1 00:00:02,410 --> 00:00:06,640 So can you start by saying your name and your current affiliation? 2 00:00:07,270 --> 00:00:12,190 Sure. My name is Pederick and I'm a department a lecturer at the school. 3 00:00:13,090 --> 00:00:19,120 Thanks very much. And without telling me your entire life story, but just to give a little bit of your background. 4 00:00:19,810 --> 00:00:29,680 Can you tell me just roughly from how you first got interested in sciences, which I understand you studied first to how you got to where you are now? 5 00:00:31,600 --> 00:00:38,380 Yeah. Curious path. So my undergrad degree was Natural Sciences at Cambridge. 6 00:00:38,980 --> 00:00:44,140 And I would never have imagined at that stage of life teaching politics. 7 00:00:45,580 --> 00:00:48,520 Certainly. Certainly not in a public policy school. 8 00:00:49,270 --> 00:00:56,890 And I at Cambridge, you start off doing all of the different natural sciences and you sort of whistle them down. 9 00:00:57,610 --> 00:01:07,269 In my final year, I did population modelling and evolutionary genetics, which sounds completely disconnected to my job now, 10 00:01:07,270 --> 00:01:13,060 except when it came to COVID and then actually being able to understand that world, 11 00:01:13,090 --> 00:01:18,580 even though it's obviously moved on and I need to do it, undergrad level has been very, very helpful. 12 00:01:19,510 --> 00:01:26,049 And then from undergrad, I did actually mean to do a Ph.D. in these subjects, but I became a journalist. 13 00:01:26,050 --> 00:01:31,870 I was offered a job first in San Francisco, getting meant to do the Ph.D. in evolutionary genetics, 14 00:01:31,870 --> 00:01:35,650 but then worked for the economist in the nature covering science. 15 00:01:36,310 --> 00:01:42,040 Then out of the blue was asked to be a correspondent for The Economist in South America. 16 00:01:42,760 --> 00:01:47,650 And I thought, Why not? I'm at the time I was about 26, 27. 17 00:01:48,040 --> 00:01:51,370 No mortgage, no kids. Let's do it. And then I. 18 00:01:51,850 --> 00:01:56,979 I became dissatisfied with what I guess political correspondence, 19 00:01:56,980 --> 00:02:05,740 considered evidence with my natural science brain and applied to come here for graduate school, basically in the politics department. 20 00:02:07,130 --> 00:02:11,330 So not seizing control of that mic at that stage. You didn't do the master's in public policy. 21 00:02:11,330 --> 00:02:16,100 You did. You did. I did. The Mphil in comparative government, which is a two year. 22 00:02:16,100 --> 00:02:25,339 Yes, nicely data driven course. And then I did the speech at the Dphil and kept the same supervisor who's been a huge influence on me. 23 00:02:25,340 --> 00:02:30,190 And he said Tim Power, who is now head of social sciences. 24 00:02:30,200 --> 00:02:36,150 Back then he was just doing Brazilian politics. So and. 25 00:02:38,440 --> 00:02:41,920 Yes. So you've you've moved between research and journalism. 26 00:02:42,280 --> 00:02:46,270 How do you think that those two professions complement one another? 27 00:02:48,010 --> 00:02:54,010 But it's an interesting question. So. I mean, it depends how you do research. 28 00:02:54,020 --> 00:03:01,910 The research I do today feels very much like a more thorough job of the journalism I used to do. 29 00:03:02,930 --> 00:03:08,990 But if you work for me particularly, you cover science and you cover it for someone like nature, 30 00:03:09,440 --> 00:03:16,210 it's very much a case of reporting on truth seeking, right? 31 00:03:17,120 --> 00:03:22,100 As though it's a first cut of of what we know at the present day. 32 00:03:22,700 --> 00:03:26,359 And I feel like particularly The Cove, it work or in general, 33 00:03:26,360 --> 00:03:33,139 the kind of work that often gets done in a school of public policy, a school of government is trying to get at that. 34 00:03:33,140 --> 00:03:40,940 Just you have months and big datasets to answer the questions instead of a few days and the right people to call. 35 00:03:42,490 --> 00:03:47,490 That's is. So what kinds of questions really drive you? 36 00:03:50,100 --> 00:03:57,509 Gosh. Wow, what a big one. All kinds of questions on honestly, all kinds of questions. 37 00:03:57,510 --> 00:04:02,760 When I was an undergrad, I used to go to lectures from all subjects. 38 00:04:04,470 --> 00:04:08,310 Really? Really. I would go to anthropology, lectures, everything. 39 00:04:09,570 --> 00:04:14,970 But I think it was the way of answering questions. 40 00:04:15,690 --> 00:04:21,420 I felt like science taught you the best way to answer the questions, at least that we figured out so far. 41 00:04:22,350 --> 00:04:29,970 And one year of my undergrad, I did history and Philosophy of Science, which is affectionately known as his [INAUDIBLE]. 42 00:04:30,310 --> 00:04:43,470 I mean, people who did that course and I think I've just it's hard to I am very probably curious about the world I'm fascinated by. 43 00:04:44,920 --> 00:04:54,160 Why people are the way they are, why decisions get made, even though collectively they can seem bonkers. 44 00:04:55,260 --> 00:05:02,080 Um. Why even, you know, animals evolve the way they do and. 45 00:05:03,970 --> 00:05:08,770 Yeah. I hope I just keep trying to answer questions that I find interesting. 46 00:05:08,810 --> 00:05:14,140 It's there's very few questions I find uninteresting about the world at large. 47 00:05:15,760 --> 00:05:25,370 That sounds like a great start period. So let's just move on to be more specific. 48 00:05:25,690 --> 00:05:33,580 Can you remember where you were, what you were doing when you first heard that there was something happening in China that might be worrying? 49 00:05:37,400 --> 00:05:45,160 I think like most people, it was probably around. Early January, late December, around New Year's Eve. 50 00:05:45,160 --> 00:05:55,320 And again, it didn't seem like it was necessarily going to be a big deal at that time and you sort of keep your eye on it. 51 00:05:55,540 --> 00:05:59,800 So when I was in South America, I had to report on swine flu. 52 00:06:00,340 --> 00:06:03,790 And most people I spoke to at the time, experts said this is going to be huge. 53 00:06:04,660 --> 00:06:10,000 Mexico's economy is going to be completely destroyed. And, of course, it wasn't nearly as big as we thought. 54 00:06:10,030 --> 00:06:21,579 So I think I even actually went to Brazil around Valentine's Day for a week, and I was beginning to get nervous. 55 00:06:21,580 --> 00:06:26,780 That week I was in Brazil. That was when things started to be happening in Italy, in Iran. 56 00:06:26,800 --> 00:06:31,550 Just the beginning. And when I got back, I thought, fear I better not travel again. 57 00:06:31,560 --> 00:06:42,470 But still, even the last week of term Hillary Tim early March I remember it felt day by day like 58 00:06:42,830 --> 00:06:49,310 the order of magnitude of importance of this thing was just exploding quite literally. 59 00:06:49,760 --> 00:06:57,630 I think, you know, I had all these other plans of what I was going to do and teaching and all the rest of it, and it just fit the following term. 60 00:06:58,490 --> 00:07:08,240 And I was going to go to a Jamie Cullum concert of sorts, and you just realise each day your view of what was possible shrank by almost 50% per day. 61 00:07:08,240 --> 00:07:11,240 That's how it felt. Yeah. 62 00:07:11,240 --> 00:07:16,340 So it was when it hit to you, it was that patch where you realised it was going to be global. 63 00:07:16,370 --> 00:07:22,520 Mm hmm. So at what point did you and your colleagues decide that this was something that you, 64 00:07:22,850 --> 00:07:30,890 your your research team here and your colleagues here could actually do something about and pivot your research in that direction. 65 00:07:31,730 --> 00:07:39,440 So the project is really originally all down to Tom Hale, and Tom and I teach in Hillary. 66 00:07:39,800 --> 00:07:44,660 We teach the politics course that all the most students you have to take. 67 00:07:45,290 --> 00:07:51,350 He does the international relations bit, which is at the end and I do the comparing inside countries back to the beginning. 68 00:07:52,700 --> 00:08:05,599 And we were we run seminars every Friday and that last Friday of the issue we were supposed to be discussing was the Greek sovereign default. 69 00:08:05,600 --> 00:08:15,140 And it just seems so out of place. And so obviously we you just couldn't keep the conversation on it. 70 00:08:15,980 --> 00:08:18,600 Equally, we have incredible students here. 71 00:08:18,620 --> 00:08:26,780 So you have our typical student who's been out in the world for about two decades doing things in terms of public, public policy. 72 00:08:27,590 --> 00:08:36,560 And so I remember one of my students in that seminar that last Friday of ten, um, I was a bit annoyed about because he wrote to me saying, 73 00:08:36,660 --> 00:08:45,590 I'm going to leave halfway through because I've got to go and test something about kind of online teaching or online meetings. 74 00:08:46,310 --> 00:08:54,980 And I thought to myself it had to be during my seminar, clearly, and then it turned out it was for the World Health Organisation, 75 00:08:56,120 --> 00:09:02,930 not some cute project for teaching here and he was a consultant for the W.H.O. 76 00:09:03,290 --> 00:09:15,890 And so it was really in that moment of confusion about I guess what was going on with the exams that were coming up for students, what you know, 77 00:09:16,400 --> 00:09:21,110 the feeling that we couldn't really stick with the agenda for what we were teaching in the course, 78 00:09:21,770 --> 00:09:27,590 that the idea, I think crystallised that we don't really know what governments are doing and nobody does. 79 00:09:28,220 --> 00:09:35,180 And what is quite unique about this community in the Blavatnik school is our students. 80 00:09:35,180 --> 00:09:44,900 We have about 100 and some are between 120, 150 master's students each year, and they tend to come from 50, 60, 70 countries. 81 00:09:45,910 --> 00:09:54,520 And if you are trying to have a community that can understand intelligently public policy announcements. 82 00:09:54,670 --> 00:09:59,750 But in any of the world's official languages, this is probably one of the best in the world to start with. 83 00:10:01,530 --> 00:10:04,919 And so yeah, that was Friday seminars. 84 00:10:04,920 --> 00:10:15,300 And then I got a call from Tomo on Monday and yeah, we met on a video call, which I don't think he'd ever done before at that point. 85 00:10:15,310 --> 00:10:20,780 And yeah, the idea was born. He'd been cooking up for the bare bones of it over the weekend. 86 00:10:21,240 --> 00:10:28,080 So what was the idea? Tell me about it. The idea was just a very simply stop recording what governments were doing. 87 00:10:29,070 --> 00:10:33,230 And at that time there wasn't much in place. 88 00:10:33,240 --> 00:10:39,840 There was the Italian lockdown, there was the way you handle lockdown, and there was stuff going on in Iran. 89 00:10:42,030 --> 00:10:47,340 But it was at that particular juncture where there was a sense that it was going to be global. 90 00:10:48,660 --> 00:10:54,960 Now, the challenge was if you were recording public policies, how do you do it before you really know what that cannot be? 91 00:10:55,650 --> 00:11:08,490 And so our original coding scheme simply said, Well, let's come up with the main areas of closure policies, schools, transport and so on. 92 00:11:09,780 --> 00:11:14,910 And let's have the simplest possible ordinal scale to measure strength of policy. 93 00:11:14,940 --> 00:11:18,780 So nothing recommended closure required closure. 94 00:11:19,530 --> 00:11:22,980 And we had an indicator for some sort of public health campaign. 95 00:11:23,580 --> 00:11:29,610 And that was how the tracker started recorded on Excel spreadsheets, which seems a very long time ago now. 96 00:11:30,780 --> 00:11:38,490 And then over time we have expanded that. So we now got more than 20 indicators and far more nuanced ways of coding the strength of policy. 97 00:11:39,540 --> 00:11:48,090 So that's the it's give it its name. It's called the Oxford COVID 19 government government response track in response to this. 98 00:11:48,540 --> 00:11:52,050 It's very helpful that after the Oxford bit, the rest is an alphabet code. 99 00:11:54,990 --> 00:12:04,290 And you talked about the involvement of the students. So how were you gathering the data to feed into the tracker? 100 00:12:04,890 --> 00:12:13,500 So the extraordinary thing about this project is that it is entirely, entirely volunteer based has been from the beginning. 101 00:12:14,160 --> 00:12:22,560 And now we have a core research team involved in the actual organisation of it, who we pay. 102 00:12:23,130 --> 00:12:27,420 But we've had over a thousand people come through as volunteers. 103 00:12:28,370 --> 00:12:33,480 At this point. And so how do we get them together? 104 00:12:33,500 --> 00:12:41,270 So at the beginning it was willing students and I am kind of in awe of them 105 00:12:41,270 --> 00:12:44,900 because they had exams a few weeks away and they didn't know what was going on. 106 00:12:45,470 --> 00:12:54,590 And the idea of giving of hours of your day to a project that you didn't know if it was going to be big or small or whatever at that moments, 107 00:12:55,340 --> 00:13:01,940 you know, hats off to them. That's really the vocation of public policy as opposed to just the outcome of your Oxford degree. 108 00:13:03,110 --> 00:13:06,769 And since then, it's expanded to different communities. 109 00:13:06,770 --> 00:13:09,910 So we have a. 110 00:13:11,330 --> 00:13:15,320 Quips. So other universities in other countries have got involved. 111 00:13:17,060 --> 00:13:24,020 We had to add we have a collaboration with Microsoft, so some of their employees can get time off to do volunteer work. 112 00:13:25,400 --> 00:13:29,870 Yeah, we sort of it seems to find its way into new contacts. 113 00:13:29,870 --> 00:13:34,519 And then these people create a new community bubble that wants to get involved. 114 00:13:34,520 --> 00:13:37,790 And lots of medical professionals actually do it. Mm hmm. 115 00:13:38,030 --> 00:13:41,320 So what does each participant have to actually do? Right. 116 00:13:41,330 --> 00:13:42,380 So. Good question. 117 00:13:43,430 --> 00:13:55,190 So we ask about four, five, 6 hours a week and they will be allocated, uh, perhaps a country to coach for that week or a few countries. 118 00:13:55,190 --> 00:14:02,960 And that means that they have to get to grips with the coding manual and then for each of the policies. 119 00:14:03,800 --> 00:14:10,280 So for how the strength of school closures, for if there's different vaccine policies. 120 00:14:10,280 --> 00:14:17,600 Now we code, we look at test and trace all sorts of things. They have to look up what official government policy is. 121 00:14:18,530 --> 00:14:25,010 And so they have to check government websites and all sorts of official forms, sometimes quality media as well, 122 00:14:25,460 --> 00:14:34,160 to sort of check whether things are really happening on the grounds for some of the indicators and then they input this into an online database. 123 00:14:36,090 --> 00:14:42,600 And that's that. What is really hard to explain to a lot of people is when someone I'm thinking of my mom, 124 00:14:42,600 --> 00:14:48,179 when they say that when someone's in their coding for the week, it's life, you know, 125 00:14:48,180 --> 00:14:50,610 from their laptop in Peru, 126 00:14:51,090 --> 00:15:00,600 it then automatically via an API ends up on the Financial Times websites feed through to all sorts of epidemiological modelling around the world. 127 00:15:01,290 --> 00:15:08,310 We do have a sort of a check of everyone's coding, but that takes about a week or two to do. 128 00:15:09,390 --> 00:15:15,870 So the last little bit of data is slightly more kind of a rough cut than everything, a bit older, but that's how it works. 129 00:15:16,950 --> 00:15:26,580 So when it comes to looking at the effectiveness or comparing the effectiveness of policy of government policy responses in different countries, 130 00:15:26,940 --> 00:15:35,050 that that's that's a research job for which your resource is actually acting as the the database. 131 00:15:35,280 --> 00:15:43,650 Is that research being done all over the world, or are you do you do you hope that that a basket of information here? 132 00:15:44,610 --> 00:15:50,040 So the idea the philosophy has been we hold nothing. Everything is available instantly. 133 00:15:50,400 --> 00:15:55,830 It's available free of charge. And we make it as easy as possible for people to use. 134 00:15:58,020 --> 00:16:00,419 We do do a bit of research ourselves, 135 00:16:00,420 --> 00:16:08,999 but it's primarily a data project and this certainly it would make no sense for us to try and hold the data and we wouldn't have the skills, 136 00:16:09,000 --> 00:16:13,440 the capacity to do different types of modelling that would be very necessary for this. 137 00:16:13,800 --> 00:16:23,400 We have ourselves had a few papers about what works and thinking about a different way so that the first way we try to 138 00:16:23,400 --> 00:16:32,070 think about it is thinking about how different combinations of policies correlates with how deaths and cases vary. 139 00:16:32,820 --> 00:16:38,250 We did some very, quite simple modelling compared to the kind of modelling that you would. 140 00:16:38,940 --> 00:16:46,679 The people I used to study with ages ago would do where they have much more nuanced idea of what your counterfactual is. 141 00:16:46,680 --> 00:16:52,890 And that's the natural curve of the disease is the typical statistical workhorses of the social sciences. 142 00:16:53,130 --> 00:16:54,690 It's quite hard to get to that. 143 00:16:55,440 --> 00:17:07,470 But where we've I think really made progress is combining our data with behavioural data from mobile phone mobility and from survey data, 144 00:17:08,130 --> 00:17:16,020 and then we can look quite closely at how different policies affect behaviour, either self-reported in surveys or objectively, 145 00:17:16,020 --> 00:17:20,729 but which people tend to have smart things about how people are moving around. 146 00:17:20,730 --> 00:17:23,970 And that's yielded some really interesting insights. Mm hmm. 147 00:17:24,240 --> 00:17:28,140 Tell me a bit more about that. So what are the results? Which are the most interesting results? 148 00:17:28,650 --> 00:17:40,740 So a few. So one of the big policy questions early on was whether something called behavioural fatigue or pandemic fatigue existed. 149 00:17:41,940 --> 00:17:51,089 And this was very contentious because at least Sweden and at one point the UK suggested, well, if you just had this sweet spot, 150 00:17:51,090 --> 00:17:54,809 if you know when the policies are going to be really effective because people give up on them, 151 00:17:54,810 --> 00:17:57,510 they get sick of them, then we need to, you know, 152 00:17:57,510 --> 00:18:04,230 essentially wait and bring in the herd closure policies at just the right moments rather than put them in place now. 153 00:18:04,590 --> 00:18:07,770 And that was a big issue in March 2020. 154 00:18:09,720 --> 00:18:16,050 So we looked at this particular question, so how long are able people able to stick to the policies? 155 00:18:16,890 --> 00:18:23,550 And what we broadly found for the physical distancing policy so they stay at home and so on, 156 00:18:24,780 --> 00:18:34,620 was that people didn't manage to adhere to these protective behaviours nearly as much as they did in the first month over time, 157 00:18:35,370 --> 00:18:42,929 but it never fully decreased. So there was a decrease and then almost hitting a threshold, that kind of internal compromise I guess, 158 00:18:42,930 --> 00:18:46,770 for how much you can handle and hold on to in the long run. 159 00:18:48,750 --> 00:18:57,210 And this was this is remarkably consistent across countries and also across different societal groups. 160 00:18:57,870 --> 00:19:04,110 So, for example, it might be that different ages or certainly different genders have different initial compliance behaviours, 161 00:19:04,500 --> 00:19:11,130 but the patterns of reduction over time just completely echo each other for different groups. 162 00:19:13,110 --> 00:19:20,340 Which is fascinating because if you see these same patterns in rich and poor countries and among employed people and unemployed people, 163 00:19:20,760 --> 00:19:25,950 it suggests that the logic isn't fundamentally about whether you can afford to stick to the policies. 164 00:19:26,880 --> 00:19:33,690 And if you then compare the patterns we see for physical distancing, which is a very. 165 00:19:34,740 --> 00:19:42,530 Actually costly kind of behaviour compared to something like mask wearing, which is very cheap behaviour. 166 00:19:42,770 --> 00:19:49,100 You see radically different patterns. So physical distancing has this descent to a threshold. 167 00:19:49,550 --> 00:19:55,700 Mask wearing is just just rises and rises and rises, controlling for the strength of policy. 168 00:19:55,700 --> 00:20:00,460 So people adopt mask wearing over and above what governments tell them to. 169 00:20:01,730 --> 00:20:11,719 And then if you think about why that might be, well, mask wearing is habituated, kind of like wearing a seat belt or helmet. 170 00:20:11,720 --> 00:20:14,900 Like if you try and drive without wearing a seatbelt, you feel kind of naked. 171 00:20:16,460 --> 00:20:23,760 Where is staying? At home, day after day after day is more like continuing to do press ups over time. 172 00:20:23,760 --> 00:20:31,520 Like each one gets harder, it's cost accumulating. And so that in itself is very fascinating. 173 00:20:31,520 --> 00:20:36,350 It also suggests that, you know, risk perception isn't a fundamental driver either, 174 00:20:37,190 --> 00:20:42,710 because if it was risk that was bothering you, makes you do everything as much as possible that could protect you. 175 00:20:43,730 --> 00:20:49,340 Right. So in some ways, to me that project sort of showed that, you know, 176 00:20:49,340 --> 00:20:56,570 the behavioural economist behavioural sciences have probably got the best insights as to what's going on, at least for that kind of question. 177 00:20:57,320 --> 00:21:02,600 The other thing that we found that was fascinating with that project was comparing 178 00:21:02,990 --> 00:21:11,780 countries with different levels of trust in institutions and in strangers in society. 179 00:21:12,470 --> 00:21:19,550 And certainly very early on in the pandemic, the assumption was that the more that people trusted their governments, 180 00:21:19,880 --> 00:21:21,350 the more they would do what they were told. 181 00:21:22,810 --> 00:21:29,500 And yet we saw countries with high levels of trust in governments have really high debts and low debts just all over the map. 182 00:21:30,850 --> 00:21:40,240 And what we saw was that when we compared the adherence patterns to protective behaviours, that high and low trust countries behaved exactly the same. 183 00:21:41,050 --> 00:21:48,460 But when we looked at whether people trusted strangers in their society, interpersonal trust, we saw divergent patterns. 184 00:21:49,030 --> 00:21:58,180 So if you trust strangers a lot, you're far more likely to stick to the physical distancing protective behaviours than if you don't trust them. 185 00:21:58,780 --> 00:22:03,550 And there are inter-country differences in this level of trust in both governments and strangers. 186 00:22:04,450 --> 00:22:08,410 See, we looked at above and below the median and yes, there's quite a big, big difference. 187 00:22:08,620 --> 00:22:14,440 Examples of countries. Yeah. So Germany would be a high trust country, but for inter-government and strangers. 188 00:22:15,970 --> 00:22:20,350 Yes. But at least in the the data I have about Germany and its government. 189 00:22:20,650 --> 00:22:26,020 So one of the big questions that we don't know, of course, is how much levels of trust have changed during the pandemic. 190 00:22:27,100 --> 00:22:30,729 Actually, for Germany, there is now as an outlier, a country where there are good surveys. 191 00:22:30,730 --> 00:22:33,850 But for most countries we don't know much about that. 192 00:22:34,360 --> 00:22:43,510 And I wouldn't be surprised if something this salience in people's lives sort of shifts trust far more quickly than social science is used to. 193 00:22:44,350 --> 00:22:47,740 Brazil would be a country where people don't trust strangers very much. 194 00:22:49,370 --> 00:22:59,680 And so the think the kind of the the political science logic behind all of this is actually collective action theory, just recast. 195 00:22:59,690 --> 00:23:05,149 So if you're trying to create a public good, say, public goods like climate change, reduction, 196 00:23:05,150 --> 00:23:09,530 all that stuff, in this case, the public good would be reducing the infection rates. 197 00:23:10,630 --> 00:23:13,060 And you have individual costs of staying at home. 198 00:23:14,080 --> 00:23:19,540 The only reason you would want to pay that individual cost is if you trust that everyone else is doing the same. 199 00:23:20,050 --> 00:23:24,610 If you don't trust that, then why would you pay that high individual cost? 200 00:23:24,670 --> 00:23:29,610 You're not going to create the public good. And that's. 201 00:23:30,150 --> 00:23:33,660 And that is what was found. That is. What was that? Yes. Yeah. 202 00:23:39,380 --> 00:23:42,530 Uh. Yes. 203 00:23:42,530 --> 00:23:45,559 We might go into some some more findings, 204 00:23:45,560 --> 00:23:51,650 but I didn't ask before about whether you needed to raise funding to support the work you mentioned, you know, have some paid staff. 205 00:23:52,250 --> 00:23:56,480 And how how easy was it to find the funding to do this work? 206 00:23:58,040 --> 00:24:04,850 At the beginning, I mean, all of the institutions that create grants weren't ready. 207 00:24:05,180 --> 00:24:14,890 I mean, no one was ready at the beginning. And so the first few months, I mean, we worked so hard, we were doing everything, the quitting. 208 00:24:15,500 --> 00:24:22,610 You know, I remember sort of 2 a.m. on a Saturday night trying to code Middle East countries with Google Translate open. 209 00:24:25,130 --> 00:24:28,520 And then I think it was about three, three months. 210 00:24:28,520 --> 00:24:31,750 And Rush gave us some money for this project. 211 00:24:31,760 --> 00:24:38,420 We heard about it not that much, but when you're desperate for a few research assistance just so you can sleep, 212 00:24:39,170 --> 00:24:41,900 that's pharmaceutical company, their pharmaceutical company. 213 00:24:42,410 --> 00:24:50,030 And then since then, we've had a number of funders and, you know, the main thing you need to pay for is just the research assistants to, 214 00:24:50,420 --> 00:24:54,230 you know, manage the contributors, the emails, check the data, all that stuff. 215 00:24:54,950 --> 00:24:58,250 And so actually our main funder has been the Blavatnik Foundation, 216 00:24:59,390 --> 00:25:04,850 but we've applied for various funds since then and been quite, you know, been quite successful. 217 00:25:05,450 --> 00:25:10,610 So, you know, we have a an agreement with the Cabinet Office and Tom and I, 218 00:25:11,060 --> 00:25:16,160 every Tuesday during COVID have joined a pool of academic advisers to them. 219 00:25:16,880 --> 00:25:20,360 Oh, you've printed other of my questions. Oh, yes, it was. 220 00:25:20,750 --> 00:25:25,100 Thanks. It was quite lovely. 221 00:25:26,300 --> 00:25:37,060 And. So, yes, I mean, clearly, this was something that was worked very collectively on this with a very wide range of people. 222 00:25:37,450 --> 00:25:40,600 Did that feel different from how research is normally done? 223 00:25:41,250 --> 00:25:52,150 Um, no. It's, you know, research can be a very competitive arena to be, you know, it's extraordinary, particularly, I think. 224 00:25:53,170 --> 00:25:55,870 So I now have tenure. But when it started, I didn't. 225 00:25:56,410 --> 00:26:04,250 And a few people told me it was bonkers because obviously you can't keep up all your current projects and your 226 00:26:04,390 --> 00:26:11,020 the main emphasis was day to day to day to the other people needed in an emergency rather than your own, 227 00:26:11,440 --> 00:26:18,639 you know, publications or whatever. Um, yes. 228 00:26:18,640 --> 00:26:21,760 So I mean, where to start with how it's different? 229 00:26:22,870 --> 00:26:27,880 First of all, just the, the collective energy. 230 00:26:28,520 --> 00:26:42,640 Um, so it's, it's volunteer driven. So just harnessing and, and frankly enjoying that community spirit is how the project has carried on, 231 00:26:43,360 --> 00:26:47,860 um, to this day almost, you know, a year and a half in. 232 00:26:49,640 --> 00:26:57,320 And that's just a world away from, you know, impact factors and how do you compare to other people and, 233 00:26:57,340 --> 00:27:06,340 and all of that stuff I've learned so much about, um, I guess community science and frankly about leadership as well. 234 00:27:06,340 --> 00:27:17,320 We have a very sort of open hearted way of running the tracker, um, very much as I have collected a feel of what skills do you want to learn? 235 00:27:17,530 --> 00:27:21,459 How can we help you do that? It's, um, it is exhausting. 236 00:27:21,460 --> 00:27:27,550 It's more time often to do it that way than just to sit in the corner and run your models and write papers. 237 00:27:27,550 --> 00:27:33,760 But it's taught me so much, and none of it would have been possible if we'd been more selfish about it. 238 00:27:33,940 --> 00:27:42,460 MM. That's really interesting. Yes. 239 00:27:42,680 --> 00:27:47,809 And. Yeah. 240 00:27:47,810 --> 00:27:56,230 I've asked you about how individually the volunteers do that data gathering and you put it together that you've, um, 241 00:27:56,240 --> 00:28:02,620 and I noticed from one of your papers that you've used data from our world in data and they've, you've used your data. 242 00:28:02,630 --> 00:28:12,200 So are there other, other collective groups out there who are now collaborating really across the whole sort of data driven landscape? 243 00:28:13,280 --> 00:28:17,090 Yeah, so I have I mean, it's mixed, if I'm honest. 244 00:28:17,390 --> 00:28:28,700 It's been very much, um, kind of, um, I would say mushrooming of data sources at each stage, right of, you know, in John Hopkins, 245 00:28:28,700 --> 00:28:33,620 at least probably in the beginning and it's been a bit of a trying to put together a 246 00:28:33,620 --> 00:28:37,770 patchwork as these kind of different projects have got off the ground to check the data, 247 00:28:37,810 --> 00:28:42,140 can talk to it to each other's different datasets. 248 00:28:43,190 --> 00:28:46,879 And, you know, sometimes it's worked. 249 00:28:46,880 --> 00:28:54,800 Everyone has been insanely busy. That's the thing about the people running these data projects that if there's one barrier to collaboration, 250 00:28:54,800 --> 00:28:58,310 it's generally not the philosophy of doing emergency work. 251 00:28:58,910 --> 00:29:05,389 It's just the you know, we have four minute meetings sometimes that kind of you're in that world. 252 00:29:05,390 --> 00:29:09,620 It's unrelenting. But things have settled a bit now. 253 00:29:09,620 --> 00:29:16,399 And certainly I've started working quite closely with someone in payroll quality 254 00:29:16,400 --> 00:29:22,640 who's been running surveys in lots of countries every two weeks to try and align 255 00:29:22,700 --> 00:29:26,120 or create synergies between the questions that are asked that you can only get 256 00:29:26,120 --> 00:29:30,860 at and survey data that are relevant to interpreting how policies are working. 257 00:29:32,530 --> 00:29:37,390 Yeah. Because that's the big question really, isn't it? It's either you're collecting data about what the policies are, 258 00:29:37,780 --> 00:29:43,510 but you really need to look at the effectiveness of those policies, which talk about, you know, how people actually behave. 259 00:29:44,020 --> 00:29:47,349 And that's that side of it. You're dependent on other people to collect. 260 00:29:47,350 --> 00:29:51,159 Is that right? Yes. Yeah. 261 00:29:51,160 --> 00:29:54,300 I mean, I. Yes. 262 00:29:54,320 --> 00:29:57,800 In short, yeah. 263 00:30:00,080 --> 00:30:11,450 So I mean, just to go back to sort of the big question, I mean, how how easy is it to make comparisons between countries or between substate entities? 264 00:30:12,230 --> 00:30:21,860 I mean, are they similar enough for you to make perhaps if you seem to I mean, do do the comparisons stand up one against the other? 265 00:30:22,490 --> 00:30:27,830 And you talked about using quite a simple scale to presumably try and get around that problem. 266 00:30:28,040 --> 00:30:33,349 Yes, it is. The answer probably depends on how what you what you're trying to do with your comparison. 267 00:30:33,350 --> 00:30:40,190 Right. If you're trying to answer the big questions about what works using policy data, 268 00:30:40,190 --> 00:30:48,020 then that's quite a bit harder than, if you like, simply comparing the strength of policy in different countries. 269 00:30:49,790 --> 00:30:58,250 And we have an interpretation guide for every possible ordinal jump up for every possible indicator. 270 00:30:58,730 --> 00:31:08,630 That is pretty exhaustive. You know, if, for example, schools are only opening for exams but aren't opening for any classes, 271 00:31:08,960 --> 00:31:16,790 whether how you code that in our system will be in the interpretation guides if buses are being extra cleaned. 272 00:31:16,790 --> 00:31:20,210 But that isn't affecting the bus timetable, it's in the interpretation guide. 273 00:31:22,220 --> 00:31:31,310 So that kind of thing. I think we I mean, we have meetings every week to reflect on whether we're consistent enough, 274 00:31:31,310 --> 00:31:35,600 whether we're detailed enough internally with our code book. 275 00:31:35,600 --> 00:31:37,940 And then we have calls every week, 276 00:31:38,420 --> 00:31:46,850 multiple calls with different coding teams where we answer questions from all of the coders about issues and questions they have about what policies, 277 00:31:46,850 --> 00:31:54,650 how to code particular policies. And then we have WhatsApp groups which acts like help desks, which the research assistants man. 278 00:31:56,150 --> 00:32:04,310 And actually one thing to point out about whether you can truly compare, I think one sort of silence, 279 00:32:05,060 --> 00:32:14,209 huge benefit of this project that is fairly unrecognised is that because we have 280 00:32:14,210 --> 00:32:18,530 this objective code book to compare the strength of policies that I think, 281 00:32:18,590 --> 00:32:24,770 you know, stands up to a lot of battering and questioning, which it gets every single day, pretty much. 282 00:32:25,670 --> 00:32:35,060 It's really, I think as a political scientist, move the conversation on in an age where there's so much questioning of the facts. 283 00:32:36,000 --> 00:32:40,590 It's the question of, well, we're doing this and you're not. 284 00:32:40,680 --> 00:32:45,410 Is that really true? Our policies are stronger than yours, even though they're quite different. 285 00:32:45,420 --> 00:32:48,570 And it's hard to seemingly compare just anecdotally. 286 00:32:49,620 --> 00:32:53,580 That's just not been a noisy part of the conversation at all. 287 00:32:53,730 --> 00:32:58,050 We've just moved the conversation on to what is working and what's not. 288 00:32:59,700 --> 00:33:06,990 What about if you've got a variable like how authoritarian a country is or even how 289 00:33:07,410 --> 00:33:14,250 well they score on your on your stringency and other levels of what's being done? 290 00:33:15,060 --> 00:33:18,720 But then you've got different levels of social inequality in different countries. 291 00:33:19,110 --> 00:33:24,110 Does that have an impact on the results? Out of the economic inequality. 292 00:33:25,170 --> 00:33:29,580 So what do these things do? Affect results in different ways. 293 00:33:29,610 --> 00:33:40,020 So I wrote a paper about different forms of authoritarianism with some other academics here in the school to say Joe Wolff and my tutor, 294 00:33:41,730 --> 00:33:50,670 sort of thinking about how leaders with an authoritarian style might respond to this kind of opportunity or moment in time. 295 00:33:52,860 --> 00:33:58,439 And what we argue is that depending on your type of authoritarian, you go one of two directions. 296 00:33:58,440 --> 00:34:04,230 If you seize the moment and you own right, I can put in put in place the really strong policies and then hold them there. 297 00:34:05,010 --> 00:34:12,120 I say the big issue is whether there are sunset clauses or if you're the more science denying. 298 00:34:12,120 --> 00:34:21,599 I prefer small governments. You're in a bit of a corner when a pandemic comes along because you can't necessarily say, wait, time for big government. 299 00:34:21,600 --> 00:34:22,830 Let's learn from science. 300 00:34:23,370 --> 00:34:31,680 And then you were in the Bolsonaro Trump camp of policies are very, very weak compared to other countries, at least at the federal level. 301 00:34:33,960 --> 00:34:40,260 And then for different questions about what works on the ground, how people are responding in terms of their behaviours, 302 00:34:40,260 --> 00:34:44,460 then things like questions of inequality and so on become far more prominent. 303 00:34:44,670 --> 00:34:51,960 Right. But I guess the all these different variables matter in quite complex ways, depending on what your question is. 304 00:34:54,010 --> 00:34:57,219 And and I thought it was interesting that they. 305 00:34:57,220 --> 00:35:05,560 Were you surprised to discover that the the kind of wealth in the sense of the you know, 306 00:35:05,560 --> 00:35:14,260 the economic ranking of countries didn't necessarily relate to how effective they were at implementing that could be presenting prevention policy. 307 00:35:14,560 --> 00:35:20,380 I think we all were. I mean, particularly not just sort of just about wealth. 308 00:35:21,580 --> 00:35:27,940 I think when we look at reports that were written before this pandemic about countries preparedness to handle a pandemic, 309 00:35:28,450 --> 00:35:32,379 all of these indices, which is way off. Absolutely way off. 310 00:35:32,380 --> 00:35:35,900 So the U.S. and the U.K. did very well on those. Yes. Good evening. 311 00:35:36,250 --> 00:35:39,730 Yeah, but then the performance didn't match up to that. 312 00:35:40,240 --> 00:35:45,850 Exactly. I think leadership going early. 313 00:35:46,450 --> 00:35:50,920 Coordinated policies have been very, very important. 314 00:35:52,990 --> 00:35:56,320 More so than how much you spend on your health system, necessarily. 315 00:35:56,470 --> 00:36:03,820 Mm hmm. Can you give an example of a country that would be thought of as a low to middle income country, but actually which did pretty well. 316 00:36:05,830 --> 00:36:10,160 So I guess Vietnam for a long period of time did very well. 317 00:36:13,210 --> 00:36:18,760 It's I would say a lot of things. It's I don't want things changing covered so quickly. 318 00:36:19,250 --> 00:36:26,140 Ask me this question. I don't know. Even a month ago, I would have said southern African countries, some of them. 319 00:36:27,370 --> 00:36:33,130 South Africa certainly had some big waves, but some of them have done very well, although, you know, the data is really, really patchy. 320 00:36:33,670 --> 00:36:41,170 And in some ways, Omicron has shone a light on just how patchy the data is because. 321 00:36:42,170 --> 00:36:48,299 Sort of speaking though in his rookie he genetics play rather than my own work but I saw 322 00:36:48,300 --> 00:36:54,300 a sort of a genetic tree a family tree of all the different variants and the deltas, 323 00:36:54,300 --> 00:37:01,380 the betas, the alphas, they all came off the same tree, the omega, and it looks like it split in around February 2020. 324 00:37:02,470 --> 00:37:05,710 Which means one of various things. 325 00:37:07,730 --> 00:37:13,790 And so, you know, could be in animals or that time or whatever. 326 00:37:13,790 --> 00:37:17,719 But one of the options, probably low probability. 327 00:37:17,720 --> 00:37:21,860 But who's to say is that there's other versions of the one patting out that 328 00:37:21,860 --> 00:37:26,870 family tree that we've never found that haven't blasted out like on the corners, 329 00:37:26,870 --> 00:37:30,080 but were there somewhere along the way and no one was looking. 330 00:37:31,470 --> 00:37:34,680 So it's hard to answer the question about foreign countries. 331 00:37:34,740 --> 00:37:42,450 I think one of your papers talked about how some countries had had four waves and some that were a couple who won the journey had one wave. 332 00:37:42,460 --> 00:37:46,470 But I don't know if that's still true, as everybody had more than one wave now. 333 00:37:47,280 --> 00:37:51,420 Question Which country was that only had one wave. Um. 334 00:37:52,200 --> 00:37:58,799 I'd have to check. It's funny, like, even so, I've got a paper under review right now with The Lancet. 335 00:37:58,800 --> 00:37:59,879 And in that paper, 336 00:37:59,880 --> 00:38:08,180 we draw a quite a stark contrast between what we refer to as the two main strategies of mitigator countries and eliminate countries. 337 00:38:08,230 --> 00:38:08,640 Oh, yes. 338 00:38:09,090 --> 00:38:18,270 So mitigator countries being countries like the UK that have just tried to keep the number of cases kind of below what the health system could handle. 339 00:38:18,270 --> 00:38:22,380 Squash the sombrero. They really haven't had that. 340 00:38:22,590 --> 00:38:28,140 Yeah, that was that was that was the Boris. I think you flatten the curve, squash the sombrero. 341 00:38:28,170 --> 00:38:30,380 Yes. Oh. 342 00:38:30,870 --> 00:38:39,480 And then countries like Australia being eliminated like they the moment is the slightest hint of a case or the possibility of community transmission. 343 00:38:39,750 --> 00:38:47,730 So make sure it doesn't go any further. We're not going to have this as a sort of a feature of of population biology. 344 00:38:49,740 --> 00:38:56,040 And, you know, what's happened in Australia and New Zealand in the period this paper's been under review, 345 00:38:56,340 --> 00:39:00,330 it looks like China's the only eliminator left strategically now. 346 00:39:01,020 --> 00:39:04,860 So such is the world of COVID research. 347 00:39:04,860 --> 00:39:09,930 It moves so quickly you just can't keep up that. The implication being that the elimination strategy doesn't work. 348 00:39:09,990 --> 00:39:19,590 You can't fight it that way. I don't want to agree with that because I think the elimination strategy worked 349 00:39:19,590 --> 00:39:25,710 for a very long time and was potentially very preferable to what we got, 350 00:39:26,370 --> 00:39:33,479 um, on many grounds. And that particular paper is about mental health and we find it on the grounds of mental health apart from, 351 00:39:33,480 --> 00:39:38,190 and that's completely aside from the even more important issues of deaths. 352 00:39:38,190 --> 00:39:41,730 Right. And hospitalisations and long COVID and all the rest of it. 353 00:39:42,480 --> 00:39:55,070 Yeah, I there's a lot to be said for eliminating. So you talked over you mentioned that you were having weekly conversations with. 354 00:39:56,630 --> 00:40:04,650 The Cabinet today was the Cabinet Office. That was what you said. And. Can you see evidence that your work has directly influenced policy? 355 00:40:08,380 --> 00:40:12,430 I can see evidence that our work has. Really? 356 00:40:13,470 --> 00:40:22,020 Very deeply infiltrated the thinking of the Cabinet Office to the extent that they even talk in the language of indicators, 357 00:40:22,020 --> 00:40:25,740 which sounds a bit like a Chinese takeaway menu. 358 00:40:25,770 --> 00:40:33,030 So for example, if I say to one of our coders C7, they know it's about internal movement closures, 359 00:40:33,030 --> 00:40:41,400 right in the cabinet of his speak in this language as well. I can certainly say that their slides that they distribute inform. 360 00:40:42,470 --> 00:40:47,270 Important people with reflect you know our work. 361 00:40:47,990 --> 00:40:55,309 I it's hard to draw a straight line between, you know, our discussions on these calls with other academics of different stripes. 362 00:40:55,310 --> 00:41:01,160 So geneticists, epidemiologists and so on. And the policy outcomes coming from the government. 363 00:41:01,220 --> 00:41:08,460 Mm hmm. And is that something you expected to be happening at this stage in your career? 364 00:41:09,120 --> 00:41:19,790 I mean, how do you feel about that personally? I'm. It's the question of how do you feel about anything is not something you ever have time to drink. 365 00:41:20,950 --> 00:41:24,100 Said that to me. I said, don't have time to think about it. 366 00:41:24,130 --> 00:41:26,830 It's announced that people people have given me. 367 00:41:28,120 --> 00:41:36,849 It's strange to say, I think, you know, before that I would be quite in touch with how I felt about different things I was doing at work. 368 00:41:36,850 --> 00:41:45,250 So if I was giving a talk, you know, and I noticed, like, if I prepared enough, everything goes out the window and you're moving at this pace. 369 00:41:45,910 --> 00:41:54,399 I've been used to now, you know, joining conferences and probably I didn't know how to this or not, but it all went fine a number of times. 370 00:41:54,400 --> 00:42:01,510 I've had to join calls where we're presenting in different venues and knowing that my colleagues are doing the 10 minutes before me and having 371 00:42:01,510 --> 00:42:08,650 to kind of write my bits while they're talking and then do my bit and jump off and do the next one or be in multiple meetings at the same time. 372 00:42:09,400 --> 00:42:13,480 It's been a, you know, a hamster wheel like nothing else. 373 00:42:14,610 --> 00:42:23,090 Hmm. That's that's the question I was getting to see what this might be with your journalist hat on rather than your public policy hat, but. 374 00:42:24,400 --> 00:42:32,860 I mean, one of the difficulties for policymakers has been that there hasn't always been a clear consensus coming from the biomedical community. 375 00:42:33,010 --> 00:42:38,979 Why do you think it's been difficult for biomedical scientists to come up with a clear, 376 00:42:38,980 --> 00:42:44,440 unified message of I suppose one obvious example is in the UK case this. 377 00:42:45,810 --> 00:42:50,160 Feeling in the first week or so that herd immunity was what they were going to go for. 378 00:42:50,460 --> 00:42:53,700 So completely throwing elimination out the window, just letting it run through, 379 00:42:53,940 --> 00:42:59,519 and then suddenly realising that that was going to lead to to overwhelming the hospitals. 380 00:42:59,520 --> 00:43:07,200 And we would have to backtrack on that. I don't think that many sensible by this were pushing for herd immunity. 381 00:43:07,650 --> 00:43:12,810 I think when I heard that phrase, you know, even with literally undergrads, 382 00:43:13,290 --> 00:43:19,590 that's 15 years old, having studied the concept of herd immunity and population models. 383 00:43:20,010 --> 00:43:26,160 You can see I remember doing the back of the envelope calculation and when they gave the 384 00:43:26,400 --> 00:43:32,670 not I and I knew in about a minute that they were talking about more than 400,000 deaths. 385 00:43:32,920 --> 00:43:38,610 Right. And that's you know, that's with, you know, my basic decade undergrad. 386 00:43:39,840 --> 00:43:46,080 I think. Yeah, I, yeah, I think herd immunity was a terrible idea at the beginning. 387 00:43:46,500 --> 00:43:55,350 Um, particularly, I mean, for something that isn't nearly as dangerous as Gohmert's, then there are arguments for it, but for something that can. 388 00:43:56,460 --> 00:44:01,350 Kill quite a lot of people. Um, just ethically unthinkable. 389 00:44:02,010 --> 00:44:09,300 Um, I think, you know, the evidence around some things has changed as the data has come in. 390 00:44:09,870 --> 00:44:15,420 To its credit, the World Health Organisation is often criticised for this, changed its tune on masks. 391 00:44:17,490 --> 00:44:20,910 And but I think that is a very positive thing. 392 00:44:21,090 --> 00:44:24,900 It's not. It's evidence of egos. Right. 393 00:44:27,030 --> 00:44:37,590 But it's it's been a really tough job to know the evidence on things that, you know, I wrote The Lancet called me very early on. 394 00:44:38,730 --> 00:44:43,440 And one of the news editors and just said, honey, you know, what do you think of some of the unanswered questions? 395 00:44:44,190 --> 00:44:48,509 And I said, well. Again, this is very early on. 396 00:44:48,510 --> 00:44:54,510 It's like when can we get antibody tests? So these very, very difficult closure policies are coming in. 397 00:44:55,050 --> 00:45:03,360 We want to know, you know, when people are safe because it's only and we don't have any vaccines, you don't have any drugs, you know. 398 00:45:03,390 --> 00:45:08,160 But doctors have to be in the hospital. Right. So who among them is safe and who is? 399 00:45:10,980 --> 00:45:14,549 And I am interviewing all the top people around that. 400 00:45:14,550 --> 00:45:21,390 And and at the time, you know, we thought the best evidence was SA's 1.0. 401 00:45:22,050 --> 00:45:27,330 So those two points there is this one is and the you know the that's 1718 years old 402 00:45:27,330 --> 00:45:32,370 and the frozen blood files and the people who survived that are still immune to it. 403 00:45:32,940 --> 00:45:38,700 Right. So, but so we thought early on that immunity would be very long lasting. 404 00:45:39,540 --> 00:45:42,480 But now we know that it's kind of not the case. 405 00:45:44,250 --> 00:45:51,450 We thought we didn't realise in the epidemiological modelling early on that you had asymptomatic transmission. 406 00:45:51,660 --> 00:45:58,140 Right. And that meant that entirely different structure of models needed to be used. 407 00:45:59,640 --> 00:46:06,420 So I think the biomedical community has I mean, I can't imagine how stressful it is. 408 00:46:06,420 --> 00:46:17,180 I know what it's been like for us. But overall, I think they, you know, they have stepped up in a big way and not just been to too many unknowns, too. 409 00:46:18,240 --> 00:46:28,379 Yes, I do. 70. Yeah. I think you know, a big change of public policy not just to do is disease t with climate change and you know, 410 00:46:28,380 --> 00:46:32,010 researchers in general have to communicate uncertainty to policymakers. 411 00:46:32,010 --> 00:46:41,950 And that's such a difficult job. When I worked for The Economist in the Science Desk, we used to have a bit of a joke that is science journalist. 412 00:46:41,950 --> 00:46:49,360 We would report the average, the paper we'd be reporting from would have an average and a variant statistic. 413 00:46:50,020 --> 00:46:53,650 And then when we actually talk to policymakers and often. MP would. 414 00:46:54,540 --> 00:46:57,840 Ask me at parties, you know, what should I do in this? You've written about it. 415 00:46:58,110 --> 00:47:07,230 They just want to know a direction up or down. So much gets stripped away and you try to have that conversation with them about variants, right? 416 00:47:07,770 --> 00:47:11,219 And it's hard. Oh, getting hot. Yeah, yeah, yeah. 417 00:47:11,220 --> 00:47:15,690 I think it's. Yeah, yeah. 418 00:47:20,510 --> 00:47:28,249 So yes, is it possible at this stage to draw any conclusions about different policy approaches and their 419 00:47:28,250 --> 00:47:35,090 effect on this other the spread of disease or pressure on health systems or the economic impacts? 420 00:47:35,300 --> 00:47:35,540 I mean, 421 00:47:35,540 --> 00:47:46,130 is it you've made some you've recently made some recommendations that have been taken up in the paper from the Global Preparedness Monitoring Board. 422 00:47:46,430 --> 00:47:54,470 Yeah. And so what were your recommendations? And so that paper is interesting because they asked us to. 423 00:47:55,950 --> 00:48:02,200 Really think about why a lot of policies that you know, there are many good policy recommendations. 424 00:48:02,340 --> 00:48:08,370 There's a sea of great PDFs out there. But implementation and pickup is very, very patchy. 425 00:48:08,910 --> 00:48:15,809 And so that conversation is really around trying to understand why so many of the recommendations 426 00:48:15,810 --> 00:48:20,820 that would have been very helpful if they had been implemented up to this point had not been. 427 00:48:21,920 --> 00:48:30,270 And so we sort of we looked into that, but really to reaffirm the ones that were most important and should be back on the table. 428 00:48:31,890 --> 00:48:34,560 So things like increased funding for the W.H.O., 429 00:48:35,070 --> 00:48:45,180 thinking about some sort of a international body or council to try and coordinate decision making, that kind of thing in terms of. 430 00:48:46,230 --> 00:48:52,020 Thinking about packages of policies are the things that we code, which is the question that very often get asked. 431 00:48:54,430 --> 00:49:01,700 I mean, it's. I usually say they work is a simple thing to say, right? 432 00:49:02,060 --> 00:49:08,870 But different countries use different combinations of different contexts, which is why it gets very complicated. 433 00:49:09,850 --> 00:49:19,110 On closure policies. I think the thing that I just you don't actually need to code to say you don't need all this data is. 434 00:49:19,410 --> 00:49:28,320 Well logically you know if people live in households and this you know people get COVID and passed on for about two weeks. 435 00:49:28,950 --> 00:49:33,060 So you have one person comes home with COVID. Imagine they just got it. 436 00:49:33,600 --> 00:49:38,940 So then they've got it for two weeks and possibly for two weeks. Then they spread it to someone else in the household. 437 00:49:39,180 --> 00:49:42,210 Who? Everyone else in the household. They can have it for two weeks. 438 00:49:43,200 --> 00:49:48,629 Right. So I think what you want to do with closure policies is to make sure that households 439 00:49:48,630 --> 00:49:52,890 aren't mixing and you want to keep them in place for long enough for that logical, 440 00:49:52,890 --> 00:49:57,750 roughly a month period to be done. And you want them to be effective. 441 00:49:59,660 --> 00:50:09,680 I think one of the things that we have not done well is we've had very long lockdowns that have been very costly to people in their everyday lives, 442 00:50:11,210 --> 00:50:15,380 and it's been done with poor messaging, and we don't code that. 443 00:50:15,740 --> 00:50:22,060 But there's a fascinating study from Italy. It compares two regions with the same policies. 444 00:50:22,550 --> 00:50:27,070 And in one region, they basically say, you know, we're doing this for as long as it takes. 445 00:50:27,290 --> 00:50:33,800 Keep going. And then another reason they set clear goals, you know, case rates, number of days. 446 00:50:34,250 --> 00:50:38,270 And in that second region, people adhere to the policy so much better. 447 00:50:39,220 --> 00:50:46,570 And it goes back to what I was talking about with the pandemic fatigue, with this logic of if you just tell someone to keep doing press ups, 448 00:50:46,720 --> 00:50:51,700 just keep going, motivated, they're going to be compared to counting them down, you know. 449 00:50:54,190 --> 00:51:00,999 So the the broad point I want to make and it's it's strange how you have to keep making 450 00:51:01,000 --> 00:51:07,540 this and I had to make it a lunch of UK US legislators recently is the policies worked, 451 00:51:08,110 --> 00:51:13,460 they cut deaths, they cut cases, but they should be done intelligently because they are. 452 00:51:13,480 --> 00:51:16,840 Some of them are very costly for mental health, education and so on. 453 00:51:23,830 --> 00:51:35,050 So. Yes. I've been reading your papers and from what you've just said, I came to the conclusion that essentially you're arguing for. 454 00:51:36,220 --> 00:51:39,640 A form of world government in this specific area. 455 00:51:40,610 --> 00:51:45,349 Which. Is itself a politically contentious ideas. 456 00:51:45,350 --> 00:51:49,280 And the idea that I mean, yes, we've got the World Health Organisation, 457 00:51:49,790 --> 00:51:56,389 but it depends on all the individual countries agreeing and so it tends to make policy will change 458 00:51:56,390 --> 00:52:03,230 policy rather slowly as you separate them off it to go from trying to to change its policy on masks and. 459 00:52:04,250 --> 00:52:07,010 But you seem to be suggesting there should be a short cut. 460 00:52:07,980 --> 00:52:15,299 That is a is a global organisation or globally trusted organisation that can that can move quite 461 00:52:15,300 --> 00:52:21,150 quickly and which people other people have to other countries or countries have to conform to. 462 00:52:21,630 --> 00:52:25,650 Not sure we call it a world government. I don't think I was saying that slightly. 463 00:52:25,650 --> 00:52:39,770 But, you know, I think a you know, a strengthened, more nimble, a legitimate council would be would would be helpful. 464 00:52:40,440 --> 00:52:49,020 You know, more personally, I think and that's not in the report because it would have taken a lot more work than we had resources to do at a time. 465 00:52:49,470 --> 00:52:56,340 I think one of the issues, which is very hard to pull off in international relations in its current state, 466 00:52:56,970 --> 00:53:04,050 is that this has been essentially a regional pandemic, the way it's moved around the world. 467 00:53:04,170 --> 00:53:10,920 It was you know, it was in China and it was big in Europe and and in New York and then shifted to South America. 468 00:53:11,120 --> 00:53:14,760 Now you've got it in Africa. And you know, the. 469 00:53:15,800 --> 00:53:25,370 What the show has done too much, given its budget of essentially a large U.S. hospital as its annual budget has been going into this. 470 00:53:26,770 --> 00:53:30,430 And it should have pandemics. And it's absolutely. 471 00:53:31,500 --> 00:53:35,760 But I think there is a case for strengthening regional bodies as well. 472 00:53:36,730 --> 00:53:42,330 But apart from the African Union, maybe to some extent ASEAN, nobody. 473 00:53:42,660 --> 00:53:46,530 China is not in that. It's not easy to pull off. 474 00:53:46,740 --> 00:53:56,320 Mm hmm. Yeah. But it's I mean, I think it's interesting because we've got other big problems globally. 475 00:53:56,890 --> 00:54:07,450 Climate change is the obvious one. And and I wonder whether you think there are lessons from the necessity of of global exchange of data 476 00:54:07,840 --> 00:54:13,120 in the pandemic that might actually help to shift the dial a bit on something like climate change? 477 00:54:14,230 --> 00:54:21,450 Well, I sincerely hope so. As she told me, my colleague, we all have this. 478 00:54:21,460 --> 00:54:27,400 He's building an ad, a tracker of policies for climate. 479 00:54:27,790 --> 00:54:39,819 It's very much the same model. But I think actually one of the things that I hope we get to do next year, so I have my fingers crossed. 480 00:54:39,820 --> 00:54:49,540 I don't want to jinx it by talking about it now is that we get to run surveys in other countries regularly where we probe how 481 00:54:50,410 --> 00:54:58,660 linked people's thinking is or how maybe their perspectives might have shifted on the value of international coordination. 482 00:55:02,240 --> 00:55:14,030 That's the you know. I hope so. I also think that many aspects of how the world has managed this pandemic have felt like a car crash in slow motion, 483 00:55:14,030 --> 00:55:17,130 particularly vaccine distribution. Um. 484 00:55:17,630 --> 00:55:21,710 Entirely predictable. Car crash information. 485 00:55:22,740 --> 00:55:27,870 And so because, because countries took a me first approach. 486 00:55:27,970 --> 00:55:33,270 Yeah. If you were to say okay well the world's structural inequalities are going to be how this ends up, 487 00:55:33,720 --> 00:55:40,340 you know, and essentially that is what has happened. So. 488 00:55:41,800 --> 00:55:46,210 Well, we'll see. In short, I mean, Glasgow wasn't. 489 00:55:48,480 --> 00:55:51,870 I heard the jamboree that we could have all hoped for. 490 00:55:51,870 --> 00:55:59,700 Kind of. Yeah. The emergence of a sense of global unity through a pandemic were emerging after the pandemic. 491 00:56:00,420 --> 00:56:05,810 But we might get there. I'd like to think it was a possibility still. 492 00:56:11,020 --> 00:56:15,130 So I may be a little bit now to you more personally. 493 00:56:15,430 --> 00:56:23,230 So how did the provisions that were made in this country, lockdowns and so on, impact on what you personally were able to do? 494 00:56:24,250 --> 00:56:28,200 It was. Wow. 495 00:56:28,290 --> 00:56:35,419 To start with that one. Um, well, I, you know, a lot of my colleagues have kids, 496 00:56:35,420 --> 00:56:43,220 so and home schooling happens what they were able to do personally in terms of their research, you know, I got blown out of the water. 497 00:56:44,000 --> 00:56:49,040 Um, and I don't know how they kept teaching. Um, I don't have kids. 498 00:56:50,240 --> 00:56:58,670 I. So I think it's probably worth saying the first lockdown, um, uh, so late March onwards, 499 00:56:58,700 --> 00:57:02,960 2020, I didn't have a meal with another human being for four months. 500 00:57:06,210 --> 00:57:11,190 It was actually fine. You know, if you say that, that's about to happen to you. 501 00:57:11,520 --> 00:57:16,590 I don't think anyone would particularly want to roll the dice on their psychology, just seeing how that one plays out. 502 00:57:17,190 --> 00:57:20,880 But I. I do think that. 503 00:57:22,180 --> 00:57:24,940 The track is in some ways being a kind of therapy. 504 00:57:25,720 --> 00:57:35,820 And in that I didn't really feel any sort of psychological impacts from that experience because it's every day, multiple times a day. 505 00:57:35,830 --> 00:57:40,330 I was seeing a huge group of people brought together by a sense of. 506 00:57:41,880 --> 00:57:46,320 You know, public service and. 507 00:57:47,620 --> 00:57:54,810 Um, also a sense of agency as well, when we all felt we didn't have any hope or it was being taken away from us. 508 00:57:54,840 --> 00:57:59,250 Yeah, actually. Everyday agency. Um. So. 509 00:57:59,640 --> 00:58:05,950 I have. Exhausted by this project, but it was being propped up by its owner personally. 510 00:58:07,910 --> 00:58:12,050 No, that's the idea. That's another question I had, which was whether. 511 00:58:12,680 --> 00:58:20,630 Yeah. Having something to do that felt worthwhile. Just a little bit to do with your own well-being? 512 00:58:21,290 --> 00:58:25,100 Definitely. Yeah, definitely. Even when I wasn't sleeping very much. 513 00:58:26,510 --> 00:58:29,510 And did the the institution. 514 00:58:30,230 --> 00:58:32,070 Did you feel that that within that, of course, 515 00:58:32,150 --> 00:58:38,510 the institution that was in a sense that for colleagues who might be struggling, there was support there for them? 516 00:58:39,410 --> 00:58:39,950 Yes. 517 00:58:41,420 --> 00:58:52,400 I feel extraordinarily lucky to be part of the school because it's the youngest department in Oxford and it feels like a very, very modern workplace. 518 00:58:53,480 --> 00:59:00,620 We're constantly talking about, you know, different policies for different kinds of diversity. 519 00:59:01,720 --> 00:59:10,630 Different aspects of well-being. And those systems are there and they're prominent in our conversations before the pandemic. 520 00:59:11,980 --> 00:59:15,520 And so I think it's I wouldn't say it hasn't been a strain, 521 00:59:15,520 --> 00:59:19,719 particularly when we all had to step up and we had to move everything online for the teaching. 522 00:59:19,720 --> 00:59:26,590 And, you know, there's only so many staff and so many hours in the day, so people have felt the heat. 523 00:59:26,680 --> 00:59:30,670 But at the same time, I think this department has been incredible. 524 00:59:30,940 --> 00:59:35,590 Mm hmm. And I mean the teaching, particularly because a lot of the students are from overseas, aren't they? 525 00:59:35,800 --> 00:59:43,660 Yeah, they. Were they here or did they stay in their home countries in a complete mixture? 526 00:59:44,810 --> 00:59:56,860 Um, you know, I, so when it came to Hillary 20, 21, I think a lot of the assumption was, are you going to be teaching in person? 527 00:59:58,480 --> 01:00:03,760 And then as Christmas drew closer, you know, things looked much worse here. 528 01:00:04,420 --> 01:00:15,160 And then we had something like ten days before Hillary started to turn everything into online material and students couldn't come back. 529 01:00:16,120 --> 01:00:21,070 A lot of them. I remember taking a supervision with a student in Indonesia. 530 01:00:22,180 --> 01:00:28,270 I didn't know she was still in Indonesia and bless her, she'd stay up till three in the morning for the supervision, you know. 531 01:00:28,270 --> 01:00:33,310 And but even that she'd been locked in her little flats away from her family. 532 01:00:33,320 --> 01:00:40,780 She was in Jakarta, her family were elsewhere. And, you know, just having a kind of. 533 01:00:42,250 --> 01:00:52,180 Um. She was just so down from being a tiny, sweaty little flat, trying to stay up for classes, moonlighting, basically. 534 01:00:53,030 --> 01:00:58,060 And yeah, it's been the human experience of COVID has been. 535 01:00:59,410 --> 01:01:04,150 Off balance for everybody and completely different ways is how I feel about it. 536 01:01:05,700 --> 01:01:10,410 Yeah. And still no light at the end of the tunnel? 537 01:01:10,540 --> 01:01:19,220 Not much. Well, vaccines, I think, like keeping light. 538 01:01:19,760 --> 01:01:23,870 And in our mental health research, vaccines are really important for mental health. 539 01:01:23,930 --> 01:01:29,239 Oh, that's interesting. Would you like to expand on that? Well, it's unpublished, so. 540 01:01:29,240 --> 01:01:34,600 Oh, right. Okay. Yeah. Um, so I. 541 01:01:34,600 --> 01:01:42,770 I'm moving to a close now. Has this experience, um, raised questions you'd be interested in exploring in the future? 542 01:01:44,000 --> 01:01:47,840 Oh, a little bit of a host of questions. Yes. Yeah, I think. 543 01:01:50,330 --> 01:01:59,240 So in terms of the the COVID work, I think there's a whole stack of research and thinking that needs to be done around, 544 01:01:59,690 --> 01:02:05,390 you know, what does preparedness really mean? If we were so bad, it's indicating it before. 545 01:02:07,890 --> 01:02:11,640 Which is important. But equally, the next pandemic might be ten years away. 546 01:02:11,640 --> 01:02:20,260 It might be 100 years away. So one thing that every country needs to think about is the building back agenda. 547 01:02:21,340 --> 01:02:25,299 And I so we I haven't talked about this, 548 01:02:25,300 --> 01:02:33,040 but we run surveys in Brazil looking at impacts and I was presenting them about education impacts on an online conference. 549 01:02:33,040 --> 01:02:37,959 And there was a guy talking about earthquakes in Pakistan. 550 01:02:37,960 --> 01:02:47,590 And I did think, what on earth are you doing on this panel? But what he was there to show to discuss is stuck with me ever since. 551 01:02:47,590 --> 01:02:52,780 And that was when they look at earthquakes that affected part of Pakistan. 552 01:02:52,780 --> 01:02:58,030 And suddenly children in those regions had to study at home for about four months. 553 01:02:58,540 --> 01:03:06,970 But the same cohort elsewhere in the country went to normal school for months of study at home. 554 01:03:08,970 --> 01:03:13,770 After going back to school by the time that they were 18, added up to two years of lost learning. 555 01:03:15,000 --> 01:03:21,540 So for me, the building back agenda is underestimated. 556 01:03:22,110 --> 01:03:32,730 And what we know so far is that inequalities that existed in the world before COVID have been entrenched. 557 01:03:33,390 --> 01:03:39,930 But I think there's a lot to be concerned about, about how much they mean they might be exacerbated from this point on. 558 01:03:41,250 --> 01:03:46,500 And I think that should be one of the sort of the guides of how you prioritise the building back agenda. 559 01:03:47,730 --> 01:03:52,740 And lots of other questions for me about trust in government and all sorts of things that I want to work on. 560 01:03:52,740 --> 01:03:59,220 But I think the building back agenda is super important and just because of its urgency at this moment. 561 01:04:01,040 --> 01:04:07,070 So it's like the. Naughty Bush, is it? 562 01:04:09,610 --> 01:04:17,589 I mean, do you think the pandemic has offered opportunities for for work on comparative government that wouldn't have been there otherwise, 563 01:04:17,590 --> 01:04:23,080 in which, as you say, you have I've raised, you know, in some ways opened a lot of doors for questions to be asked. 564 01:04:23,910 --> 01:04:28,450 Yeah. It's funny, I just research. When we were testing this, you asked me what I had for lunch. 565 01:04:29,180 --> 01:04:32,549 I had lunch with a philosopher, and we were talking about this very issue. 566 01:04:32,550 --> 01:04:37,620 And he he was saying the pandemic isn't really done anything with philosophy. 567 01:04:37,620 --> 01:04:47,160 It hasn't really opened up any new questions or allowed people to write books like that for, you know, the empirical social sciences. 568 01:04:47,640 --> 01:04:57,060 This is such a traumatic event that I don't I feel a bit uncomfortable calling in opportunity. 569 01:04:57,060 --> 01:05:00,450 But, you know, that's why I was asking the question. 570 01:05:00,450 --> 01:05:05,340 But it's been a moments to learn about the world and about societies. 571 01:05:08,410 --> 01:05:15,639 And has the experience changed your attitude or your approach to your work and 572 01:05:15,640 --> 01:05:20,530 all things you'd like to see change in the future of how research is conducted? 573 01:05:22,110 --> 01:05:25,260 Hum. I think. 574 01:05:26,770 --> 01:05:30,730 So also answer this in both a personal and professional way, I think personally. 575 01:05:32,320 --> 01:05:40,900 There's been so much work and so much urgency that literally I could not have slept if I you know, you know, 576 01:05:40,930 --> 01:05:48,810 there were days when you're getting 700,000 emails and I had to turn off the ping just because it was anxiety. 577 01:05:48,990 --> 01:05:52,330 You make a cup of tea and it's Ping-Pong ping pong, just impossible. 578 01:05:53,770 --> 01:06:00,729 So I think personally, I think a lot of Oxford academics are used to the feeling of overworking, 579 01:06:00,730 --> 01:06:07,990 but it made me work out what I needed to do for my own well-being and stick to that no matter what. 580 01:06:09,460 --> 01:06:15,580 Even if, you know, heads of state literally want to talk to me sometimes that, you know, I need to go for a walk at some point today. 581 01:06:17,710 --> 01:06:24,430 And then I think in terms of how research is done, it has had, you know, a profound impact on me. 582 01:06:24,640 --> 01:06:35,049 I think the and coming from the natural sciences, where research is by default a team sport moving to the social sciences, 583 01:06:35,050 --> 01:06:39,730 where it's by default an individual or a pair or a very small group. 584 01:06:40,960 --> 01:06:48,340 I had the instinct when I moved across that it should be a team sport and it was structured basically. 585 01:06:49,060 --> 01:06:56,889 And I hope that projects like The Tracker make the point of how how many synergies 586 01:06:56,890 --> 01:07:02,590 there are and how there are things that are only possible when you play as a team. 587 01:07:04,090 --> 01:07:07,120 And the other thing that I think really needs to be thinking a little bit. 588 01:07:08,190 --> 01:07:16,799 Is the credit system so often that people who are not just in the social sciences, 589 01:07:16,800 --> 01:07:25,290 but people who've done sequencing and so on have been post-doc saving, people who have been at very insecure stages of their career. 590 01:07:26,070 --> 01:07:32,340 And they have done work which doesn't may or may not lead to papers. 591 01:07:32,700 --> 01:07:37,770 They've done it because it's been needed by the world and the trackers of data projects, 592 01:07:38,190 --> 01:07:44,700 you know, and creating datasets or writing in really informative Twitter thread. 593 01:07:44,700 --> 01:07:48,870 Sometimes academics have set the record straight on a whole bunch of things. 594 01:07:50,470 --> 01:07:59,010 It doesn't get you anywhere in terms of career review boards, that kind of thing, and I think that needs some consideration at the moment. 595 01:08:00,740 --> 01:08:07,050 Good. Anything that I haven't asked you about that you feel I should have done that? 596 01:08:07,360 --> 01:08:13,399 I know you've asked a lot. No, it was lovely. 597 01:08:13,400 --> 01:08:13,820 Thank you.