1 00:00:03,880 --> 00:00:07,720 Speaker 1: In twenty fourteen, a cluster of Ebola cases emerged in 2 00:00:07,800 --> 00:00:10,840 Speaker 1: Guinea in West Africa. It was the start of what 3 00:00:10,880 --> 00:00:15,680 Speaker 1: would become the largest Ebola outbreak on record. Ebola is 4 00:00:15,720 --> 00:00:18,279 Speaker 1: one of the most deadly pathogens on Earth, and it 5 00:00:18,320 --> 00:00:22,320 Speaker 1: started to move through densely populated cities at the time. 6 00:00:22,880 --> 00:00:27,800 Speaker 1: Two scientists, Christian Happy and Party Sabbetti, were already fighting 7 00:00:27,840 --> 00:00:31,480 Speaker 1: other infectious diseases in West Africa, and when Ebola hit, 8 00:00:31,880 --> 00:00:35,320 Speaker 1: they threw everything they had at containing the outbreak, even 9 00:00:35,320 --> 00:00:37,320 Speaker 1: if it meant putting themselves in danger. 10 00:00:37,800 --> 00:00:41,080 Speaker 2: I might get Itbora, but my thinking is is better 11 00:00:41,120 --> 00:00:46,080 Speaker 2: for me and a few orders to die and saved 12 00:00:46,200 --> 00:00:48,880 Speaker 2: millions of people than be selfish and lefty. Disease spread 13 00:00:49,280 --> 00:00:52,320 Speaker 2: and eventually paid a high price by seeing menials of 14 00:00:52,320 --> 00:00:53,239 Speaker 2: people dying around me. 15 00:00:54,440 --> 00:00:58,120 Speaker 1: Today, on the show the twenty fourteen Ebola Outbreak and 16 00:00:58,160 --> 00:01:01,040 Speaker 1: how the lessons we learned from that moment may help 17 00:01:01,120 --> 00:01:05,480 Speaker 1: us prevent future pandemics. I'm Jacob Goldstein and this is Incubation, 18 00:01:05,760 --> 00:01:16,760 Speaker 1: a show about viruses. First today is my conversation with 19 00:01:16,840 --> 00:01:20,520 Speaker 1: Christian Happy. I talked with him about his work hunting viruses, 20 00:01:20,640 --> 00:01:23,720 Speaker 1: and in particular about his role in fighting the twenty 21 00:01:23,760 --> 00:01:27,760 Speaker 1: fourteen Ebola outbreak. Christian grew up in Cameroon and he 22 00:01:27,800 --> 00:01:30,880 Speaker 1: decided at an early age to become a scientist. He 23 00:01:31,000 --> 00:01:32,880 Speaker 1: was inspired by malaria. 24 00:01:33,520 --> 00:01:35,600 Speaker 2: I grew up in the rainforest area and there was 25 00:01:35,640 --> 00:01:38,280 Speaker 2: a lot of malaria there. I grew up knowing that 26 00:01:38,319 --> 00:01:42,840 Speaker 2: my mother has lost two of my siblings to malaria. 27 00:01:43,680 --> 00:01:46,080 Speaker 2: I think the day I met up my mind to 28 00:01:46,120 --> 00:01:49,240 Speaker 2: become a scientist was when I was about eight nine 29 00:01:49,320 --> 00:01:52,560 Speaker 2: years and I got one of the worst part of 30 00:01:52,680 --> 00:01:57,280 Speaker 2: maya that I could ever remember, and my mom took 31 00:01:57,320 --> 00:02:00,320 Speaker 2: me to the hospital. I got treated by nouns and 32 00:02:00,360 --> 00:02:04,400 Speaker 2: then on the way back, she stopped under a tree 33 00:02:04,640 --> 00:02:08,320 Speaker 2: and I was lying against a tree, and I do 34 00:02:08,360 --> 00:02:11,440 Speaker 2: remember asking my mom, why was that sick? 35 00:02:12,639 --> 00:02:13,720 Speaker 3: Why is it that there's no. 36 00:02:14,480 --> 00:02:19,200 Speaker 2: Treatment or cure or anything to prevent this disease from 37 00:02:19,320 --> 00:02:23,919 Speaker 2: hitting me this hard? And our response was I have 38 00:02:23,960 --> 00:02:27,840 Speaker 2: no idea, And that day I remember telling her and 39 00:02:27,960 --> 00:02:31,079 Speaker 2: when I grew up, I will find a cure. 40 00:02:31,919 --> 00:02:35,000 Speaker 1: Christian Happy did in fact grow up to study malaria 41 00:02:35,160 --> 00:02:38,160 Speaker 1: and other infectious diseases that are common in West Africa. 42 00:02:38,840 --> 00:02:42,800 Speaker 1: He got a PhD in molecular parasitology, and then he 43 00:02:42,840 --> 00:02:45,639 Speaker 1: did a postdoc at Harvard. He was at Harvard when 44 00:02:45,639 --> 00:02:48,920 Speaker 1: he met Pardis Sabbetti, the other scientist will hear from 45 00:02:48,919 --> 00:02:52,320 Speaker 1: on today's show. In two thousand and eight, Christian started 46 00:02:52,360 --> 00:02:55,880 Speaker 1: working on a disease called loss of fever. That experience 47 00:02:56,000 --> 00:02:58,200 Speaker 1: laid the groundwork for the work he would do when 48 00:02:58,240 --> 00:03:01,920 Speaker 1: the Ebola outbreak came. Loss of fever, though not as 49 00:03:01,960 --> 00:03:05,120 Speaker 1: well known as Ebola, is deadly and it's endemic in 50 00:03:05,160 --> 00:03:06,000 Speaker 1: West Africa. 51 00:03:06,160 --> 00:03:08,400 Speaker 2: We find ourselves in a place where whole families have 52 00:03:08,440 --> 00:03:11,120 Speaker 2: been wiped out by these virus. I realized that at 53 00:03:11,160 --> 00:03:14,760 Speaker 2: that point that hospital is a nogal area. I'm being 54 00:03:14,800 --> 00:03:17,120 Speaker 2: told that the place is called like a burier ground. 55 00:03:17,200 --> 00:03:19,640 Speaker 2: When you go there, you get infected by this mysterious disease. 56 00:03:19,680 --> 00:03:23,000 Speaker 2: There's no care for, it's almost impossible to diagnose, and 57 00:03:23,040 --> 00:03:25,960 Speaker 2: then you die. And the first thing I did when 58 00:03:26,000 --> 00:03:27,639 Speaker 2: I get to that place is to ask a lot 59 00:03:27,639 --> 00:03:30,680 Speaker 2: of questions, and I realized, one, they don't have diagnostics. 60 00:03:31,080 --> 00:03:34,360 Speaker 2: Two they need to be trained, and then thirdly we 61 00:03:34,400 --> 00:03:36,600 Speaker 2: need to actually make sure that we provide them with 62 00:03:36,720 --> 00:03:39,640 Speaker 2: the necessary drugs and medicine in order to actually help 63 00:03:39,760 --> 00:03:42,720 Speaker 2: stop this while we're doing the research and guess what, 64 00:03:43,200 --> 00:03:45,600 Speaker 2: I leave the place and then come back to Boston, 65 00:03:45,720 --> 00:03:47,880 Speaker 2: have a discussion with by this and we started guarding 66 00:03:47,920 --> 00:03:51,280 Speaker 2: the resources to go there. And then within a few 67 00:03:51,320 --> 00:03:55,360 Speaker 2: months we set up the first molecular diagnostics for last 68 00:03:55,360 --> 00:03:59,200 Speaker 2: of fever lab. Molecular diagnostics Labs for lasta fever in Nigeria. 69 00:04:00,000 --> 00:04:02,360 Speaker 1: Okay, you and parties, you set up this a lab 70 00:04:02,440 --> 00:04:05,080 Speaker 1: at a hospital. It lets the people at the hospital 71 00:04:05,120 --> 00:04:08,880 Speaker 1: diagnose loss of fever in patients there. What happens next. 72 00:04:08,800 --> 00:04:11,640 Speaker 2: We cut down the test fatality rate in the hospital 73 00:04:11,640 --> 00:04:14,720 Speaker 2: from ninety percent to about twenty three point eight percent. 74 00:04:15,120 --> 00:04:19,520 Speaker 2: Why because when they used to have disease outbreak, people 75 00:04:19,600 --> 00:04:21,920 Speaker 2: will come from Germany or a team of researcher will 76 00:04:21,960 --> 00:04:24,560 Speaker 2: come from Germany, take the sample to Germany and then 77 00:04:24,600 --> 00:04:27,120 Speaker 2: they will have the result only about a month three 78 00:04:27,160 --> 00:04:28,200 Speaker 2: weeks to a month after. 79 00:04:28,680 --> 00:04:32,720 Speaker 1: So there were people who had this potentially fatal disease 80 00:04:32,760 --> 00:04:35,919 Speaker 1: in a hospital in rural Nigeria and in order to 81 00:04:36,080 --> 00:04:38,800 Speaker 1: test for the disease, people had to take a sample 82 00:04:38,839 --> 00:04:42,960 Speaker 1: from the patients from rural Nigeria to Germany and do 83 00:04:43,040 --> 00:04:43,440 Speaker 1: the test. 84 00:04:43,560 --> 00:04:46,839 Speaker 2: Yes to Germany, Yes, and if you take about a 85 00:04:46,920 --> 00:04:48,640 Speaker 2: month for them to have the result. 86 00:04:48,520 --> 00:04:50,479 Speaker 1: And would the person be dead by then. 87 00:04:50,440 --> 00:04:52,960 Speaker 2: By the time they get the result, ninety percent of 88 00:04:53,000 --> 00:04:57,599 Speaker 2: those people were dead. But the introduction we brought the 89 00:04:57,600 --> 00:05:01,480 Speaker 2: diagnostics by the patient bedside, that's really what our very 90 00:05:01,520 --> 00:05:02,440 Speaker 2: first huge impact. 91 00:05:02,839 --> 00:05:06,320 Speaker 1: And so that's the just a simple, simple idea, which 92 00:05:06,360 --> 00:05:09,800 Speaker 1: is like, if somebody might have a deadly disease, let's 93 00:05:09,880 --> 00:05:13,600 Speaker 1: figure it out right here, rather than sending a simple 94 00:05:14,040 --> 00:05:14,839 Speaker 1: to Germany. 95 00:05:15,440 --> 00:05:18,800 Speaker 2: That's the whole idea, normal senning simple to Germany, doing 96 00:05:18,839 --> 00:05:22,440 Speaker 2: it within Africa, using the data within Africa to inform 97 00:05:22,560 --> 00:05:25,080 Speaker 2: policy makers in real time so that they can use 98 00:05:25,120 --> 00:05:26,440 Speaker 2: the data to respond faster. 99 00:05:27,200 --> 00:05:31,599 Speaker 1: So now let's jump forward to twenty fourteen and we 100 00:05:31,760 --> 00:05:37,239 Speaker 1: have what's gonna become the biggest recorded ebola outbreak in history. 101 00:05:38,400 --> 00:05:39,159 Speaker 1: Tell me about that. 102 00:05:39,920 --> 00:05:44,560 Speaker 2: The Ebola outbreak start in the remote part of Guinea 103 00:05:44,839 --> 00:05:48,960 Speaker 2: and at that time, the world is looking away. The 104 00:05:49,040 --> 00:05:52,520 Speaker 2: disease is crawling and the disease is killing people. And 105 00:05:52,640 --> 00:05:54,560 Speaker 2: the first thing we did at the time is sit 106 00:05:54,560 --> 00:05:57,400 Speaker 2: down and think about what we can do to support 107 00:05:57,440 --> 00:06:00,559 Speaker 2: the response. Can we activate this This is so system 108 00:06:00,600 --> 00:06:03,080 Speaker 2: that we were setting up through this epidemic, and we 109 00:06:03,160 --> 00:06:07,240 Speaker 2: identify diagnostics as the weakest link of all of the 110 00:06:07,279 --> 00:06:08,479 Speaker 2: things of the chain. 111 00:06:09,120 --> 00:06:10,520 Speaker 3: And the reason is being simple. 112 00:06:10,680 --> 00:06:12,880 Speaker 2: If you cannot diagnose a disease, it's going to be 113 00:06:12,960 --> 00:06:14,840 Speaker 2: very difficult for you to contain it because you need 114 00:06:14,880 --> 00:06:16,920 Speaker 2: to know what you're trying to contain. 115 00:06:17,520 --> 00:06:20,160 Speaker 1: You need to know who's got the infectious disease, and. 116 00:06:20,120 --> 00:06:21,560 Speaker 3: You need to go who has infection. 117 00:06:22,279 --> 00:06:27,039 Speaker 2: And we decided then to start training people in Syralllion 118 00:06:27,120 --> 00:06:31,279 Speaker 2: because Syrillleon was neighbor to Guinea, and we train people 119 00:06:31,279 --> 00:06:34,520 Speaker 2: in Nigeria because Nigeria being the most popular country in 120 00:06:34,560 --> 00:06:37,000 Speaker 2: West Africa. But then the second thing is also the 121 00:06:37,040 --> 00:06:40,320 Speaker 2: fact that we were already working in Syraleion on last fear. 122 00:06:40,920 --> 00:06:43,599 Speaker 2: So those two countries where we're working, we prepare the 123 00:06:43,600 --> 00:06:47,400 Speaker 2: ground by training people on how to diagnose a bowler. 124 00:06:47,960 --> 00:06:51,520 Speaker 2: We also go quickly to develop a molecular diagnostics test 125 00:06:51,560 --> 00:06:53,520 Speaker 2: for a bowler. We test and do the validation in 126 00:06:53,560 --> 00:06:58,359 Speaker 2: the lab. Around May twenty twenty fourteen, we confirmed the 127 00:06:58,400 --> 00:06:59,280 Speaker 2: first case. 128 00:06:59,720 --> 00:07:02,599 Speaker 3: Of boler in Seria leone spreading out of Guinea. 129 00:07:03,440 --> 00:07:05,960 Speaker 1: So the disease is spreading. It has started in Guinea. 130 00:07:06,440 --> 00:07:09,800 Speaker 1: Now you've identified it in Sierra Leone, but it hasn't 131 00:07:09,880 --> 00:07:14,560 Speaker 1: yet reached Nigeria. Yes, you are in Nigeria. Yeah, and 132 00:07:14,800 --> 00:07:20,040 Speaker 1: a passenger arrives at Legos Airport with a suspected case 133 00:07:20,320 --> 00:07:23,840 Speaker 1: of ebola, first suspected case in the country. You're there, Yes, 134 00:07:24,200 --> 00:07:26,800 Speaker 1: what do you do at that moment? That day? 135 00:07:27,680 --> 00:07:30,320 Speaker 2: On that day, I got a call, a very strange 136 00:07:30,320 --> 00:07:34,440 Speaker 2: call around eight four pm and that call is coming 137 00:07:34,520 --> 00:07:39,040 Speaker 2: from one of the foremost virologists in Africa. He calls 138 00:07:39,080 --> 00:07:42,960 Speaker 2: me and he says there is a suspectic case of 139 00:07:43,000 --> 00:07:48,320 Speaker 2: a boler in Legos. There's a lot of argument about 140 00:07:48,920 --> 00:07:51,679 Speaker 2: whether it is or it is not. But I've told 141 00:07:51,720 --> 00:07:53,920 Speaker 2: the public health authority is that the only person that 142 00:07:53,920 --> 00:07:56,640 Speaker 2: will give me a result, and I believe is Christian 143 00:07:56,760 --> 00:07:58,720 Speaker 2: because I know his ability. 144 00:07:59,360 --> 00:08:02,440 Speaker 1: They're saying like we were one person in this country 145 00:08:02,480 --> 00:08:04,000 Speaker 1: to do the test, and that person is. 146 00:08:03,960 --> 00:08:07,880 Speaker 2: You, right, I mean, the stexts are very high. And 147 00:08:07,960 --> 00:08:10,440 Speaker 2: he tells me what do we do? And I told 148 00:08:10,520 --> 00:08:13,680 Speaker 2: him that evening, I am driving from a house to 149 00:08:13,720 --> 00:08:16,400 Speaker 2: the lab that they should send the sample. We'll meet 150 00:08:16,440 --> 00:08:18,120 Speaker 2: in my lab and I'll collect the sample and then 151 00:08:18,160 --> 00:08:19,160 Speaker 2: do the testing that night. 152 00:08:19,800 --> 00:08:21,960 Speaker 1: And what are you thinking. What do you think when 153 00:08:22,000 --> 00:08:22,960 Speaker 1: you get this call? 154 00:08:24,000 --> 00:08:27,680 Speaker 2: Of course, it sends you chills in your vertebrate because 155 00:08:28,640 --> 00:08:31,760 Speaker 2: this is a suspected case in a country of two 156 00:08:31,840 --> 00:08:35,800 Speaker 2: hundred and thirty million people. So if this spread, the 157 00:08:35,920 --> 00:08:39,760 Speaker 2: consequences are going to be very disastrous, not only for Nigeria, 158 00:08:40,320 --> 00:08:42,600 Speaker 2: for Africa and then the rest of the world. 159 00:08:43,160 --> 00:08:47,360 Speaker 1: And this patient collapsed at the airport. The nightmare scenario. 160 00:08:47,559 --> 00:08:50,439 Speaker 2: He collapsed at the airport, He's taken into a hospital, 161 00:08:50,640 --> 00:08:54,719 Speaker 2: He's probably been handled by people with are suspicion, and 162 00:08:55,920 --> 00:08:58,240 Speaker 2: I am to face it. I was going to deal 163 00:08:58,280 --> 00:09:01,800 Speaker 2: with the beasts, which means I may not come back 164 00:09:01,800 --> 00:09:05,280 Speaker 2: home alive because we don't have a BIS facility, be. 165 00:09:05,280 --> 00:09:09,080 Speaker 1: A self force like the super safe High Security facility. 166 00:09:08,640 --> 00:09:11,160 Speaker 3: The super safe HAST best safety level. 167 00:09:11,000 --> 00:09:13,160 Speaker 1: Four and you don't have that. So you mean by 168 00:09:13,200 --> 00:09:15,599 Speaker 1: going to do this test, you might get a bolder. 169 00:09:15,920 --> 00:09:16,840 Speaker 3: I might get a border. 170 00:09:17,160 --> 00:09:21,120 Speaker 2: But my thinking is it's better for me and a 171 00:09:21,160 --> 00:09:25,480 Speaker 2: few orders to die and save millions of people. If 172 00:09:25,480 --> 00:09:27,920 Speaker 2: it is confirmed, then be selfish and left the disease 173 00:09:27,920 --> 00:09:31,640 Speaker 2: spread and eventually paid a high price by seeing millions 174 00:09:31,640 --> 00:09:34,120 Speaker 2: of people dying around me. So I turn to my 175 00:09:34,200 --> 00:09:36,920 Speaker 2: wife and I tell hell, this is a situation. I 176 00:09:36,960 --> 00:09:39,160 Speaker 2: want to go do this. If I don't make it 177 00:09:39,200 --> 00:09:43,480 Speaker 2: back home, take care of the children, it to be okay. 178 00:09:44,320 --> 00:09:46,480 Speaker 2: And she said, I'll pray for you. You'll be back. 179 00:09:46,520 --> 00:09:48,440 Speaker 2: I know this is what you've always wanted to do. 180 00:09:48,840 --> 00:09:50,920 Speaker 2: You always want to work for the people. You always 181 00:09:50,920 --> 00:09:53,520 Speaker 2: want to save lives. I left home with a back, 182 00:09:53,600 --> 00:09:57,120 Speaker 2: with a change of clothes and I drive on that 183 00:09:57,280 --> 00:10:00,640 Speaker 2: road is a lonely road in Nigeria at time, one 184 00:10:00,640 --> 00:10:02,760 Speaker 2: of the most dangerous ord in Nigeria. But I drave 185 00:10:02,800 --> 00:10:04,360 Speaker 2: alone in the night and I go to the lab 186 00:10:04,400 --> 00:10:06,960 Speaker 2: and then the ambulance come from Legos and meet me. 187 00:10:07,920 --> 00:10:10,600 Speaker 2: And I went into the place. We have a bs 188 00:10:10,600 --> 00:10:13,160 Speaker 2: A level too. It's a laminar. I mean'd be very 189 00:10:13,160 --> 00:10:17,520 Speaker 2: low level of contentment and we prepared zero point five 190 00:10:17,559 --> 00:10:21,400 Speaker 2: percent bleach and I will all put up our ppees 191 00:10:21,920 --> 00:10:25,319 Speaker 2: and we're in a very tiny small room without air conditioners. 192 00:10:25,640 --> 00:10:28,240 Speaker 3: I'm sweating like a pig into the ppe. 193 00:10:28,760 --> 00:10:32,160 Speaker 2: My eyes and fast side is fuggy because you know 194 00:10:32,240 --> 00:10:33,000 Speaker 2: of the heat. 195 00:10:33,040 --> 00:10:34,560 Speaker 1: Your safety goggles are fugging. 196 00:10:34,720 --> 00:10:38,079 Speaker 2: I'm wearing the safety goggle and that is fugging up 197 00:10:38,200 --> 00:10:41,040 Speaker 2: and then I'm really looking through that and then working 198 00:10:41,120 --> 00:10:43,600 Speaker 2: in the night very carefully. 199 00:10:43,280 --> 00:10:45,760 Speaker 1: And it's what, you have a blood simple I got 200 00:10:45,760 --> 00:10:48,640 Speaker 1: a blood sample, yeah, okay, and ran the first test 201 00:10:49,280 --> 00:10:51,360 Speaker 1: and then the first test turned out to be positive. 202 00:10:52,040 --> 00:10:54,080 Speaker 3: That really sends chills in my spine. 203 00:10:54,160 --> 00:10:56,679 Speaker 1: I mean, this disease is transmitted through blood, and you're 204 00:10:56,720 --> 00:10:58,960 Speaker 1: sitting there working with the blood. 205 00:10:59,440 --> 00:10:59,720 Speaker 3: Yeah. 206 00:10:59,840 --> 00:11:02,320 Speaker 2: But my mind is like started running through the number 207 00:11:02,360 --> 00:11:05,040 Speaker 2: of people that came across this guy. How many people 208 00:11:05,080 --> 00:11:09,000 Speaker 2: are being exposed? You asked yourself many questions. I repeated 209 00:11:09,040 --> 00:11:12,199 Speaker 2: the test just for confirmation purpose, and then that test 210 00:11:12,320 --> 00:11:15,160 Speaker 2: actually the result is consistent, and were repeated it the 211 00:11:15,200 --> 00:11:18,040 Speaker 2: tod time and then in the morning around like six 212 00:11:18,040 --> 00:11:20,160 Speaker 2: o'clock the result turns out to be positive. 213 00:11:20,440 --> 00:11:22,560 Speaker 3: Then I know that while we got a problem in 214 00:11:22,559 --> 00:11:23,120 Speaker 3: the country. 215 00:11:23,520 --> 00:11:25,880 Speaker 2: I do court the then Minister of Health and I 216 00:11:25,920 --> 00:11:28,720 Speaker 2: told him that, sir, we have a baller in the country. 217 00:11:29,480 --> 00:11:32,160 Speaker 2: I remember for about a minute when I told him this, 218 00:11:32,760 --> 00:11:36,840 Speaker 2: the line was quiet. And then the question was how 219 00:11:36,840 --> 00:11:39,240 Speaker 2: do we organize ourselves the reality that at that time 220 00:11:39,320 --> 00:11:40,520 Speaker 2: were really not organized. 221 00:11:40,880 --> 00:11:43,400 Speaker 1: What do you and your colleagues do over the coming days. 222 00:11:43,559 --> 00:11:45,760 Speaker 2: We start saying Okay, when I know that this case 223 00:11:45,840 --> 00:11:48,319 Speaker 2: is confirmed, we start thinking about who are the people 224 00:11:48,320 --> 00:11:49,720 Speaker 2: that came in contact with this case. 225 00:11:50,040 --> 00:11:52,720 Speaker 1: So people are like going door to door, who are 226 00:11:52,720 --> 00:11:56,360 Speaker 1: doing knocking on doors saying you need to come with me. 227 00:11:56,440 --> 00:11:57,680 Speaker 1: You were exposed to a bowler. 228 00:11:57,840 --> 00:12:01,640 Speaker 2: Basically, that is exactly what im because we form immediately 229 00:12:01,760 --> 00:12:05,040 Speaker 2: form an emergency operation center. Were actually create a group 230 00:12:05,080 --> 00:12:08,319 Speaker 2: that is tracking down with these individuals, and that group 231 00:12:08,360 --> 00:12:11,520 Speaker 2: is led by a general and we're getting people out 232 00:12:11,559 --> 00:12:13,240 Speaker 2: of their homes. Once we tuck you down, you know 233 00:12:13,280 --> 00:12:15,760 Speaker 2: we're in contact. We're taking them to a solution center. 234 00:12:16,120 --> 00:12:18,600 Speaker 2: And this is how we're actually picking the cases much 235 00:12:18,640 --> 00:12:21,640 Speaker 2: more earlier before the cases become serious and we start 236 00:12:21,679 --> 00:12:22,320 Speaker 2: managing them. 237 00:12:22,840 --> 00:12:25,960 Speaker 1: So this is at this moment when there is this 238 00:12:27,120 --> 00:12:31,400 Speaker 1: large outbreak in several countries near Nigeria. You have this 239 00:12:31,480 --> 00:12:35,760 Speaker 1: first case in Nigeria. You're trying to do testing and 240 00:12:35,800 --> 00:12:39,199 Speaker 1: tracing really aggressively in Nigeria. Does it work? 241 00:12:39,840 --> 00:12:40,640 Speaker 3: It works? 242 00:12:41,240 --> 00:12:41,760 Speaker 1: It works. 243 00:12:42,000 --> 00:12:45,040 Speaker 2: It works because we're able to issolate as many cases 244 00:12:45,080 --> 00:12:48,360 Speaker 2: as possible. We're basically also providing the results the test 245 00:12:48,480 --> 00:12:51,040 Speaker 2: result in the E real time and sharing these with 246 00:12:51,080 --> 00:12:54,400 Speaker 2: public health authorities and guess what Nigeria comes out of 247 00:12:54,440 --> 00:12:58,200 Speaker 2: it with only twenty cases and AED debt and in 248 00:12:58,200 --> 00:13:02,760 Speaker 2: the country of two and tenty million, that is unprecedented. 249 00:13:02,840 --> 00:13:06,040 Speaker 3: It's unbelievable. But then it shows that it works. 250 00:13:06,440 --> 00:13:09,160 Speaker 2: We showed in the context of our program that you 251 00:13:09,240 --> 00:13:11,199 Speaker 2: need two major things to contain an epidemic. 252 00:13:11,559 --> 00:13:13,240 Speaker 3: One is speed, that is. 253 00:13:13,160 --> 00:13:16,160 Speaker 2: Your speed of execution, your speed of response, your speed 254 00:13:16,200 --> 00:13:17,680 Speaker 2: of characterizing the virus. 255 00:13:17,920 --> 00:13:19,679 Speaker 3: And then secondly is accuracy. 256 00:13:19,960 --> 00:13:22,559 Speaker 2: If you do this with accuracy, you should be able 257 00:13:22,600 --> 00:13:23,679 Speaker 2: to contain any epidemic. 258 00:13:24,360 --> 00:13:27,520 Speaker 1: Thank you so much for your time. I really appreciate it. 259 00:13:27,600 --> 00:13:30,480 Speaker 3: Thank you. 260 00:13:31,880 --> 00:13:34,720 Speaker 1: Christian Happy is the director of the African Center of 261 00:13:34,760 --> 00:13:40,960 Speaker 1: Excellence for Genomics of Infectious Diseases at Redeemers University in Ede, Nigeria. 262 00:13:51,240 --> 00:13:53,360 Speaker 1: So for the next part of the show, we're going 263 00:13:53,440 --> 00:13:56,240 Speaker 1: to talk to parties, yep. And I'm just curious to 264 00:13:56,280 --> 00:13:59,200 Speaker 1: hear you talk about your friendship with her, your work 265 00:13:59,240 --> 00:13:59,559 Speaker 1: with her. 266 00:14:00,720 --> 00:14:04,079 Speaker 2: I do call parties my academic betaph I will say 267 00:14:04,679 --> 00:14:07,520 Speaker 2: it is a friendship that is transcending, you know, the 268 00:14:07,640 --> 00:14:10,120 Speaker 2: RUSSA that we're doing, it will become family. 269 00:14:10,720 --> 00:14:12,760 Speaker 1: For the second half of today's show, I talked with 270 00:14:12,880 --> 00:14:17,400 Speaker 1: Pardise Sabbetti. Partise is a computational geneticist. She has an 271 00:14:17,520 --> 00:14:20,440 Speaker 1: MD from Harvard and a doctorate from Oxford and she 272 00:14:20,560 --> 00:14:23,640 Speaker 1: runs a lab at the Broad Institute. And she says 273 00:14:23,800 --> 00:14:25,760 Speaker 1: she and Christian have been through a lot together. 274 00:14:26,400 --> 00:14:28,640 Speaker 4: Well, Christian is like, you know, we call each other 275 00:14:28,640 --> 00:14:31,000 Speaker 4: our academic better haves. That's what he always says. And 276 00:14:31,040 --> 00:14:33,360 Speaker 4: I always calling my writer Di because we've been in 277 00:14:33,400 --> 00:14:38,080 Speaker 4: situations where it's like, what are we even in? You know, 278 00:14:38,840 --> 00:14:41,280 Speaker 4: We've been in some really banana situations together. 279 00:14:41,800 --> 00:14:44,640 Speaker 1: Parties and Christian's work on malaria and loss of fever 280 00:14:45,160 --> 00:14:49,720 Speaker 1: and ebola has made them really world experts on how 281 00:14:49,760 --> 00:14:53,520 Speaker 1: to prevent outbreaks. Later in the show, I'll talk to 282 00:14:53,560 --> 00:14:57,760 Speaker 1: Parties about her book on culture and Epidemics, but we 283 00:14:57,880 --> 00:15:01,720 Speaker 1: started by discussing this outbreak surveillance system that she and 284 00:15:01,800 --> 00:15:05,320 Speaker 1: Christian have developed to reduce the risk of future pandemics. 285 00:15:05,800 --> 00:15:08,560 Speaker 1: It's called Sentinel, and it grew out of the work 286 00:15:08,760 --> 00:15:11,320 Speaker 1: that Christian and Parties did on loss of virus and 287 00:15:11,360 --> 00:15:14,480 Speaker 1: in bola. Sentinel was created to solve a couple of 288 00:15:14,560 --> 00:15:19,480 Speaker 1: deceptively simple problems, including getting fast, accurate tests to the 289 00:15:19,520 --> 00:15:21,440 Speaker 1: places where people are getting. 290 00:15:21,120 --> 00:15:25,640 Speaker 4: Sick we need detection of pathogens wherever they're occurring, because 291 00:15:25,680 --> 00:15:27,840 Speaker 4: these pathogens all look the same and we don't know, 292 00:15:28,120 --> 00:15:30,520 Speaker 4: you know, like what's brewing inside of us until it's 293 00:15:30,520 --> 00:15:34,720 Speaker 4: pretty late. It's no coincidence that every time we identified 294 00:15:35,280 --> 00:15:38,360 Speaker 4: a virus in the world it was when somebody had 295 00:15:38,400 --> 00:15:41,320 Speaker 4: access to testing, right, Like, for most of these viruses, 296 00:15:41,400 --> 00:15:43,560 Speaker 4: we don't see them, and so LASA viruses won't the 297 00:15:43,560 --> 00:15:45,520 Speaker 4: one we started looking at, and everywhere we looked we 298 00:15:45,560 --> 00:15:48,840 Speaker 4: found it. Like in twenty twelve, we were saying, we 299 00:15:48,960 --> 00:15:52,360 Speaker 4: think Ebola and Lasa are circulating undetected in West Africa, 300 00:15:52,400 --> 00:15:54,240 Speaker 4: and if we just set up the capability to test 301 00:15:54,240 --> 00:15:56,480 Speaker 4: for it, we'd find it. And we are already doing that. 302 00:15:56,840 --> 00:15:59,800 Speaker 4: When Abola appeared in West Africa, it appeared in neighboring 303 00:16:00,920 --> 00:16:02,880 Speaker 4: So what exactly. 304 00:16:02,400 --> 00:16:05,440 Speaker 1: Were you setting up just just before that twenty fourteen 305 00:16:05,440 --> 00:16:06,320 Speaker 1: Ebola outbreak. 306 00:16:06,760 --> 00:16:09,560 Speaker 4: We essentially set up the capacity to train individuals on 307 00:16:09,600 --> 00:16:13,240 Speaker 4: the continent to do diagnostic testing and the tools to 308 00:16:13,280 --> 00:16:16,440 Speaker 4: share that information and track pathogens. So it was basically 309 00:16:16,520 --> 00:16:19,200 Speaker 4: a training program as well as the tools and technology 310 00:16:19,240 --> 00:16:22,480 Speaker 4: to detect pathogens around West Africa at the time. 311 00:16:23,080 --> 00:16:25,560 Speaker 1: And then how did that play out as the Ebola 312 00:16:25,600 --> 00:16:26,960 Speaker 1: outbreak emerged. 313 00:16:27,560 --> 00:16:31,520 Speaker 4: Honestly, our timing is kind of wild in that we were, 314 00:16:31,680 --> 00:16:33,840 Speaker 4: you know, working and working and working to get the grant. 315 00:16:34,040 --> 00:16:37,080 Speaker 4: We got the grant in like December of twenty thirteen, 316 00:16:37,120 --> 00:16:40,920 Speaker 4: and we launched the program in March of twenty fourteen. 317 00:16:41,440 --> 00:16:44,560 Speaker 4: And right at that moment, like literally the program launched, 318 00:16:44,560 --> 00:16:47,120 Speaker 4: we're like meeting with the World Bank leadership, you know there, 319 00:16:48,000 --> 00:16:51,920 Speaker 4: and that was when they identified Ebola and Guinea. We're 320 00:16:51,960 --> 00:16:54,840 Speaker 4: like just hearing Ebolas here and we're there. The frustration 321 00:16:55,040 --> 00:16:58,480 Speaker 4: is deep. It's exciting because at least we're there, but 322 00:16:58,520 --> 00:17:01,120 Speaker 4: we haven't even set up anything yet. But we were 323 00:17:01,160 --> 00:17:03,240 Speaker 4: able to move so we were able to set up 324 00:17:03,280 --> 00:17:07,640 Speaker 4: diagnostics March twenty fourteen. You know, the cases were detected 325 00:17:07,640 --> 00:17:10,719 Speaker 4: in Guinea, We had working diagnostics within a matter of 326 00:17:10,760 --> 00:17:11,840 Speaker 4: a week or two from there. 327 00:17:12,280 --> 00:17:15,480 Speaker 1: So what did you learn from that experience that led 328 00:17:15,520 --> 00:17:20,600 Speaker 1: to this kind of next generation surveillance system that you 329 00:17:20,920 --> 00:17:21,560 Speaker 1: are working with. 330 00:17:22,040 --> 00:17:25,240 Speaker 4: So, I mean the idea of Sentinel it is predicated 331 00:17:25,280 --> 00:17:29,320 Speaker 4: on having these different technologies. So there's three kinds, Like 332 00:17:29,720 --> 00:17:32,680 Speaker 4: there are these point of need tests, things that could 333 00:17:32,680 --> 00:17:36,119 Speaker 4: be as sensitive as PC are tests that you know, 334 00:17:36,160 --> 00:17:39,000 Speaker 4: you could run in villages at home that could pick 335 00:17:39,080 --> 00:17:41,840 Speaker 4: up the pathogen everyone's worried about circulating or ones that 336 00:17:41,920 --> 00:17:45,479 Speaker 4: are known to circulate. These other kinds of tests that 337 00:17:45,560 --> 00:17:47,280 Speaker 4: you could do a lot more of, and you could 338 00:17:47,280 --> 00:17:49,080 Speaker 4: test a lot of different pathogens and a lot of 339 00:17:49,119 --> 00:17:51,680 Speaker 4: people and just be like picking up what's making people 340 00:17:51,680 --> 00:17:53,040 Speaker 4: sit coming into the hospitals. 341 00:17:53,240 --> 00:17:55,679 Speaker 1: And as I understand it, some of these tests are 342 00:17:55,760 --> 00:18:00,000 Speaker 1: using using a technology related to crisper to the genie 343 00:18:00,119 --> 00:18:01,520 Speaker 1: editing tool. Tell me about that. 344 00:18:01,840 --> 00:18:04,600 Speaker 4: Sure people know about crisper as the technology to edit 345 00:18:04,600 --> 00:18:07,439 Speaker 4: genes and how it might be changing gene therapy and 346 00:18:07,600 --> 00:18:10,320 Speaker 4: designer babies and all that stuff, But what is crisper. 347 00:18:10,359 --> 00:18:14,200 Speaker 4: It was actually originally discovered in nature as a bacteria's 348 00:18:14,280 --> 00:18:18,479 Speaker 4: immune system to viruses. It was designed in nature to 349 00:18:18,640 --> 00:18:22,240 Speaker 4: detect and to destroy viruses. So that's what it's good at. 350 00:18:22,400 --> 00:18:26,439 Speaker 4: It's good at detecting viruses. And there's certain classes of 351 00:18:26,560 --> 00:18:29,280 Speaker 4: crisper that have properties that make them really good for 352 00:18:29,440 --> 00:18:33,679 Speaker 4: just detection. You could engineer that detection signal to like 353 00:18:34,200 --> 00:18:37,000 Speaker 4: light up when something is found in a sample, and 354 00:18:37,040 --> 00:18:39,800 Speaker 4: that could be used as a diagnostic. So it's very 355 00:18:39,880 --> 00:18:42,960 Speaker 4: very sensitive, very very specific. It makes really good point 356 00:18:43,000 --> 00:18:45,879 Speaker 4: of need tests, and it's also so sensitive specific you 357 00:18:45,880 --> 00:18:48,520 Speaker 4: could combine it in a multiplex test and do a 358 00:18:48,520 --> 00:18:49,879 Speaker 4: lot of testing simultaneously. 359 00:18:50,960 --> 00:18:55,240 Speaker 1: Okay, so those are the point of need tests. And 360 00:18:55,280 --> 00:18:59,359 Speaker 1: then there's this other category, which is the genetic sequencing 361 00:18:59,400 --> 00:19:02,040 Speaker 1: machines that you were able to get into hospitals in 362 00:19:02,080 --> 00:19:04,720 Speaker 1: West Africa. What do those allow you to do. 363 00:19:04,720 --> 00:19:07,119 Speaker 4: In the clinics when people come into the clinics or hospitals. 364 00:19:07,160 --> 00:19:08,720 Speaker 4: You want to be able to test for like a 365 00:19:08,760 --> 00:19:10,840 Speaker 4: patient comes in and you test for twenty things right 366 00:19:10,880 --> 00:19:12,520 Speaker 4: at the same time, because it could be any of 367 00:19:12,560 --> 00:19:12,880 Speaker 4: those things. 368 00:19:12,920 --> 00:19:14,959 Speaker 5: We don't know a lot of times. 369 00:19:14,560 --> 00:19:17,720 Speaker 1: Like in that instance, are you just like what are 370 00:19:17,760 --> 00:19:21,520 Speaker 1: all the pathogens in this person's body right now? 371 00:19:21,560 --> 00:19:23,280 Speaker 5: Is that what you're doing in that Yes, that is 372 00:19:23,320 --> 00:19:24,000 Speaker 5: the goal of it. 373 00:19:24,080 --> 00:19:26,919 Speaker 4: We're not there like we know everything, but we but 374 00:19:26,960 --> 00:19:27,959 Speaker 4: it's getting close to that. 375 00:19:28,760 --> 00:19:31,120 Speaker 1: I mean, that's like the dream right then you can 376 00:19:31,240 --> 00:19:34,280 Speaker 1: just say, like tell me everything that's there, Like that's 377 00:19:34,320 --> 00:19:35,760 Speaker 1: the one that I thought you couldn't do. 378 00:19:35,880 --> 00:19:36,080 Speaker 4: Yeah. 379 00:19:36,240 --> 00:19:37,840 Speaker 1: Yeah, And you're saying you're getting there. 380 00:19:37,760 --> 00:19:39,280 Speaker 5: Well, you were getting there, yeah exactly. 381 00:19:39,359 --> 00:19:42,480 Speaker 4: Right now, It's like expensive and time consuming, but like yes, 382 00:19:42,560 --> 00:19:44,520 Speaker 4: that would be the kind of holy grail, right, And 383 00:19:44,560 --> 00:19:47,920 Speaker 4: so we've been building these like really super exciting tools 384 00:19:48,160 --> 00:19:51,040 Speaker 4: that allow people to really in a very user friendly way, 385 00:19:51,520 --> 00:19:55,080 Speaker 4: see you know what, all the different pathogens that they're detecting, 386 00:19:55,119 --> 00:19:57,840 Speaker 4: where they're occurring, and start to really kind of make 387 00:19:57,920 --> 00:19:59,560 Speaker 4: sense of all of that data. 388 00:20:00,080 --> 00:20:05,720 Speaker 1: And has there been an instance more recently when you 389 00:20:05,760 --> 00:20:08,679 Speaker 1: were able to use this set of tools to you know, 390 00:20:08,920 --> 00:20:09,760 Speaker 1: stam an outbreak? 391 00:20:10,000 --> 00:20:10,359 Speaker 5: Definitely? 392 00:20:10,440 --> 00:20:12,000 Speaker 4: Well, yeah, I mean the funny thing about the kind 393 00:20:12,040 --> 00:20:14,199 Speaker 4: of work we do is like when we're successful, you 394 00:20:14,240 --> 00:20:17,360 Speaker 4: don't know about it, right, yes, right, right, Yeah. 395 00:20:17,440 --> 00:20:19,720 Speaker 1: I don't know all of the pandemics. We didn't, Yeah, 396 00:20:19,960 --> 00:20:20,800 Speaker 1: because of that's it. 397 00:20:21,200 --> 00:20:24,200 Speaker 4: Well, and so we we have lots of great successes. 398 00:20:24,440 --> 00:20:27,200 Speaker 4: The fact that when COVID hit, we had working COVID 399 00:20:27,240 --> 00:20:31,840 Speaker 4: diagnostics in serri Leone, Senegal and Nigeria our main sentinel 400 00:20:31,880 --> 00:20:35,280 Speaker 4: sites before any US hospital, like any that's sort of 401 00:20:35,280 --> 00:20:36,399 Speaker 4: like what you don't hear about. 402 00:20:37,440 --> 00:20:39,840 Speaker 1: So okay, so that's your work on this, you know, 403 00:20:40,560 --> 00:20:46,000 Speaker 1: very small scale, right, genetic screening looking for molecules in patients. 404 00:20:47,080 --> 00:20:50,119 Speaker 1: I want to sort of go up many many orders 405 00:20:50,119 --> 00:20:53,600 Speaker 1: of magnitude here make a big turn and talk about 406 00:20:53,840 --> 00:20:58,680 Speaker 1: your work on thinking about outbreaks at the scale of society, 407 00:20:58,800 --> 00:21:01,760 Speaker 1: at the scale of country in the world. You actually 408 00:21:01,760 --> 00:21:04,639 Speaker 1: co wrote a book about this side of things. It 409 00:21:04,680 --> 00:21:08,119 Speaker 1: came out before the COVID pandemic in twenty eighteen. It 410 00:21:08,200 --> 00:21:11,160 Speaker 1: was called Outbreak Culture. Tell me about that. 411 00:21:11,320 --> 00:21:17,400 Speaker 4: Yeah, So Outbreak Culture was kind of born from conversations 412 00:21:17,440 --> 00:21:21,119 Speaker 4: I had with a journalist, Laras LAHI just kind of 413 00:21:21,200 --> 00:21:23,240 Speaker 4: processing what we were in. 414 00:21:23,400 --> 00:21:25,280 Speaker 5: Like, I think I said that, you know, the bowl 415 00:21:25,280 --> 00:21:33,720 Speaker 5: outbreak was really like a lot. I mean a lot. 416 00:21:33,760 --> 00:21:33,959 Speaker 5: You know. 417 00:21:34,000 --> 00:21:35,920 Speaker 4: Obviously now people know the kind of a lot that 418 00:21:36,000 --> 00:21:38,080 Speaker 4: pandemics can be from COVID, but it was different because 419 00:21:38,080 --> 00:21:40,359 Speaker 4: it was like it was extreme and nobody else seemed 420 00:21:40,359 --> 00:21:41,919 Speaker 4: to know about it, about us, but there was this 421 00:21:41,960 --> 00:21:44,040 Speaker 4: group of people that were like just deep in it, 422 00:21:44,720 --> 00:21:47,600 Speaker 4: and we just saw a societal breakdown, you know. So 423 00:21:47,880 --> 00:21:52,439 Speaker 4: I often say that viruses expose and exploit the cracks 424 00:21:52,480 --> 00:21:56,240 Speaker 4: in our society. They prey upon us and the division 425 00:21:56,240 --> 00:21:58,480 Speaker 4: between us. And at the end of the day, it's 426 00:21:58,480 --> 00:22:01,359 Speaker 4: like outbreaks are crucible, right there is this It's just 427 00:22:01,400 --> 00:22:04,600 Speaker 4: like something that's insidious. It spreads without you knowing that 428 00:22:04,640 --> 00:22:07,560 Speaker 4: weaponizes your neighbor, that suddenly makes your neighbor like a 429 00:22:07,720 --> 00:22:11,159 Speaker 4: dangerous threat. And so it's kind of everything is so 430 00:22:11,359 --> 00:22:14,840 Speaker 4: much going on, you know, and the lockdowns and all 431 00:22:14,880 --> 00:22:18,520 Speaker 4: of it. So it essentially was this realization I was 432 00:22:18,520 --> 00:22:20,920 Speaker 4: trying to like process what was going on and why 433 00:22:21,080 --> 00:22:26,280 Speaker 4: was this so insane? And one of Nathan Yosback, who 434 00:22:26,400 --> 00:22:29,080 Speaker 4: was my team, he had great insights always and he 435 00:22:29,320 --> 00:22:31,920 Speaker 4: basically when he was in first came back to Sierra Leone, 436 00:22:31,960 --> 00:22:34,360 Speaker 4: he had said something like, it's funny thing is nobody 437 00:22:34,359 --> 00:22:35,679 Speaker 4: seems to even care about the virus. 438 00:22:35,720 --> 00:22:39,359 Speaker 5: The virus is like the backdrop, you know, it's what 439 00:22:39,440 --> 00:22:42,280 Speaker 5: does that mean? It's so proper between people. 440 00:22:42,280 --> 00:22:44,560 Speaker 4: It's a political battle, you know, and the virus is 441 00:22:44,600 --> 00:22:47,320 Speaker 4: the thing around it, but it's actually about who's in charge. 442 00:22:47,560 --> 00:22:47,800 Speaker 4: You know. 443 00:22:48,600 --> 00:22:53,560 Speaker 5: There's so much politics, and I think ultimately the most that. 444 00:22:53,600 --> 00:22:57,200 Speaker 1: Is wild foreshadowing, right, And like, yeah, I mean you 445 00:22:57,400 --> 00:22:59,280 Speaker 1: you wrote about this and the book came out in 446 00:22:59,320 --> 00:23:03,280 Speaker 1: what twenty eighteen or something, and like when you talk 447 00:23:03,320 --> 00:23:06,120 Speaker 1: about that, when you say, what everybody starts talking about 448 00:23:06,160 --> 00:23:10,440 Speaker 1: is politics, like that's like, you know, chills down my spine. 449 00:23:10,600 --> 00:23:13,560 Speaker 4: Yeah, right, I had a lot of people who were like, 450 00:23:13,640 --> 00:23:15,639 Speaker 4: thank goodness you wrote this book, because then I just 451 00:23:15,840 --> 00:23:17,200 Speaker 4: knew what was happening during COVID. 452 00:23:18,040 --> 00:23:21,280 Speaker 1: There's a really deep human nature thing that happens, right. 453 00:23:21,320 --> 00:23:23,639 Speaker 1: I mean, that's the interesting piece of the book. In 454 00:23:23,680 --> 00:23:26,960 Speaker 1: a certain way. It's like it's not because some particular 455 00:23:27,080 --> 00:23:31,399 Speaker 1: politician is craven or something right as you describe it. 456 00:23:31,400 --> 00:23:33,800 Speaker 1: It's just like, this fundamental thing that. 457 00:23:33,720 --> 00:23:36,600 Speaker 4: Happened doesn't have to happen by the way. It doesn't. 458 00:23:37,200 --> 00:23:39,080 Speaker 4: I thought out the places where people were doing it 459 00:23:39,119 --> 00:23:41,080 Speaker 4: the right way. Early in the outbreak. I was like 460 00:23:41,119 --> 00:23:43,520 Speaker 4: advising everybody in their mother and you know, kind of 461 00:23:43,560 --> 00:23:46,560 Speaker 4: just around talking to people, and I stumbled upon this 462 00:23:46,680 --> 00:23:50,520 Speaker 4: like really just interesting place at Colorado Mesa University in 463 00:23:50,560 --> 00:23:51,680 Speaker 4: Grand Junction, Colorado. 464 00:23:52,119 --> 00:23:53,600 Speaker 5: They had, you know, a. 465 00:23:53,560 --> 00:23:59,159 Speaker 4: Community that was very diverse politically, ideologically, ethnically, and they 466 00:23:59,160 --> 00:24:01,399 Speaker 4: were trying to figure out how to work through it. 467 00:24:01,880 --> 00:24:04,440 Speaker 4: And they didn't have money. They couldn't just throw money 468 00:24:04,480 --> 00:24:06,280 Speaker 4: the problem and test everybody, so they had to figure 469 00:24:06,280 --> 00:24:08,200 Speaker 4: out how to be creative. And that's where we brought 470 00:24:08,240 --> 00:24:10,639 Speaker 4: our data analytics system to them, and we brought some 471 00:24:10,720 --> 00:24:13,760 Speaker 4: of our support to help them be like smart about it. 472 00:24:14,080 --> 00:24:16,200 Speaker 4: And the things that they did that were different from 473 00:24:16,359 --> 00:24:19,359 Speaker 4: all the other places in the United States was they 474 00:24:19,680 --> 00:24:23,639 Speaker 4: tested smartly. They supported testing in the community, not just 475 00:24:23,840 --> 00:24:26,760 Speaker 4: like testing themselves. I would always basically liken it to 476 00:24:27,320 --> 00:24:30,360 Speaker 4: being in a place where there's wildfires and a shortage 477 00:24:30,359 --> 00:24:32,080 Speaker 4: of fire alarms, and you went and you bought like 478 00:24:32,119 --> 00:24:34,240 Speaker 4: a thousand of them and putting them in your own 479 00:24:34,280 --> 00:24:37,280 Speaker 4: home and nobody around you has one, Like you will 480 00:24:37,280 --> 00:24:39,120 Speaker 4: pick up a fire when it gets to your house, 481 00:24:39,160 --> 00:24:41,600 Speaker 4: but at that point it's burning to the ground. Wouldn't 482 00:24:41,600 --> 00:24:43,400 Speaker 4: it be smarter to set up, you know, put fire 483 00:24:43,400 --> 00:24:45,920 Speaker 4: alarms in every one of your neighbor's homes so when 484 00:24:45,960 --> 00:24:48,119 Speaker 4: they get it, you get the signal. Right, And that's 485 00:24:48,160 --> 00:24:52,119 Speaker 4: basically what we proved in using like an epidemiological model 486 00:24:52,400 --> 00:24:56,480 Speaker 4: that yes, actually even randomly testing your neighbors will be 487 00:24:56,480 --> 00:24:59,240 Speaker 4: better than testing yourself. That's like one example, right, And 488 00:24:59,280 --> 00:25:01,879 Speaker 4: they were thinking that way. We were being creative and 489 00:25:01,920 --> 00:25:04,920 Speaker 4: we weren't just in this like isolated box of protect myself, 490 00:25:04,960 --> 00:25:05,680 Speaker 4: protect myself. 491 00:25:06,760 --> 00:25:10,000 Speaker 1: So we are at this place now where the whole 492 00:25:10,080 --> 00:25:17,240 Speaker 1: idea of pandemics and epidemiology is is weirdly politicized, and 493 00:25:17,359 --> 00:25:22,000 Speaker 1: it seems like we would be screwed essentially socially politically 494 00:25:22,080 --> 00:25:24,919 Speaker 1: if there were to be another outbreak. What do we 495 00:25:24,960 --> 00:25:25,640 Speaker 1: do about that? 496 00:25:26,080 --> 00:25:28,160 Speaker 4: I think that we need to be way less toxic 497 00:25:28,200 --> 00:25:29,359 Speaker 4: in the way we talk about it. You know, we 498 00:25:29,440 --> 00:25:32,560 Speaker 4: have to basically decharge how we think about these things together, 499 00:25:32,960 --> 00:25:35,800 Speaker 4: and we have to use every like mini event as 500 00:25:35,840 --> 00:25:40,600 Speaker 4: an opportunity of like building better systems. When RSV is 501 00:25:40,640 --> 00:25:43,600 Speaker 4: going through the schools, we can help parents think about 502 00:25:43,680 --> 00:25:46,800 Speaker 4: basically just really think about what are the parents' incentives, right, 503 00:25:47,240 --> 00:25:50,040 Speaker 4: Parents who don't want their kids vaccinated, they're just scared parents. 504 00:25:50,160 --> 00:25:50,800 Speaker 5: At the end of the day. 505 00:25:50,840 --> 00:25:52,239 Speaker 4: We need to tell them about how to keep their 506 00:25:52,320 --> 00:25:55,280 Speaker 4: kids safe. And that's what's important, you know. And we 507 00:25:55,280 --> 00:25:59,120 Speaker 4: should be showing value at every point and respecting autonomy 508 00:25:59,440 --> 00:26:02,520 Speaker 4: where we can and trying to you know, support that conversations. 509 00:26:03,800 --> 00:26:05,879 Speaker 1: Is there anything we didn't talk about, anything else you 510 00:26:05,880 --> 00:26:06,639 Speaker 1: want to talk about? 511 00:26:06,880 --> 00:26:08,399 Speaker 4: I guess the thing I just don't express to you 512 00:26:08,560 --> 00:26:11,920 Speaker 4: is the joy that we have in Sentinel. You know, 513 00:26:12,040 --> 00:26:14,080 Speaker 4: when I think about Sentinel, why it matters to me, 514 00:26:14,320 --> 00:26:17,680 Speaker 4: right is as somebody who's a nerd at the heart, 515 00:26:17,800 --> 00:26:20,560 Speaker 4: you know, I love math, and I love computer science, 516 00:26:20,600 --> 00:26:23,119 Speaker 4: and I love building things, and I love it. But 517 00:26:23,160 --> 00:26:25,600 Speaker 4: I also just want to say it's joyous and the 518 00:26:25,720 --> 00:26:28,440 Speaker 4: joy that is important responding to outbreaks, Like you will 519 00:26:28,480 --> 00:26:31,000 Speaker 4: not respond to an outbreak by walking around with your 520 00:26:31,000 --> 00:26:33,240 Speaker 4: head down. You're going to respond to this kind of 521 00:26:33,280 --> 00:26:36,800 Speaker 4: existential threat by pure joy. And so I love our 522 00:26:36,840 --> 00:26:39,680 Speaker 4: training programs because they are fire. The students are that 523 00:26:39,840 --> 00:26:42,119 Speaker 4: we like, you know, would go canoeing on the Charles 524 00:26:42,200 --> 00:26:44,800 Speaker 4: River altogether, Like we're creating a network of people who 525 00:26:44,800 --> 00:26:47,440 Speaker 4: are friends with each other. So to me, I think 526 00:26:47,440 --> 00:26:50,240 Speaker 4: that's like the most powerful part. And what I'm really 527 00:26:50,240 --> 00:26:52,280 Speaker 4: proud of is that we're bringing the best technology, but 528 00:26:52,320 --> 00:26:56,280 Speaker 4: we're bringing it with like extreme joy and communication and friendship. 529 00:26:56,760 --> 00:26:59,520 Speaker 4: That's how we will overcome the next pandemic, you know, 530 00:26:59,760 --> 00:27:00,720 Speaker 4: by doing it together. 531 00:27:05,000 --> 00:27:06,840 Speaker 1: Thank you for your time. It was great to talk 532 00:27:06,880 --> 00:27:10,520 Speaker 1: with you, Kate, to talk with you too. Partis Sabetti 533 00:27:10,680 --> 00:27:13,760 Speaker 1: is a professor in the Department of Immunology and Infectious 534 00:27:13,800 --> 00:27:17,040 Speaker 1: Disease at the Harvard School of Public Health. She is 535 00:27:17,080 --> 00:27:20,920 Speaker 1: also an Institute member at the Broad Institute of MIT 536 00:27:21,160 --> 00:27:24,760 Speaker 1: and Harvard. Thanks to both my guests today, Christian, Happy 537 00:27:24,840 --> 00:27:29,640 Speaker 1: and partis Sabbetti. Next week on the show, Epstein Barr 538 00:27:29,960 --> 00:27:32,760 Speaker 1: the first cell for Christ's say Somebody's on his side, 539 00:27:32,760 --> 00:27:36,880 Speaker 1: I'll tell You. Incubation is a co production of Pushkin 540 00:27:36,960 --> 00:27:40,920 Speaker 1: Industries and Ruby Studio at iHeartMedia. It's produced by Kate 541 00:27:41,000 --> 00:27:44,720 Speaker 1: Ferby and Brittany Croman. The show is edited by Lacey Roberts. 542 00:27:44,880 --> 00:27:48,680 Speaker 1: It's mastered by Sarah Bruguerer, fact checking by Joseph Friedman. 543 00:27:49,200 --> 00:27:52,640 Speaker 1: Our executive producers are Lacey Roberts and Matt Romano. I'm 544 00:27:52,720 --> 00:27:53,520 Speaker 1: Jacob Goldstein. 545 00:27:53,680 --> 00:28:05,159 Speaker 3: Thanks for listening.