1 00:00:00,570 --> 00:00:04,500 Can you just start by saying your name and what your current position is? 2 00:00:05,580 --> 00:00:13,559 I'm Somali Bajaj, and I'm literally a default student in the Department of Biology and also part of NARC, 3 00:00:13,560 --> 00:00:16,770 which is the environmental research doctoral training program. 4 00:00:17,010 --> 00:00:21,780 All right. Okay. And what does that stand for? Environmental research. 5 00:00:22,020 --> 00:00:25,130 All right. Yes. Doctoral training. Okay. Yes. Yes. 6 00:00:26,070 --> 00:00:38,250 And so tell me a little about yourself. How did you first get interested in science in general and the kind of work you're interested in specifically? 7 00:00:39,960 --> 00:00:44,520 So I did my undergrad instead back in India in 2010. 8 00:00:44,600 --> 00:00:48,569 And while I was doing my undergrad at a couple of research projects, 9 00:00:48,570 --> 00:00:57,590 trying to understand where I can apply stats in real life, I worked on some gastro intestinal diseases. 10 00:00:57,600 --> 00:01:02,850 I worked on sizing charts for women and how that should be standardised. 11 00:01:03,000 --> 00:01:10,920 But then I realised that what I really am interested in is learning a bit more about where this can actually be applied stats in general, 12 00:01:11,680 --> 00:01:14,760 which in the biomedical field people are always thinking. 13 00:01:15,600 --> 00:01:24,729 Yeah, because while I was getting exposed to generally applying stats and finance and just gender, a lot of companies take or take on data analysts. 14 00:01:24,730 --> 00:01:29,820 Somehow that did not excite me as much, which is I think, okay, 15 00:01:31,560 --> 00:01:39,959 I did do a short stint in a consulting firm for about three or four months, but then soon realised that I wasn't excited about it. 16 00:01:39,960 --> 00:01:46,060 So I left my job and just started working as a researcher and billing in a hospital, 17 00:01:46,060 --> 00:01:51,270 a government hospital in India with the bio stats unit so that I worked on coeliac 18 00:01:51,270 --> 00:01:55,560 disease and how so many people have coeliac disease but do not really know about it. 19 00:01:56,040 --> 00:02:05,790 And while I was doing that, my professor there suggested that because I'm interested in this field should probably study a bit more and 20 00:02:06,000 --> 00:02:13,650 suggested I study bio stats so a bit more focussed on the medical field or just public health in general. 21 00:02:14,550 --> 00:02:23,129 So while I was applying for Masters and Ballots that I didn't really know anyone from my field or from my university in general who had studied bios, 22 00:02:23,130 --> 00:02:29,520 that they all went on to do a bit more data analyst type of jobs or maybe an MBA in general. 23 00:02:30,900 --> 00:02:35,940 While I was applying, I also started working as a consultant for Public Health Foundation of India, 24 00:02:35,940 --> 00:02:42,930 and that just solidified my interest in applying stats to the health public health field. 25 00:02:43,320 --> 00:02:49,410 And then I got a scholarship to go and study at Harvard for my masters in Bio stat. 26 00:02:49,410 --> 00:02:57,330 And that was really eye opening because my undergrad, as much fun as it was, it was quite theoretically the stats. 27 00:02:57,330 --> 00:03:01,020 And that's why throughout I wanted to know where can I really use all of this? 28 00:03:01,800 --> 00:03:03,810 But at Harvard it was very much applied. 29 00:03:03,820 --> 00:03:11,460 We had a lot of methodological subjects which were somewhat easier for me to understand because I'd done a lot of theory, 30 00:03:11,820 --> 00:03:18,510 but it was the applied aspect, which was very new, very exciting and somewhat difficult to understand at first. 31 00:03:19,200 --> 00:03:23,069 But while I was doing that, I was still interested in studying all these things. 32 00:03:23,070 --> 00:03:29,850 Where can I really apply it? So then I worked with a couple of professors at Harvard working as a research assistant, 33 00:03:30,210 --> 00:03:35,070 but mostly on non-communicable diseases because that was the big department there. 34 00:03:35,070 --> 00:03:42,120 And most of the people, at least at that time, used to work on non-communicable diseases at Harvard or the School of Public Health. 35 00:03:44,070 --> 00:03:51,570 So when I was finishing up my master's, I felt like I wanted to still know I can apply, but I was still not very excited about it. 36 00:03:51,570 --> 00:03:57,660 I wasn't excited about applying stats at the population level, so I did not know a lot of biology, 37 00:03:57,660 --> 00:04:03,150 so I wasn't really looking to do something at the cellular level to something at the population level. 38 00:04:04,350 --> 00:04:11,549 But which is I keep using the word exciting, but that's exactly what I was looking for and I didn't quite find something. 39 00:04:11,550 --> 00:04:17,660 So I didn't apply for a Ph.D. then, but I got married after that and my husband and I, we he was working. 40 00:04:17,800 --> 00:04:22,230 So we decided that I can move here and to the UK. 41 00:04:22,230 --> 00:04:32,160 Exactly. To the UK. And then I got a job at the infectious disease department in Imperial and I was working on HIV for the first 9 to 10 months, 42 00:04:32,820 --> 00:04:35,460 and that's when I felt like, Oh, this is exciting. 43 00:04:36,360 --> 00:04:44,189 People's behaviours can affect how a disease spreads and it's not just your behaviour affecting yourself, 44 00:04:44,190 --> 00:04:51,270 it's how your behaviour affects everyone else. Which is where I thought, okay, that's where I want to maybe focus a bit more on. 45 00:04:52,200 --> 00:04:56,980 I worked on Alzheimer's also for a bit and those are two different things, aren't they? 46 00:04:57,200 --> 00:05:05,359 It was. I mean it was, yeah. I would have wanted to continue with HIV, but because of the visa situation, 47 00:05:05,360 --> 00:05:14,540 I ended up working with a very important person in this field who was at Oxford many, many years ago. 48 00:05:14,810 --> 00:05:22,820 Roy Anderson He started the department there, and it was it was a lot of fun learning about Alzheimer's, which is a brand new field. 49 00:05:22,820 --> 00:05:29,540 But again, I knew that it just solidified again my idea that I wasn't quite interested in non-communicable diseases. 50 00:05:30,740 --> 00:05:38,330 And then a colleague of mine there called Ben Lambert, who was again at Oxford and I was at Exeter, 51 00:05:38,630 --> 00:05:46,640 suggested that I get in touch with a few people in Oxford who are working on interesting diseases, infectious diseases. 52 00:05:46,940 --> 00:05:50,960 He's a statistician himself, so I felt like I could follow his advice. 53 00:05:51,980 --> 00:05:57,260 And then I met Moritz Cranmer and Jose Lorenzo, who are now my supervisors, 54 00:05:57,260 --> 00:06:03,229 and they were the first people I met when I was thinking maybe I can pursue a Ph.D. in this. 55 00:06:03,230 --> 00:06:05,870 It's been years, and this is the only thing that excites me. 56 00:06:05,870 --> 00:06:14,209 And I think it was just meeting them just the first time I met them and just seeing how excited and passionate they were about the work. 57 00:06:14,210 --> 00:06:19,070 I knew that I just wanted to apply for a Ph.D. with them, and that's exactly what I did. 58 00:06:19,070 --> 00:06:26,540 I did apply in Oxford with other PIs, but they were always going to be my main supervisors, 59 00:06:26,930 --> 00:06:30,469 and I was just so excited to work with them because they had such interesting 60 00:06:30,470 --> 00:06:36,950 ideas and they were just so passionate about solving so many problems that exist. 61 00:06:36,950 --> 00:06:47,180 And that's exactly what I did. And my Ph.D. was going to be on mosquito borne viruses, and that was supposed to start in 2020. 62 00:06:48,110 --> 00:06:51,290 But that's when COVID happened. Exactly. Yes. 63 00:06:51,300 --> 00:06:57,200 And let's just take a step back on that. So you were still at Imperial at the beginning of 2020? 64 00:06:57,230 --> 00:07:04,400 Exactly. Yeah. So can you remember where you were when you first heard that there was an outbreak of a respiratory disease going on in China? 65 00:07:04,930 --> 00:07:10,219 Yeah. So I used to live a bit. I was not quite in London. 66 00:07:10,220 --> 00:07:14,240 I was in a town called Staines, so I didn't travel to work every day. 67 00:07:14,240 --> 00:07:19,280 And I was just listening on news and seeing people. 68 00:07:19,700 --> 00:07:29,059 Moritz actually write on Twitter. And this was only John when he wrote something about this airborne virus that's circulating in China. 69 00:07:29,060 --> 00:07:30,320 Nobody was talking about it, 70 00:07:30,500 --> 00:07:42,020 but I still remember seeing his tweet at that time and then slowly seeing a few tidbits of news coming up in there and then actually talking to Roy. 71 00:07:42,020 --> 00:07:54,229 Right. I wrote him a name of saying that I feel like we can work from home and I don't want to be susceptible or put other people at risk. 72 00:07:54,230 --> 00:07:58,790 So is it okay if I work from home? I do not know what's happening, but is it okay? 73 00:07:58,790 --> 00:08:02,390 And he was very much on board and he's like, Let let's do that for the whole team. 74 00:08:02,660 --> 00:08:05,690 He sent out an email, but that was very early on, I think. 75 00:08:05,930 --> 00:08:11,510 But that's when we all started. Again, it's privileged to be able to work from home, but that's what we did. 76 00:08:11,870 --> 00:08:18,529 Whether you were in a field where you were likely to be more conscious of how important I mean to you what you understood about the spread of disease, 77 00:08:18,530 --> 00:08:21,860 which most people didn't do? Exactly. Yes. Yes. 78 00:08:22,190 --> 00:08:26,210 And did you do any work on COVID while you were still at Imperial? 79 00:08:26,240 --> 00:08:31,910 MM Yeah. So initially I still remember it was, like I said, quite early on, 80 00:08:31,940 --> 00:08:40,009 our team just in the department decided that we would probably work from home if possible, but there were people still coming in. 81 00:08:40,010 --> 00:08:48,170 And I remember we had this meeting where they called volunteers to just come and help with data scraping from the web. 82 00:08:48,440 --> 00:08:58,549 What a sort of hackathon. So we all sat in a room and we were literally we were assigned countries where a few news articles were coming up from, 83 00:08:58,550 --> 00:09:02,330 and our task was to actually go throughout the day, 84 00:09:02,330 --> 00:09:08,270 just go online, try to extract all the PDFs from the Ministry of Health or whatever news articles, 85 00:09:08,720 --> 00:09:14,209 Twitter, blogs, and get a number of cases that are being reported. 86 00:09:14,210 --> 00:09:21,620 So actually just manually creating a long list because there wasn't any set database to inform all of this. 87 00:09:21,620 --> 00:09:29,690 So literally just sitting, writing in an Excel and curating data from basics, 88 00:09:29,750 --> 00:09:40,820 and that was my first introduction to working on COVID and how reliable with those sources, in retrospect, I'm as reliable as they could be, I guess. 89 00:09:40,940 --> 00:09:45,020 Yeah, Yeah, I think it's it's so difficult. 90 00:09:45,920 --> 00:09:56,059 Oh, it was so difficult at that point to have a reliable global database, which I think it still is difficult to find. 91 00:09:56,060 --> 00:09:59,450 But there are people working and have created these. Excellent. 92 00:09:59,500 --> 00:10:04,659 Databases which have line lists, or even if they don't have lined list, they have some sort of aggregated data. 93 00:10:04,660 --> 00:10:08,709 So that is that exists now. 94 00:10:08,710 --> 00:10:16,480 But was difficult then. Yes. And were you able to see the dynamics of the how the virus was spreading through that work? 95 00:10:16,690 --> 00:10:23,110 Yeah. I mean, I wasn't particularly doing that. There were more senior researchers actually doing the analysis. 96 00:10:23,110 --> 00:10:26,190 So they used to we used to have, I don't know, 97 00:10:26,310 --> 00:10:35,469 weekly calls where they were updating us on estimates of are not for this disease or this new virus that is spreading. 98 00:10:35,470 --> 00:10:41,200 And I was just it was amazing to be able to see all of this from the front seat, really, 99 00:10:41,950 --> 00:10:49,930 and some excellent scientists just working on this and to be able to understand how it is difficult when an unknown outbreak happens. 100 00:10:50,350 --> 00:11:00,069 How how do you actually go about using all these techniques that you've learned which cater to really clean datasets and organise datasets, 101 00:11:00,070 --> 00:11:01,870 But how do you work with messy datasets? 102 00:11:02,980 --> 00:11:10,629 That was the first time I was seeing all of that, and it was exciting and you'd ask me if I'd worked in Imperial. 103 00:11:10,630 --> 00:11:21,750 I, after I finished my Alzheimer's project, Imperial actually had has a group or is it a group or it's called J Idea. 104 00:11:21,760 --> 00:11:31,030 Jamal Infection. I'm sorry, I don't know what it stands for, but J idea they funded to investigate. 105 00:11:31,360 --> 00:11:38,410 I guess infectious diseases spread and I was taken on by Katrina. 106 00:11:39,130 --> 00:11:45,340 How do you spell out you get a d h. 107 00:11:47,920 --> 00:11:53,350 E r Ryanair h a u. 108 00:11:53,380 --> 00:11:59,470 C gear. Yeah. She's an infectious disease modeller, economic modeller. 109 00:11:59,470 --> 00:12:12,549 And she was. So there were almost two sections in the department, one which was doing active research in the infectious disease EPI of Autism remit. 110 00:12:12,550 --> 00:12:23,110 And Katherine was leading more of the economic impact this disease was this disease had on different societies and different parts of the world. 111 00:12:23,920 --> 00:12:26,409 So she recruited me for about six months over there. 112 00:12:26,410 --> 00:12:33,490 I was looking, particularly in England, how lockdowns and other different scenarios of if we have a partial lockdown, 113 00:12:33,730 --> 00:12:37,299 what sectors would be affected if we have a full lockdown, 114 00:12:37,300 --> 00:12:43,570 What sectors are going to be affected most when we lift the lockdown more from an economic perspective? 115 00:12:43,570 --> 00:12:54,970 So I think that was one of the earlier studies combining epidemiology and economic impact before we knew it's going to impact the economy. 116 00:12:55,300 --> 00:13:03,670 So you were able to use your statistics both to look at the that the as you say, the epidemiology and to a sense that the virology, 117 00:13:03,670 --> 00:13:08,500 the biology, the biology end of it and the economic end of it at the same time, 118 00:13:08,740 --> 00:13:12,910 and do the both those things and this is a general question really how important 119 00:13:12,910 --> 00:13:19,570 are non-biological factors in the spread of an infectious disease like COVID? 120 00:13:19,900 --> 00:13:22,660 Oh, massively. I think that oh, I mean, 121 00:13:22,720 --> 00:13:33,190 that is hopefully going to be a chapter in my thesis because it's it's I think a lot of it is behaviour driven because you 122 00:13:33,190 --> 00:13:40,420 might have a very mild disease and you might have people responding very differently to if the disease was very deadly. 123 00:13:40,690 --> 00:13:47,140 And that is going to make all the difference and communicating the severity of disease. 124 00:13:47,150 --> 00:14:00,850 And I think communication and behaviour, human behaviour which is dictated by fear or by solidarity, it's it's dictated by so many aspects of life. 125 00:14:01,330 --> 00:14:05,860 But I think it's behaviour that drives how disease spreads in the end. 126 00:14:05,980 --> 00:14:15,100 Mm hmm. So you said yes, you you were due to start a PhD on mosquito borne illness. 127 00:14:16,040 --> 00:14:21,870 Sorry. Was it viruses or substance in that would have been in October 2020. 128 00:14:21,880 --> 00:14:28,690 So did you indeed come to us with an idea? I moved on 1st of October, and like I said, I was on a doctoral training programme, 129 00:14:28,690 --> 00:14:35,799 so my first year was really taking a few classes and deciding what my project was going to be do My project was decided. 130 00:14:35,800 --> 00:14:41,020 So it did change because I was going to get well. 131 00:14:41,020 --> 00:14:46,000 I was taking classes as part of the doctoral training programme in my first few months. 132 00:14:46,330 --> 00:14:49,510 Mm hmm. And was that sometimes I do this at the end. 133 00:14:49,520 --> 00:14:51,640 We might as well do that since we've arrived. 134 00:14:51,650 --> 00:14:59,290 That I mean, when you say taking classes, by that time were you able to meet in person again with the classes happening online? 135 00:14:59,320 --> 00:15:02,799 No, everything was online. Yeah, I barely met anyone. 136 00:15:02,800 --> 00:15:08,350 We it was. I think it was the rule of meeting six. 137 00:15:08,350 --> 00:15:14,139 Yes. So we used to meet a cohort in groups of six and university parks. 138 00:15:14,140 --> 00:15:22,300 Really. And everything was just online, you know, I was just sitting in a cute little house in Oxford and studying the courses. 139 00:15:22,390 --> 00:15:28,240 Yeah. Yes. So that made your experience as a PhD student very different from what it should have been? 140 00:15:28,360 --> 00:15:31,689 Yeah, yeah, yeah, I suppose so. Yes. No, no, no. 141 00:15:31,690 --> 00:15:41,840 But yes. No, you know. But were you able to get to know your your colleagues and your fellow students in your cohort despite the limitations. 142 00:15:41,860 --> 00:15:49,600 Yeah. I think the programme tried their best to have social events online. 143 00:15:51,640 --> 00:15:55,930 I think just through the classes I knew everyone's faces. I just didn't know how tall they were. 144 00:15:56,470 --> 00:16:00,490 Sure I know their faces. I know how they talk, what they interested in. 145 00:16:00,700 --> 00:16:07,060 When you see them in your life, you just realise how tall or short the person is, if that is the only difference, I guess. 146 00:16:08,380 --> 00:16:18,670 Yeah, I know, but I think the programme tried as much as they could and I did make a few friends and I'm in touch with a few of them right now. 147 00:16:19,420 --> 00:16:26,780 Yeah, I think they did a good job. And you hinted that your big topic did shift. 148 00:16:28,200 --> 00:16:32,579 What was going on? Yeah. So what what did you mean? 149 00:16:32,580 --> 00:16:37,770 And were you getting involved in studies during that first year, even though it was mainly a talk course? 150 00:16:38,280 --> 00:16:41,099 Yeah, exactly. You know, it was more on me. 151 00:16:41,100 --> 00:16:52,890 I reached out to moderates and Jose saying that I am quite keen and I do have time because during the third term, 152 00:16:53,220 --> 00:16:55,080 during the third term of the first year, 153 00:16:55,080 --> 00:17:02,450 you're supposed to find a project and understand what you want to work on and just get a feel for what your Ph.D. might be on. 154 00:17:02,880 --> 00:17:09,960 But because I already knew I wanted to work with Moretz and Jose, so I just asked them if I can start working on some projects. 155 00:17:10,860 --> 00:17:18,810 And I'm like, Throughout this period there have been so many projects which have started and ended just because of interest, 156 00:17:19,350 --> 00:17:23,729 time constraints, capacity and things like that. 157 00:17:23,730 --> 00:17:27,970 But one of the projects that took off quite early on in my Ph.D., 158 00:17:27,990 --> 00:17:34,710 which also became part of my first chapter, was looking at the spread of alpha variant of COVID in England. 159 00:17:35,220 --> 00:17:39,840 And that's how I really just started with my Ph.D. in the third term. 160 00:17:40,530 --> 00:17:43,860 So remind me about as it's getting a bit distant now. 161 00:17:44,550 --> 00:17:48,180 We started with, I think what was just called the Wuhan variant. 162 00:17:48,570 --> 00:17:55,310 When did Alpha come along? I think it was the end of 2020, Yeah. 163 00:17:55,530 --> 00:17:59,660 Yeah. Was it called something that was that the one that was called the Kent variant. Yes exactly. 164 00:17:59,710 --> 00:18:07,800 Yeah. Yeah. Yeah exactly. Yeah. Which supposedly originated or was found in Kent in Greater London. 165 00:18:08,910 --> 00:18:15,330 So who was doing I mean. Yes. So the detection of these variants is that's a job for people who are working in genomics. 166 00:18:16,170 --> 00:18:19,590 So who were your collaborators, who were who were doing that kind of work? 167 00:18:19,950 --> 00:18:29,070 So we have a team somewhat it is very involved with Cog-uk, which is the Consortium for the Genomics in UK, 168 00:18:29,460 --> 00:18:37,410 and we have our collaborators in Edinburgh who do a lot of the genomics plus by Brazil was very involved. 169 00:18:37,740 --> 00:18:49,500 So the whole team really and they were mostly doing the genomics analysis then and I did the stats analysis from here. 170 00:18:49,740 --> 00:18:58,260 That's how we structured that project. And how did, what was the shape of the way the alpha variant spread? 171 00:18:59,520 --> 00:19:06,240 So what I was looking at, what was established was that we found it quite early on in Kent and Greater London. 172 00:19:06,660 --> 00:19:14,010 And then what I was interested in through more, it was so, you know, it's found. 173 00:19:14,010 --> 00:19:18,809 Yeah. So it's going to be spreading faster in Kent and Greater London obviously. 174 00:19:18,810 --> 00:19:28,170 But can we show that through the final geography analysis and can we order through show that through the stats analysis just to have a robust 175 00:19:28,170 --> 00:19:38,760 sort of result and then try to understand why is it spreading faster in certain locations And know the other obvious answer is mobility, 176 00:19:38,940 --> 00:19:48,479 but just to put numbers to it. So what we found was that number of imports from Kent and London into different pockets of 177 00:19:48,480 --> 00:19:58,590 England was greatly associated with where it started spreading and it seems quite intuitive. 178 00:19:58,920 --> 00:20:07,829 But the purpose of all of this was that international import is important, but once you it, 179 00:20:07,830 --> 00:20:13,350 it's really important to understand what scale at which you're looking at for your results. 180 00:20:13,350 --> 00:20:23,010 And over here it was subnational levels and understanding the epicentre and how different areas are connected to these epicentres, 181 00:20:24,120 --> 00:20:31,050 just putting numbers to really these results. So it wasn't about the transmissibility of the virus itself, it was more about the people. 182 00:20:31,380 --> 00:20:39,240 Oh, I mean, yeah, I think it's going to be very difficult to talk about transmissibility with a lot of certainty at the population level. 183 00:20:39,330 --> 00:20:47,100 Ideally, to assess transmissibility, you would do a controlled experiment, but you can't really do that. 184 00:20:47,640 --> 00:20:54,360 I think at the population level, like I said, so many factors apart from your question, a very important question. 185 00:20:54,630 --> 00:21:01,230 What really affects transmission over and above biological properties of a pathogen? 186 00:21:01,740 --> 00:21:11,250 And I think it's really difficult to disentangle this at the population level with a lot of precision and accuracy. 187 00:21:11,520 --> 00:21:12,809 And that's what we talk about. 188 00:21:12,810 --> 00:21:19,950 There is a little bit there might be and there were other controlled biological experiments which said Alpha was a bit more transmissible. 189 00:21:20,640 --> 00:21:25,610 But how much more transmissible? And how much of that is attributed at the population level? 190 00:21:26,000 --> 00:21:30,890 I think it's sometimes difficult to make that connection when you just have observational data, 191 00:21:31,430 --> 00:21:38,480 like at the population level, it's all mobility and intrinsic properties that led to its spread. 192 00:21:38,750 --> 00:21:46,280 Mm hmm. So how did you collect that kind of data? Data on mobility and the human behaviour side of it. 193 00:21:47,150 --> 00:21:51,590 So, okay, I feel like all of this stems from what Moore had said access to, 194 00:21:51,590 --> 00:22:00,920 but he had access to all the data which was at the local authority level in England or the mobile provider, 195 00:22:01,280 --> 00:22:05,940 and also Google mobility data, which was quite useful. 196 00:22:05,960 --> 00:22:12,140 I mean, all these mobility leaders that have their own set of biases and problems really, 197 00:22:12,920 --> 00:22:18,799 but we try to make as much use of them as possible and try to understand what 198 00:22:18,800 --> 00:22:23,960 sort of biases are coming from different sources of the same sort of data. 199 00:22:24,380 --> 00:22:31,490 And that's exactly what we did. Just use aggregate population level data about how many trips are being made between 200 00:22:31,490 --> 00:22:36,950 two different locations in England and use that to inform mobility patterns. 201 00:22:37,010 --> 00:22:43,850 Mm hmm. And did you go on from that to look at the other variants that emerged as time went by? 202 00:22:43,940 --> 00:22:53,180 Yeah, exactly. So again, the second paper, which is again part of my first chapter, was looking at the spread of Delta in England, 203 00:22:53,960 --> 00:23:03,530 which was originated or found in India first and then spread quite substantially in England as well. 204 00:23:03,980 --> 00:23:08,840 And yeah, I could talk about the results of that. 205 00:23:09,090 --> 00:23:19,010 Yes, yes, yes. Again, so we had a Edinburgh team leading the final geography or the genomic analysis, 206 00:23:19,010 --> 00:23:29,000 which I think one of the main results they found was that if you remember around I guess late April 21, was it? 207 00:23:29,240 --> 00:23:39,230 Yes, late April 21, we had hotel quarantine for people travelling from India because this had spread and had a deadly wave in India. 208 00:23:39,710 --> 00:23:42,950 So actually that's what the politicians decided to do. 209 00:23:42,950 --> 00:23:46,250 But more often than not, it's already too late. 210 00:23:47,270 --> 00:23:50,950 If you're finding these variants, it's likely that there are lot, 211 00:23:50,960 --> 00:23:57,800 many parents already seated in England, but they did decide to do this hotel quarantine. 212 00:23:57,800 --> 00:24:05,690 And what the genomic analysis found was the quarantine did help in the slowing of the spread of the variant. 213 00:24:06,080 --> 00:24:13,570 But the genes, if you were to think about one person infecting another or infecting another, the genes, 214 00:24:13,700 --> 00:24:17,660 the genes of which ended up being the largest in England of the Delta variant, 215 00:24:17,930 --> 00:24:22,530 were already seeded in England before they implemented the hotel quarantine. 216 00:24:22,660 --> 00:24:25,760 So how useful is it? 217 00:24:25,940 --> 00:24:39,380 It might be, but I think it has to be backed with other non-pharmaceutical interventions that you can do within the country or prepare the country. 218 00:24:41,240 --> 00:24:52,940 And what I looked at in that is how people were mixing within locations and how that affected the growth of Delta in different parts of the country. 219 00:24:52,940 --> 00:25:00,860 And again, it was mobility because the lockdown had opened and people were mixing more freely within their areas. 220 00:25:00,860 --> 00:25:08,480 And even if they were not moving a lot between areas, even because it was already seated in different parts of England, 221 00:25:09,110 --> 00:25:14,330 local mobility patterns were associated with the spread of Delta. 222 00:25:14,420 --> 00:25:23,030 Mm hmm. And I should probably of asked you this before, but how do you turn that into statistics, into a numerical problem that you can address? 223 00:25:24,830 --> 00:25:34,700 Right. So we do have model. So models of when I think about models, I've always thought about why what is the purpose of modelling? 224 00:25:34,700 --> 00:25:39,349 What I think is to get some signal from noise. 225 00:25:39,350 --> 00:25:45,649 It's the signal versus noise. Comparison is quite common in the field where the purpose is. 226 00:25:45,650 --> 00:25:55,820 You have all this data, can you find some trends? Can you find association between two different moving parts of the vehicle? 227 00:25:55,820 --> 00:26:01,820 Really? And what we looked at was the outcome being how fast Delta was growing in proportion 228 00:26:01,820 --> 00:26:08,090 to the other variant which was around and what was changing along with it. 229 00:26:08,100 --> 00:26:13,190 So it was mobility. We accounted for vaccination status that was also changing over time. 230 00:26:13,910 --> 00:26:17,389 We accounted for a number of inputs which really didn't have an effect. 231 00:26:17,390 --> 00:26:21,620 As we were talking, it was more local mobility. So having all these moving parts. 232 00:26:22,050 --> 00:26:27,330 And then having your outcome and then seeing which one is more associated with not saying it caused it, 233 00:26:27,570 --> 00:26:32,280 because that's another different discussion and it requires a lot more detailed analysis. 234 00:26:32,640 --> 00:26:41,390 But when you can see two moving parts that can give you an insight into what the mechanism might really be, and that's how we do the modelling. 235 00:26:41,400 --> 00:26:42,820 That's the only explanation. 236 00:26:44,250 --> 00:26:53,550 And and to what extent were the studies that you were doing, you and your colleagues were doing influencing policy at the national level? 237 00:26:54,550 --> 00:27:05,190 Oh, I'm not entirely sure. These were somewhat retrospective even of real time, but I think by the time again, 238 00:27:05,190 --> 00:27:12,270 moderates was quite involved with a lot of the other groups that were in forming government policies. 239 00:27:12,280 --> 00:27:19,980 So I'm not quite sure how much of the preliminary results were being relayed on to the government. 240 00:27:20,470 --> 00:27:25,220 Exactly. Yet this was part of Sage four. He was part of SPI am definitely. 241 00:27:25,980 --> 00:27:32,190 But yeah, I'm not sure how much he was relaying or how much he, you know, not I'm not sure. 242 00:27:32,200 --> 00:27:43,139 But our results did work. Our papers did come out as retrospective analysis, but not too delayed, if that makes sense, within a few months. 243 00:27:43,140 --> 00:27:46,770 But through the whole peer review process, it takes a lot of time. 244 00:27:46,770 --> 00:27:56,760 But I'm sure there were feed ins I don't like say it said, Has your PHC become entirely about COVID? 245 00:27:56,790 --> 00:28:00,710 I hope the mosquito borne virus is being forgotten or nothing. 246 00:28:01,740 --> 00:28:12,990 I thankfully, I have some excellent researchers around me who are still working on many important diseases that are relevant to the entire world. 247 00:28:13,320 --> 00:28:18,600 So I've been part of small projects looking at dengue, Zika and chicken. 248 00:28:18,600 --> 00:28:28,740 Go now in Mexico with one of my colleagues, Bernardo, and I hope to do more of that, at least in the next at least in the last year. 249 00:28:29,040 --> 00:28:36,869 I do want to, if not after my PhD, because I do understand, I know it's COVID and it might get boring after a point, 250 00:28:36,870 --> 00:28:42,660 but I think there's just so much data that I it's almost impossible not to make use of all then 251 00:28:42,840 --> 00:28:49,970 access to data that you have to understand all these mechanisms that govern disease spread. 252 00:28:51,150 --> 00:28:54,960 So how far through are you at the moment? When do you intend to submit? 253 00:28:55,590 --> 00:28:59,190 I'm supposed to submit by October 20, 24. 254 00:28:59,460 --> 00:29:03,550 Oh, right. Okay. Yeah, I suppose it's a four year program. Yes, yes, yes. 255 00:29:03,570 --> 00:29:08,460 My son did one, so it's a nice, um. 256 00:29:09,490 --> 00:29:12,760 Uh. Yes. 257 00:29:12,760 --> 00:29:17,709 I wonder I've got a question about collaboration because, um, I mean, 258 00:29:17,710 --> 00:29:23,920 obviously your field is very interdisciplinary, so you will always have been collaborating with others. 259 00:29:23,920 --> 00:29:30,670 But did you have a sense that working on COVID, there was a greater sense of sharing, 260 00:29:30,670 --> 00:29:36,130 of openness, of data, of bringing more people in who perhaps might not have been? 261 00:29:36,550 --> 00:29:39,310 Absolutely. No, I think that's a brilliant question. 262 00:29:39,310 --> 00:29:47,410 And I think if you just look at the number of authors that you see on the paper, that's changed massively. 263 00:29:48,070 --> 00:29:54,430 People argue that it might not be reflective of how much people work or how much work people have put in, 264 00:29:54,430 --> 00:30:04,420 but I think it's almost amazing how much people are recognising the importance of interdisciplinary work. 265 00:30:04,960 --> 00:30:14,650 I mean, both my papers and now have been with people from a completely different field, which I do not completely understand. 266 00:30:14,650 --> 00:30:25,000 But together we can bring so much insight and almost sometimes do robust checks to the kind of result the counterpart is bringing, 267 00:30:25,150 --> 00:30:30,940 which I think is excellent because you're not only talking about your results being robust, 268 00:30:30,940 --> 00:30:36,760 you're also talking about or you can also look at mechanisms through which they get the same results, 269 00:30:37,300 --> 00:30:47,860 which is brilliant, I think, and getting perspectives not only so when I talk about Delta growing faster in a certain place, 270 00:30:48,130 --> 00:30:53,620 I would also want to maybe collaborate with social scientists to understand why was that really happening? 271 00:30:53,620 --> 00:30:59,469 Why were people moving around in one place more than the other, which you can't really just get from the data you have? 272 00:30:59,470 --> 00:31:07,510 So it's important to if you really want to make a change, you have to have you need to have this multi-layered collaboration to solve a big crisis. 273 00:31:07,510 --> 00:31:15,640 And I think COVID really was shining a light on this interdisciplinary work going around the globe. 274 00:31:15,650 --> 00:31:24,250 And when you also asked about data access and how it was definitely a problem initially, and even now, 275 00:31:24,250 --> 00:31:35,079 I think that cases where data is not made available freely, a lot of it is to do with just ethics of data governance. 276 00:31:35,080 --> 00:31:44,860 And I think that a lot of people, like I said, working on this and my supervisor also has a collaboration with Google called Global Dot Health, 277 00:31:45,100 --> 00:31:49,930 where they're trying to make lots of international linguists, 278 00:31:50,230 --> 00:31:55,600 linguists available to scientists all over the world so that we can take science forward. 279 00:31:57,460 --> 00:32:04,630 And so are you fairly optimistic that the lessons that have been learned through working on the pandemic might continue to apply in the future? 280 00:32:05,950 --> 00:32:10,089 I hope so. I mean, I would definitely hope so. 281 00:32:10,090 --> 00:32:21,190 I do see a change in general about data sharing and science sharing, really idea of sharing and the fact that things have become online. 282 00:32:21,190 --> 00:32:29,320 I know it's been problematic for so many reasons, but I think access to just knowledge has become greater, 283 00:32:30,940 --> 00:32:35,710 sometimes not in the direction you would want it, 284 00:32:36,550 --> 00:32:45,040 but I think there have been so many positives and negatives from this that you can maybe focus on positives when you want to at least. 285 00:32:45,040 --> 00:32:48,220 And and I was struck that you mentioned when you were at Harvard, 286 00:32:48,680 --> 00:32:55,510 the main focus was on non-communicable diseases, which are obviously the biggest problem in Western societies. 287 00:32:56,410 --> 00:33:01,660 Infectious diseases is still an absolutely enormous problem, mainly in the in the global south. 288 00:33:02,710 --> 00:33:05,740 Is that the area that you want to continue to work in? 289 00:33:05,740 --> 00:33:09,879 And do you think the fact that everybody's been looking at infectious disease for the last 290 00:33:09,880 --> 00:33:14,950 three years might raise the profile of what sometimes get called neglected diseases, 291 00:33:14,950 --> 00:33:18,010 even though they affect millions of people? Absolutely. 292 00:33:18,020 --> 00:33:25,210 You know, when I say the focus was on non infectious diseases, it was more so at Harvard, 293 00:33:25,300 --> 00:33:28,480 the School of Public Health, They have a Department of Infectious diseases. 294 00:33:28,480 --> 00:33:38,709 But when you talk about the Department of Epidemiology or Department of Bio stat, all of our case studies are guided by non-communicable diseases. 295 00:33:38,710 --> 00:33:46,720 So I was never really exposed to methods from stats that I can apply to infectious diseases in the regular modules. 296 00:33:46,990 --> 00:33:50,800 I would have to go to the infectious diseases modules to actually see that. 297 00:33:53,110 --> 00:33:57,429 Yes, I mean, I mentioned Roy earlier. 298 00:33:57,430 --> 00:34:01,690 He has a big group working on neglected tropical diseases. 299 00:34:02,560 --> 00:34:09,130 Some of them truly are neglected tropical diseases. They're very niche in terms of where they exist. 300 00:34:10,630 --> 00:34:18,390 I mean, I. I really, really do hope that people understand the importance of studying infectious disease and spending, 301 00:34:18,780 --> 00:34:22,270 as you can say, more funds into this whole research area. 302 00:34:22,290 --> 00:34:29,939 But unfortunately, I mean, I think of it as an excellent example of when people actually do that. 303 00:34:29,940 --> 00:34:42,870 It's when it affects them. The Western world. You know, I, I remember talking to my family back in India when COVID was happening, 304 00:34:42,870 --> 00:34:51,599 and they were just talking about how so many people didn't really care about COVID because they had just so many other diseases, 305 00:34:51,600 --> 00:34:56,810 so many other problems in life that COVID is just one of them. 306 00:34:56,820 --> 00:35:05,010 And having lockdowns when so many people can't afford to really stay at home and their 307 00:35:05,010 --> 00:35:11,639 livelihoods depend on getting wages for the and you're asking them to stay at home. 308 00:35:11,640 --> 00:35:16,200 And then it was we literally are surrounded by so many other diseases. 309 00:35:16,200 --> 00:35:21,300 What is one more disease going to do to us? It is a disease of the privileged. 310 00:35:21,330 --> 00:35:29,729 I think over in general. I know a lot of people won't agree with it, but it's just my personal opinion. 311 00:35:29,730 --> 00:35:33,719 I've seen how people respond to it and what they feel about it. 312 00:35:33,720 --> 00:35:39,510 It isn't politically driven, it is truth driven, and a lot of people won't get it. 313 00:35:39,510 --> 00:35:43,380 But I know a lot of people in India will get it because you see that impact. 314 00:35:44,640 --> 00:35:48,720 You know, that's very interesting and sobering perspective. 315 00:35:49,170 --> 00:35:53,460 So, I mean, just coming back to to you yourself living here in the UK, 316 00:35:53,910 --> 00:35:59,850 how threatened did you feel by the possibility of actually catching the virus yourself? 317 00:36:02,760 --> 00:36:06,030 Like I said, I think of when I was growing up in India. 318 00:36:06,330 --> 00:36:12,690 I mean, I've been very privileged to grow up in a safe environment where I was taken care of. 319 00:36:12,690 --> 00:36:22,139 I had access to a lot of health care, but I was always cognisant of the problems that exist, at least to some extent. 320 00:36:22,140 --> 00:36:30,660 I wouldn't say I've been brilliant at understanding how the world functions, but I was cognisant to some extent and I've always I mean, 321 00:36:30,660 --> 00:36:33,239 I was always scared when I when I was living in Staines, 322 00:36:33,240 --> 00:36:43,709 we were living in a three storey apartment sort of set up and I was more scared for the person living downstairs. 323 00:36:43,710 --> 00:36:53,730 It was just an elderly man, so I didn't want to get infected just so that I do not touch surfaces while going up to my apartment, 324 00:36:53,730 --> 00:36:56,970 which ends up infecting this person. 325 00:36:58,320 --> 00:37:04,440 But maybe deep down I was of course scared of all these complications that were happening to young, healthy individuals. 326 00:37:04,440 --> 00:37:09,000 Also not downplaying the is how long COVID has affected people. 327 00:37:09,390 --> 00:37:13,800 But if I was completely honest, not entirely scared. 328 00:37:14,400 --> 00:37:20,700 And also I think the health care system, even though it was very overburdened, I've had a very good experience with the NHS. 329 00:37:20,700 --> 00:37:26,759 You know, even during COVID, I had a bad day. 330 00:37:26,760 --> 00:37:32,549 I was not feeling too well and the ambulance had rushed and they took such good care of me. 331 00:37:32,550 --> 00:37:39,270 It was the middle of the pandemic and I did have faith in the health system, you know. 332 00:37:40,920 --> 00:37:44,219 And how did it impact on on your ability to work? 333 00:37:44,220 --> 00:37:50,549 I mean, obviously, you chose to work from home like like so many people where you were. 334 00:37:50,550 --> 00:37:55,770 You working longer hours, do you think? Sometimes I think sometimes not entirely. 335 00:37:55,770 --> 00:38:02,759 I think of generally in general, I do not push myself because I know I will burn out and it happens quite quickly. 336 00:38:02,760 --> 00:38:05,850 If I do that. I'm very aware of how much I can work. 337 00:38:06,120 --> 00:38:11,249 I try to stick to 9 to 5, even when it's strict deadlines. 338 00:38:11,250 --> 00:38:14,700 I try to be just more productive. Not everybody can do that. 339 00:38:14,940 --> 00:38:17,700 I know it's difficult, but I think that's how I've grown up. 340 00:38:17,700 --> 00:38:26,249 So I used to just finish my work by five sun setting, work setting and yeah, yeah, I was able to do that. 341 00:38:26,250 --> 00:38:34,650 I think working from home was okay for me. I my husband is lovely, so it was so nice to be able to spend so much time with him again. 342 00:38:34,890 --> 00:38:40,440 I know people have had difficult times, but I was just, I think, lucky again, 343 00:38:40,440 --> 00:38:45,780 just counting my blessings and also how privileged we were to be able to afford to do that. 344 00:38:47,820 --> 00:38:53,380 But yeah, it was okay for me if not good. Yeah. 345 00:38:53,610 --> 00:39:02,910 And, and did the, the, the pandemic conditions change or, or I mean things like going out for walks. 346 00:39:03,090 --> 00:39:09,000 Did you have routines that you adapted to, to kind of fit around the constraints. 347 00:39:09,270 --> 00:39:13,040 Yeah yeah yeah. We we. You. You're interested in dance? 348 00:39:13,050 --> 00:39:16,110 I seem to have read somewhere. I do dance? Yes. Yeah. 349 00:39:16,230 --> 00:39:19,700 Oh. Oh, yeah. It's been a while. 350 00:39:19,710 --> 00:39:22,860 But I used to dance for my college back in undergrad. 351 00:39:23,190 --> 00:39:26,610 Um, I. Oh, we're not into sports, so we. 352 00:39:26,910 --> 00:39:34,290 We bought new bikes, and we used to just cycle around winter and just go for really long bike rides. 353 00:39:34,290 --> 00:39:39,689 And that was most of the time. And then my cousin's back in India. 354 00:39:39,690 --> 00:39:46,230 We had a regular Zoom call where we were all working out, which everyone was doing around the world, but we had that. 355 00:39:46,500 --> 00:39:50,100 I was celebrating buddies, my grandparents buddies. 356 00:39:50,100 --> 00:39:52,110 We were having a little quiz online, 357 00:39:52,530 --> 00:40:00,030 but I think I just reconnected with my family a lot during this time and it was so nice to have everyone fighting this together. 358 00:40:00,030 --> 00:40:08,129 And um, I, but I do remember, I know this isn't the question, but, well, you could ask me the impact. 359 00:40:08,130 --> 00:40:16,140 I think the impact was okay for me, but I remember my granddad use he was quite active, used to play tennis table tennis, 360 00:40:16,140 --> 00:40:26,220 swim and because he was susceptible or was at high risk, he was confined to his house and that had a very severe impact on him. 361 00:40:27,780 --> 00:40:31,530 He, he was, he was he wanted to go out. He wasn't scared. 362 00:40:31,530 --> 00:40:37,680 He wanted to just play. But then it's all this we didn't allow him really. 363 00:40:37,710 --> 00:40:41,860 And it it did not impact like in a good way. 364 00:40:41,910 --> 00:40:47,580 And he did whatever he could use to walk around in the house cycle stationary cycles. 365 00:40:47,580 --> 00:40:51,090 But yeah, I just remember not everybody had the same. 366 00:40:51,750 --> 00:40:54,900 No, no. Everybody has a very, very different experience. 367 00:40:55,020 --> 00:41:00,270 Some people really enjoyed it, but, you know, some people were climbing up the walls. 368 00:41:00,330 --> 00:41:05,460 Yeah, exactly. Yeah, yeah. Yes. And things like remote working. 369 00:41:07,440 --> 00:41:11,190 Have you seen that that that's been adopted more widely. 370 00:41:11,190 --> 00:41:15,420 Are you using that more in your work now. Uh, virtual meetings and so on? 371 00:41:15,540 --> 00:41:21,150 I think so, yeah. I think it's become, like I said, very easy access to knowledge. 372 00:41:21,510 --> 00:41:27,570 So all you may be setting up a meeting with someone in the US used to feel like a big deal, 373 00:41:28,290 --> 00:41:33,990 but now it's just so common just setting up a meeting with someone and emailing people. 374 00:41:34,470 --> 00:41:40,740 Just the idea that you can email a very senior person and they will respond to you because everyone is online now in a way. 375 00:41:41,460 --> 00:41:44,590 I don't know what I used to think. Well, people not on their computers and would, 376 00:41:44,800 --> 00:41:47,780 but it could have just been because you were very junior before and the idea 377 00:41:47,800 --> 00:41:51,730 you just couldn't envisage that this person would respond to your instruction. 378 00:41:51,930 --> 00:41:57,230 And actually they do. Yeah, exactly. Well, I feel the words become closer. 379 00:41:57,960 --> 00:42:04,920 You're talking to people sitting in extremely different parts of the world and working on the same idea. 380 00:42:05,210 --> 00:42:07,380 It feels so much more connected. 381 00:42:07,380 --> 00:42:14,370 And in general, some days when I don't feel like coming into work just because I'm in a writing mode, I can go to a café and work. 382 00:42:14,370 --> 00:42:19,199 I can just work from home. My husband's been working remotely for most of the time. 383 00:42:19,200 --> 00:42:26,339 Even though he work, he goes to work sometimes and mostly for the social aspect of getting his team together. 384 00:42:26,340 --> 00:42:30,930 But he's quite productive at work, at home, and he has his own set up. 385 00:42:30,930 --> 00:42:36,330 So yeah, it's made life more flexible, I would definitely say. 386 00:42:37,560 --> 00:42:43,709 Yeah. And has the experience of the work you've done through the pandemic changed your 387 00:42:43,710 --> 00:42:47,970 thinking about the kinds of problems that you might like to work on in the future? 388 00:42:48,090 --> 00:42:56,520 Absolutely. Yes. Like I was saying, and you just how you put these questions into numerical models, 389 00:42:56,970 --> 00:43:01,350 when I did put them and I got these results, there was another question. 390 00:43:01,350 --> 00:43:04,589 Like I said, why is there more mobility in one place? 391 00:43:04,590 --> 00:43:11,460 Why is that really happening now that I see that this impact so apparent gross or variant of concern is growing, 392 00:43:11,790 --> 00:43:16,080 Why is that really happening, going deeper or digging deeper into the mechanism? 393 00:43:16,980 --> 00:43:27,540 And that's what I've been working on, is understanding how socio economic status, how equity in disease transmission impact or disease spreads. 394 00:43:27,570 --> 00:43:38,040 And I've been very fortunate to be working as part of a collaboration between Oxford University and Ernst and 395 00:43:38,040 --> 00:43:48,870 Young to answer a question that UK agency put forward about how testing was useful as an intervention in the UK. 396 00:43:49,740 --> 00:43:54,360 So we were made to do a lot of free and tests and PCR tests. 397 00:43:54,750 --> 00:44:04,470 So they are doing an incredible job doing the self evaluation, external self evaluation of whether the interventions in testing were really helpful. 398 00:44:04,800 --> 00:44:10,980 And through that we've looked at different sectors testing in schools, testings and testing. 399 00:44:11,030 --> 00:44:18,170 In health care settings. And what I focussed on is whether testing was really equitable because these tests are free, 400 00:44:18,180 --> 00:44:23,149 but are they really equitable in the sense that you released them? 401 00:44:23,150 --> 00:44:27,590 They are free and everybody should be taking them and everybody should be reporting it. 402 00:44:27,860 --> 00:44:29,540 But are people really doing that? 403 00:44:30,350 --> 00:44:36,650 That's what I've looked at and we've just submitted our report to you to say, Oh, we're in the process of finalising and submitting, 404 00:44:37,670 --> 00:44:46,040 but I hope to have those results and the set of extra results as part of my next chapter and hopefully publish soon. 405 00:44:46,550 --> 00:44:53,110 So did you have to collaborate with behavioural sciences on that? We do have that to interview people about their behaviour. 406 00:44:53,120 --> 00:45:02,300 Yes, I didn't do that. I did the quantitative analysis, but we have an excellent team from Eli from Oxford doing all the qualitative analysis, 407 00:45:02,570 --> 00:45:10,700 understanding what was really happening by talking to people, going through a lot of reports that people had collected back in the day. 408 00:45:11,090 --> 00:45:16,040 So we have a lot of qualitative analysis and quantitative analysis. 409 00:45:17,090 --> 00:45:23,000 And is, I mean, is it too early for you to be thinking about postdoc positions or have you started to plan for that? 410 00:45:23,360 --> 00:45:29,209 I have age. I haven't thought about what it would be, but I'm quite certain it's going to be in research. 411 00:45:29,210 --> 00:45:35,120 And like I've mentioned hundreds of times, I absolutely love my supervisors. 412 00:45:35,450 --> 00:45:40,340 Ben, Crystal Moritz, Josie and I have so much to learn from them. 413 00:45:40,880 --> 00:45:47,850 So even then give me the settings because Ben Lambert, yet he was the one who connected me to my other two supervisors. 414 00:45:47,860 --> 00:45:54,079 He was my colleague in Imperial, but he got me involved in this UK tourism budget as well. 415 00:45:54,080 --> 00:45:58,560 Bristol Donnelly, Crystal Dunn and Moritz Kramer and Jose Llorente. 416 00:46:00,710 --> 00:46:08,120 Yeah, I mean, I've been I've enjoyed my time so much and I'm still enjoying it. 417 00:46:08,270 --> 00:46:14,270 And every time I speak to them, they're just so passionate about what they do and I have so much to learn from them. 418 00:46:14,660 --> 00:46:21,530 I feel like I've been researching hopefully around them for a bit longer before I can be a bit more independent. 419 00:46:21,530 --> 00:46:26,330 But I think I'll always want to collaborate with excellent scientists that have found you.