1 00:00:04,480 --> 00:00:08,740 So could you just start by saying your name and your current position? 2 00:00:09,550 --> 00:00:14,860 Yes. So I'm Naomi Campbell, and again, I have two affiliations, actually. 3 00:00:15,460 --> 00:00:26,200 You could say three or four. I'm a professor at the Future of Opera, which is in Colombo in the Department of Immunology and Molecular Medicine. 4 00:00:26,920 --> 00:00:33,910 I'm and because this is obviously about interview with Oxford and COVID, 5 00:00:34,270 --> 00:00:41,650 I'm academic at Oxford and I've been academic at Oxford since 2008 and since last year, 6 00:00:41,920 --> 00:00:51,970 last July, actually, I have also joined Drugs for Neglected Diseases Initiative and I headed the global Dengue programme. 7 00:00:52,630 --> 00:00:56,420 Well, that's a lot of responsibility. That's right. 8 00:00:56,560 --> 00:00:59,890 So going right back to your earliest childhood, really. 9 00:01:00,160 --> 00:01:04,090 Can you tell me a little bit about how you got interested in science? Yes. 10 00:01:04,260 --> 00:01:08,800 So so I am based in Sri Lanka and Sri Lankan, been living in Sri Lanka. 11 00:01:09,550 --> 00:01:15,040 That and I think very interested in animals and biology. 12 00:01:15,700 --> 00:01:22,120 And when I was watching BBC documentaries in all these things in Africa, I have to say that it was not in Sri Lanka. 13 00:01:22,120 --> 00:01:29,990 I watched it because my early childhood, the three years I spent in the UK because my parents came over studies. 14 00:01:30,100 --> 00:01:36,820 So I used to watch these documentaries by BBC about, you know, people going and discovering things. 15 00:01:37,570 --> 00:01:43,720 And that's really, really wanted, you know, I wanted to be a scientist and discover things. 16 00:01:44,080 --> 00:01:54,040 And that had been my aim throughout. And so, so even in Sri Lanka, you know, science in Sri Lanka is not really developed even now. 17 00:01:54,040 --> 00:02:01,110 And my childhood, it was very much underdeveloped. And so there was no opportunity to science. 18 00:02:01,120 --> 00:02:07,360 And anyway, for my A-levels I did biology and entered medical school and became a doctor. 19 00:02:07,880 --> 00:02:11,709 And my still, my dream was to become a scientist. 20 00:02:11,710 --> 00:02:21,340 And I was very much science research at the institute. And after doing my internships in shops, all that, I got the opportunity to, 21 00:02:21,640 --> 00:02:26,950 I got the opportunity to do a in Oxford through a Commonwealth scholarship, 22 00:02:27,460 --> 00:02:39,430 and I did my PhD dphil with him at the whim of the Weatherall Institute for Miller Weatherall Institute of Medicine, I think for him. 23 00:02:40,390 --> 00:02:43,660 Yeah. So that's how I sort of entered science. 24 00:02:44,440 --> 00:02:46,750 And what was the subject of your PhD or your Dphil? 25 00:02:47,110 --> 00:02:55,030 Yeah, it was cutaneous immunology, a tool specifically to tell us as to why this was used to fight us clotting chickenpox. 26 00:02:55,510 --> 00:03:06,610 And I was very much interested in infection and immunity and this and specifically actually to let you know, before I came to do my dphil, 27 00:03:06,940 --> 00:03:16,750 I was very much interested in dengue because when I was an intern of dengue, he had come to Sri Lanka about five years before I was reminded. 28 00:03:17,230 --> 00:03:23,980 But it was a very few cases. But during my internship there were lots of kids getting, you know, having dengue about three deaths. 29 00:03:23,990 --> 00:03:29,920 And so I really wanted to find out why do kids get to visiting and whether people would find a treatment. 30 00:03:29,930 --> 00:03:35,200 And so I started doing studies on data even before coming to Oxford. 31 00:03:35,620 --> 00:03:40,510 And just tell me tell me a little bit more about dengue. What what what is what kind of illness is it? 32 00:03:41,060 --> 00:03:52,120 Well, so it's a mosquito borne virus and it is currently the most fastest evolving emerging white infection in the world. 33 00:03:52,540 --> 00:03:59,420 It's estimated to infect around 390 million people a year, of which one fourth are asymptomatic infections. 34 00:04:00,140 --> 00:04:03,070 So we are talking about about 100 million infections per year. 35 00:04:03,520 --> 00:04:08,559 And these are obvious in the endemic countries, which is near the tropics where you get mosquitoes. 36 00:04:08,560 --> 00:04:10,990 So you wouldn't have dengue in countries like the UK. 37 00:04:11,320 --> 00:04:18,520 And there are many people in the UK, high income countries which are not aware about the devastation I think it causes. 38 00:04:18,760 --> 00:04:23,170 But because people are very much used to COVID, how the hospitals overburdened. 39 00:04:23,500 --> 00:04:27,970 So people understand when you see overburdening of healthcare systems. 40 00:04:28,000 --> 00:04:33,940 Now earlier they wouldn't understand what it meant. So this is what happens every time. 41 00:04:33,940 --> 00:04:37,600 You know, dengue is so so we have to dengue seasons in Sri Lanka now. 42 00:04:37,990 --> 00:04:42,190 So and that's when we have a ridiculous amount of patients sometimes. 43 00:04:42,880 --> 00:04:51,010 And our health systems get overburdened as seen in all developed countries as well during COVID. 44 00:04:51,190 --> 00:04:54,280 And so this has been getting worse every year. 45 00:04:55,120 --> 00:05:00,580 And so when I was doing my internship, it was very scary because we didn't know much about this. 46 00:05:00,610 --> 00:05:04,450 Now we do know and. So the mortality rates are less. 47 00:05:05,200 --> 00:05:11,660 Earlier, when countries initially experienced the mortality rates for like 10%, I mean, that's a 10% mortality. 48 00:05:12,970 --> 00:05:14,860 It is ridiculous. 49 00:05:15,130 --> 00:05:25,860 But now, because we understanding what it is in most countries is less than 0.2% because we don't have a treatment this and no vaccine. 50 00:05:26,150 --> 00:05:30,410 So I was very much intrigued about thinking about viral immunology. 51 00:05:30,670 --> 00:05:37,810 And so this was an excellent opportunity to come to Greenwald's lab and to do a dphil under his supervision. 52 00:05:39,010 --> 00:05:42,130 Okay, So that. So what year did that take you up to? 53 00:05:42,160 --> 00:05:46,090 So you came in 2008, is that right? No, I came in 2004. 54 00:05:46,150 --> 00:05:52,600 Oh, right. Yes. And I did my dphil and had my graduation in early 2008. 55 00:05:53,290 --> 00:06:01,299 And since then, you know, because Graham was wanted to continue working. 56 00:06:01,300 --> 00:06:03,850 And, you know, it was a very interesting area to work on. 57 00:06:03,850 --> 00:06:10,299 So although I worked on chicken pox immunity to chicken pox during my difficult after I got back Sri Lanka, 58 00:06:10,300 --> 00:06:14,680 dengue was a really, really important area given the importance of it. 59 00:06:15,020 --> 00:06:19,060 So and Graham really wanted to work on dengue as well. 60 00:06:19,090 --> 00:06:24,850 So both of us started working and collaborating on dengue. 61 00:06:25,210 --> 00:06:29,370 So when Corbett came Hang on a minute, hang on a minute, we haven't got there yet. 62 00:06:29,380 --> 00:06:37,840 So just tell me a little bit about a bit more about what you were able to do on dengue between all August in 2019. 63 00:06:38,260 --> 00:06:43,740 Yeah, So, so 2008 to 2020. So I'm talking about 12 years, right? 64 00:06:44,490 --> 00:06:47,280 So we did do quite a bit on dengue. 65 00:06:47,290 --> 00:07:00,390 I mean, so basically in dengue y people, some people get severe disease is you have these blood vessels and fluid, they become very permeable. 66 00:07:00,640 --> 00:07:07,120 So sweet leaks out of the blood vessels and the fluid accumulates in your crew cavities, your peritoneal cavities. 67 00:07:07,120 --> 00:07:12,940 And and when fluid leaks, your blood pressure drops. So and then you get hypertension and shock. 68 00:07:13,060 --> 00:07:18,700 So that's a big shock syndrome or I'll actually end the blood supply to our vital organs is diminished. 69 00:07:18,940 --> 00:07:25,450 So you get liver issues and then you write us directing the liver, causing hepatitis as well, a failure, so on. 70 00:07:25,840 --> 00:07:29,180 And also what you call dengue haemorrhagic fever because it causes bleeding. 71 00:07:29,200 --> 00:07:35,560 So you do get this bleeding tendencies. So as I said, it's not everybody who gets this leakage. 72 00:07:35,770 --> 00:07:42,549 So the number one question we wanted to answer was what causes leakage and and why do some people get it? 73 00:07:42,550 --> 00:07:47,230 You know, those two, it simply seem like the most important questions to answer. 74 00:07:47,980 --> 00:07:51,850 And so we we to see to understand what causes leakage. 75 00:07:51,850 --> 00:07:58,450 We looked at T-cells, we were looking at antibodies and we were looking at mediators that specifically cause especially leakage. 76 00:07:58,750 --> 00:08:01,980 And we found it important media to follow basic activating factors. 77 00:08:02,290 --> 00:08:10,930 And for us there was this drug which was widely used even in the UK for chronic earlier added allergies called Uptodate, 78 00:08:11,320 --> 00:08:14,559 which is an anti I mean it you said it has histamine obviously, 79 00:08:14,560 --> 00:08:21,170 but apart from that it had platelet activating factor receptor blocking abilities so we did to treat it 80 00:08:21,170 --> 00:08:27,070 so because you know it was important part of visibility and you had a receptor blocker we thought okay, 81 00:08:27,370 --> 00:08:30,520 let's go ahead and try you to put it in a sophisticated trial. 82 00:08:30,730 --> 00:08:36,220 So we were able to see so the bench to bedside we were able to consider, you seem to think of trials. 83 00:08:36,640 --> 00:08:47,920 And so so we did the first trial in hospital which looks promising and and we got an early signal to say that if you gave it earlier, 84 00:08:48,130 --> 00:08:52,030 it might be more effective. So we've been tied to the second clinical trials, 85 00:08:52,990 --> 00:08:58,690 which we did outpatient so that you could get patients earlier and rather than 86 00:08:58,690 --> 00:09:03,580 waiting for them to be in hospital and and the data was looking quite good, 87 00:09:03,820 --> 00:09:09,600 but we had to stop the trial chart of 31 patients because of COVID. 88 00:09:10,810 --> 00:09:15,130 I see. Right. So we finally arrived at COVID. 89 00:09:15,370 --> 00:09:24,909 So can you I'm asking everybody this. Can you remember when you first heard about what was going on in Newcastle and and how long 90 00:09:24,910 --> 00:09:29,020 it was before you realised this was going to be something that affected the whole world? 91 00:09:29,500 --> 00:09:38,650 Yes. And so so it was in December because as a person working on viruses, you know, you, you keep an eye on these things and so on. 92 00:09:38,890 --> 00:09:42,540 And both my husband and my son were planning to go to China. 93 00:09:42,750 --> 00:09:47,440 My son had never gone and they were planning to go to China Boy's trip in March. 94 00:09:47,830 --> 00:09:51,700 And and when I when I was suspended, I said, you know, I don't think you're going to China. 95 00:09:51,850 --> 00:09:54,940 And they were like, I mean, this is a few cases of pneumonia. 96 00:09:54,940 --> 00:09:59,460 You are overreacting. A typical, you know, like difficult case, you know, death. 97 00:09:59,650 --> 00:10:02,700 And of course we are going with and I said, I don't think you very much this the. 98 00:10:02,820 --> 00:10:07,530 And look good. This looks pretty bad. And this is not looking good at all. 99 00:10:07,950 --> 00:10:14,730 And you see that? That's how it was end of December when things were not looking good. 100 00:10:15,120 --> 00:10:20,880 And, you know, as a part of our collaboration, me and Graham, I visit Oxford once a year, sometimes twice a year. 101 00:10:21,150 --> 00:10:30,930 Graham also comes to Sri Lanka once a year because, you know, apart from the virtual meetings we have, it's always important to meet. 102 00:10:30,930 --> 00:10:36,329 But when you meet, you know, face to face, it's always you can communicate that. 103 00:10:36,330 --> 00:10:40,500 I think. I think, you know, I think we accept that even after COVID, 104 00:10:40,740 --> 00:10:46,860 after we all the success of the virtual world, face to face communication is so much better. 105 00:10:46,860 --> 00:10:53,280 So. So we do we we were meeting twice a year and gram usually comes in January you see 106 00:10:53,640 --> 00:10:59,550 so Graham did come come end of January to Sri Lanka for his annual collaborator 107 00:10:59,640 --> 00:11:13,890 visit and that's when you know UK had reported that he landed UK was reporting the first cases and and the Sri Lanka reported the first COVID patient. 108 00:11:14,400 --> 00:11:18,830 And and you know he's obviously very excited about science and AI. 109 00:11:19,050 --> 00:11:26,400 And so we sort of I remember this we were planning actually not in the lab, 110 00:11:26,730 --> 00:11:33,440 but both of us were like sitting very relaxed casually and thinking, okay, this doesn't sound look good, obviously. 111 00:11:33,450 --> 00:11:38,319 And by end of January, people knew that it was, you know, a bad thing to happen. 112 00:11:38,320 --> 00:11:45,060 And and what should we be looking at? And we were designing all we were deciding on all the things we should be doing. 113 00:11:45,870 --> 00:11:50,040 What should we be looking at? Antibodies, T cells, other things, you know. 114 00:11:50,510 --> 00:11:54,719 But but because, you know, ethics applications can be troublesome. 115 00:11:54,720 --> 00:12:00,470 So we wanted to, you know, to apply from time to time asking for things that hold you back. 116 00:12:00,480 --> 00:12:10,770 Right. So so what do we report, What samples do we need and all that we were planning end of January and yeah, 117 00:12:11,280 --> 00:12:24,150 so that's how how things basically evolved and yeah so it was, it was good that we could have a face to face discussion about COVID in January itself. 118 00:12:25,830 --> 00:12:30,299 And just I think just initial background. 119 00:12:30,300 --> 00:12:37,140 Can you just give me a quick sort of thumbnail of how the COVID epidemic evolved in Sri Lanka itself? 120 00:12:37,500 --> 00:12:44,160 Yes. So just to let you know, so when I look back about how people with affected Sri Lanka, 121 00:12:44,460 --> 00:12:49,230 I mean, obviously COVID affected every all countries and in a bad way, 122 00:12:49,530 --> 00:12:57,260 but I think especially specifically low and middle income countries and low income countries, they were also good sites to meet of of COVID. 123 00:12:57,270 --> 00:13:02,340 And that's might be surprising when I say that's right, but I'll expand myself. 124 00:13:02,550 --> 00:13:10,880 What I mean by that. So when we actually came to Sri Lanka, there were only apart from the private labs which were doing their thing. 125 00:13:11,310 --> 00:13:14,129 So we had like two private labs which would do real time. 126 00:13:14,130 --> 00:13:22,770 PCR There was only one Ministry of Health lab and our university lab which could do realtime PCR cooking. 127 00:13:23,160 --> 00:13:28,240 And so we are talking about this, that sort of facilities and, and we and you know, 128 00:13:28,290 --> 00:13:34,670 the funding, we are very closely working with the clinicians who manage it. 129 00:13:35,070 --> 00:13:41,700 So we have the National Institute of Infectious Diseases in Sri Lanka, which was the Incident Management Centre. 130 00:13:41,700 --> 00:13:46,200 So all that, that is the place we have patients within Yemen. 131 00:13:46,290 --> 00:13:53,960 So all our clinical trials were conducted in this hospital. All our other studies are conducted in these hospitals and we had, you know, 132 00:13:54,000 --> 00:13:59,610 working with this hospital very closely for like ten years, actually more than ten years. 133 00:14:00,210 --> 00:14:04,950 And with the key physicians of the hospital for a very, very long time, even putting on books, 134 00:14:04,950 --> 00:14:13,590 actually even for my Ph.D. And but then when COVID came, that hospital was the first one to be converted to a COVID hospital. 135 00:14:14,100 --> 00:14:18,780 You see, because this and actually it's huge of infectious diseases with previous people, idiot. 136 00:14:19,160 --> 00:14:23,070 And you know, Sri Lanka was under the British rule for a while. 137 00:14:23,460 --> 00:14:30,180 And so and earlier there were several pandemics, you know, like, dude, I'm talking about in the 18th century, 19th century. 138 00:14:30,480 --> 00:14:33,299 So this hospital was built as a quarantine hospital. 139 00:14:33,300 --> 00:14:41,700 So it's not in the heart of Colombo residential area, but it's sort of, you know, in in in an isolated area. 140 00:14:41,910 --> 00:14:45,959 But now, of course, you can notice it in actual area of that. 141 00:14:45,960 --> 00:14:54,650 I know so and because we had this connection and then it became the COVID hospital, people naturally, then, you know, handling their samples, 142 00:14:54,660 --> 00:15:02,190 it's talking from, you know, for diagnostics, basically, you know, they were patients coming in and you needed to do you have to find them, right? 143 00:15:02,740 --> 00:15:06,430 I mean, diagnose. So you were getting all the PCR samples. 144 00:15:06,640 --> 00:15:17,620 We and, and because we got a fix very soon we were getting all the blood samples to look at biomarkers, antibodies and all the T-cells and, and Yeah. 145 00:15:17,620 --> 00:15:29,290 So and with that we were able to our lab you way engage in training many other labs setting setup that we just couldn't be two labs. 146 00:15:29,800 --> 00:15:30,490 That's crazy right. 147 00:15:31,150 --> 00:15:40,150 And so we were involved in a lot of training but at the same time as the, you know, like the Pioneer Lab, we had to be involved in doing lots of PCR. 148 00:15:40,930 --> 00:15:49,509 And we our lab ended up doing a 24 seven service for so many months during the pandemic. 149 00:15:49,510 --> 00:15:53,320 So, I mean, how many samples a day would that represent? 150 00:15:53,750 --> 00:16:03,010 So, so when we started with, we didn't have automated RNA extractors automated, so it was all manual, you see. 151 00:16:03,010 --> 00:16:09,350 And when the when the virus initially. So this is just to be, you know, biosafety level lab to lab. 152 00:16:09,640 --> 00:16:17,320 And so initially people in my life, my students were a little bit scared to handle, you know, I mean, by your working in the hood. 153 00:16:17,320 --> 00:16:25,300 But if the electricity goes, which sometimes happens in Sri Lanka, okay, then then the safety cabinet is not safe anymore using. 154 00:16:25,670 --> 00:16:31,660 So so I was actually inactivating all the samples for the first one and a half months. 155 00:16:31,870 --> 00:16:36,190 So every single sample that came into our lab, I was inactivating it. 156 00:16:36,460 --> 00:16:41,220 But after that, all, all the research, my students, postdocs, assistants, 157 00:16:41,230 --> 00:16:45,820 everybody took over their duties themselves, realising, you know, that's fine. 158 00:16:45,820 --> 00:16:52,900 So, so we were doing this 24/7 service and apart from that, you know, we then we just got the sequence. 159 00:16:53,010 --> 00:17:03,400 So just for you to understand how, how things were in Sri Lanka, we, we had never sequenced white us in Sri Lanka, we had never sequenced. 160 00:17:03,640 --> 00:17:07,450 That's interesting. And now it was coffee was here and you know, 161 00:17:07,470 --> 00:17:14,049 you could be sending samples overseas for sequencing because there were no flights working, you see, So so then off to speak. 162 00:17:14,050 --> 00:17:20,560 And so after pulling a lot of strings in the sense, you know, it was a time that everybody came together and, 163 00:17:20,560 --> 00:17:23,950 you know, you could do things that you that we possible earlier. 164 00:17:24,430 --> 00:17:33,940 So so the Sri Lankan ambassador to Singapore intervened put stuff in the in the flight and sent us sequencing reagents and and all that. 165 00:17:33,940 --> 00:17:37,899 So we managed to actually sequence Sri Lanka the Sri Lankan way. 166 00:17:37,900 --> 00:17:42,280 This is just just for in the beginning in April 2020. 167 00:17:42,280 --> 00:17:47,559 And I think that was a huge achievement given that we had never sequenced the virus in Sri Lanka. 168 00:17:47,560 --> 00:17:54,490 So we did a whole genomic sequencing of SARS-CoV-2 just focussed in in April 2020. 169 00:17:54,820 --> 00:17:57,910 And from that time onwards we did a lot of troubleshooting, 170 00:17:57,910 --> 00:18:04,360 lot of learning because initially a lot of samples we sequenced didn't work actually done, would understand when you're starting a new thing. 171 00:18:04,780 --> 00:18:13,780 But but then they did and you know, the W.H.O. wants 1% of samples sequenced at least so we did manage to. 172 00:18:14,440 --> 00:18:22,510 But by last year, mid-last year we would be like point 5% positivity like the samples sequence. 173 00:18:22,900 --> 00:18:26,379 So and all that sequence in our lab. 174 00:18:26,380 --> 00:18:33,040 So that was quite, quite nice to have the sequencing thing going on as well. 175 00:18:33,700 --> 00:18:38,790 And so and. During this time. 176 00:18:39,240 --> 00:18:43,950 You know, of course, Oxford made vaccines and did all these things. 177 00:18:43,950 --> 00:18:46,710 So I'll come to the work that we did with Oxford. 178 00:18:46,980 --> 00:18:52,980 But people were developing a lot of vaccines and everybody was trying to get hold of vaccines, including Sri Lanka. 179 00:18:53,370 --> 00:19:00,410 And so we did give a huge order to serum institute India for the Oxford vaccine. 180 00:19:00,420 --> 00:19:03,989 It is the same, you know, tetanus vaccine. 181 00:19:03,990 --> 00:19:07,500 But then they were they wanted it for India because of the situation. 182 00:19:07,500 --> 00:19:11,610 So they couldn't give us enough and they want to give us the pain, in other words. 183 00:19:11,970 --> 00:19:22,710 So then Sri Lanka was entertaining Chinese vaccines, the Sinopharm, the Sputnik, trying to get some Pfizer Moderna. 184 00:19:23,040 --> 00:19:28,610 So in the end, Sri Lanka used all of these vaccines with Sinopharm being the predominant vaccine. 185 00:19:28,950 --> 00:19:41,280 And by that time, you know, there was very little even although Sinopharm and Sputnik, we started using this on W.H.O., given EU way by the WTO. 186 00:19:41,760 --> 00:19:51,329 I mean, eventually Sinopharm was going to be the best view of satellite and our data sort of we were working on t cell responses to sinopharm, 187 00:19:51,330 --> 00:19:59,540 the antibody responses that was not published by the manufacturers on the developers themselves and all this work we actually did with also. 188 00:19:59,550 --> 00:20:04,200 So if I actually describe the work from the beginning to the end until now, 189 00:20:04,200 --> 00:20:09,300 the work we did with Oxford, you won't believe the amount of work which we did. 190 00:20:10,290 --> 00:20:13,710 Try me. Try me. Okay, Start at the beginning. 191 00:20:15,810 --> 00:20:23,850 So initially, you know, after Graham left and he managed to leave before the borders closed in the UK. 192 00:20:25,110 --> 00:20:28,530 Lucky for him, he he left with February. 193 00:20:29,130 --> 00:20:36,570 And so we what we initially started looking at antibodies and how, 194 00:20:36,720 --> 00:20:46,200 how these work and if I'm from Oxford we were also having some collaborations with Duke and with Singapore so and the surrogate utilising the test. 195 00:20:46,200 --> 00:20:55,170 They were developing this they sent us and so we were looking at because we do not have B category three or BSL three facilities. 196 00:20:55,500 --> 00:21:04,290 So to do INITIALISING antibody test still we don't have in Sri Lanka, we can't isolate the SARS-CoV-2 because we don't have recently. 197 00:21:04,440 --> 00:21:10,620 That said, but anyway, we were working on antibodies and doing different tests and you know, 198 00:21:10,650 --> 00:21:18,420 because we had been working on things for a long time and people who get to building immune mediated disease, 199 00:21:18,420 --> 00:21:23,960 I mean you react strongly to the virus and you end up getting the cytokine storm, which you get to building. 200 00:21:24,060 --> 00:21:29,460 So and when you look at looked at Kobe in light of April, sort of. 201 00:21:30,400 --> 00:21:34,540 Colby, although we didn't know a lot about what we did look like the same I did. 202 00:21:34,540 --> 00:21:41,410 I mean, you get the virus, but by the time you get all this pneumonia, it was because we were doing repeated. 203 00:21:42,520 --> 00:21:47,169 It looks like when you're sort of getting like when the white e-mail is being cleared, 204 00:21:47,170 --> 00:21:51,770 or at least the values were increasing, you get these complications. 205 00:21:51,810 --> 00:21:56,530 So the initial work we did was comparing the site of files with COVID. 206 00:21:56,710 --> 00:22:00,360 And the reason we did it was to see because apart from the quality, 207 00:22:00,370 --> 00:22:10,930 we had identified several other drug targets for the target and we wanted to look with any of those could work on COVID to understand the differences. 208 00:22:11,380 --> 00:22:17,680 Corbett And then so that was our initial work. And yes, there were certain similarities and differences. 209 00:22:17,890 --> 00:22:23,440 And so we initially published that because these are important things to note. 210 00:22:23,770 --> 00:22:31,210 And then we went on to look at antibodies, how the antibody levels courses, the duration of antibody levels, 211 00:22:31,450 --> 00:22:38,530 the decay, the differences in antibodies in people who have severe cold with mild COVID. 212 00:22:38,770 --> 00:22:45,819 And one important thing in Sri Lanka, because Sri Lanka and usually when pharmaceutical with policy you see so I think you 213 00:22:45,820 --> 00:22:50,350 could never when pharmaceutical and policy activity kind point if I'm not mistaken, 214 00:22:51,190 --> 00:22:56,200 I think we left it too late for that. So it was never going to be possible. 215 00:22:56,710 --> 00:23:02,470 Once they never closed the airports or checked anybody, it was just it got away from us. 216 00:23:02,510 --> 00:23:09,760 So. So Sri Lanka from the beginning had a theoretical I mean, it had a suitable policy for some time and it didn't work. 217 00:23:09,760 --> 00:23:17,770 We realised it did work. By end of 2020. But so we did have no virus, speed it for like three months in the middle of 2020. 218 00:23:18,100 --> 00:23:24,220 But anyway, so there was a lot of community testing and also for people to be discharged from hospital, 219 00:23:25,060 --> 00:23:29,830 you need a tour, two PCR, two days apart, a 24 hours apart. 220 00:23:30,310 --> 00:23:38,170 And also we were getting PCR test from the same patient in 50 days or something to see if this is a real patient, could this person go home? 221 00:23:38,530 --> 00:23:44,530 So we had all these if you know how the virus behaves and everything, you know, and today they will be positive. 222 00:23:44,530 --> 00:23:50,050 Tomorrow they have high viral loads again. So they will go home to some people in hospital for two months. 223 00:23:50,470 --> 00:23:53,890 You see, they're completely fine and they were getting depressed. 224 00:23:53,890 --> 00:24:02,620 Also imagine, I mean, you're stuck in hospital and because of feel countries have to hold a policy back in 2020 of those times, 225 00:24:03,280 --> 00:24:08,740 whether you had an infection like that, you do this or not, if you didn't feel positive, you were in hospital. 226 00:24:09,220 --> 00:24:18,010 So so some people clear the virus very early, but some people were asymptomatic, but they had, you know, persistence, waiting for like. 227 00:24:19,050 --> 00:24:27,860 Two months. Like the picture was positive and then it found that actually the antibody responses were different in these individuals. 228 00:24:28,380 --> 00:24:32,460 And this was interesting. King's interesting developments how? 229 00:24:33,030 --> 00:24:40,540 Because I think other countries, all the high income countries, they had given up on these things, but very early this year. 230 00:24:41,270 --> 00:24:44,220 I mean, so we had this opportunity to look at these things. 231 00:24:44,580 --> 00:24:51,570 And because I was closely working with Grimm and, you know, Oxford had everybody was working with everybody else. 232 00:24:51,960 --> 00:24:56,200 And tell Tell Don West was working. 233 00:24:56,280 --> 00:25:01,470 Yes. So the tail down was very closely working with Grimm to look at T cells. 234 00:25:01,890 --> 00:25:11,070 And she's she's an immunologist as well. Yes. And I had no power because she was she's in the Withdrawal Institute of Molecular Medicine. 235 00:25:11,070 --> 00:25:14,280 And, you know, since my days, I had known her. 236 00:25:14,640 --> 00:25:21,150 So she was working closely with Grimm on T cells, and they were publishing all the data on T cells. 237 00:25:21,570 --> 00:25:29,310 And so Oxford had this source then also joined our collaboration to look at T cells, which we were looking. 238 00:25:30,270 --> 00:25:31,430 So we did that. 239 00:25:31,440 --> 00:25:38,670 So just for the benefit of people who don't have the background, why was it particularly important to look at T cells as opposed to antibodies? 240 00:25:38,910 --> 00:25:46,020 And so because antibodies sort of prevent infection or are I mean, 241 00:25:46,020 --> 00:25:52,350 one of the roles of antibodies is to prevent infection and also they would reduce the severity to some extent. 242 00:25:52,560 --> 00:25:58,830 But as we know now, specifically now with all the variants coming up, the antibodies are not preventing infection very well. 243 00:25:59,070 --> 00:26:06,150 But still, people don't get severe disease if they've especially been vaccinated and had fought with both like hybrid immunity. 244 00:26:06,570 --> 00:26:13,500 Because although the white has changed because you have lots of lots of T-cells targeting various areas of the virus, 245 00:26:14,250 --> 00:26:20,820 the T cells are doing their work and and the antibody can target a virus if it is outside a cell. 246 00:26:21,570 --> 00:26:27,960 But once a virus is inside a cell, the antibodies can sort of access it. 247 00:26:28,290 --> 00:26:30,060 You see it because antibodies outside. 248 00:26:30,360 --> 00:26:40,440 But the T cells actually they are very good at recognising infected cells and and basically deliver signals so that the the cells commit suicide. 249 00:26:40,650 --> 00:26:47,910 So, so, so this is why t cells are very, very important and of course know T cells. 250 00:26:48,090 --> 00:26:53,410 I mean you have different populations like, like in the UK and Sri Lanka if technique wise. 251 00:26:53,430 --> 00:27:00,990 And one of the questions that we wanted to ask at a moment with UK was having quite a bad time with COVID. 252 00:27:01,230 --> 00:27:09,730 But in our studies, at least with the variant that was spreading around, we didn't seem to be getting a lot of severe disease at that point. 253 00:27:09,730 --> 00:27:13,000 But, but it could be transmission differences in transmission as well. 254 00:27:13,740 --> 00:27:19,790 But I'll come across your surveillance studies, which re the transmission I mean, the seropositivity was, 255 00:27:20,340 --> 00:27:27,299 but it could be the weather because it's hot and hot and humid and, you know, ventilation because because it's hot and humid. 256 00:27:27,300 --> 00:27:34,670 We always have our doors and windows open in all the buildings which which we now know that Kuwait is airborne and all. 257 00:27:35,440 --> 00:27:41,190 So I think those things made a huge difference, which people on transmission, 258 00:27:41,520 --> 00:27:47,159 disease, severity and all that, which we didn't realise during the early outbreak. 259 00:27:47,160 --> 00:27:51,450 But anyway, there was all this help. 260 00:27:51,780 --> 00:27:56,400 The British government was doing a lot of things to try and do something about COVID. 261 00:27:56,730 --> 00:28:00,610 And so the British High Commission in Sri Lanka actually approved. 262 00:28:00,930 --> 00:28:07,010 I mean, they contacted me and said, you know, do you have any contacts with the UK and what it is? 263 00:28:07,020 --> 00:28:11,700 And I said, Yeah, I mean, you're working with Oxford, We've been working with a long time. 264 00:28:12,000 --> 00:28:21,150 So then the British High Commission, the Foreign and Commonwealth Office gave us a research grant for, for to, you know, to for collaborative work. 265 00:28:21,390 --> 00:28:29,850 And, and you know, at the beginning of the outbreak for the Foreign and Commonwealth Office to actually help us in back there by it was 266 00:28:30,480 --> 00:28:39,959 because in Sri Lanka we didn't have funds and you see the government was spending its its funds on the control programmes. 267 00:28:39,960 --> 00:28:45,060 Obviously the government of Sri Lanka did not have any funds at all to give for research. 268 00:28:46,050 --> 00:28:49,620 And you know, the threat of the situation that we are using. 269 00:28:49,950 --> 00:28:56,099 So there was absolutely no funds for research and so we were just using some funds which we had here and there, 270 00:28:56,100 --> 00:29:01,050 which was very limited funds and basically we didn't have funds to do this with. 271 00:29:01,380 --> 00:29:10,410 So the Foreign and Commonwealth Office that the Grant research grant we got from the Commonwealth Office in 2020, 272 00:29:11,040 --> 00:29:15,180 like in June 2020 actually was really, really important. 273 00:29:15,450 --> 00:29:19,870 Excuse me. Yeah. 274 00:29:19,890 --> 00:29:24,360 So. But in the meantime, apart from working with Tower. 275 00:29:25,740 --> 00:29:31,140 Then Prosser and Townsend's group presented Townsend himself to his group. 276 00:29:32,060 --> 00:29:35,750 Developed this amazing antibody test. And so. 277 00:29:36,080 --> 00:29:42,000 Which I'm sure you are aware of. Yes, I've interviewed Alan. So he he told me about the test and I very much like to hear it, 278 00:29:42,080 --> 00:29:46,850 and he told me about you using it in Sri Lanka, but I'd like to hear about it from your perspective. 279 00:29:47,030 --> 00:29:51,109 So I saw this antibody test, which you don't need any equipment. 280 00:29:51,110 --> 00:29:56,810 You just need a. Want to get your blood and you can do it. 281 00:29:57,170 --> 00:30:01,309 And it was very, very easy to do and very easy to interpret. 282 00:30:01,310 --> 00:30:06,410 So and so Graham put us in touch with Professor Townsend. 283 00:30:06,860 --> 00:30:10,780 And so evolvement of brain confidence. 284 00:30:11,480 --> 00:30:21,980 I mean, they sent us the first reagents for the Wuhan variant, which we used to do a field survey in, in, in Colombo. 285 00:30:22,250 --> 00:30:25,940 So the community based service was about to get a survey. 286 00:30:25,950 --> 00:30:31,969 So it was the Colombo Security Council, which had the highest number of COVID cases. 287 00:30:31,970 --> 00:30:37,940 We didn't actually have cases in the rural areas by the end of 2020, not much, 288 00:30:37,940 --> 00:30:45,560 not much of cases in rural areas because of travel restrictions between districts, you know, suitable with policy and all that. 289 00:30:46,190 --> 00:30:56,810 But when we did a pseudo survey in 2020 in the Colombo Montagu difficulty, we found that 25% had been exposed to COVID, you see. 290 00:30:57,050 --> 00:31:07,910 And so and we compared these the neutralising how to get neutralising antibody tests developed by you can use with with the Townsend's. 291 00:31:08,990 --> 00:31:12,920 I see. And it worked. I mean, it was, you know, sort of the same. 292 00:31:13,250 --> 00:31:24,920 And the similarities and plus, the town obviously is like very cheap to use and, you know, but easy to do and ideal for countries like ours. 293 00:31:25,020 --> 00:31:30,560 It was a life saver and and and research wise. 294 00:31:30,680 --> 00:31:37,550 So then they developed other. Apart from looking at the antibodies to the initial, you know, variant, 295 00:31:37,940 --> 00:31:42,469 of course then we got alpha, beta, delta gamma, you know, you know, I mean all that. 296 00:31:42,470 --> 00:31:48,140 So they managed to develop reagents to look at antibodies specifically for these different variants. 297 00:31:48,530 --> 00:31:54,010 And so after. And so by end of 2020, December 2020, you know that. 298 00:31:54,700 --> 00:31:59,180 And the Pfizer vaccine was already registered. AstraZeneca was immediately registered. 299 00:31:59,540 --> 00:32:09,530 And so this was a still people were not very sure because there was apart from the trials data and trials done very fast, 300 00:32:09,920 --> 00:32:15,380 even in the scientific community and in the medical community, people were like, okay, do we have enough data? 301 00:32:15,980 --> 00:32:20,809 How how if this and we didn't have real world data and there were a lot of questions like, 302 00:32:20,810 --> 00:32:26,810 okay, and in Sri Lanka, we we didn't get the AstraZeneca vaccine initially. 303 00:32:26,820 --> 00:32:29,840 We was the Serum Institute vaccine, which is the same of course. 304 00:32:30,080 --> 00:32:36,230 But again, people were like, okay, there is no data of the vaccine developed in tiramisu. 305 00:32:36,320 --> 00:32:39,710 After all, it's a different vaccine manufacturer that was selected. 306 00:32:40,160 --> 00:32:52,040 How is the immunogenicity, all that? So then we we were very much involved in doing the initial immunogenicity studies for the the COVAXIN, 307 00:32:52,040 --> 00:32:58,610 which is the Serum Institute vaccine in human trials is not a trial. 308 00:32:58,790 --> 00:33:02,060 It is actually real world deployment. 309 00:33:02,240 --> 00:33:03,740 Yes. Right. Yes. Yes. 310 00:33:04,040 --> 00:33:15,650 So so as all these vaccines were approved by the national regulator and and in the national regulator to assess these vaccines, I mean, 311 00:33:15,950 --> 00:33:24,020 because they had they had to be people who knew about vaccines because these were not like W.H.O., who are, you know, like not approved as such. 312 00:33:25,340 --> 00:33:31,010 So you had to sort of countries have to assess the data themselves. So I was also in the vaccine regulator. 313 00:33:31,820 --> 00:33:34,850 So I was in the committee that approved COVID vaccines. 314 00:33:35,210 --> 00:33:40,370 So I actually received all the dossiers, confidential dossiers of all these. 315 00:33:40,700 --> 00:33:48,080 So not just the published data, but the actual vaccine, you know, like huge five dossiers. 316 00:33:48,410 --> 00:33:54,740 And so by that time I realised, you know, we have to do these studies ourselves to look at the immunogenicity. 317 00:33:55,190 --> 00:33:59,330 And as in all countries, the vaccines were initially given to healthcare workers. 318 00:34:00,160 --> 00:34:04,470 So we looked at this Serum Institute's head of vaccine in Healthcare Workers. 319 00:34:04,500 --> 00:34:10,990 So we initially started giving the vaccine on 29th of January 2021 in Sri Lanka. 320 00:34:11,380 --> 00:34:21,480 So from that day, so we immediately got a large cohort of healthcare workers, actually 2200, and did immunogenicity study. 321 00:34:21,490 --> 00:34:24,610 So the first study was published in Nature Communications. 322 00:34:24,880 --> 00:34:29,240 So how long after the vaccination did you look at it? 323 00:34:30,160 --> 00:34:35,860 One month after three months, after then six months and so on. 324 00:34:36,050 --> 00:34:40,090 So we did. So we did that for just mutagenesis studies. 325 00:34:40,510 --> 00:34:43,320 The initial one was one month after because that data, 326 00:34:43,330 --> 00:34:51,310 what people wanted that data and also in that paper we looked at data for the T cell responses and so on. 327 00:34:51,580 --> 00:34:56,140 And in Sri Lanka. So we did get this vaccine from Serum Institute. 328 00:34:57,010 --> 00:35:00,850 And so the healthcare workers were given and then subsequently other people were given. 329 00:35:01,450 --> 00:35:03,640 And so there was an issue with the second dose. 330 00:35:03,670 --> 00:35:09,670 Now, there were not enough vaccines for the second dose, you see, and there was no other vaccine for the second dose. 331 00:35:09,670 --> 00:35:11,980 I know I know that you can mix and match, 332 00:35:12,250 --> 00:35:17,680 but for us to even mix and match with all the all the doses that you want to know, there were no second doses. 333 00:35:18,070 --> 00:35:25,990 And and initially, you see it was the time death was supposed to be four weeks, then it was eight weeks to a week. 334 00:35:26,140 --> 00:35:37,600 Then ultimately and and here we were at the time of fighting and no vaccines and there was no data for like if you give the vaccine at 16 weeks worth, 335 00:35:37,600 --> 00:35:44,470 as this time did, the immunogenicity change like so? 336 00:35:44,500 --> 00:35:48,580 And how did the immunogenicity immune responses to different variants. 337 00:35:48,910 --> 00:35:58,240 When you prolong the time gap between the vaccines, I mean, from from the Oxford vaccine data, what they showed was the gap was increased. 338 00:35:58,270 --> 00:36:00,430 Yes, the immune responses were better. 339 00:36:01,570 --> 00:36:11,260 But but for the Wuhan, the initial variant at that time, like might be coming up to May 2021, we had all these things alpha, beta, gamma and delta. 340 00:36:11,260 --> 00:36:16,719 Right? So, so and the council, it had developed all these variants. 341 00:36:16,720 --> 00:36:24,610 It was available to us. We were then looking at antibodies for all these different variants in AstraZeneca, 12 weeks worth for 16 weeks. 342 00:36:24,610 --> 00:36:27,670 And so and that was important data. 343 00:36:28,030 --> 00:36:36,249 And, and in the meantime, we were also had the sinopharm brought over and again, 344 00:36:36,250 --> 00:36:43,000 there was no data for any variants, very limited T data on t cell functionality of, 345 00:36:43,240 --> 00:36:51,390 you know, like just like the frequency or the magnitude, but apart Formula T cells produced in these do they do all these other functions. 346 00:36:52,480 --> 00:37:03,190 So and along with the tell tale and tell us even while in all the cases studies which we did for vaccines and even for Sputnik, 347 00:37:03,190 --> 00:37:12,130 there was very limited data published. So we also looked for these t cell data for the functionality of T cell responses. 348 00:37:12,550 --> 00:37:22,840 Again, antibodies of the question a lot of people wanted, the general public wanted was that antibody responses for different variants because by mid, 349 00:37:24,820 --> 00:37:33,010 you know, like June, July 2021, that's after what was happening in India with the Delta and Delta heading into treatment. 350 00:37:33,010 --> 00:37:36,970 That that was one of the questions people really wanted to know. 351 00:37:37,330 --> 00:37:44,470 So and so we were covering all the T cells, antibodies for all these different vaccines. 352 00:37:44,890 --> 00:37:49,540 And then subsequently, you know, different countries were using different vaccines. 353 00:37:49,750 --> 00:37:56,710 And in Sri Lanka specifically, you know, things were getting I mean, there was a lot of political piece to these things. 354 00:37:57,370 --> 00:38:05,820 And, you know, and a lot of people were like, look, if majority of Sri Lankans are given sinopharm, why can't we get Pfizer? 355 00:38:06,010 --> 00:38:09,309 Why can't we get you know, the healthcare workers were given AstraZeneca. 356 00:38:09,310 --> 00:38:12,120 Everybody else is giving inferior with Chinese people. 357 00:38:13,010 --> 00:38:21,330 You can you could and you could understand how how how it would have gone, how it went right and and how the media also. 358 00:38:22,820 --> 00:38:25,670 So so you needed a head to head comparison. 359 00:38:25,930 --> 00:38:31,120 I mean, each each of the vaccine studies would be reporting look at the efficacy data with this much this much. 360 00:38:31,480 --> 00:38:39,670 But you can't compare like that between like the immune responses of between different studies done in different countries. 361 00:38:39,940 --> 00:38:47,139 So we had this golden opportunity. I mean, it is the same population of Sri Lankan population of it's heterogeneous but 362 00:38:47,140 --> 00:38:52,120 still is the Sri Lankan population and it is one lab doing all these essays. 363 00:38:53,560 --> 00:38:59,260 And so so we did a time series of four weeks, three months, six months. 364 00:38:59,840 --> 00:39:03,260 Of all these vaccines at the same time point. 365 00:39:03,650 --> 00:39:10,549 And we did a head to head comparison and and yeah, so that data is, I believe, 366 00:39:10,550 --> 00:39:15,920 useful for everybody because it's there's very much consistency between assays 367 00:39:16,280 --> 00:39:22,610 because it's the same lab so and yeah so so it was it was comparing Moderna, 368 00:39:22,970 --> 00:39:28,310 AstraZeneca, Sputnik, the Sinopharm and also the Sputnik. 369 00:39:28,310 --> 00:39:34,760 Again, we got first doses, but we didn't get second doses. And then Russia said that actually you don't need a second dose. 370 00:39:35,270 --> 00:39:39,320 They are actually using one in one dose and the one dose vaccine is marketed as looking great. 371 00:39:40,100 --> 00:39:47,059 So then we looked at people immune responses, time series of people who had received two doses of the specific vaccine with us. 372 00:39:47,060 --> 00:39:59,480 One because they said it's enough, but they didn't have any data to show was to say okay is the same number seen at all you know suffice I you Yeah. 373 00:39:59,600 --> 00:40:02,720 So what what were the what were the results of your comparisons. 374 00:40:03,230 --> 00:40:15,800 I mean the one dose of Sputnik induce fileless antibody responses and t cell responses like longitudinally and at different time points. 375 00:40:16,130 --> 00:40:19,910 Then the two doses of the Sputnik vaccine, which I don't think Jesus Christ. 376 00:40:20,390 --> 00:40:23,030 And what about the other comparisons between the other. Yeah. 377 00:40:23,030 --> 00:40:28,370 So the others are the comparisons are actually the Moderna gave the highest antibody responses, 378 00:40:29,330 --> 00:40:35,540 Sputnik and AstraZeneca and the two Adenoviral vector vaccines in people who had received 379 00:40:35,540 --> 00:40:43,700 two doses had equally followed by Sputnik one dose followed by two doses of sinopharm. 380 00:40:44,900 --> 00:40:53,450 Yeah. So I saw it look like the Sinopharm was not inducing adequate antibody responses. 381 00:40:53,840 --> 00:40:57,800 But what was interesting is, you know, then they'll be protected, right? 382 00:40:58,460 --> 00:41:01,790 Sorry, I didn't hear what you said. Yes, yes, yes. 383 00:41:02,960 --> 00:41:05,510 And it was causing havoc in all these countries. 384 00:41:05,510 --> 00:41:13,220 And everybody was very scared, I mean, because it was so transmissible and, you know, again, increasing hospitalisations either. 385 00:41:13,460 --> 00:41:20,800 So we got the big one just for a tiny bit because we then got a two and we got to empty them. 386 00:41:21,200 --> 00:41:26,120 But strangely, our hospitalisations were not that high. 387 00:41:26,870 --> 00:41:34,850 And our mortgage rates because duty does really crazy and our hospitalisations and deaths were not I mean, 388 00:41:35,750 --> 00:41:48,230 it was not bad in the sense of during the outbreak we had around in the height of the the Delta outbreak, we had like 200, 220 deaths per day. 389 00:41:49,640 --> 00:41:55,130 But that to me, chronic was like a ten, which is, you know, so much less. 390 00:41:55,490 --> 00:42:06,950 And so and people expected more people to like that not to be the case especially given that the Chinese vaccine but was, 391 00:42:07,400 --> 00:42:10,550 you know, possibly not more immunogenic. 392 00:42:10,880 --> 00:42:16,790 But what what sort of cover it excuse me what sort of coverage of the population had you managed to achieve by then? 393 00:42:16,960 --> 00:42:22,130 So it's like this. So the vulnerable population had been vaccinated. 394 00:42:22,610 --> 00:42:28,460 So we did we do have a anti-vax movement, but it is not at all as strong as what you're seeing in the West. 395 00:42:28,870 --> 00:42:34,550 So so because of that, all the vulnerable people were immunised except a few people. 396 00:42:34,940 --> 00:42:39,200 And in January 2022 we got the booster doses rolled out as well. 397 00:42:39,550 --> 00:42:45,650 So we did have like 18% of the population boosted by much, but it was the people who needed it. 398 00:42:46,310 --> 00:42:50,180 And and also of sinopharm. 399 00:42:50,180 --> 00:42:53,840 You know, as soon as people got sinopharm, they also got a Delta infection. 400 00:42:54,170 --> 00:43:00,880 So, so sinopharm worked in that way in the sense although it might have what we see in China right now, fine. 401 00:43:00,920 --> 00:43:07,129 So I mean, they had sinopharm, but they're getting infection long after they were they were vaccinated. 402 00:43:07,130 --> 00:43:10,160 So then the immunity would have been and they're having issues. 403 00:43:10,460 --> 00:43:16,540 But in Sri Lanka, you know, one month after getting fully vaccinated, you got infection and, 404 00:43:16,790 --> 00:43:20,120 you know, one month after you are fully vaccinated, that vaccine would hold, 405 00:43:20,540 --> 00:43:27,830 you see, and then you get very strong immunity because actually you get two doses of the vaccine and a third dose, the infection at a third dose. 406 00:43:28,160 --> 00:43:37,730 So so I think that that contributed a lot to how how then when we came back, we didn't have a huge issue in Sri Lanka. 407 00:43:38,240 --> 00:43:41,570 And yeah, so as of now we are having a. 408 00:43:43,580 --> 00:43:47,270 Almost all the weddings here in Sri Lanka. 409 00:43:47,930 --> 00:43:53,420 But we are not doing a lot of testing because I'm sure you are aware that we are having an economic crisis. 410 00:43:54,740 --> 00:44:00,530 So we don't have a lot of discussion about that. But at the same time, we do not have patients. 411 00:44:00,690 --> 00:44:09,980 You know, our hospitals filled with lots of patients with with COVID, the patients who do have pneumonia and going to hospital. 412 00:44:10,550 --> 00:44:13,700 The the vast majority have influenza. 413 00:44:14,670 --> 00:44:21,230 And in 2022 we had about three times more hospitalisations funding you than quoted. 414 00:44:21,810 --> 00:44:33,300 So just coming back to the big story and that has been so many when you speak to other countries on the equator, 415 00:44:33,420 --> 00:44:41,550 wow, you have a hot, humid climate throughout and that leads to a lack of ventilation because, 416 00:44:41,880 --> 00:44:42,120 I mean, 417 00:44:42,150 --> 00:44:48,400 you can be putting air conditioning all the time in a lot of places don't have air conditioning because you open your doors and windows you find. 418 00:44:48,430 --> 00:44:53,020 Right. So and you have the fans going on that. 419 00:44:53,130 --> 00:45:02,520 So I think that actually really helps in reducing transmission because our schools are designed like that. 420 00:45:04,260 --> 00:45:09,960 They don't have windows, actually. That also is where most of the schools are like that. 421 00:45:10,290 --> 00:45:15,300 All hospitals are like that. You see all the windows and everything are open all the time. 422 00:45:15,960 --> 00:45:20,160 Which I think in in those environments and most of the offices are like that. 423 00:45:20,160 --> 00:45:27,300 So I think. And but but of course the problem of dengue is death in a big way. 424 00:45:27,750 --> 00:45:32,870 And so that's why I said as far as Sri Lanka is concerned, for 2022, we had four, 425 00:45:32,900 --> 00:45:37,410 three times more hospital admissions, at least a dinghy that then floated. 426 00:45:37,680 --> 00:45:44,340 Okay. So your experience with COVID, what kind of impact has that had on your dengue work, if at all? 427 00:45:44,820 --> 00:45:46,250 Oh, yes. So. 428 00:45:47,480 --> 00:45:58,030 So it is slightly so, of course, in 2020 and 2021, because of the scale of work we were doing, apart from doing the research that I described before, 429 00:45:58,080 --> 00:46:05,700 looking at sequencing, looking at different variants, immunogenicity of these vaccines going out and getting the samples and all that. 430 00:46:07,080 --> 00:46:12,390 I mean, that's a that's a lot of time that we were also running a diagnostic lab for PCR. 431 00:46:12,420 --> 00:46:19,110 Right. You had me and we were offering antigen tests. We were offering. So, so we were doing all that. 432 00:46:19,110 --> 00:46:27,690 And we don't it's not like we have a huge staff. So there was no opportunity to do much dengue research in 2020 and 2021. 433 00:46:28,120 --> 00:46:36,630 However, luckily for us and rest of the didn't countries, we didn't have much dengue at all in both years. 434 00:46:36,870 --> 00:46:41,130 Now you will ask me why not? I mean, it's a mosquito borne infection. 435 00:46:41,820 --> 00:46:45,030 And did the mosquitoes disappear and. No, they did not. 436 00:46:45,060 --> 00:46:53,610 So this this was surprising because we have collaborations ongoing with our heads who has a large both in Nicaragua and also in Latin America. 437 00:46:53,970 --> 00:46:58,650 And when we were speaking to our colleagues in other Asian countries, I mean, 438 00:46:59,280 --> 00:47:04,350 they think he had disappeared and people weren't in the sense it was a really low number of cases. 439 00:47:04,970 --> 00:47:10,140 They were they were basically and it's not the patients were not coming to hospital because if they didn't, 440 00:47:10,380 --> 00:47:16,200 we would have more than 10% mortality, you see. So and we saw that people were coming. 441 00:47:16,200 --> 00:47:26,930 So we were really confused and maybe what we did was so I told you, you know, we had three months of COVID free period in 2020. 442 00:47:27,240 --> 00:47:30,750 So then we opened our schools. Then we got dengue again. 443 00:47:30,930 --> 00:47:34,520 We school, then we got Kobe closed our schools. Dengue event. 444 00:47:34,950 --> 00:47:43,470 So it looks like these social distancing restrictions had a greater effect on daily than COVID. 445 00:47:46,030 --> 00:47:51,010 Which is very surprising. But I think you see, if you get a school environment, 446 00:47:51,340 --> 00:47:57,310 the kids are sitting close together and you have one mosquito biting a kid and there are lots of legs very close by. 447 00:47:58,660 --> 00:48:05,290 The mosquito bite, the mosquito doesn't have to travel, you know, like 100, 200 metres and and bite another one. 448 00:48:05,500 --> 00:48:08,560 And then, you know, so it's easy to jump for the mosquito. 449 00:48:09,460 --> 00:48:13,510 So social distancing made it difficult for the mosquito. 450 00:48:13,870 --> 00:48:17,100 And so we didn't have a patient. 451 00:48:17,270 --> 00:48:23,500 So this is this is why we because our work is very much dependent on getting patient samples, 452 00:48:25,060 --> 00:48:32,020 because we've always been working on four samples because we have enough and more patients with dengue. 453 00:48:32,350 --> 00:48:36,940 And so there were not many dengue patients in both. 454 00:48:37,120 --> 00:48:40,690 So, you know, we're here so we could be concentrated on COVID. 455 00:48:41,110 --> 00:48:45,310 And it's only in 2022 that we started working on dengue again. 456 00:48:45,400 --> 00:48:49,390 So we did basically do much with funding in the two years. 457 00:48:49,510 --> 00:48:58,060 Hmm. Now, I'm just wondering whether the level of international collaboration, different sources of funding, 458 00:48:59,170 --> 00:49:07,360 what you learnt about how the immune response works, whether any of that will feed into research that you do in the future on dengue? 459 00:49:07,810 --> 00:49:22,470 There's no doubt about it, absolutely. So because there are a lot of similarities and differences between didn't concur with us and just to, you know, 460 00:49:22,750 --> 00:49:32,560 because I've been working on both know to get to clear my head, you know, you have to clear your head first to generate research ideas and to think. 461 00:49:33,010 --> 00:49:44,319 So me and Graham, I wrote this review article on compare the comparison of dengue in COVID because a lot of similarities in the T cell responses, 462 00:49:44,320 --> 00:49:47,650 even pathogenesis and so on between dengue in Hawaii. 463 00:49:48,010 --> 00:49:56,469 But but actually, when I wrote that review article, one thing I realised we had been there for two years for a ridiculous amount of years, 464 00:49:56,470 --> 00:50:03,040 about 30 or 40, 50 years, that there were so much less work done on dengue then forward. 465 00:50:04,420 --> 00:50:08,560 And that was that was, I think, shocking. 466 00:50:09,020 --> 00:50:18,009 You see, I mean, and the differences because of lack of funding for dengue, not because dengue costs less disease severity, 467 00:50:18,010 --> 00:50:25,239 but just because there is less funding for dengue and and presumably because COVID affected people in high income countries. 468 00:50:25,240 --> 00:50:32,530 High income countries. Because because right now I told you in in many countries, the case fatality rates are less than 0.2%. 469 00:50:33,370 --> 00:50:40,000 But in India, I mean, something like 2.6% at best, large mortality rate. 470 00:50:40,450 --> 00:50:45,100 And in some Asian countries, it's like 15.5%. 471 00:50:45,460 --> 00:50:52,410 And if you look at the current COVID mortality rates, it's less than point 1%, you see. 472 00:50:52,660 --> 00:50:56,360 So it's very much less than point 1% we are talking about. 473 00:50:56,800 --> 00:50:59,950 And so and we have so many different COVID vaccines. 474 00:51:00,470 --> 00:51:05,380 So we do have drugs for it. But still, there's a lot of drug development happening for four weeks. 475 00:51:05,710 --> 00:51:12,100 And but but when you speak to somebody about the non-availability of drugs for dengue, people would be like, 476 00:51:12,460 --> 00:51:20,260 but what about what about the Wolbachia project that's going to control the Wolbachia mosquito project, which is targeting vectors? 477 00:51:20,440 --> 00:51:27,460 Oh, yes, yes, yes, yes. So don't we have that? I mean, I mean, that's like having social distancing for [INAUDIBLE]. 478 00:51:27,910 --> 00:51:36,340 That's one measure, but that can't be the only missions. And of course, Takeda has developed a dengue vaccine which applies to face trials. 479 00:51:37,480 --> 00:51:40,480 Sorry, what was the name of the company again? EDA. Takeda? 480 00:51:41,870 --> 00:51:52,240 Takeda. Yeah, yeah, yeah. Have you seen It has been approved in Indonesia and it has been given approval 481 00:51:52,360 --> 00:51:57,400 by the European Medical Association and it's looking at approval in Asia. 482 00:51:58,090 --> 00:52:02,940 And again, if people would be like, but don't you, like, have a promising dengue vaccine? 483 00:52:03,070 --> 00:52:06,910 Why would you want to have drugs for dengue? 484 00:52:07,270 --> 00:52:13,540 And that's like asking, you know, you have so many different vaccines for COVID, why would you want to have drugs for it? 485 00:52:13,720 --> 00:52:20,100 And if you are somebody that like despite you do, bye, you will do need drugs for coolie. 486 00:52:20,150 --> 00:52:23,930 People don't think you're mad. I mean, why are you even asking that question right now? 487 00:52:23,980 --> 00:52:29,920 For dengue, you have just one vaccine which has the same efficacy as a cold vaccine, mind you. 488 00:52:30,100 --> 00:52:32,950 Okay. So the theory that any vaccine is good. 489 00:52:33,310 --> 00:52:41,290 But if it doesn't prevent infection, it's not like a measles vaccine and it reduces hospitalisation in about 80%. 490 00:52:41,800 --> 00:52:45,840 Okay. So basically the same. Because he will be backseats. 491 00:52:46,290 --> 00:52:51,839 And so which means people are still going to get dinghy and all of that and and we don't know 492 00:52:51,840 --> 00:52:56,760 the duration of immunity that is going to hold because it's and we have to provide assistance. 493 00:52:57,500 --> 00:53:02,470 So but still, people would ask why would you want a dinghy now? 494 00:53:02,880 --> 00:53:08,460 And you have all these things and that just shows you don't know how hospitals are overloaded. 495 00:53:08,940 --> 00:53:15,630 Healthcare systems are burdened with dengue and and how dengue affects kidnapped mothers. 496 00:53:16,110 --> 00:53:24,930 And just to let you know, in 2017, when when Sri Lanka had the largest outbreak, similar to Colgate, 497 00:53:25,350 --> 00:53:31,560 if if dengue affects pregnant women, they're very much more likely to get sick and die off. 498 00:53:31,580 --> 00:53:38,460 Then you see. And in 2017, the number one cause of maternal mortality girl was dengue. 499 00:53:38,940 --> 00:53:41,100 And that that's that's scary. 500 00:53:41,310 --> 00:53:50,040 And and there was this large study done in Brazil which showed that if you compare a non-pregnant female versus a pregnant female, 501 00:53:50,370 --> 00:53:55,680 the chances of dying when you get dengue haemorrhagic fever was 450 times more if you're pregnant. 502 00:53:56,470 --> 00:54:05,070 Okay. So so so and and people with pregnant women get thank you all the time because we 503 00:54:05,070 --> 00:54:09,870 have increasing each year the number of dengue is increasing with climate change. 504 00:54:09,870 --> 00:54:15,300 You see with global warming, the vector densities increase and all of that. 505 00:54:15,310 --> 00:54:19,210 So so that's really impacting pregnancy we have. 506 00:54:19,230 --> 00:54:24,030 And also people with co-morbidities, people who have diabetes, 507 00:54:24,030 --> 00:54:29,820 obesity for get are likely to have severe COVID, but they're also more likely to get severe. 508 00:54:30,990 --> 00:54:34,050 So so the risk factors are sort of similar. 509 00:54:35,580 --> 00:54:41,250 And we know that in most parts of the world, even in low middle income countries, 510 00:54:41,460 --> 00:54:48,330 you have a double burden of disease, the infectious diseases and all these diabetes obesity issues. 511 00:54:49,020 --> 00:54:52,770 And Sri Lanka and many endemic countries have this the same situation. 512 00:54:52,770 --> 00:55:01,829 So the dengue thing has got worse because now you have the population of developing severe diseases is more because of this obesity diabetes thing. 513 00:55:01,830 --> 00:55:04,860 So. So there's a lot of angles to look at. 514 00:55:04,880 --> 00:55:12,390 Thank you. And so I'm really happy also to be working at Drug for Neglected Diseases Initiative. 515 00:55:12,720 --> 00:55:18,840 So realising all these issues with dengue and that dengue affects the neglected population, 516 00:55:18,840 --> 00:55:23,880 it's not just a neglected diseases effects a neglected population, which is the Olympics. 517 00:55:24,510 --> 00:55:31,800 They have to the thing in the portfolio in 2021 and they recruited me as the head of dengue in July. 518 00:55:32,520 --> 00:55:37,500 And so the idea is to actually learning from experience, from COVID, 519 00:55:37,680 --> 00:55:42,060 where the initial thing was to repurpose drugs, etc., so that you can move faster. 520 00:55:42,900 --> 00:55:50,250 But at the same time learning from quoted, you have to do a lot of pre-clinical testing so that you don't waste drugs in clinical trials. 521 00:55:50,820 --> 00:56:00,940 So to proper treating for testing before you move on the trials, learning from all that the India is moving ahead with this lab, 522 00:56:01,300 --> 00:56:09,720 the dengue strategy of discovering a treatment solution for the needs and how it's done is by forming this alliance, 523 00:56:11,310 --> 00:56:23,700 which is in a lot of research institutions of lower middle income countries coming together to develop treatment for something that facing themselves, 524 00:56:23,700 --> 00:56:26,070 which is dengue. So that. 525 00:56:26,580 --> 00:56:36,300 So this alliance is formed by partners from in Malaysia Mahidol University Thailand STI which is the translation have is Duke 526 00:56:36,340 --> 00:56:45,840 from New Delhi Fiocruz and you have them from Brazil which which are the endemic countries and also part of this alliance. 527 00:56:46,230 --> 00:56:50,360 So we have a pre-clinical working group which works on the pre-clinical clock 528 00:56:50,380 --> 00:56:55,230 of it being log antibiotic testing and drugs that affect the immune system. 529 00:56:55,590 --> 00:56:58,350 And we have also initiated the clinical working group. 530 00:56:58,800 --> 00:57:07,290 There are people with the know we are looking at clinical trial design and all that with the clinicians, 531 00:57:07,300 --> 00:57:15,120 dengue experts from from these countries so that we find what sort of endpoints are we looking at, how should we design our trials? 532 00:57:16,050 --> 00:57:20,790 So we are talking with drug repurposing because it's fostered by the same time we 533 00:57:20,790 --> 00:57:26,639 are very much open to people like industry who might be because some industry 534 00:57:26,640 --> 00:57:30,900 partners already do have some antivirals that they would like to collaborate and 535 00:57:31,470 --> 00:57:36,390 actually work with the Olympics and with the alliance to take the drugs forward. 536 00:57:37,530 --> 00:57:43,620 So. So do you think that the extra collaboration that went on internationally? 537 00:57:44,170 --> 00:57:55,990 During COVID has helped to give some impetus to the development of this new institute and encouraged us to stay together. 538 00:57:57,460 --> 00:58:07,240 The reason why I think maybe we are alive today because this I think it's difficult to find people who haven't got COVID by now. 539 00:58:07,660 --> 00:58:16,809 It is. Well, it seems when I got with, I had two doses of AstraZeneca and one dose of Pfizer got into me. 540 00:58:16,810 --> 00:58:24,160 So when I got cold, it was pretty mild. But it wouldn't have been the case if if probably if I hadn't been vaccinated. 541 00:58:24,490 --> 00:58:33,070 And the reason why we have all these vaccines and drugs is because of these international collaborations, people actually coming together, 542 00:58:33,580 --> 00:58:41,160 putting their differences aside and working for a cause, which is, you know, let's all of us get out of this alive, the sort of thing. 543 00:58:42,040 --> 00:58:46,930 I think that that was what brought people together. I mean, let's get out of this alive. 544 00:58:47,170 --> 00:58:49,110 Either we get all of us get out alive. 545 00:58:49,120 --> 00:58:55,840 I mean, it's not going to be one or two people for any infectious disease if is going to be one or two people who are going to just you know, 546 00:58:56,620 --> 00:59:00,220 you can't close borders and stop infectious diseases. 547 00:59:00,610 --> 00:59:10,059 So. So and, you know, it worked for people were sharing their data, people working together and, you know, ironing out a lot of differences. 548 00:59:10,060 --> 00:59:19,750 And it was so beautiful to see how how everybody was just working together and the ability that actually, 549 00:59:19,750 --> 00:59:21,910 you know, the world is not a bad face after all. 550 00:59:22,670 --> 00:59:29,020 But what we make it to be, people are actually nice people and people like to work together and work for a good cause. 551 00:59:29,260 --> 00:59:39,399 So I think that actually is very inspiring and this is a very inspiring for the alliance that is there for find the freedom, 552 00:59:39,400 --> 00:59:40,570 the solution for doing it. 553 00:59:40,810 --> 00:59:49,870 And I think when a lot of people come together, something is likely to be more successful than individuals working in isolation. 554 00:59:50,200 --> 00:59:57,130 So so I'm quite hopeful that I mean, obviously that's going to be failures because we know that science is very high, risky. 555 00:59:58,120 --> 01:00:03,580 I mean, most of the time that are failures then things going right. 556 01:00:04,000 --> 01:00:07,680 I mean, 90% of failures and I mean more than 90%. 557 01:00:07,990 --> 01:00:13,060 So I would say 95% of failures and 5% success in any science project. 558 01:00:13,180 --> 01:00:21,190 And so. So I think but given those risks, which I think everybody who works in science accepts, 559 01:00:21,580 --> 01:00:28,930 it is very promising that when people come together, you achieve much, much more and much faster than working in isolation. 560 01:00:29,230 --> 01:00:35,730 Mm hmm. So let me just ask you a couple of questions about how working through the pandemic impacted on you, 561 01:00:35,740 --> 01:00:45,610 and particularly as Sri Lanka had this zero-covid policy, how did the restrictions impact on what you were able to do or how you were able to work? 562 01:00:46,180 --> 01:00:51,399 Yes. So the Sri Lanka had a policy in 2020 and 2021. 563 01:00:51,400 --> 01:00:57,760 They gave up that idea. Yes, yes, yes. It became obvious that, you know, it's not going to happen. 564 01:00:58,480 --> 01:01:10,960 So it didn't impact my work because, you see, we were the the PCR lab, the doing the things for the Multiplicity Council and all that. 565 01:01:11,320 --> 01:01:14,480 And we were doing the sequencing and all this so. 566 01:01:14,740 --> 01:01:22,990 So we were doing a 24/7 service which meant that we will open like seven days, 24 hours. 567 01:01:23,200 --> 01:01:27,820 So, so you were, you were excluded. I mean, you didn't have to observe these. 568 01:01:27,850 --> 01:01:33,430 No, no, no. Because actually, I went to work on seven days, which sounds a little bit sad. 569 01:01:34,630 --> 01:01:37,690 And what kind of how many hours in the day were you doing yourself? 570 01:01:37,720 --> 01:01:48,490 Yes. So. So it was like I was on the site, at least, you know, 12 hours a day, but remotely even longer. 571 01:01:48,790 --> 01:01:53,649 And and also, apart from the research work I was doing, I was as I said, 572 01:01:53,650 --> 01:02:00,100 I was in the National Medical Regulatory Expert group with vaccines, evaluating all these vaccines. 573 01:02:00,790 --> 01:02:07,990 Then I was in all these Ministry of Health committees, which were looking at all these diagnostics of, 574 01:02:08,260 --> 01:02:13,750 you know, guideline committees, you know, then then put to put out rules. 575 01:02:14,680 --> 01:02:18,879 You know, they they there were a lot of, you know, all these. So I was I was in all that. 576 01:02:18,880 --> 01:02:25,790 And apart from that. I had something I didn't enjoy with the media presence. 577 01:02:26,060 --> 01:02:34,940 So I was also put in the frontline of media, which which was actually done by the Ministry of Health, 578 01:02:35,570 --> 01:02:41,240 because me coming from a university background, I think I had the ability to explain things. 579 01:02:41,240 --> 01:02:42,340 Maybe you do. 580 01:02:44,270 --> 01:02:54,620 And also because I was not in the Ministry of Health and I was sort of outsider and a researcher up there was people maybe tend to believe me more, 581 01:02:54,620 --> 01:03:01,100 but I see. I don't know. But but, you know, when you get into media, there are good size and bad sides. 582 01:03:01,850 --> 01:03:10,940 And you would have known that a lot of scientists who were in media, but it was both so and so. 583 01:03:10,940 --> 01:03:19,550 I was on media a lot and I was doing a lot of webinars and a lot of, you know, 584 01:03:20,300 --> 01:03:25,040 a lot of updates for doctors about various, you know, why are we doing it this way? 585 01:03:25,040 --> 01:03:32,960 And I mean, in 2021, September to November, I was doing like three webinars per week. 586 01:03:33,230 --> 01:03:39,170 And so so we are talking about a huge amount of webinars, student talks, public talks. 587 01:03:39,830 --> 01:03:42,559 So it was a huge amount of work. 588 01:03:42,560 --> 01:03:51,650 And when I look back at and just wondering what I would not want to repeat now, but how did you feel about it at the time? 589 01:03:51,650 --> 01:03:58,400 I mean, I've asked other people this and some people say that the excitement and adrenaline just carried them through 590 01:03:58,930 --> 01:04:07,520 and other people found it awful and terribly stressful and needed to find ways to support their wellbeing. 591 01:04:07,520 --> 01:04:13,459 Where would you say you fell on? I mean, the excitement, the excitement of the research, 592 01:04:13,460 --> 01:04:19,400 because the research was very exciting and and more than more than just the research being exciting, 593 01:04:19,670 --> 01:04:24,889 you know, you knew that you were doing something which was very important to Sri Lanka, you see. 594 01:04:24,890 --> 01:04:30,740 I mean, if you didn't step in, then, you know, you had to step in. 595 01:04:30,740 --> 01:04:33,930 I mean, you felt like you were doing something. 596 01:04:33,960 --> 01:04:40,610 You were a very bad person. If you didn't do that, like, I would like like, like now, if I didn't do that in 20, 597 01:04:40,610 --> 01:04:46,970 20, 2021, how I would be feeling about myself, that I was a really bad person. 598 01:04:47,180 --> 01:04:51,710 And it matters to me that I feel that I'm not a bad person, you see, 599 01:04:52,940 --> 01:04:58,010 because the impression you have about yourself is the most important question, I would say. 600 01:04:58,250 --> 01:05:05,420 So I would not could not live with myself if I didn't do all that, that I could have helped. 601 01:05:05,420 --> 01:05:10,850 But I didn't start off feeling so and so because everybody needed help. 602 01:05:11,390 --> 01:05:15,800 I thought I had to help. And if I didn't, I was being a really, really bad person. 603 01:05:16,130 --> 01:05:26,090 So. So the fact that I was doing good and because then that that really this despite I was talking about the media, 604 01:05:26,090 --> 01:05:31,190 despite, you know, sometimes backlash over that and, you know, social media and so on still, 605 01:05:31,190 --> 01:05:41,210 because I was doing something good and I was helping and I would not be able to live with myself if I didn't, it was okay. 606 01:05:41,460 --> 01:05:45,650 Yeah. And were you able to take any time to do anything for yourself? 607 01:05:45,650 --> 01:05:51,950 I mean, just go for walks or do some yoga or so that was the good part of it. 608 01:05:51,950 --> 01:05:58,309 Again, I mean, when the restrictions were lifted, I did go running and stuff and yeah, 609 01:05:58,310 --> 01:06:04,940 and so but when the restrictions of from time to time when restrictions were lifted, I did all that. 610 01:06:04,940 --> 01:06:11,870 But I did gain weight, I would say which, which I have managed to finally get off now. 611 01:06:13,580 --> 01:06:16,820 Yeah. Yeah. Right. 612 01:06:17,240 --> 01:06:20,990 I think, I think we've covered everything. It's been wonderful. Thank you. 613 01:06:21,170 --> 01:06:24,350 So. Yeah. 614 01:06:24,380 --> 01:06:33,560 So this is the final question. Has the experience of working through the pandemic changed your attitude or your approach to your work? 615 01:06:33,560 --> 01:06:44,690 And is there anything you'd like to see change in the future? Yeah I think it very much I would say, because as I told you in Sri Lanka, 616 01:06:44,690 --> 01:06:51,110 we had never sequenced a virus before a whole genome sequencing and just started doing that and doing it. 617 01:06:51,110 --> 01:06:56,030 And that's clear. That gave the confidence that I mean, 618 01:06:56,030 --> 01:07:05,599 if you want to use you could you see and and all all the other work that we did and the need for able to generate a lot of funding, 619 01:07:05,600 --> 01:07:09,469 the things that we got after the British High Commission gave us funding and we 620 01:07:09,470 --> 01:07:14,000 started off work and we've actually very visible and actually delivering results. 621 01:07:14,330 --> 01:07:18,440 Then the W.H.O. gave us funds, the Sri Lankan office, 622 01:07:18,440 --> 01:07:23,660 and they continue to support us in a big with the WTO, Sri Lanka and then the World Bank gave us funds. 623 01:07:23,900 --> 01:07:30,920 So. So and then you understand that actually if you can work, you do get funding in the end. 624 01:07:31,280 --> 01:07:38,960 And and if you want to do something and if you put your mind to it, actually you can I mean, you give it you give a lot of confidence. 625 01:07:38,970 --> 01:07:44,380 Yeah. I decided to do it and I think I'm going to do that. 626 01:07:44,420 --> 01:07:47,460 And then. Yeah. That's great. 627 01:07:47,490 --> 01:07:49,340 Thanks so much. I'm going to.