 --> 00:19:40,019
And I was beginning to challenge and we'll talk about the pandemic in the world

147
00:19:40,020 --> 00:19:44,910
circled back to talking about how that's had an impact on your work globally.

148
00:19:45,120 --> 00:19:50,740
Yeah. So how did you first become aware that there was a pandemic in the offing?

149
00:19:50,760 --> 00:19:54,990
Do you remember? You know what you were doing? I've asked everybody this question.

150
00:20:03,920 --> 00:20:14,930
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.

151
00:20:15,260 --> 00:20:20,090
Well, yeah. I mean, so sort of.

152
00:20:20,480 --> 00:20:32,360
So circling back to what I was saying about training people and and nurturing talent in country, after ten years and in Thailand,

153
00:20:32,810 --> 00:20:42,170
I worked with some really brilliant Thai mathematicians and modellers and and I led a group there.

154
00:20:42,170 --> 00:20:45,240
And when I left that group, the,

155
00:20:46,350 --> 00:20:55,130
the Thai deputy head of group took over as the head of group and is now that group is almost entirely Thai

156
00:20:55,550 --> 00:21:06,710
nationals who are working on policy relevant modelling work for Thailand and I still collaborate with them a lot.

157
00:21:08,330 --> 00:21:22,070
So I just left, I left my beloved group behind in Thailand and, and I was looking forward to Oxford life and,

158
00:21:22,700 --> 00:21:29,809
you know, going to places and, and, you know, in the English language and all that sort of thing.

159
00:21:29,810 --> 00:21:36,440
And, and, and then we started to hear these reports.

160
00:21:36,480 --> 00:21:52,070
And so that would have been in late 2019 when we were hearing reports from from China of of this new this new virus.

161
00:21:52,760 --> 00:21:56,690
And I remember thinking.

162
00:21:59,000 --> 00:22:02,090
If they're finding it there, it's probably everywhere already.

163
00:22:04,700 --> 00:22:08,509
That's that was my suspicion, because, I mean,

164
00:22:08,510 --> 00:22:17,780
we sort of teach this to our students that the difference between a reported case and the underlying number of

165
00:22:17,780 --> 00:22:25,520
cases that you would need to have and how long the epidemic could be going before you see that first case.

166
00:22:26,290 --> 00:22:32,450
And so, yeah, that was quite a sobering and scary thought.

167
00:22:34,010 --> 00:22:44,270
And yet, sadly, then reports started coming in from different countries.

168
00:22:45,170 --> 00:22:54,260
And then it was here. And I remember saying to my husband, I'm not going to get involved.

169
00:22:57,980 --> 00:23:06,320
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,

170
00:23:06,320 --> 00:23:15,040
we have not just lots of modellers but lots of modelling groups and they, you know, I've just been doing malaria.

171
00:23:15,050 --> 00:23:19,220
It's not, you know, I did a bit different thing.

172
00:23:19,220 --> 00:23:22,250
It's a parasite, it's a virus. It's not. Yes, yeah.

173
00:23:22,370 --> 00:23:24,910
It's people who have the flu models,

174
00:23:25,100 --> 00:23:34,639
the best people to pivot or people who are working on the source pandemic previously, the best people to pivot across.

175
00:23:34,640 --> 00:23:46,010
And that's who did. And so that was that was my original intention.

176
00:23:47,480 --> 00:23:57,470
And I just couldn't resist writing my own model because there were news reports and

177
00:23:58,400 --> 00:24:03,530
decisions that were affecting me personally and predictions that were being made.

178
00:24:03,530 --> 00:24:08,090
And I felt really.

179
00:24:08,330 --> 00:24:13,729
So I just wanted to check. Well, I would have predicted with my model.

180
00:24:13,730 --> 00:24:17,600
So. So what were the key elements that you put into your models?

181
00:24:18,170 --> 00:24:23,210
What were the kind of red flags that popped up that made you think, I need to investigate that?

182
00:24:27,160 --> 00:24:38,440
So there are a few key things. That's the absolutely the most important thing to include in a COVID model is age structure.

183
00:24:40,690 --> 00:24:49,840
Because of this, it's during a relationship between age and likelihood of severity of disease.

184
00:24:49,850 --> 00:24:55,700
So with that age structure, I would argue the COVID model is not fit for purpose.

185
00:24:58,120 --> 00:25:01,930
So that's the first step to create an age structured model.

186
00:25:03,430 --> 00:25:11,980
The next step for me was I actually included loss of immunity and reinfection quite early on.

187
00:25:14,890 --> 00:25:25,050
It wasn't found for months and I suspect that as well.

188
00:25:25,060 --> 00:25:28,270
But that's because like me working on malaria. I see.

189
00:25:28,990 --> 00:25:40,480
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.

190
00:25:40,900 --> 00:25:48,050
Similarly for COVID. So. Just because we hadn't seen it.

191
00:25:48,200 --> 00:25:52,610
I had a very strong feeling that it was coming.

192
00:25:53,210 --> 00:26:01,700
So if you look at earlier versions of the models that my I and my team produced,

193
00:26:01,700 --> 00:26:11,720
they all had this arrow back from R to S so being recovered to se being susceptible.

194
00:26:13,940 --> 00:26:18,200
And unfortunately, we were right about that as well.

195
00:26:18,310 --> 00:26:25,250
Um, I was hoping we wouldn't be because as soon as you have that flow back and you allow for reinfection,

196
00:26:25,250 --> 00:26:30,260
then zero-covid is impossible, not impossible.

197
00:26:30,260 --> 00:26:39,170
That would be. But it's extremely unlikely and would require a huge investment of resources to to achieve.

198
00:26:41,210 --> 00:26:46,400
So you said you made your model essentially for your own satisfaction, but presumably published it having having done it.

199
00:26:47,570 --> 00:26:58,910
Well, what happened was, um, I, I then contacted a few of my colleagues and said, you know, what have you been doing?

200
00:27:00,170 --> 00:27:03,229
I made a model. Did you make a model?

201
00:27:03,230 --> 00:27:19,460
And so my, my, some of my colleagues who have since joined my group that they've been writing models as well.

202
00:27:20,570 --> 00:27:31,070
So one person, Ricardo Alquist, had had produced a very similar model with very similar ideas behind it.

203
00:27:32,190 --> 00:27:34,550
As I said, we merged and he said, Yeah, I'm in.

204
00:27:35,390 --> 00:27:51,230
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.

205
00:27:51,620 --> 00:27:55,670
And he'd written a model for, um, the hospital demand.

206
00:27:56,330 --> 00:28:02,900
So we added to our structured transmission model hospital demand.

207
00:28:03,840 --> 00:28:12,080
Um, and initially we, we used the output of our model as the input to his model.

208
00:28:12,110 --> 00:28:17,240
So you were basically saying if this many people get COVID badly, this number of hospital beds is what we're going to need.

209
00:28:17,270 --> 00:28:28,060
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.

210
00:28:29,090 --> 00:28:34,980
But that one was a big shift where instead of linking to, you know,

211
00:28:35,060 --> 00:28:42,500
sort of using two separate models and taking out of one and adding and putting into the other, we we merged them together.

212
00:28:43,490 --> 00:28:53,090
And then we had a model that looked at the epidemiology but also at the hospital demand and the mortality.

213
00:28:53,800 --> 00:29:04,700
Um, and then we had one that was suitable for Western settings where, um,

214
00:29:05,180 --> 00:29:10,740
so the first one was more focussed on, on low and middle income countries was, it was general ocean.

215
00:29:11,690 --> 00:29:17,780
So to make it suitable for low and middle income settings, um, sadly,

216
00:29:17,960 --> 00:29:30,830
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.

217
00:29:31,340 --> 00:29:36,860
And that's a very important element of the model that you have to include.

218
00:29:37,520 --> 00:29:44,059
So this started to happen because I have colleagues in Thailand,

219
00:29:44,060 --> 00:29:53,060
I have colleagues I have students from all over the world, former students, current students, collaborators.

220
00:29:53,700 --> 00:29:59,540
Um, so the next step was essentially we said, look, we made a model so useful to you.

221
00:30:00,340 --> 00:30:03,520
Um, and quite a few people said yes.

222
00:30:04,730 --> 00:30:11,900
And so we created the International COVID Modelling Consortium, um, como.

223
00:30:12,500 --> 00:30:16,630
And so my colleague Rima Shrestha was involved in that.

224
00:30:16,640 --> 00:30:22,820
She's, she's an economics and policy and financing expert.

225
00:30:23,660 --> 00:30:31,639
And so now we had a consortium with, um, my students involved.

226
00:30:31,640 --> 00:30:39,290
I know remote Akhmatova from Kyrgyzstan, for example, and Caroline Franco from Brazil.

227
00:30:39,860 --> 00:30:45,620
And we basically made this model.

228
00:30:46,590 --> 00:30:51,180
Available to any anyone who would like to use it.

229
00:30:53,040 --> 00:30:59,040
We created an interface so that they could run it without needing to use the code.

230
00:30:59,610 --> 00:31:05,580
And we also shared the code. So if they wanted to take the code directly and modify, then they did.

231
00:31:06,030 --> 00:31:13,049
So the Thai group did that and so did the Brazil group and other countries use the interface.

232
00:31:13,050 --> 00:31:29,550
And yeah, that was the COMO Consortium was initiated and that basically became my whole life then for about 18 months.

233
00:31:30,210 --> 00:31:38,250
And is it still running? It is, but it's very different now to how it was while the pandemic was raging.

234
00:31:39,570 --> 00:31:53,340
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

235
00:31:53,340 --> 00:32:01,110
a consortium was to empower national modellers to do their own modelling for their own country.

236
00:32:02,520 --> 00:32:09,810
So there was a lot of training that we did on in real time as people needed it.

237
00:32:10,980 --> 00:32:17,850
We we actually had also some stress management sessions.

238
00:32:18,840 --> 00:32:25,200
We all needed that and especially some people who were, you know,

239
00:32:27,540 --> 00:32:43,050
sort of watching while there was some catastrophic epidemiological, uh, fallout from this disease in their countries.

240
00:32:45,060 --> 00:32:49,320
And you sort of feel powerless while this is happening.

241
00:32:49,950 --> 00:32:59,540
Um, so we, you know, we tried to provide also, you know, not just an environment for, um,

242
00:33:00,330 --> 00:33:09,960
academic collaboration, but also for support internationally sharing ideas, training.

243
00:33:10,680 --> 00:33:15,330
Um, yeah, it was, it was an interesting time.

244
00:33:16,350 --> 00:33:22,260
And did the consortium members have a hotline to their individual governments and all the health services?

245
00:33:22,470 --> 00:33:37,260
Yes, they did. So we had a mixture of people who would join some people who had the modelling skills and background,

246
00:33:37,320 --> 00:33:47,550
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.

247
00:33:48,150 --> 00:33:53,820
And on the other hand, we had people who had all of the policy,

248
00:33:53,820 --> 00:34:00,660
they were already in an institution or linked to an institution, they didn't have the modelling background.

249
00:34:00,670 --> 00:34:04,590
So that's where we used the interfaces.

250
00:34:04,590 --> 00:34:14,760
And I mean this is why the model changed so many times because we would get requests coming in,

251
00:34:16,300 --> 00:34:21,780
our partners would say, well, you know, we need this, we need this thing in the model.

252
00:34:21,780 --> 00:34:27,599
Have you considered this thing? Have you considered you know, our country wants to explore this intervention, but it's not a new model.

253
00:34:27,600 --> 00:34:35,040
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.

254
00:34:35,040 --> 00:34:38,310
Isolation and all the all the non-pharmaceutical interventions.

255
00:34:38,490 --> 00:34:46,889
Yes. And we did them one by one as we discovered them because it was such a vibrant community.

256
00:34:46,890 --> 00:34:54,120
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.

257
00:34:54,630 --> 00:35:02,490
And then there would be a sort of burst of activity as we updated the model to include

258
00:35:02,490 --> 00:35:09,390
all of these interventions that were either being used or were on the table to be used.

259
00:35:09,900 --> 00:35:23,010
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.

260
00:35:23,710 --> 00:35:31,140
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.

261
00:35:31,770 --> 00:35:42,150
And when we were doing one of the updates, I thought I found a bug and we were already supporting with that model.

262
00:35:42,660 --> 00:36:01,870
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.

263
00:36:01,880 --> 00:36:08,000
So that was a few times of an all nighter to try and find the bug.

264
00:36:08,720 --> 00:36:14,930
And we also sort of made it into a game and I said I'd send her a voucher,

265
00:36:15,920 --> 00:36:25,490
£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?

266
00:36:26,780 --> 00:36:31,820
Yeah. Mm hmm. And have you been able to?

267
00:36:32,240 --> 00:36:36,830
Is it. Have we got enough distance from it now to be able to evaluate how effective

268
00:36:37,370 --> 00:36:42,080
the input from your your model was to the policies of the various countries?

269
00:36:42,980 --> 00:36:48,290
I mean, I feel like we're in that phase now. The problem is that everyone's very tired.

270
00:36:48,560 --> 00:36:53,590
Yes. So the people that would would and should evaluate it often.

271
00:36:54,140 --> 00:37:06,290
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.

272
00:37:06,800 --> 00:37:12,580
And so, for example, that that's the thing with scenario analysis.

273
00:37:12,590 --> 00:37:24,830
If if one of the scenarios that we simulated or that one of our partners simulated came to pass, um, if, if the,

274
00:37:25,340 --> 00:37:29,990
the decision makers went down that road or close to that road, then we can,

275
00:37:30,860 --> 00:37:36,950
we can assess how accurate the model was in making that prediction under those assumptions.

276
00:37:37,340 --> 00:37:46,550
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.

277
00:37:47,340 --> 00:37:54,290
Um, but, you know, the, the thing that we all have to accept,

278
00:37:55,520 --> 00:38:07,040
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.

279
00:38:07,850 --> 00:38:15,740
And so and that was flawed, presumably things like number of cases, number of deaths, even.

280
00:38:16,370 --> 00:38:20,660
You're not necessarily getting the right numbers, are you? No, you're not.

281
00:38:22,100 --> 00:38:25,310
And so the data are uncertain.

282
00:38:25,520 --> 00:38:28,800
The future is uncertain. People's behaviour is uncertain.

283
00:38:28,940 --> 00:38:29,960
So many things.

284
00:38:30,860 --> 00:38:52,040
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?

285
00:38:52,700 --> 00:38:55,580
And if I think it's going to help, I'll go ahead.

286
00:38:56,210 --> 00:39:11,510
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,

287
00:39:11,510 --> 00:39:24,530
the decision makers, what all the uncertainties are, because the temptation in such an extreme environment is to, is to hunt for certainty.

288
00:39:25,280 --> 00:39:32,570
And so model output is very compelling because it looks like the real data.

289
00:39:34,820 --> 00:39:47,210
And so to, to communicate that kind of uncertainty, um, is I think really the responsibility of the modeller.

290
00:39:48,500 --> 00:39:55,200
And that's what we, we did, we did a lot of work in that, in that area,

291
00:39:55,220 --> 00:40:05,960
both training ourselves and our partners and also engaging in that kind of discussion with the policymakers as well.

292
00:40:07,700 --> 00:40:12,430
So that you mentioned that there are lots and lots of models in this country, in the US and places like that.

293
00:40:12,650 --> 00:40:17,990
And to what extent did your work have an impact on what was going on in the UK?

294
00:40:18,920 --> 00:40:35,000
Um, it's increasing. So actually interestingly Spi-m is led by my former supervisor.

295
00:40:35,060 --> 00:40:38,240
All right. So this is a this is the modelling subgroup of sage.

296
00:40:38,270 --> 00:40:44,390
Yes. Yes, exactly. So I called Graham.

297
00:40:45,010 --> 00:40:53,410
A few times, will Graham Medley medley a few times through throughout this phase.

298
00:40:53,770 --> 00:40:57,790
And the first time I called, I said, Well, can I help? I've made a model.

299
00:40:58,900 --> 00:41:04,500
But it seems like you've got enough models. And he agreed.

300
00:41:04,510 --> 00:41:11,380
And I agreed that it was, you know, at the time it was the best thing.

301
00:41:12,550 --> 00:41:19,000
The best use of this particular model was to support partners like our partners in Afghanistan,

302
00:41:19,000 --> 00:41:29,620
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.

303
00:41:29,980 --> 00:41:40,950
Having said that, we're the Oxford community is an interesting one where,

304
00:41:41,890 --> 00:41:47,260
you know, you the the vaccine one of the main vaccines was being developed here.

305
00:41:48,010 --> 00:41:53,200
A lot of the treatment with the treatments were being tested.

306
00:41:53,230 --> 00:41:56,890
The studies. Yes, the principal trial and the recovery time.

307
00:41:57,070 --> 00:41:57,940
Absolutely.

308
00:41:59,410 --> 00:42:16,330
So we have collaborated with both the Oxford Vaccine Group and and the Recovery Trial Group to explore the potential impact of vaccine with,

309
00:42:16,480 --> 00:42:19,059
um, with the vaccine group,

310
00:42:19,060 --> 00:42:30,850
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.

311
00:42:31,560 --> 00:42:35,470
And but we actually used a different model for both of those.

312
00:42:36,040 --> 00:42:43,780
Um, because, uh, the, the, the questions were more around.

313
00:42:44,630 --> 00:42:53,170
Um, so for the vaccine one, we were interested in how the roll out could be done as efficiently as possible.

314
00:42:53,170 --> 00:42:58,240
So while you were rolling out, how do you save as many lives during that roll out as you can?

315
00:42:58,910 --> 00:43:02,130
Um, and would that be different in different countries?

316
00:43:02,620 --> 00:43:06,280
And is that to do with who you give the vaccine to first and.

317
00:43:06,580 --> 00:43:13,390
Yeah. Yeah. And, you know, how far do you go with the first dose before you go back round to give the second dose?

318
00:43:13,990 --> 00:43:22,450
And that was interesting because the risk groups are so different in size.

319
00:43:23,470 --> 00:43:30,310
You're looking at an order of magnitude between a low income setting and a high income

320
00:43:30,310 --> 00:43:34,270
setting in terms of the proportion of the population that are in a risk group.

321
00:43:34,930 --> 00:43:49,450
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.

322
00:43:49,750 --> 00:43:54,280
So any other way round? No, it's ten times more lethal here.

323
00:43:54,550 --> 00:43:57,720
More lethal here. Because it's ten times as many.

324
00:43:58,420 --> 00:44:03,050
The proportion of the people. Yeah, mostly, obviously.

325
00:44:03,070 --> 00:44:09,160
Yes. It's ten times higher. Right. Right. So this is so the because I know a lot of people are saying, oh,

326
00:44:09,160 --> 00:44:12,820
we don't seem to be getting many cases in Africa, but that must be just because they can't count.

327
00:44:14,060 --> 00:44:19,750
But it really meant it really was because they simply had a different age structure in their population.

328
00:44:20,110 --> 00:44:23,920
Yeah, that's part of it. There's probably several reasons.

329
00:44:26,650 --> 00:44:32,230
Uh, yes. Surveillance was an issue, but not in every country.

330
00:44:32,650 --> 00:44:36,880
Some African countries had excellent surveillance systems and others didn't.

331
00:44:37,640 --> 00:44:41,470
And probably similarly in in Europe.

332
00:44:42,700 --> 00:44:57,690
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,

333
00:44:57,760 --> 00:45:09,850
that the high income settings have ten times higher proportion of the population who will be at risk of this disease.

334
00:45:10,890 --> 00:45:21,700
Um, and so that brings in a very interesting question about balance then, because.

335
00:45:24,620 --> 00:45:33,020
If you have limited resources to take them away from malaria, TB and HIV and put them into COVID.

336
00:45:34,250 --> 00:45:44,210
And so there was an enormous amount of pressure on every country and every administration to lock down the country.

337
00:45:46,460 --> 00:45:53,600
But when you lock down, you pay and you pay in money and you pay in lives,

338
00:45:54,050 --> 00:46:00,560
and even more so in low income settings where there isn't furlough and there isn't.

339
00:46:01,010 --> 00:46:10,730
And when you when you lock down, you actually interrupt health programs that are saving babies lives like malaria.

340
00:46:11,150 --> 00:46:18,050
So I think to understand that difference and to understand that violence and.

341
00:46:21,030 --> 00:46:26,610
To put it into the context of each country is so important.

342
00:46:27,210 --> 00:46:32,940
Same virus, different challenges. Mm hmm.

343
00:46:33,960 --> 00:46:41,280
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.

344
00:46:41,370 --> 00:46:44,940
I mean, to what extent has COVID changed?

345
00:46:45,970 --> 00:46:51,690
Um, I don't know what you envisaged yourself doing when you arrived here, but how, how have things changed?

346
00:46:51,690 --> 00:46:54,030
And how has that I mean, it sounds awful to say,

347
00:46:54,030 --> 00:47:00,090
but has there been a kind of silver lining in terms of extra resources, extra people, new problems to work on?

348
00:47:04,620 --> 00:47:12,360
One thing that I was saying in 2018 and 2019 was.

349
00:47:16,560 --> 00:47:27,210
It'd be really important to have people who know their own country doing modelling for their own country before there's a disaster.

350
00:47:29,640 --> 00:47:37,350
And I was trying to get funding support for this,

351
00:47:38,280 --> 00:47:50,280
and it was really difficult to to convince funders that this was something to invest in rather than the research,

352
00:47:50,280 --> 00:47:56,400
because it didn't fall into a suitable category for research funding.

353
00:47:59,280 --> 00:48:04,470
And then we have the COMO Consortium and we proved it.

354
00:48:05,400 --> 00:48:10,560
So and did that need funding or was it that essentially a kind of all hands at the pump?

355
00:48:10,830 --> 00:48:23,819
I was so lucky. It started off as purely voluntary and the level of commitment of the people involved was astounding.

356
00:48:23,820 --> 00:48:31,800
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.

357
00:48:34,050 --> 00:48:37,200
And so we started it's purely voluntary.

358
00:48:37,320 --> 00:48:44,280
And some of us were lucky enough to sort of, you know, be able to pivot as part of our job.

359
00:48:44,280 --> 00:48:55,410
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,

360
00:48:55,620 --> 00:49:04,320
and they had a daily rate that they went for supporting us and working with us on a voluntary basis.

361
00:49:06,840 --> 00:49:20,040
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.

362
00:49:20,700 --> 00:49:27,880
So I just did not have time to make a formal application for funding to go to a normal funder,

363
00:49:27,900 --> 00:49:36,870
because I just couldn't spare the time and it would have taken weeks and I didn't have that kind of time.

364
00:49:38,190 --> 00:49:47,910
Where is the application for the Rapid Response Fund was was quick and low, low intensity.

365
00:49:48,330 --> 00:49:53,280
So it went for it didn't get it the first time, but I got lots of good feedback.

366
00:49:53,280 --> 00:50:08,700
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,

367
00:50:09,240 --> 00:50:16,230
you know, giving up and come to work with us first and and then working our way through the team.

368
00:50:16,800 --> 00:50:26,580
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.

369
00:50:27,420 --> 00:50:32,940
Yeah. And what about since you mentioned it's still going we're back to voluntary.

370
00:50:33,330 --> 00:50:35,760
Oh, yeah. Yeah.

371
00:50:36,480 --> 00:50:44,580
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.

372
00:50:44,650 --> 00:50:51,240
Yeah. So that's at that time. Yeah. It's, it's only it's in the plan.

373
00:50:52,200 --> 00:50:56,940
One thing I did do with I did consulting work.

374
00:50:57,000 --> 00:51:06,570
So because we had published in a lot about COVID and we tried to keep that bit of it up then,

375
00:51:09,570 --> 00:51:20,460
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.

376
00:51:21,360 --> 00:51:30,150
So that's one. This was the governments, companies that were advising governments of pharma companies.

377
00:51:31,380 --> 00:51:37,020
So we were able to sort of refill the coffers a bit with with that.

378
00:51:37,050 --> 00:51:49,560
And you're talking about evaluation earlier on now, you know, now would have been the time to apply for funding to support COMO.

379
00:51:49,560 --> 00:52:04,900
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,

380
00:52:05,860 --> 00:52:12,780
the, the testing, the mass testing program that was deployed in, in England.

381
00:52:13,280 --> 00:52:23,420
So. That's an evaluation that we're going to be using a range of methods, including modelling to, to, to perform.

382
00:52:23,900 --> 00:52:28,670
And that's happening over the next six months. But that's very interesting.

383
00:52:28,670 --> 00:52:33,770
And it will that feed into the inquiry into the effectiveness of the UK's COVID response.

384
00:52:34,520 --> 00:52:42,829
This is separate from the inquiry. This is a this is an evaluation that so far do a lot of self evaluation.

385
00:52:42,830 --> 00:52:49,370
And this is part of their evaluation of themselves, which I think is really exciting.

386
00:52:50,120 --> 00:52:55,910
So that's something that is just starting for for us.

387
00:52:58,320 --> 00:53:02,630
But and that will include the cost effectiveness, presumably.

388
00:53:03,320 --> 00:53:12,070
Yes. And so I'm just thinking of but there have been there's been it's been a catalyst.

389
00:53:12,080 --> 00:53:30,080
The a consortium and and partners have sort of experienced a sort of is yes, this catalysed a lot of research and research leadership.

390
00:53:30,500 --> 00:53:37,760
She's really exciting. So my Kyrgyzstan colleagues are going for their own funding.

391
00:53:37,830 --> 00:53:41,120
They're getting their own funding. They have got it. And they're getting more.

392
00:53:42,320 --> 00:53:52,280
And they've it's given them it's opened doors for them that weren't open before.

393
00:53:52,790 --> 00:54:03,860
So in that way, it's a good thing. And my partners in South Africa, they were not members of the Komen consortium.

394
00:54:04,220 --> 00:54:10,430
They they they were members of their own national consortium in South Africa.

395
00:54:12,110 --> 00:54:17,630
They've just been awarded some funding for on for their ongoing work.

396
00:54:18,140 --> 00:54:23,180
So I think that's even better than, you know, I'd like some funding to.

397
00:54:23,180 --> 00:54:25,910
But, you know, it's have to be funded.

398
00:54:26,810 --> 00:54:35,330
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.

399
00:54:35,780 --> 00:54:38,870
That's that's that's an amazing thing. Yeah.

400
00:54:39,680 --> 00:54:44,149
And as I say, you were relatively new to Oxford, although I'm sure you knew lots of people here already.

401
00:54:44,150 --> 00:54:52,010
But do you think the fairly unprecedented degree of collaboration that went on across medical sciences,

402
00:54:52,010 --> 00:54:56,870
division and beyond that has enabled you to sort of get to know what else is going on in

403
00:54:56,870 --> 00:55:02,749
Oxford more quickly than you would have done in areas like epidemiology and public health,

404
00:55:02,750 --> 00:55:05,960
the kinds of things you're interested in. Definitely.

405
00:55:08,150 --> 00:55:13,730
I think, you know, the idea that, you know, we're interested in vaccines.

406
00:55:13,760 --> 00:55:23,180
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,

407
00:55:23,180 --> 00:55:33,730
we're interested in dexamethasone or well, I'll lend our contacts and Peter Horby then, you know, we're interested in awareness.

408
00:55:33,740 --> 00:55:38,060
So I'll. I'll call Sarah Walker. This is.

409
00:55:38,960 --> 00:55:43,760
Yeah, I mean, the environment at Oxford was perfect for this.

410
00:55:44,780 --> 00:55:56,600
For those of us in Oxford, for this internal funding, that meant that we could keep going was also incredible.

411
00:55:58,040 --> 00:56:09,379
But also the Oxford brand internationally was extremely helpful for our modelling partners

412
00:56:09,380 --> 00:56:19,580
who were part of Como to for their credibility when they were engaging with policymakers.

413
00:56:19,910 --> 00:56:24,140
And sometimes they were, they were a little bit having to repel.

414
00:56:28,500 --> 00:56:34,170
Some of the competition from other modelling groups from outside the country.

415
00:56:35,520 --> 00:56:39,150
And it was a it was competition. It was not collaboration.

416
00:56:39,630 --> 00:56:47,880
And in those cases, having the Oxford name at their back was with very valuable to them.

417
00:56:50,450 --> 00:56:53,810
Hmm. That's very interesting. Um,

418
00:56:55,880 --> 00:57:01,459
I noticed from your website that one of the things that you're interested in is integrating

419
00:57:01,460 --> 00:57:06,740
strategies for multiple diseases so that you can model everything you've told me already.

420
00:57:06,740 --> 00:57:14,300
Sounds horrendously complicated already. So to try and build models to deal with more than one disease at the time seems extraordinary.

421
00:57:14,480 --> 00:57:18,020
Is that something you've had time to think about again or is it is that.

422
00:57:18,260 --> 00:57:20,600
Well, the fact that it's been rumbling along.

423
00:57:23,030 --> 00:57:29,780
This this actually came from the malaria work because you have this blanket term malaria, but actually ends at that term.

424
00:57:29,780 --> 00:57:35,210
There's five different species and they're all different, different diseases.

425
00:57:35,660 --> 00:57:41,180
And they have some interaction, but not not much.

426
00:57:43,520 --> 00:57:49,300
So they they have some interaction. If you're doing vector control, they have some interaction.

427
00:57:49,350 --> 00:57:53,360
Some of the treatments work on both diseases, but not all.

428
00:57:54,560 --> 00:58:08,060
And so in order to write malaria models for especially for Southeast Asia or Asia in general or well,

429
00:58:08,060 --> 00:58:13,610
basically as soon as you leave Africa, malaria everywhere else is a multi species problem.

430
00:58:15,290 --> 00:58:17,840
And even in some parts of Africa it is.

431
00:58:18,410 --> 00:58:31,430
So, um, we would, we were a while ago before the pandemic, myself and my colleague Shehu Cee-Lo from South Africa,

432
00:58:31,880 --> 00:58:38,930
who's a former student of mine, and Raymond Shweta, who I mentioned earlier, who's the financing expert.

433
00:58:40,430 --> 00:58:51,140
We worked together on a project where, uh, we, we were looking at the.

434
00:58:55,210 --> 00:59:03,370
The simulation of potential elimination of malaria from the Asia Pacific region.

435
00:59:03,880 --> 00:59:08,550
My goodness. Yeah. Yeah. I think that's a big task.

436
00:59:09,700 --> 00:59:16,330
And so we that was only a year as well to do that work.

437
00:59:16,660 --> 00:59:20,560
And so we had to manage expectations about what you can actually do in that time.

438
00:59:21,160 --> 00:59:24,400
But what we did to make the model not a year to eliminate malaria.

439
00:59:24,460 --> 00:59:27,640
No, exactly. Yeah. Just to make the model. Yes. Yes.

440
00:59:28,120 --> 00:59:42,760
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.

441
00:59:43,780 --> 00:59:50,580
So we have falciparum malaria and vivax malaria in Asia as well as in fact you have all five species,

442
00:59:50,590 --> 00:59:54,940
but those are the two most prevalent with falciparum being the most lethal.

443
00:59:56,050 --> 01:00:01,900
Um, so we had to make a multi species model for that project.

444
01:00:04,030 --> 01:00:12,190
So we made a multi species, multi patch economic epidemiological model for the Asia Pacific for malaria.

445
01:00:12,730 --> 01:00:15,940
And patches being small areas.

446
01:00:16,180 --> 01:00:20,860
So part. So we find that in there it was countries with the patch.

447
01:00:21,340 --> 01:00:27,650
But the patch model can also be applied. You just have to choose what the patches and what the region is.

448
01:00:27,670 --> 01:00:34,660
And you can use this structure to, to explore spatial heterogeneity at any level.

449
01:00:36,790 --> 01:00:46,720
So this was the approach we took and the the development of the multi species model was so challenging

450
01:00:47,140 --> 01:01:00,010
that we came up with a mathematical shortcut and this approximation of the full model meant that in,

451
01:01:00,320 --> 01:01:08,170
say you have one species and you have five equations, you have another species and you have seven equations.

452
01:01:08,680 --> 01:01:12,880
Then the full model for both species, then you need 35 equations.

453
01:01:13,660 --> 01:01:18,639
So then if you bring in a third species, you can imagine how many equations you end up with.

454
01:01:18,640 --> 01:01:22,270
You can't even write them down without making errors.

455
01:01:23,950 --> 01:01:31,970
What we really want is we want to be able to have five plus 712 equations and then it's manageable.

456
01:01:31,990 --> 01:01:34,690
You can write each model separately and then you wipe them out.

457
01:01:35,290 --> 01:01:45,580
And so the approximation we came up with, with, uh, a way to wire these species models up together.

458
01:01:46,300 --> 01:01:53,950
And so then the work, uh, so we did it, and then we proved it.

459
01:01:54,340 --> 01:02:05,079
So the work that we're doing now is a paper will come out soon proving y proving that that

460
01:02:05,080 --> 01:02:10,120
approximation is appropriate and under what conditions you can use an approximation like that.

461
01:02:10,720 --> 01:02:16,600
And once we've proved it, we can then use it for any infectious diseases.

462
01:02:17,080 --> 01:02:24,340
So putting any species together, as long as they conform to these rules that allow us to use that approximation.

463
01:02:24,400 --> 01:02:29,230
Mm hmm. So, yeah, the next thing.

464
01:02:29,800 --> 01:02:38,470
The things that interest me, uh, uh, the same diseases that share the same mosquito, the same vector.

465
01:02:39,290 --> 01:02:42,790
Um, so dengue, chikungunya, yellow fever.

466
01:02:43,750 --> 01:02:53,770
These all share the same vector. So if you think of independently, interventions against them independently may not be cost effective,

467
01:02:54,460 --> 01:03:02,290
but if the intervention has added value against other diseases, then you start to see the possibility that things become cost effective.

468
01:03:02,980 --> 01:03:14,590
Um, and similarly, other interactions, the things where you could have multiple vaccines or concurrent vaccines for respiratory diseases.

469
01:03:15,160 --> 01:03:21,760
And so that's another area where we can use this technique to integrate different

470
01:03:21,760 --> 01:03:27,460
diseases together and look at integrated strategy that's more efficient.

471
01:03:28,750 --> 01:03:36,649
Yeah. Mm hmm. Yes.

472
01:03:36,650 --> 01:03:45,740
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?

473
01:03:50,750 --> 01:03:55,790
I think it's raised awareness, yes, a lot.

474
01:03:58,130 --> 01:04:03,500
I think if you before the pandemic, if someone said, what do you do for a living?

475
01:04:03,500 --> 01:04:10,340
And I said, I'm a mathematical model, they just wouldn't know what that was in general.

476
01:04:11,510 --> 01:04:21,280
And so it always has to come with a description of what modern mathematical modelling is, but of, well,

477
01:04:21,290 --> 01:04:29,809
the now from now on, if you say you're a modeller, people know what you are, whether they think you are useful.

478
01:04:29,810 --> 01:04:43,320
But another issue. I think there's there's been some there's been some good and bad for all of us.

479
01:04:43,340 --> 01:04:49,900
Well, a model is only as good as whatever you put into it is also the way you end with it.

480
01:04:50,420 --> 01:05:00,680
I think where you see the problem, in my view, is where you have a huge amount of uncertainty.

481
01:05:01,520 --> 01:05:10,310
The models, none of them can accurately predict into the future that far.

482
01:05:10,790 --> 01:05:18,680
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.

483
01:05:19,220 --> 01:05:23,570
And so you can have these different scenarios. Go back to the scenario analysis.

484
01:05:23,840 --> 01:05:36,350
It's all about the scenario analysis. And I think where things have gone wrong is that you may have several different scenarios.

485
01:05:36,350 --> 01:05:48,290
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.

486
01:05:48,290 --> 01:05:51,980
And that's the one that you focus on.

487
01:05:52,880 --> 01:06:01,490
And then when things go wrong, that's the problem.

488
01:06:02,240 --> 01:06:08,110
And so I think that a bit of that was happening,

489
01:06:08,120 --> 01:06:20,120
this kind of cherry picking of this across this huge range of possibilities, the few that suit a particular agenda.

490
01:06:20,780 --> 01:06:29,240
And, you know, it's very hard to recognise sometimes when it's happening to you because you're you're so busy.

491
01:06:30,620 --> 01:06:44,990
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.

492
01:06:46,040 --> 01:06:55,010
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.

493
01:06:55,760 --> 01:07:03,120
And so I think that was, you know, sort of a negative end of the exposure to the field.

494
01:07:04,820 --> 01:07:12,990
But there is, of course, lots of positive lots and lots of positive ends of the exposure as well.

495
01:07:13,110 --> 01:07:24,560
I mean, one of my partners, he was this is Kerala, Steve, from Egypt.

496
01:07:24,710 --> 01:07:35,570
He, he was working at the Eastern Mediterranean office at the show and he was in the technical team, very interested in modelling.

497
01:07:36,080 --> 01:07:39,860
Soon as the pandemic hit. So does anyone know about modelling?

498
01:07:40,340 --> 01:07:55,010
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.

499
01:07:56,960 --> 01:08:09,530
And so he joined the Como Consortium and was an amazing contributor as well as a beneficiary of it.

500
01:08:09,540 --> 01:08:16,009
And you know, he would tell me some of his experiences where they would be in a room with heads of

501
01:08:16,010 --> 01:08:22,340
state and they would get a standing ovation for the support they've been giving.

502
01:08:23,690 --> 01:08:28,880
So, you know, that profile was definitely raised.

503
01:08:30,410 --> 01:08:35,570
I just got a few more questions about how the pandemic impacted on you personally.

504
01:08:37,060 --> 01:08:41,110
From lockdown. Presumably we're working from home like everybody else.

505
01:08:42,580 --> 01:08:48,730
What difference does that make to how you were able to work and what were any of those changes positive her?

506
01:08:49,030 --> 01:08:54,790
Yeah, I mean, so I just moved to Oxford and I couldn't get a mortgage for three months because

507
01:08:55,450 --> 01:08:59,770
I've been living abroad for so long because I wanted to move into a house.

508
01:09:00,220 --> 01:09:10,210
So I was my husband and I were both working at home, staying in this tiny little flat, waiting to move.

509
01:09:11,680 --> 01:09:15,249
And then we were about to move and so we bought a load of stuff.

510
01:09:15,250 --> 01:09:25,000
So then we were in a tiny little flat with like teetering piles of crockery that we bought for our food creeping around them.

511
01:09:25,330 --> 01:09:34,000
And so, you know, in the early stages of the model development and the work and sort of forming the COMO Consortium,

512
01:09:35,860 --> 01:09:42,640
we were I was doing it in this little flat with with my little personal computer.

513
01:09:43,990 --> 01:09:56,290
And then we we did actually manage to move just before they the rule came in to not allow anyone to move.

514
01:09:56,620 --> 01:09:58,870
We managed to get in just before that happened.

515
01:09:59,380 --> 01:10:09,640
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,

516
01:10:10,000 --> 01:10:19,750
there was a constant stream of of requests for support and people needing this, needing that, wanting to talk all of this going on.

517
01:10:20,140 --> 01:10:29,590
And, you know, all I had to stand in the back garden to to for the phone reception for the wi fi.

518
01:10:29,590 --> 01:10:36,670
So we solved that in a couple of days with a with a wi fi device.

519
01:10:37,030 --> 01:10:41,400
So it sounds like a silly minor thing, but it was instant.

520
01:10:42,040 --> 01:10:48,339
It was my lifeline. And, you know, I didn't much remote working previously.

521
01:10:48,340 --> 01:10:53,020
Obviously with all these international colleagues, remote working was presumably not a new thing for you.

522
01:10:53,110 --> 01:10:57,010
For me, it was really easy actually, because I've done it a lot.

523
01:10:58,090 --> 01:11:06,160
I've actually supervised two PhD students completely remotely.

524
01:11:07,660 --> 01:11:09,879
So one one student was based,

525
01:11:09,880 --> 01:11:23,320
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.

526
01:11:24,370 --> 01:11:28,760
And she thought [INAUDIBLE] she'll see Lal from South Africa.

527
01:11:28,780 --> 01:11:42,370
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.

528
01:11:42,580 --> 01:11:47,860
Yeah, I mean I actually I don't mind working from home at all.

529
01:11:48,220 --> 01:11:49,540
I quite like it actually.

530
01:11:49,540 --> 01:12:00,369
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,

531
01:12:00,370 --> 01:12:07,509
12, 16 hours a day sometimes. And so if you're working normally, I think it's fine.

532
01:12:07,510 --> 01:12:12,069
But if you're if you're working with that level of intensity, the only way to do it.

533
01:12:12,070 --> 01:12:24,940
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?

534
01:12:25,240 --> 01:12:36,640
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.

535
01:12:37,900 --> 01:12:46,690
And I was actually I mean the the area of Oxford where I live is really beautiful.

536
01:12:46,690 --> 01:12:55,930
And so I did a lot of walking. I took a week off during lockdown and dug a wildlife pond in my garden.

537
01:12:56,590 --> 01:13:04,030
So that was an adventure into what people buried in the garden over the years.

538
01:13:05,410 --> 01:13:12,790
And so, yeah, I think we all had our own coping mechanisms.

539
01:13:13,060 --> 01:13:19,540
I had a few online drinks with friends and chats and that sort of thing.

540
01:13:19,540 --> 01:13:25,959
And yeah, in terms of like my working environment, we,

541
01:13:25,960 --> 01:13:35,290
we had a lot of support from a friend of mine who's a leadership trainer, and so he did some sort of more.

542
01:13:36,970 --> 01:13:47,230
Social elements of working as a community online and, you know, sort of communication training and that sort of thing.

543
01:13:47,230 --> 01:14:07,540
And we had help from the Oxford behavioural, the behavioural group who offer behavioural therapy, normally one of their main clients, the new doctors.

544
01:14:08,050 --> 01:14:17,230
And so they also gave us some sessions as well on time management, stress management, that sort of thing.

545
01:14:17,860 --> 01:14:22,810
So yeah, that was quite good. Yeah, so that sounds very good.

546
01:14:23,020 --> 01:14:31,989
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.

547
01:14:31,990 --> 01:14:40,030
Yeah. And did you feel that the fact that you were working on something important helped to support you through that difficult time?

548
01:14:41,320 --> 01:14:51,930
Yeah, I mean. It was it was a huge adrenaline buzz.

549
01:14:52,560 --> 01:15:02,130
To be honest, we you know, it was terrible.

550
01:15:02,640 --> 01:15:04,530
It was terrible thing that was happening.

551
01:15:05,370 --> 01:15:23,850
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.

552
01:15:24,720 --> 01:15:33,300
And so, you know, a lot of us miss it a bit.

553
01:15:35,100 --> 01:15:38,550
We certainly miss the the closeness that we had with each other.

554
01:15:39,130 --> 01:15:46,290
And that has continued. Well, yes, it has. But it was never going to have the same intensity as it did then, because,

555
01:15:47,100 --> 01:15:51,570
you know, people have to go back to their normal work and their normal lives.

556
01:15:53,250 --> 01:15:59,810
But at one point, we were having online meetings where everyone had to shut their cameras down.

557
01:16:00,200 --> 01:16:05,780
Bandwidth was overwhelmed. And so I knew pretty much everybody.

558
01:16:07,560 --> 01:16:11,310
I could recognise nearly everyone in the consortium just from their voice.

559
01:16:11,940 --> 01:16:24,420
And, you know, we would sometimes have, you know, 30, 40, 50, even 100 people on a call every week.

560
01:16:25,020 --> 01:16:38,880
And, you know, as I said, the work it was, you know, because you were in that sort of zone, you had to innovate,

561
01:16:40,380 --> 01:16:50,640
didn't have time to you didn't have the time and resources to to sort of kick an idea around.

562
01:16:50,640 --> 01:16:56,370
You had to innovate. And you had to do things. You had to find solutions to problems really quickly.

563
01:16:57,870 --> 01:17:08,370
Working with other people is yeah, it was, you know, in that way it was it's unsustainable because of the intensity.

564
01:17:08,370 --> 01:17:11,940
But it was fascinating and exciting.

565
01:17:12,210 --> 01:17:16,440
Mm hmm. And did you personally feel threatened by the infection itself?

566
01:17:18,630 --> 01:17:28,180
No, I think because of it's a bit of a reality check, working with people from different countries.

567
01:17:29,490 --> 01:17:39,150
And so whenever I started feeling sorry for myself, I would sort of think, well, hang on a minute, it's not that bad.

568
01:17:41,220 --> 01:17:47,100
I have a chronic kidney disease, stage three, which is really mild.

569
01:17:47,280 --> 01:17:51,540
It's this world is it doesn't sound good. It's it's stable.

570
01:17:52,350 --> 01:17:55,620
So I am in a mild risk group. I'm not in a high risk group.

571
01:17:56,610 --> 01:18:02,129
So I was lucky I got the vaccine, you know, quite early on.

572
01:18:02,130 --> 01:18:07,110
And, you know, so I took reasonable precautions.

573
01:18:07,110 --> 01:18:11,340
But, you know, I didn't feel threatened.

574
01:18:12,960 --> 01:18:20,970
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.

575
01:18:21,670 --> 01:18:24,690
You can, you know, who is who is under the extreme risk.

576
01:18:24,870 --> 01:18:30,930
You know, and and actually, there were some really, you know, the.

577
01:18:32,960 --> 01:18:36,800
Bad things that were happening to people at that time in other countries.

578
01:18:36,810 --> 01:18:44,360
And, you know, compared to what I was likely to face is really different kettle of fish.

579
01:18:45,180 --> 01:18:46,370
You know, so for example,

580
01:18:46,370 --> 01:18:57,320
people starving because of lockdown or people dying because they can't get to know that there wasn't any space at the hospital,

581
01:18:59,570 --> 01:19:04,540
you know, that that wasn't going to happen to me. So, yeah.

582
01:19:04,630 --> 01:19:09,710
So it gives you perspective. Yeah. So last question.

583
01:19:10,130 --> 01:19:17,900
How has the experience of working through the pandemic changed your attitude or your approach to your work?

584
01:19:17,900 --> 01:19:30,510
And is there anything you'd like to see changed in the future? I think it's.

585
01:19:32,710 --> 01:19:46,900
It's given me hope that there's going to be some preparation in the future, both sort of within country,

586
01:19:46,900 --> 01:19:54,230
within our country and institutionally, but also, you know, in a global sense.

587
01:19:57,330 --> 01:20:03,730
As it changed my attitude. It's made me more humble.

588
01:20:06,010 --> 01:20:19,780
It's made me less sure of myself. But it's given me more confidence to sort of pursue some of the.

589
01:20:22,790 --> 01:20:26,500
The ambitions I had. Yeah.

590
01:20:28,030 --> 01:20:32,470
And to work with others to that to that end, it sounds like. Absolutely, yeah.

591
01:20:34,020 --> 01:20:41,350
Yeah. And it's also made me feel like I could relax a little bit.

592
01:20:41,560 --> 01:20:52,300
And because there's some incredible people out there to do this work, and I think I can relax and collaborate with them.

593
01:20:52,330 --> 01:20:52,630
Yeah.