1 00:00:01,490 --> 00:00:05,870 OK. The recording is happening. We're very lucky to have Nick Drew joining us today. 2 00:00:05,870 --> 00:00:13,580 He's the professor at London School of Hygiene and Tropical Medicine, as well as a professor at the graduate school at UC Berkeley. 3 00:00:13,580 --> 00:00:16,580 So there was some debate over whether or not he'd talk about COVID, 4 00:00:16,580 --> 00:00:24,560 but we opted to ask him to speak about something that wasn't covered today so we can have a bit of variety in our lives. 5 00:00:24,560 --> 00:00:27,770 So you'll be I'll keep people on mute except for Nick. 6 00:00:27,770 --> 00:00:36,200 And then if you have questions, please put them into chat and I will try and note if something is an urgent question, 7 00:00:36,200 --> 00:00:40,220 where if it is, I might politely interrupt Nick to to ask it. 8 00:00:40,220 --> 00:00:44,930 Otherwise, I will keep them till the end and try to cover the range of questions as if it's urgent. 9 00:00:44,930 --> 00:00:52,430 And I haven't noticed it. Please do the raise hand button and then I will be even more likely to notice you. 10 00:00:52,430 --> 00:00:59,710 OK, thank you very much for that, and I will hand over to Nick, who will now share his screen. 11 00:00:59,710 --> 00:01:10,240 Thank you very much, Crystal. Thanks for the invitation. It's a great pleasure to speak about something other than COVID 19 for a while. 12 00:01:10,240 --> 00:01:19,030 Let me just share the screen, so I very much appreciate the opportunity to talk about something else that's close and dear to my heart. 13 00:01:19,030 --> 00:01:32,800 And as I say to my friends, I only wish I could go back to normal life with Ebola and dengue instead of COVID 19, which always raises a few eyebrows. 14 00:01:32,800 --> 00:01:39,250 But that's the nature of life as a statistician involved with infectious diseases. 15 00:01:39,250 --> 00:01:48,310 So what I'm going to talk about today is a trial that is in progress to eliminate 16 00:01:48,310 --> 00:01:54,500 or reduce the burden of disease from dengue fever and the statistical aspects. 17 00:01:54,500 --> 00:02:04,900 So I'll give a background as to what's going on for those of you who may not be familiar with this approach to controlling dengue. 18 00:02:04,900 --> 00:02:10,300 So this is a study I'm going to talk about is sponsored by the World Mosquito Programme. 19 00:02:10,300 --> 00:02:21,070 You can see there in Spanish. It used to be known as the Eliminate Dengue programme and indicate why it's changed its name. 20 00:02:21,070 --> 00:02:34,090 It will become obvious in a few minutes. There've been many, many people involved in this trial, including Crystal here and Neil Ferguson at Imperial. 21 00:02:34,090 --> 00:02:40,090 And, of course, the local investigators at Yogyakarta. 22 00:02:40,090 --> 00:02:48,220 This programme comes out of the lab of Scott O'Neill at Monash University in Australia and several other people. 23 00:02:48,220 --> 00:02:58,540 And the last person I want to acknowledge is my graduate student, Suzanne Duvall, who just graduated with her Ph.D. last week. 24 00:02:58,540 --> 00:03:02,050 There's a bunch of references here. I'm not expecting you to look at those. They're just here. 25 00:03:02,050 --> 00:03:11,050 If you ever download this or want the slides so you can have the background for some of the references that described some of the work, 26 00:03:11,050 --> 00:03:18,020 that's in this talk. So let me say a little bit about dengue fever. 27 00:03:18,020 --> 00:03:23,600 It's one of the most important mosquito borne viral diseases, of course, along with malaria, 28 00:03:23,600 --> 00:03:33,230 it's very rapidly increasing, with roughly a 30 fold increase in global incidence in the last 50 years. 29 00:03:33,230 --> 00:03:38,690 A significant part of the world is at risk of infection, and that number is growing. 30 00:03:38,690 --> 00:03:49,490 I'll show a map in a second. It leads to an enormous number of infections per year and disease burden and dengue. 31 00:03:49,490 --> 00:03:56,750 When you're first infected with one of the four serotypes of the virus that causes dengue, 32 00:03:56,750 --> 00:04:09,980 it's likely that you suffer what would be most equivalent to a severe case of influenza with joint pains and the like for an end. 33 00:04:09,980 --> 00:04:15,950 But it's rarely fatal. But what can happen with the because of immunity reaction? 34 00:04:15,950 --> 00:04:24,050 If you're subsequently reinfected by another serotype, it can often lead to much more serious health complications, 35 00:04:24,050 --> 00:04:34,790 including dengue haemorrhagic fever, and that causes a lot of hospitalisations and a significant number of deaths per year. 36 00:04:34,790 --> 00:04:42,890 Dengue is transmitted primarily, but not universally, by the species of mosquito in order to elicit a fight. 37 00:04:42,890 --> 00:04:49,670 So a different species from the primary mosquito that causes malaria. 38 00:04:49,670 --> 00:04:56,870 And here is a map of where a decision play lives in the world or a heat map here, 39 00:04:56,870 --> 00:05:04,430 with red being very high concentration of very good conditions for breeding over the subject. 40 00:05:04,430 --> 00:05:12,500 Slowly declining to the blue and the grey to cold and not appropriate for. 41 00:05:12,500 --> 00:05:15,860 For the life cycle, for a dosage of type, 42 00:05:15,860 --> 00:05:25,550 and you can see there that it's an area with a covers a significant amount of Latin America, Africa, India and Asia. 43 00:05:25,550 --> 00:05:33,770 And just in north the northeast corner of Australia on here I. 44 00:05:33,770 --> 00:05:38,010 You can see up on the United States there, it's just creeping in. 45 00:05:38,010 --> 00:05:46,040 And in Florida, a little bit of Texas, but it's creeping up there because with global warming and climate change, 46 00:05:46,040 --> 00:05:51,560 more and more of the northern hemisphere is coming, becoming suitable for a subject. 47 00:05:51,560 --> 00:06:01,270 And now there are industries by in California not far from where I am right now, beginning to develop. 48 00:06:01,270 --> 00:06:09,100 They're also so that's becoming a more and more risk as we go on, it's not in the United Kingdom yet, 49 00:06:09,100 --> 00:06:20,380 but it will become in the United Kingdom if global warming continues at the pace it is in in due course. 50 00:06:20,380 --> 00:06:25,930 This is similar a graphic that reflects where the disease burden occurs. 51 00:06:25,930 --> 00:06:36,670 In addition to where the mosquito lives and so you can see there on the left and by far the biggest country that suffers dengue is Brazil, 52 00:06:36,670 --> 00:06:40,060 followed by Indonesia, which I'll come back to in a minute. 53 00:06:40,060 --> 00:06:49,390 Then Vietnam and so on. And so very significant effects just where you find. 54 00:06:49,390 --> 00:06:59,350 Aegypti. And so, of course, humans have battled for aeons against mosquito borne diseases, 55 00:06:59,350 --> 00:07:04,690 and there's a number of interesting public health interventions that are designed to 56 00:07:04,690 --> 00:07:14,500 try and protect humans from infection from various diseases transmitted by mosquitoes. 57 00:07:14,500 --> 00:07:17,380 And I'm not going to go through these in great detail. 58 00:07:17,380 --> 00:07:25,990 Many of them, I'm sure all of you were aware of there can be direct vector control that's trying to kill the mosquito. 59 00:07:25,990 --> 00:07:32,980 Stop it breeding by usually spraying larva sites or removing habitats. 60 00:07:32,980 --> 00:07:45,910 That's the sort of thing you would have seen during the world before the World Cup and the Olympics in Brazil because of the concern then of Zika, 61 00:07:45,910 --> 00:07:53,680 which is also transmitted by Aedes aegypti. And so you would see these images from the TV of people in white protective suits 62 00:07:53,680 --> 00:07:59,290 spraying various streets and areas as much as we're seeing now for COVID 19, 63 00:07:59,290 --> 00:08:04,450 and I'll try and resist mentioning COVID 19 as much as possible. 64 00:08:04,450 --> 00:08:09,970 It's largely been ineffective. It's a long term strategy, 65 00:08:09,970 --> 00:08:19,780 in part because the efficiency of data subject time breeding is renowned and even a small plastic bottle cap 66 00:08:19,780 --> 00:08:28,390 that's left in the street that fills with water from rain can breed an enormous number of subjects in short order. 67 00:08:28,390 --> 00:08:36,430 So it's extremely difficult to remove all possible breeding grounds and spray all of them, but it is used. 68 00:08:36,430 --> 00:08:40,030 Vector barriers have been very effective for malaria. 69 00:08:40,030 --> 00:08:48,550 That's bed nets, largely our personal insect, which is what I use when I'm travelling in a just aegypti country, 70 00:08:48,550 --> 00:08:58,900 and that certainly can work at the individual level effectively. There has been some attempt at vaccine development, 71 00:08:58,900 --> 00:09:09,370 and there is now a vaccine for dengue fever called its trade name is Dengvaxia that was developed by Sanofi and was approved for use in many, 72 00:09:09,370 --> 00:09:14,080 many countries. It was suspended. 73 00:09:14,080 --> 00:09:18,880 Use of Dengvaxia was suspended in the Philippines. 74 00:09:18,880 --> 00:09:31,000 Now I'm going to say almost a year to two years ago because of a cross immune reaction to the vaccine in the sense that individuals, 75 00:09:31,000 --> 00:09:34,410 children, particularly who were vaccinated. 76 00:09:34,410 --> 00:09:47,940 And had been naive to end any strain of the dengue virus who were subsequently infected, even though they were vaccinated, 77 00:09:47,940 --> 00:09:56,370 suffered very severe complications and since the vaccine was only about 60 percent effective, that was not a rare occurrence. 78 00:09:56,370 --> 00:10:00,360 In other words, 60 percent effectiveness is not awful. 79 00:10:00,360 --> 00:10:08,070 It's it's useful. It's it's not sufficient to eradicate dengue, but that's very helpful. 80 00:10:08,070 --> 00:10:13,140 But when you get the 40 percent of children who were vaccinated but still vulnerable, 81 00:10:13,140 --> 00:10:25,770 and then they suffer a severe reaction to a subsequent infection that caused a suspension of the licence of its use, 82 00:10:25,770 --> 00:10:31,980 and that has cast a great shadow over the use of Dengvaxia. 83 00:10:31,980 --> 00:10:39,810 And now that nor the newer sort of modern high tech strategies for controlling these diseases. 84 00:10:39,810 --> 00:10:47,070 The genetically modified mosquito development from a British company, Oxitec, is still in full operation. 85 00:10:47,070 --> 00:10:53,160 It's an attempt, and now there are many different using CRISPR technology. 86 00:10:53,160 --> 00:11:02,160 There are many variants of the Oxitec strategy to genetically modified mosquitoes to essentially interrupt the life cycle 87 00:11:02,160 --> 00:11:13,110 and not allow them to either breeders effectively or other have other consequences that limit disease transmission. 88 00:11:13,110 --> 00:11:20,760 I'm not going to talk about that. Some of you, I know, know about those experiments to test this as a strategy. 89 00:11:20,760 --> 00:11:29,490 What I am going to talk about for the rest of the talk is the second one, which is a more natural version of modification of the mosquito, 90 00:11:29,490 --> 00:11:37,860 and that is to deploy mosquitoes that are trans infected with a bacterium known as Wolbachia. 91 00:11:37,860 --> 00:11:40,590 And if those of you are not familiar with Wolbachia, 92 00:11:40,590 --> 00:11:50,140 I'll talk about it very briefly to show you why it has an impact, but this is a naturally occurring bacterium. 93 00:11:50,140 --> 00:11:52,040 And here's the little graphic, that's you. 94 00:11:52,040 --> 00:12:01,990 So you've got the red blob of the virus, and it's now appearing simultaneously with the green dots for you and or vice versa? 95 00:12:01,990 --> 00:12:02,770 Doesn't matter. 96 00:12:02,770 --> 00:12:10,120 The issue is having both at the same time changes things and let me talk about Wolbachia and what it does to the mosquito and in particular, 97 00:12:10,120 --> 00:12:21,160 what it does to dengue virus living in a mosquito. So Wolbachia is a very natural symbiotic bacteria and you all know about symbiotic bacteria. 98 00:12:21,160 --> 00:12:29,920 We carry a huge amount of symbiotic bacteria in our bodies constantly, largely in our gut. 99 00:12:29,920 --> 00:12:37,150 We depend on those bacteria. If you remove all of those bacteria from mammals, the mammals die. 100 00:12:37,150 --> 00:12:47,260 We know this from mice experiments where they're born and in a complete bacterial free environment, and they don't live for more than a few weeks. 101 00:12:47,260 --> 00:13:02,380 So mammals depend on symbiotic bacteria to digest our food, to protect those largely from other bad things, and so they don't cause us harm. 102 00:13:02,380 --> 00:13:13,030 Most bacteria don't cause us harm, and Wolbachia is such a bacterium that exists in the insect population that does not exist in warm blooded animals. 103 00:13:13,030 --> 00:13:19,720 So it can't be transmitted from a mosquito to your cat, for example. 104 00:13:19,720 --> 00:13:27,760 And so it but it's a very common, in fact, the most common symbiotic bacterium living in insect species. 105 00:13:27,760 --> 00:13:35,980 And you can see it in the graphic there. A lot of different insect species carry Wolbachia naturally. 106 00:13:35,980 --> 00:13:47,350 And if there's some doubt about this, but it largely was believed that it was not carried by Aedes aegypti, naturally, it could not be found in Egypt. 107 00:13:47,350 --> 00:13:55,990 I know it could be found in other mosquito species. And so it was not existent in the wild. 108 00:13:55,990 --> 00:14:10,780 It is a type population and. Let me just go back a second until it was determined in experiments and Scott O'Neill's lab how to trends in fact, 109 00:14:10,780 --> 00:14:18,070 a is a subject a mosquito with while back in the laboratory and then breed the mosquito 110 00:14:18,070 --> 00:14:27,080 in the laboratory to produce a number of mosquito that were carrying Wolbachia. 111 00:14:27,080 --> 00:14:29,360 And that took a significant amount of time, 112 00:14:29,360 --> 00:14:38,660 it was originally thought that this would be potentially an effective control strategy by shortening the lifespan of the mosquito, 113 00:14:38,660 --> 00:14:43,910 perhaps shortening it in some ways similar to genetic modified mosquitoes, 114 00:14:43,910 --> 00:14:51,020 so that it would shorten it below the time when they have maximal breeding and therefore reduce mosquito populations. 115 00:14:51,020 --> 00:14:54,350 That turned out to be true, but only slightly, 116 00:14:54,350 --> 00:15:04,310 and not really a significant enough effect to have any impact in the wild and reducing mosquito populations so scientifically. 117 00:15:04,310 --> 00:15:11,150 This took a great amount of effort that looked like a complete dead end. 118 00:15:11,150 --> 00:15:19,040 Because it didn't do what was hoped, and then in one of the serendipitous discoveries that science is so full of, 119 00:15:19,040 --> 00:15:31,490 someone looked at it as suggesting that we're infected with dengue and saw an effect of the presence of Wolbachia, and I'll come to that in a moment. 120 00:15:31,490 --> 00:15:38,240 Let me just point out that Wolbachia carries with it cytoplasmic incompatibility. 121 00:15:38,240 --> 00:15:42,920 So this is very important in when you introduce it in the wild. 122 00:15:42,920 --> 00:15:47,870 It's how quickly Wolbachia infections in mosquitoes can spread. 123 00:15:47,870 --> 00:15:56,630 So an infected male and a uninfected female cannot produce offspring, but in all other combinations, 124 00:15:56,630 --> 00:16:07,500 infected females with an uninfected male and with both mosquitoes being infected, Wolbachia hold all of their offspring have. 125 00:16:07,500 --> 00:16:19,590 Rollback infections through birth, so through the egg, so that cytoplasmic incompatibility means that there's very rapid transmission. 126 00:16:19,590 --> 00:16:29,610 By the way, this suggests one strategy for using Wolbachia, which is to use to release infected males into a population because they're they're 127 00:16:29,610 --> 00:16:37,050 infertile and they compete with uninfected males and therefore reduce mosquito reproduction. 128 00:16:37,050 --> 00:16:39,480 And this is actually been used in California. 129 00:16:39,480 --> 00:16:51,720 In the Central Valley, which produces a significant amount of the world's food to reduce rollback, I would reduce the subject statutory abundance. 130 00:16:51,720 --> 00:16:56,910 And so that's a different strategy. But that's not the strategy I'm going to use now. 131 00:16:56,910 --> 00:17:03,510 I'm going to talk about deployment of both male and female, and there are some reasons for doing that. 132 00:17:03,510 --> 00:17:14,010 One of which is it's actually quite expensive to have to separate out when you're breeding mosquitoes separate out the males from the females. 133 00:17:14,010 --> 00:17:21,300 And so that isn't done in the experiment I'm going to discuss. 134 00:17:21,300 --> 00:17:30,840 And so that cytoplasmic incompatibility means that when you release or deploy mosquitoes with Wolbachia 135 00:17:30,840 --> 00:17:37,860 represented by the green here into an area in the wild where of course there is no Wolbachia. 136 00:17:37,860 --> 00:17:39,940 And these are representing the number of weeks. 137 00:17:39,940 --> 00:17:49,500 So these are releasing mosquitoes, deploying them usually in larvae farm in a population where there are wild. 138 00:17:49,500 --> 00:17:50,760 It is a job type. 139 00:17:50,760 --> 00:18:01,140 You can see over about three months the infection spreads very rapidly because of that cytoplasmic incompatibility and within three or four months, 140 00:18:01,140 --> 00:18:08,040 essentially, almost all the mosquitoes carry Wolbachia infection, so they're carrying the bacterium. 141 00:18:08,040 --> 00:18:17,220 So this means it's a relatively straightforward way of introducing Wolbachia into the population that doesn't have 142 00:18:17,220 --> 00:18:28,780 ecological consequences per se and reducing mosquitoes and therefore upsetting the animals that depend on mosquitoes. 143 00:18:28,780 --> 00:18:31,720 So it actually doesn't reduce that abundance at all, 144 00:18:31,720 --> 00:18:41,680 but it does ultimately rather quickly lead to a sustained infection with Wolbachia and in experiments that are now going on for many years. 145 00:18:41,680 --> 00:18:48,580 There isn't really a significant reduction. Once it's there, it's there and it doesn't disappear. 146 00:18:48,580 --> 00:19:02,690 So unlike genetically modified mosquitoes, you don't have to continuously deploy Wolbachia mosquitoes to try to keep a high level of infection. 147 00:19:02,690 --> 00:19:08,450 And so this is the experiment I was talking about that led to that evidence or some of the evidence, 148 00:19:08,450 --> 00:19:18,300 there's more evidence now, but this was where they were originally. Released in the wild in a pilot in the northeast of Australia, in Cairns, 149 00:19:18,300 --> 00:19:26,970 and you can see this was done almost 10 years ago, over a couple of month period where Wolbachia was deployed. 150 00:19:26,970 --> 00:19:36,690 By the way, Cairns is one of those areas in Australia that does suffer from dengue, usually not endemically, but introduced through travel. 151 00:19:36,690 --> 00:19:43,690 With travellers going to Asia getting infected with dengue returning, they get bitten by a local, 152 00:19:43,690 --> 00:19:51,180 a subject which sustains an outbreak for a while, and then it disappears until a new introduction. 153 00:19:51,180 --> 00:19:55,920 So there's interest there in reducing that burden. 154 00:19:55,920 --> 00:19:59,860 It was introduced in 2011 over a couple of months. 155 00:19:59,860 --> 00:20:10,050 So there you can see that the percent of wild mosquitoes in that area of Cairns, two areas of care and so we're releases were done in a pilot. 156 00:20:10,050 --> 00:20:19,610 Fashion has sustained over 100 percent now for actually much longer than five years. 157 00:20:19,610 --> 00:20:23,390 OK, so what's the big deal about getting Wolbachia into a mosquito? 158 00:20:23,390 --> 00:20:34,310 What does it do to dengue? So the key discovery was that made it reinstate interest after noticing it didn't do a great deal to the 159 00:20:34,310 --> 00:20:43,070 lifespan was that it blocked the ability of dengue virus to replicate inside the guts of the mosquito. 160 00:20:43,070 --> 00:20:52,580 So the mosquito bites and infected human it, it draws in dengue virus through the blood. 161 00:20:52,580 --> 00:20:55,940 It goes into the gut of the mosquito where it normally will replicate. 162 00:20:55,940 --> 00:21:03,050 It will then move through the body of the mosquito up to the saliva of the mosquito bites. 163 00:21:03,050 --> 00:21:14,450 Another naive human, it transmits the virus from the saliva of the mosquito into the blood stream of the human on which it's feeding. 164 00:21:14,450 --> 00:21:16,250 And that's how dengue is transmitted. 165 00:21:16,250 --> 00:21:26,240 There are these four serotypes, as I've mentioned, a very brilliantly named dengue one, dengue two, dengue three and dengue before. 166 00:21:26,240 --> 00:21:37,220 And it was noticed in the lab that the ability to of the mosquito to find dengue virus took 167 00:21:37,220 --> 00:21:44,480 from an infected mosquito after they've been infected with Wolbachia was significantly reduced 168 00:21:44,480 --> 00:21:50,750 somewhere in the order of 70 to 90 percent reduction in being able to detect dengue in the 169 00:21:50,750 --> 00:22:00,860 saliva of infected dengue infected mosquitoes after they were infected with with Wolbachia. 170 00:22:00,860 --> 00:22:05,780 And this is pretty consistent across all serotypes and most areas of the world. 171 00:22:05,780 --> 00:22:07,460 So there's various prevalence. 172 00:22:07,460 --> 00:22:15,950 Differences carry all four serotypes, and that's part of the issue around cross immunity that occurs in these countries. 173 00:22:15,950 --> 00:22:17,990 Well, that was interesting. 174 00:22:17,990 --> 00:22:28,910 But really, what's more interesting, the stakes went up considerably higher because dengue is a member of a family of viruses like the coronaviruses, 175 00:22:28,910 --> 00:22:34,160 a family of viruses and the dangly is a member of another family of versus known as 176 00:22:34,160 --> 00:22:40,730 flaviviruses and many of the familiar and horrible viruses that humans suffer from, 177 00:22:40,730 --> 00:22:48,260 including yellow fever, which was a great scourge of the 19th and early 20th century West Nile virus, 178 00:22:48,260 --> 00:22:54,320 which is a more recent worldwide concern chicken junior Zika, 179 00:22:54,320 --> 00:23:02,870 which became a huge concern several years ago, and Japanese encephalitis and so on, and the ones I've checked on the left. 180 00:23:02,870 --> 00:23:07,550 And actually, I think now this slide is slightly outdated. 181 00:23:07,550 --> 00:23:15,260 One or two on the right have also been checked that they have exactly the same response to the presence of 182 00:23:15,260 --> 00:23:24,440 Wolbachia in these viruses can't be replicated within the body of the virus within the body of the mosquito. 183 00:23:24,440 --> 00:23:30,500 So this suddenly raises the stakes because it means that it is aegypti infected with the bucket. 184 00:23:30,500 --> 00:23:37,550 Not only might be enable unable to transmit dengue, but unable to transmit yellow fever, 185 00:23:37,550 --> 00:23:42,260 chikungunya, Zika and many countries suffer from all of these viruses. 186 00:23:42,260 --> 00:23:50,180 Brazil suffers from yellow fever. Still, even though there's a very effective vaccine for yellow fever. 187 00:23:50,180 --> 00:24:00,050 They certainly suffer, as we all know, from Zika and chicken. And so suddenly, this robot here has the appearance of maybe being a magic cure all, 188 00:24:00,050 --> 00:24:08,990 it's like taking one pill or having one integrated pharmaceutical intervention that protects you from multiple diseases simultaneously, 189 00:24:08,990 --> 00:24:16,970 which is a very rare outcome pharmaceutically. And suddenly, this was a tantalising possibility. 190 00:24:16,970 --> 00:24:23,720 And so this really raised the interest of whether this could be an effective strategy 191 00:24:23,720 --> 00:24:28,310 for protecting not only against dengue and against all serotypes of dengue, 192 00:24:28,310 --> 00:24:30,950 but against these other flaviviruses. 193 00:24:30,950 --> 00:24:38,600 And so now interest around the world in places that suffer from the burden of these diseases showed great interest in this. 194 00:24:38,600 --> 00:24:47,780 And here are some of the current sites in the world where Wolbachia has been released in various levels. 195 00:24:47,780 --> 00:24:57,320 And I'm going to focus entirely on here on Indonesia, which is currently in the midst of a rather severe dengue outbreak. 196 00:24:57,320 --> 00:25:08,400 At the same time as COVID 19. But there's significant effort going on elsewhere in the world that I'd be happy to answer questions about. 197 00:25:08,400 --> 00:25:15,780 So the real question now is you're there, so this looks like a great possibility, but this is all in the lab. 198 00:25:15,780 --> 00:25:23,220 Everything I've said has been larger than the lab other than make showing that you can logistically deploy will be accurate. 199 00:25:23,220 --> 00:25:33,630 Mosquitoes and in fact, a wild population. But does it really protect people in in in the in the field? 200 00:25:33,630 --> 00:25:42,390 Does this really work at scale? Can we do this out there in in in urban and city environments? 201 00:25:42,390 --> 00:25:50,940 And so now you've got to try it. So the question when Crystal and I will remember being at a meeting where basically the 202 00:25:50,940 --> 00:25:57,850 question on the table to a bunch of infectious disease statisticians and biologists were? 203 00:25:57,850 --> 00:26:07,740 How do we design a study that will actually produce convincing evidence or not that this is it could be an effective intervention strategy? 204 00:26:07,740 --> 00:26:14,580 And that's what I'm going to talk about for the last twenty five minutes ago is that design of that study. 205 00:26:14,580 --> 00:26:26,280 And basically, we sat down and over a few days in a workshop came up with what at that point I thought it was a case control design. 206 00:26:26,280 --> 00:26:35,040 The natural experimental design was to use a cohort study because there's a natural aversion to case control studies, 207 00:26:35,040 --> 00:26:39,960 because of concern of confounding and the ability to control confounding. 208 00:26:39,960 --> 00:26:47,940 So a cohort study, I won't describe it in detail that was envisaged in this was enrolling a large 209 00:26:47,940 --> 00:26:54,360 cohort of susceptible to dengue that's largely minors and children because 210 00:26:54,360 --> 00:27:05,340 most adults in this country have developed have been infected already and are immune to one or more of the one or all of the serotypes of dengue. 211 00:27:05,340 --> 00:27:15,960 So you're you're establishing a large cohort of children and then following them in and maybe a cohort of children who are in an 212 00:27:15,960 --> 00:27:24,060 area where you've deployed Wolbachia and comparing them to a cohort of children in an area where Wolbachia has not been deployed, 213 00:27:24,060 --> 00:27:30,900 following them for several years and intermittently bleeding them to determine whether they've been infected or not. 214 00:27:30,900 --> 00:27:43,920 Because since dengue often produces symptoms that are similar to many viral infections, fever, high fever and so on. 215 00:27:43,920 --> 00:27:49,530 It's not distinguishable without a definitive test diagnostic test. 216 00:27:49,530 --> 00:28:01,330 So this raises the prospect of trying to. Recruit and retain a cohort of many, many thousand of children and bleeding them constantly. 217 00:28:01,330 --> 00:28:08,590 Many of those bleeding episodes, of course, would be uninformative to the extent they would be negative. 218 00:28:08,590 --> 00:28:16,600 We wouldn't be expecting everyone, every child to be infected with dengue over a even a several two or three year period. 219 00:28:16,600 --> 00:28:20,800 And so that was conceived of being a very difficult study to actually do 220 00:28:20,800 --> 00:28:27,910 effectively to recruit and retain and ethically draw blood samples so frequently. 221 00:28:27,910 --> 00:28:31,670 And so we moved to a more case control kind of issue. 222 00:28:31,670 --> 00:28:43,370 And here the idea was to enrol dengue cases and non dengue controls in hospitals and clinics in an area, and this is schematically represented here. 223 00:28:43,370 --> 00:28:54,130 Here are the clinics. And the idea is people present if if it right, if the infection within it rises to the level of symptoms again, 224 00:28:54,130 --> 00:28:59,770 largely in children and minors, the parents would bring them to a clinic for treatment. 225 00:28:59,770 --> 00:29:07,360 In fact, it wouldn't be clear as the arrived that they had dengue fever or some other viral infection. 226 00:29:07,360 --> 00:29:17,050 And so blood samples would be drawn and treatment would be given while test results were weighted. 227 00:29:17,050 --> 00:29:21,100 The test results would either be positive or negative for dengue. 228 00:29:21,100 --> 00:29:27,490 The positives would form the basis of your case control, design and the negatives of the words. 229 00:29:27,490 --> 00:29:34,270 Children infected with some other viral infection would be the controls. 230 00:29:34,270 --> 00:29:40,840 The controls would actually have to remove other saliva virus infections like Zika and chikungunya and so on. 231 00:29:40,840 --> 00:29:50,830 But so the controls would have to be injected with some other non-Florida virus and they would form then your ability. 232 00:29:50,830 --> 00:29:56,320 You would then check with the parents where they lived and their location. 233 00:29:56,320 --> 00:30:03,520 And then if you had an area where you'd partially deployed Wolbachia in the green areas and 234 00:30:03,520 --> 00:30:10,840 not in the way you'd look at the place of residence of the child or the infected individual, 235 00:30:10,840 --> 00:30:17,230 and look at the rates at where the cases and controls arose from and do a comparison 236 00:30:17,230 --> 00:30:23,560 there to determine if that distribution of locations differed depending on. 237 00:30:23,560 --> 00:30:32,670 From cases to controls. And that's the basic structure of the design, and I'm going to follow on as it happened, 238 00:30:32,670 --> 00:30:38,590 I quickly discovered I went back and I thought, Well, well, why is this interesting? 239 00:30:38,590 --> 00:30:39,820 So we're doing case control. 240 00:30:39,820 --> 00:30:49,030 I mentioned what you worry about confounding other factors because people don't deploy across a geographic area uniformly. 241 00:30:49,030 --> 00:30:56,500 The socioeconomic status, population density all differ substantially geographically, 242 00:30:56,500 --> 00:31:02,260 and many of those are known risk factors for infection of dengue. 243 00:31:02,260 --> 00:31:04,870 So wouldn't you worry a lot about confounding? 244 00:31:04,870 --> 00:31:16,900 Well, the beauty here is that the that we then moved forward is if we randomly allocate the regions to Wolbachia deployment, 245 00:31:16,900 --> 00:31:21,360 then in fact the exposure is randomly allocated. 246 00:31:21,360 --> 00:31:31,590 And therefore, there can't be any confounding in principle, because we have this ability to randomly export to do a random allocation of exposure. 247 00:31:31,590 --> 00:31:35,400 It's very rare and actually started when I went away from Australia. 248 00:31:35,400 --> 00:31:47,460 After thinking about this design, try to think of other experiments in epidemiology where there was a natural random allocation of exposure. 249 00:31:47,460 --> 00:31:52,350 And then you superimposed on top of that case control ascertainment, 250 00:31:52,350 --> 00:31:59,220 which is efficient because you can find cases more easily and yet not worry about confounding. 251 00:31:59,220 --> 00:32:05,460 And I look for examples, and to be honest, I didn't really find a great example. 252 00:32:05,460 --> 00:32:10,660 I'd still be curious if there are those examples in the history of epidemiology. 253 00:32:10,660 --> 00:32:17,760 What I did find was a essentially a design which mimicked what we had come up with independently, 254 00:32:17,760 --> 00:32:22,740 which is called the test negative design, and it's suddenly become very popular. 255 00:32:22,740 --> 00:32:29,790 And in fact, people are not going to talk about this. People are trying to promote test negative designs to study COVID 19. 256 00:32:29,790 --> 00:32:35,010 Currently, a test negative design had been developed initially, 257 00:32:35,010 --> 00:32:45,210 largely to test the seasonal influenza vaccine after original clinical trials to test influenza vaccine. 258 00:32:45,210 --> 00:32:49,170 We don't test them at clinical trials year after year, as you all know. 259 00:32:49,170 --> 00:33:01,470 And yet, year after year, you will see on the BBC some and the ITV you'll see some mention of the effectiveness of the seasonal flu vaccine this year. 260 00:33:01,470 --> 00:33:05,990 Is it 60 percent effective with last year's 30 percent effective? 261 00:33:05,990 --> 00:33:10,200 And you might ask yourself, Well, where do they how do they estimate the effectiveness? 262 00:33:10,200 --> 00:33:21,970 Well, those numbers are long largely coming from test negative designs, and it's largely come from taking sentinel clinics. 263 00:33:21,970 --> 00:33:28,510 Getting data on everyone showing up at the Sentinel clinic with symptoms of a viral infection. 264 00:33:28,510 --> 00:33:37,750 Drawing a sample to test for the presence of influenza or some other form of viral or some other cause of the symptoms. 265 00:33:37,750 --> 00:33:45,580 Separating the two into two groups to test positive for the influenza cases of the test negative on influenza. 266 00:33:45,580 --> 00:33:54,670 And then at the same time as you recruit the participant and take the sample asking them whether they've received the flu vaccine. 267 00:33:54,670 --> 00:34:01,400 And then you compare the frequency of vaccination amongst the flu test. 268 00:34:01,400 --> 00:34:16,160 Positives to the vaccination rate in the flu or test negatives, and that comparison leads an estimate of the efficacy of the seasonal vaccine. 269 00:34:16,160 --> 00:34:24,840 And again, now you immediately should think, wait a minute, vaccine status is not randomly allocated. 270 00:34:24,840 --> 00:34:36,680 It's clearly not, and it's clearly selectively allocated in a way that also due to factors that may also influence the risk of influenza. 271 00:34:36,680 --> 00:34:44,300 But the big advantage of why it's used in influenza is it this test negative design 272 00:34:44,300 --> 00:34:49,160 controls or eliminates the confounding due to health care seeking behaviour? 273 00:34:49,160 --> 00:35:02,390 So people who get vaccines are more likely to seek care for influenza, which is not usually a very severe disease or fatal disease. 274 00:35:02,390 --> 00:35:07,300 And so you might worry that people who get vaccines are more connected to the health, 275 00:35:07,300 --> 00:35:14,090 health or health care system and therefore seek health care when their child or they get influenza. 276 00:35:14,090 --> 00:35:24,260 And therefore, that will bias the results. But that's not possible with the tests negative design since the individual does not know on arrival at the 277 00:35:24,260 --> 00:35:30,950 clinic whether they're infected by influenza or some other virus that is only determined post recruitment. 278 00:35:30,950 --> 00:35:43,370 So this is why it's been used, and there's a significant literature about its use for in the context of influenza vaccination assessment, 279 00:35:43,370 --> 00:35:50,390 but also in other respiratory diseases and other diseases that I'm not going to talk about today. 280 00:35:50,390 --> 00:35:57,200 So now let me move on to the actual experiments in the last 10 or 15 minutes or so the site should send from here. 281 00:35:57,200 --> 00:36:02,120 And there's a lot of care going into choosing the sites because once you've moved in and you can't use this site ever, 282 00:36:02,120 --> 00:36:08,510 ever again in history to test things because you're going to contaminate the site by releasing Wolbachia. 283 00:36:08,510 --> 00:36:14,420 But the city chosen with junk Jakarta in Java in Indonesia, it's a very large city. 284 00:36:14,420 --> 00:36:22,760 I usually say it's a city the size of Oakland, but I realise that doesn't tell you much if you're a native Californian, 285 00:36:22,760 --> 00:36:27,060 but it's a fairly big city urban environment. 286 00:36:27,060 --> 00:36:36,650 There's a picture of it and the right not far from Jakarta, but it's much, much smaller than Jakarta, which is huge. 287 00:36:36,650 --> 00:36:42,860 And this was piloted in external areas around the city just for logistics. 288 00:36:42,860 --> 00:36:51,690 And then we implemented this design in starting in around 2017. 289 00:36:51,690 --> 00:36:58,470 Late 2017 and what was done was the city was by the statisticians, 290 00:36:58,470 --> 00:37:05,790 the city was divided into regions and the deployment was randomly determined by the region. 291 00:37:05,790 --> 00:37:13,440 And there you can see on the left here the map of drug Toccata broken up into these 292 00:37:13,440 --> 00:37:21,390 patches or geographic regions contiguous and then a random allocation of deployment. 293 00:37:21,390 --> 00:37:28,810 Now I should have said when when I pointed out this test negative design and the advantage of randomisation. 294 00:37:28,810 --> 00:37:37,420 And randomising exposure, unlike vaccination, this is not an intervention you can implement at the individual level because you can't 295 00:37:37,420 --> 00:37:43,810 give each individual their own personal Wolbachia mosquitoes to carry around with them. 296 00:37:43,810 --> 00:37:52,210 So you have to do it geographically. So now we've introduced a statistical complication because this is a cluster randomisation that 297 00:37:52,210 --> 00:38:00,790 we're super imposing in addition to sort of a case cohort sampling design of of outcomes. 298 00:38:00,790 --> 00:38:06,430 But there are the regions they were allocated and there were 24 clusters. 299 00:38:06,430 --> 00:38:12,310 Obviously, you can't have 2000 clusters because then the clusters become very small geographically 300 00:38:12,310 --> 00:38:18,130 and there would be spread of infected mosquitoes into neighbouring regions. 301 00:38:18,130 --> 00:38:27,970 So the spread was considered in designing how many regions that could be restart. 302 00:38:27,970 --> 00:38:35,050 The regions had to be a roughly one to two square kilometres. So there's about 24 of them are exactly 24 of them. 303 00:38:35,050 --> 00:38:39,800 At City, 12 were allocated at random. I do, too. 304 00:38:39,800 --> 00:38:46,160 Wolbachia deployment and 12 to nothing and. 305 00:38:46,160 --> 00:38:54,410 In just two, I won't go into all of the technical details because time doesn't allow it, but in choosing in these regions, 306 00:38:54,410 --> 00:39:00,980 there was an attempt to use natural boundaries like major highways or rivers to 307 00:39:00,980 --> 00:39:07,430 limit the contamination or the drift of mosquitoes from one region to another, 308 00:39:07,430 --> 00:39:15,350 so that the Wolbachia infections would be contained within the 12 clusters that had been chosen. 309 00:39:15,350 --> 00:39:28,200 And that was checked throughout the experiment, using mosquito traps to measure the prevalence of Wolbachia in all 24 regions and. 310 00:39:28,200 --> 00:39:36,950 Yeah, let me keep going here. And I should say the other thing, I was just pausing there, 311 00:39:36,950 --> 00:39:46,440 it says to the sensors a very small number of cluster randomised units here 24 and you worry about balance. 312 00:39:46,440 --> 00:39:55,770 Actually, a constrained randomisation beer was used to balance the green, the Wolbachia areas from the great on a variety of factors, 313 00:39:55,770 --> 00:40:01,500 including things like population density and and past history of dengue infection. 314 00:40:01,500 --> 00:40:10,480 And a bunch of other factors. So that was there was a constrained randomisation that I'm not going to talk about here. 315 00:40:10,480 --> 00:40:14,560 There's an interesting issue here, also in the pilot areas, 316 00:40:14,560 --> 00:40:24,490 there were two pilot areas that had been done several years before where systematic surveillance thing, decent population density, 317 00:40:24,490 --> 00:40:33,850 surveillance of severe cases haemorrhagic fever were detected and we could follow then the incidence 318 00:40:33,850 --> 00:40:41,170 of those before and after in a pilot region where Wolbachia was employed in an area where it wasn't. 319 00:40:41,170 --> 00:40:49,780 And that leads to interrupted time series. But I'm not going to talk about that that much, but here you can just sort of see, 320 00:40:49,780 --> 00:41:02,110 it's quite a difficult set of data to analyse with interrupted time series because of the great volatility and seasonal appearance of dengue fever. 321 00:41:02,110 --> 00:41:08,950 Dengue causes a certain amount of herd immunity if there's a huge outbreak and then it goes away for a year or two and then it comes back again, 322 00:41:08,950 --> 00:41:16,780 you can see this phenomenon here going on before and after you might introduce an interruption or an intervention. 323 00:41:16,780 --> 00:41:19,600 And so it was quite tricky to do that, 324 00:41:19,600 --> 00:41:25,420 and that's described in some of the results that were described in some of the references I gave at the beginning. 325 00:41:25,420 --> 00:41:30,250 But I want to go back in the last five minutes or so to looking at now. 326 00:41:30,250 --> 00:41:37,030 The results are the outcomes data from a test negative design at this clustered level. 327 00:41:37,030 --> 00:41:42,550 So here's the the raw data that you will get that's being collected. 328 00:41:42,550 --> 00:41:49,480 So in the two intervention clusters, there will be a cumulative count of the number of tests positive, 329 00:41:49,480 --> 00:41:57,280 the number of people testing positive for dengue in a negative in the in the area, in the whole population. 330 00:41:57,280 --> 00:42:02,070 The rules here represent the intervention. Areas in the control is. 331 00:42:02,070 --> 00:42:03,330 In the whole population, of course, 332 00:42:03,330 --> 00:42:10,500 there's a lot of people who get infected by one virus or another and never show up at a clinic or never get infected. 333 00:42:10,500 --> 00:42:20,070 And so what's observable to you from the way I described the data collection are only over here, ABG and age, and you don't observe this. 334 00:42:20,070 --> 00:42:26,940 Ideally, what you'd like to measure efficacy is you'd like to know the rate of dengue in the exposed area, 335 00:42:26,940 --> 00:42:32,580 which is over nine and the rate in the unexposed, which is over and see. 336 00:42:32,580 --> 00:42:41,440 But now in and steer unobserved and they're not really useful in. 337 00:42:41,440 --> 00:42:49,480 It's not really the total number of people in the population who can't usually use that as a proxy because the selection isn't appearing at a clinic. 338 00:42:49,480 --> 00:42:55,960 So I can see here the numbers of people in the population who would have shown up at 339 00:42:55,960 --> 00:43:01,330 the clinic had they been affected by a virus that produced these kind of symptoms. 340 00:43:01,330 --> 00:43:07,210 And that's really just impossible to measure. So you can't really measure and I don't see. 341 00:43:07,210 --> 00:43:15,970 But the idea of the test negative is is to exploit the fact that other viruses are not affected by, well, Baqir. 342 00:43:15,970 --> 00:43:21,400 And therefore the rate of occurrence of PNH should be an exact proportion to the sizes 343 00:43:21,400 --> 00:43:27,430 of the population that seek health care so that be over any is approximately the 344 00:43:27,430 --> 00:43:32,440 same as h over NC because the distribution of test negative should be completely 345 00:43:32,440 --> 00:43:38,080 independent of the intervention because Wolbachia does nothing to the other viruses. 346 00:43:38,080 --> 00:43:42,310 And that's of course, the key aspect to the test negative design. 347 00:43:42,310 --> 00:43:46,270 I shouldn't have said that a flu vaccine you've got to assume the flu vaccine does 348 00:43:46,270 --> 00:43:52,570 nothing with compared to other respiratory viruses that might cause similar symptoms. 349 00:43:52,570 --> 00:43:58,960 But if you assume that, then you can substitute the ratio of P over H for the the population sizes that you would 350 00:43:58,960 --> 00:44:05,930 like and you get an estimate then of this relative risk are one minus the efficacy. 351 00:44:05,930 --> 00:44:11,990 And the idea here is that you have removed or certainly severely reduced confounding. 352 00:44:11,990 --> 00:44:17,180 The problem here statistically was is that you've got this at the cluster level. 353 00:44:17,180 --> 00:44:26,930 So actually what you have is you have one of these tables for every cluster and then you collect the cumulative odds ratio across all clusters. 354 00:44:26,930 --> 00:44:32,900 But the statistics has to account for the clustering while exploiting the randomisation. 355 00:44:32,900 --> 00:44:42,770 And I was a great fan and still am of using permutation tests here, particularly given the small number of randomisation units. 356 00:44:42,770 --> 00:44:52,040 And what I want you to understand for power calculations and design was the properties of that permutation distribution, 357 00:44:52,040 --> 00:44:56,900 and that can be done relatively straightforward. I'm not going to talk about this measure. 358 00:44:56,900 --> 00:45:02,660 I'm going to talk about the the odds ratio estimates. I'm going to jump over those sites. 359 00:45:02,660 --> 00:45:08,870 The odds ratios say here's the cumulative odds ratio estimate here that I described 360 00:45:08,870 --> 00:45:14,900 before comparing the intervention areas and the control accumulating over the clusters. 361 00:45:14,900 --> 00:45:22,760 And by using finite sampling ideas and techniques, you can actually figure out from the property on the permutation distribution, 362 00:45:22,760 --> 00:45:27,920 an approximation to the variance of that under the null. 363 00:45:27,920 --> 00:45:34,970 The mean of the log odds ratio will be zero just by symmetry, but you need to know the variance. 364 00:45:34,970 --> 00:45:39,530 And here's the variance that looks complicated. It's not that complicated. 365 00:45:39,530 --> 00:45:50,640 And each of these variances within within each of these terms can be easily estimated from the observed data. 366 00:45:50,640 --> 00:45:57,560 So that allows you to simulate the permutation distribution for power calculations, 367 00:45:57,560 --> 00:46:06,100 which is important because the permutation distribution to compute is hard to do repeatedly. 368 00:46:06,100 --> 00:46:10,950 And then you can do these power calculations, and I didn't talk about this alternative, 369 00:46:10,950 --> 00:46:20,260 you're just there the odds ratio test, but it shows you that the the the the with the number of clusters we were using, 370 00:46:20,260 --> 00:46:25,450 we had reasonably good power for detecting a 50 percent reduction in dengue, 371 00:46:25,450 --> 00:46:33,520 comparing the deploy to the UN deployed areas, and I'm drawing to a close here. 372 00:46:33,520 --> 00:46:37,150 There are lots of interesting statistical questions that I've just touched on, 373 00:46:37,150 --> 00:46:42,430 and I hope the main point of talks like this are not to overwhelm you with technical details, 374 00:46:42,430 --> 00:46:48,010 but just to stimulate your interest and go away and think about things. 375 00:46:48,010 --> 00:46:52,420 You can actually just use the case only day to year. 376 00:46:52,420 --> 00:46:56,920 That's just using the test positives because of randomisation, 377 00:46:56,920 --> 00:47:06,280 because you could just compare the frequency of tests positive in the intervention area to those in the non-intervention area. 378 00:47:06,280 --> 00:47:16,090 That comparison, we've studied it, but it depends critically on the randomisation working and balancing everything else effectively. 379 00:47:16,090 --> 00:47:23,320 Interestingly, you can use it in the test negative to see and check the assumption that the 380 00:47:23,320 --> 00:47:32,830 test negative distribution should be independent of the intervention areas. 381 00:47:32,830 --> 00:47:41,890 There's interesting questions here. Well, what else can you estimate beyond estimating the overall efficacy of the intervention? 382 00:47:41,890 --> 00:47:53,170 Can you estimate and distinguish the efficacy that's due to the direct objective Wolbachia blocking, but also the herd immunity effect? 383 00:47:53,170 --> 00:47:58,660 Because if you're in an area with Wolbachia, fewer people are being infected is the idea, 384 00:47:58,660 --> 00:48:08,260 and that will mean that your risk is reduced because you're living around people in your household and next door who themselves are protected. 385 00:48:08,260 --> 00:48:16,270 So like vaccination, there will be some herd immunity. And so whether you can estimate from that data is an interesting question. 386 00:48:16,270 --> 00:48:27,340 There's also the problem of even though we've tried to protect the contamination of the infected areas by essentially taking advantage, 387 00:48:27,340 --> 00:48:34,030 the mosquitoes don't travel a great deal during their lifetime. Humans are mobile and do move across the city. 388 00:48:34,030 --> 00:48:38,530 They may go to school in a different area. They may visit grandparents in a different area. 389 00:48:38,530 --> 00:48:41,950 And so that hasn't all been measured in this study. 390 00:48:41,950 --> 00:48:50,650 We're humans, we're the test positive test negative moved in the few weeks before they showed up at the clinic, 391 00:48:50,650 --> 00:48:55,960 and that allows one to measure a sort of continuous exposure measurement. 392 00:48:55,960 --> 00:49:00,520 And how that should be used in the analysis is interesting. 393 00:49:00,520 --> 00:49:06,490 There's a considerable interest in the transferability of these efficacy estimates. 394 00:49:06,490 --> 00:49:10,250 If I estimate an odds ratio for drug Jakarta, 395 00:49:10,250 --> 00:49:18,490 will it be useful in Rio de Janeiro because where there's a completely different population distribution of mosquito distribution? 396 00:49:18,490 --> 00:49:26,440 And that's interesting because the herd immunity aspects will be different in different locations, and that's still ongoing work. 397 00:49:26,440 --> 00:49:36,190 And then in some places in Colombia in particular, there were politically opposed to doing randomisation randomisation design, 398 00:49:36,190 --> 00:49:44,440 but they were not opposed to a deployment that happened sequentially over time with a random choice of the deployment schedule, 399 00:49:44,440 --> 00:49:49,590 which leads to extending the results to stepped wedge design. 400 00:49:49,590 --> 00:49:54,340 And let me just finish this and then take some questions if there aren't any by giving an update. 401 00:49:54,340 --> 00:50:02,860 And unfortunately, that's where COVID 19 appears. This study was designed to end at the end of 2020, probably around November. 402 00:50:02,860 --> 00:50:09,040 There's still, as I said, a significant amount of dengue transmission going on in Jakarta at the moment. 403 00:50:09,040 --> 00:50:18,850 However, unfortunately, this study had to be stopped eight months early on March 18 because the triage of 404 00:50:18,850 --> 00:50:24,730 patients in the clinic with fever was modified by the appearance of COVID 19. 405 00:50:24,730 --> 00:50:29,830 There wasn't sufficient protective equipment to protect our research staff that 406 00:50:29,830 --> 00:50:35,560 were working to recruit patients with fever and to service the mosquito traps, 407 00:50:35,560 --> 00:50:46,030 and the diagnostic labs had to be seconded to support the COVID 19 testing effort going on in Indonesia. 408 00:50:46,030 --> 00:50:55,270 So the data have now been locked, and I think I'm actually expecting the data to arrive in the next week or so. 409 00:50:55,270 --> 00:50:58,720 So I can't tell you the results just yet, but I'll stop there, 410 00:50:58,720 --> 00:51:05,160 and I hope I find something to at least attract your attention not only as a great public health. 411 00:51:05,160 --> 00:51:14,660 The. Prevention, but also some very interesting statistical questions surrounding cluster randomised trials in which trials and I'll stop there. 412 00:51:14,660 --> 00:51:20,820 Thank you so much for your attention. Thank you very much, Nick. 413 00:51:20,820 --> 00:51:25,080 That was really interesting. It covers the lessons of, I mean, I've heard a lot of things before, 414 00:51:25,080 --> 00:51:29,730 but having not thought about it for a living, oh, we're getting lots of raised hands. 415 00:51:29,730 --> 00:51:35,340 OK, so since we don't have it in chat, I will just pick people, unmute them. 416 00:51:35,340 --> 00:51:42,830 So Emmanuel, I'll unmute you. And you can ask your question. 417 00:51:42,830 --> 00:51:46,590 OK, Manuel, you want to ask? Yeah, that's a cloud. 418 00:51:46,590 --> 00:51:56,210 Chris, though not OK. Oh, sorry. This is my first time, my first time hosting, so I didn't realise that. 419 00:51:56,210 --> 00:52:00,240 All right. Sorry, I actually didn't see any raised tens. 420 00:52:00,240 --> 00:52:09,390 OK, so one of the things I was thinking about was about blinding, because if you is not blinded, right, still that's. 421 00:52:09,390 --> 00:52:21,060 The obviously, the participants in the experiment were not blinded people in principle, you could determine which region you lived in. 422 00:52:21,060 --> 00:52:26,640 In fact, when I visited Yogyakarta during the trial to visit the clinics and observe, 423 00:52:26,640 --> 00:52:33,390 I think it's important for statisticians to actually go to the place where data is being collected to understand the data. 424 00:52:33,390 --> 00:52:38,760 I deliberately found out where my hotel was and whether I was in a black region or not. 425 00:52:38,760 --> 00:52:44,010 I wasn't there as it happened, but so the participants were not. 426 00:52:44,010 --> 00:52:50,340 In fact, it was a great attempt to bring the community in to this experiment and engage them, 427 00:52:50,340 --> 00:52:55,380 and there was a big public drawing of ping pong balls like the World Cup does. 428 00:52:55,380 --> 00:53:02,880 A draw was made on which region, which there. So it was certainly I'm yes, so that they knew it was fair. 429 00:53:02,880 --> 00:53:12,450 It was not. I'm blinded. However, all the clinic staff and the testing staff were blinded to where these samples were coming from in the region, 430 00:53:12,450 --> 00:53:18,810 so there was some attempt to do blinding. But clearly this was not a situation where blinding could be inferred, 431 00:53:18,810 --> 00:53:26,280 and that is interesting and using the test negative to see if there's any any impact of that, 432 00:53:26,280 --> 00:53:33,630 that will be a check to see that somehow the unblinding of participants didn't change health care seeking behaviour. 433 00:53:33,630 --> 00:53:40,200 So in different regions, we should be able to check that as I mentioned, 434 00:53:40,200 --> 00:53:45,870 the and will you have demographics about those people so that you can look to see? 435 00:53:45,870 --> 00:53:49,260 I mean, there might be a certain level of. 436 00:53:49,260 --> 00:53:57,930 Education or awareness that would affect whether or not it had even occurred to them to affect their decision to go into health care, you would. 437 00:53:57,930 --> 00:54:04,980 There's a fairly significant amount of demographic data available on the participant, not a huge amount, 438 00:54:04,980 --> 00:54:14,220 but their socioeconomic status, their exact location, their movements and a bunch of their sex, of course, and age. 439 00:54:14,220 --> 00:54:21,470 And I know a lot of standard demographic information. 440 00:54:21,470 --> 00:54:27,350 OK, so one of the other questions was how confident can you be that it doesn't affect other viruses? 441 00:54:27,350 --> 00:54:34,670 Well, you certainly have to check that and that has been done in the lab to the largest 442 00:54:34,670 --> 00:54:41,810 extent possible that I mentioned very briefly that with the appearance of Zika, 443 00:54:41,810 --> 00:54:46,790 there isn't a great deal of Zika in Indonesia, but there is a little historically. 444 00:54:46,790 --> 00:54:55,820 And so the samples are all being tested for the presence of other flaviviruses, and they would not be used as controls, 445 00:54:55,820 --> 00:55:05,660 by the way, in separately with providers simultaneously an estimate of the efficacy for other viruses distinct from dengue. 446 00:55:05,660 --> 00:55:11,660 But I don't think there will be in sufficient numbers from preliminary look at the data. 447 00:55:11,660 --> 00:55:17,090 But yeah, you have to make sure that influenza, for example, is unaffected by Wolbachia. 448 00:55:17,090 --> 00:55:28,280 And as far as we know in the lab, it has no impact on the the ability of influenza to replicate. 449 00:55:28,280 --> 00:55:36,080 But that's key. Absolutely key, correct, and that's a big issue on trying to use this design for COVID 19. 450 00:55:36,080 --> 00:55:41,300 That I am concerned about the people are promoting it because you're testing negative. 451 00:55:41,300 --> 00:55:46,400 There are usually other respiratory infections and any intervention that we might have that is 452 00:55:46,400 --> 00:55:54,960 of interest for COVID 19 is likely to have similar effects on other respiratory infections. 453 00:55:54,960 --> 00:56:02,200 So that night. Makes it much less useful. 454 00:56:02,200 --> 00:56:17,800 OK. Are there any other questions? But is it just one of the other things that I wondered if you're going to do is the. 455 00:56:17,800 --> 00:56:23,400 A new wave already planned for this. The as you get near the edges of the areas. 456 00:56:23,400 --> 00:56:32,620 Is there going to be any sort of sort of excluding people within a certain distance of the boundary with those as being? 457 00:56:32,620 --> 00:56:40,240 Likely to you're in effect, yes. So what we have observed, as I said, throughout every region, mosquitoes were trapped, 458 00:56:40,240 --> 00:56:49,300 so we know exactly the Wolbachia prevalence by the man in every region and much more frightened, granular detail than just which region. 459 00:56:49,300 --> 00:56:58,060 So there were many traps for each region. And actually so some drift finally was observed in the late part of last year. 460 00:56:58,060 --> 00:57:08,730 Some of the control areas started on the edges to show 20 percent of prevalence, so they started to be partially. 461 00:57:08,730 --> 00:57:18,090 Treated, if you will. And that data is available, so that will go into a measure of Wolbachia exposure. 462 00:57:18,090 --> 00:57:25,890 That's not just a binary, yes or no, where was an original or not, but a continuous measure and that can be done at the time level. 463 00:57:25,890 --> 00:57:32,520 So we know when you know the prevalence of Wolbachia in the region at the time you presented at the clinic. 464 00:57:32,520 --> 00:57:34,760 So that will be used. 465 00:57:34,760 --> 00:57:45,270 I mean, an extreme version, as you say, would be just to eliminate those positives and negatives that are obtained after after contamination. 466 00:57:45,270 --> 00:57:57,450 Not not many of the, I would say three or four of the 12 control regions started to show contamination in the last few months. 467 00:57:57,450 --> 00:58:03,850 So that will be an issue that will be looked at in the analysis, for sure. 468 00:58:03,850 --> 00:58:08,700 Well, the intention to treat will probably dominate because that's what we said. 469 00:58:08,700 --> 00:58:19,560 But we did pre disclose that we would look at a specifically constructed Wolbachia exposure index that would account for that contamination. 470 00:58:19,560 --> 00:58:28,380 And is there a threshold beyond which could you sort of show that as well back is sort of put into the area. 471 00:58:28,380 --> 00:58:35,650 It grit its prevalence, gradually increase and then you expect it to go to 100 in the absence of everything else. 472 00:58:35,650 --> 00:58:40,950 There's some threshold isn't there below which that won't happen. 473 00:58:40,950 --> 00:58:48,060 There would be a threat as it happens, we know all 12 treated areas, well above 90 percent, 474 00:58:48,060 --> 00:58:54,030 OK, 95, 98 percent prevalence because this was being measured throughout the entire study. 475 00:58:54,030 --> 00:59:02,470 So the and in fact the the level in the controls was close to zero percent for at least two plus years. 476 00:59:02,470 --> 00:59:09,360 So that was monitored throughout. That worked really well. So we know the experiment worked really well that way. 477 00:59:09,360 --> 00:59:13,770 It is true that let me see what was your question then. 478 00:59:13,770 --> 00:59:18,710 Well, I think that the threshold for the threshold? Yeah, yeah. 479 00:59:18,710 --> 00:59:20,580 Yeah, there is a threshold. 480 00:59:20,580 --> 00:59:30,150 And and that's why clinic ascertainment wasn't used for a month or so after deployment until until the thresholds went above, 481 00:59:30,150 --> 00:59:32,100 I think it was above 80 percent. 482 00:59:32,100 --> 00:59:39,240 You weren't allowed to recruit patients, but once above that, we started recruitment in the in the intervention areas. 483 00:59:39,240 --> 00:59:43,830 And so that was you use what's interesting from what your previous question is about it. 484 00:59:43,830 --> 00:59:53,760 In the areas now where there's been some contamination, it may allow a sense of which what threshold is needed for protection against and you 485 00:59:53,760 --> 00:59:59,130 would if you were living in an area where only 50 percent of the mosquitoes had Wolbachia, 486 00:59:59,130 --> 01:00:05,670 would that be sufficient to confer protection through a sort of herd immunity kind of thing? 487 01:00:05,670 --> 01:00:14,190 Or would you need it to be higher? No one knows that at the moment, we've clearly designed this to be a 100 percent deployment or zero percent. 488 01:00:14,190 --> 01:00:21,030 But we do now have this in between for the last few months for a few of the control areas. 489 01:00:21,030 --> 01:00:28,230 So it it would be interesting. It's unlikely we'll get really detailed data on that. 490 01:00:28,230 --> 01:00:32,280 It's only happened in the very last few months of data collection, 491 01:00:32,280 --> 01:00:44,700 but it's possible we may see if we see some drift in a controlled area of increasing or decreasing dengue as as contamination increased, 492 01:00:44,700 --> 01:00:50,410 that would suggest we could detect where that threshold might be. 493 01:00:50,410 --> 01:00:52,210 Would you have been it? 494 01:00:52,210 --> 01:01:00,340 I can understand why they didn't include the first cases, but you can imagine if you didn't put an 80 percent threshold to start accumulating data, 495 01:01:00,340 --> 01:01:09,010 you could have looked at the cases early on to get some idea of where it was going to you. 496 01:01:09,010 --> 01:01:16,000 Yes, and there may be data like that I can't recall if we if they were piloting the clinic recruitment and 497 01:01:16,000 --> 01:01:23,770 so that there was pilot data before the definitive light was flicked on for actual data collection, 498 01:01:23,770 --> 01:01:33,580 which clearly have that kind of information in the pilot studies of the before and after, which is a very powerful. 499 01:01:33,580 --> 01:01:38,170 We don't have that here, of course, because we did the deployment all at once. 500 01:01:38,170 --> 01:01:44,830 That's a little bit of the interest in step wedge design, where you not only have cross region comparison, 501 01:01:44,830 --> 01:01:52,360 but then meet the comparisons of before and after deployment, so that twice that wedge is sort of interesting in its own way. 502 01:01:52,360 --> 01:02:02,930 But it raises additional statistical complications because of temporal trends in dengue infections over time that you have to worry about with step, 503 01:02:02,930 --> 01:02:09,680 which decides that we don't have to worry about here because they were affecting all areas simultaneously. 504 01:02:09,680 --> 01:02:15,940 OK, great. Are there any further questions? I don't see anything else in the chat. 505 01:02:15,940 --> 01:02:22,090 You're OK with it. I think we've gotten just over an hour, so thank you very much, Nick, 506 01:02:22,090 --> 01:02:28,810 for making time to do this and doing it online since we couldn't have you in all sorts of. 507 01:02:28,810 --> 01:02:35,560 A sometimes when you're visiting, we have a dinner that you were promised initially. 508 01:02:35,560 --> 01:02:40,690 Yes, I look forward to getting back to London and getting back to Oxford. 509 01:02:40,690 --> 01:02:46,270 So thank you very much, everybody. Thank you. Thanks for your attention, right? 510 01:02:46,270 --> 01:02:50,140 And I will see you next week, Crystal. Yes, indeed. 511 01:02:50,140 --> 01:02:55,424 Virtually. All right. Bye bye.