1 00:00:01,015 --> 00:00:03,777 You may never have heard of Kenema, Sierra Leone 2 00:00:03,801 --> 00:00:05,333 or Arua, Nigeria. 3 00:00:05,357 --> 00:00:09,128 But I know them as two of the most extraordinary places on earth. 4 00:00:09,956 --> 00:00:15,009 In hospitals there, there's a community of nurses, physicians and scientists 5 00:00:15,033 --> 00:00:16,590 that have been quietly battling 6 00:00:16,614 --> 00:00:19,314 one of the deadliest threats to humanity for years: 7 00:00:19,338 --> 00:00:20,512 Lassa virus. 8 00:00:21,118 --> 00:00:23,256 Lassa virus is a lot like Ebola. 9 00:00:23,280 --> 00:00:26,513 It can cause a severe fever and can often be fatal. 10 00:00:27,053 --> 00:00:30,992 But these individuals, they risk their lives every day 11 00:00:31,016 --> 00:00:33,933 to protect the individuals in their communities, 12 00:00:33,957 --> 00:00:36,537 and by doing so, protect us all. 13 00:00:37,093 --> 00:00:40,001 But one of the most extraordinary things I learned about them 14 00:00:40,025 --> 00:00:42,592 on one of my first visits out there many years ago 15 00:00:42,616 --> 00:00:44,325 was that they start each morning -- 16 00:00:44,349 --> 00:00:49,292 these challenging, extraordinary days on the front lines -- by singing. 17 00:00:49,731 --> 00:00:53,070 They gather together, and they show their joy. 18 00:00:53,094 --> 00:00:54,616 They show their spirit. 19 00:00:54,640 --> 00:00:55,799 And over the years, 20 00:00:55,823 --> 00:00:58,936 from year after year as I've visited them and they've visited me, 21 00:00:58,960 --> 00:01:00,953 I get to gather with them and I sing 22 00:01:00,977 --> 00:01:03,151 and we write and we love it, 23 00:01:03,175 --> 00:01:06,718 because it reminds us that we're not just there to pursue science together; 24 00:01:06,742 --> 00:01:09,059 we're bonded through a shared humanity. 25 00:01:09,586 --> 00:01:13,909 And that of course, as you can imagine, becomes extremely important, 26 00:01:13,933 --> 00:01:16,706 even essential, as things begin to change. 27 00:01:16,730 --> 00:01:21,519 And that changed a great deal in March of 2014, 28 00:01:21,543 --> 00:01:23,889 when the Ebola outbreak was declared in Guinea. 29 00:01:24,426 --> 00:01:26,558 This is the first outbreak in West Africa, 30 00:01:26,582 --> 00:01:29,024 near the border of Sierra Leone and Liberia. 31 00:01:30,074 --> 00:01:32,596 And it was frightening, frightening for us all. 32 00:01:32,620 --> 00:01:34,532 We had actually suspected for some time 33 00:01:34,556 --> 00:01:37,198 that Lassa and Ebola were more widespread than thought, 34 00:01:37,222 --> 00:01:39,531 and we thought it could one day come to Kenema. 35 00:01:39,896 --> 00:01:42,306 And so members of my team immediately went out 36 00:01:42,330 --> 00:01:44,727 and joined Dr. Humarr Khan and his team there, 37 00:01:44,751 --> 00:01:48,428 and we set up diagnostics to be able to have sensitive molecular tests 38 00:01:48,452 --> 00:01:50,605 to pick up Ebola if it came across the border 39 00:01:50,629 --> 00:01:51,892 and into Sierra Leone. 40 00:01:51,916 --> 00:01:54,912 We'd already set up this kind of capacity for Lassa virus, 41 00:01:54,936 --> 00:01:56,093 we knew how to do it, 42 00:01:56,117 --> 00:01:57,547 the team is outstanding. 43 00:01:57,571 --> 00:02:00,808 We just had to give them the tools and place to survey for Ebola. 44 00:02:01,340 --> 00:02:02,968 And unfortunately, that day came. 45 00:02:02,992 --> 00:02:07,973 On May 23, 2014, a woman checked into the maternity ward at the hospital, 46 00:02:07,997 --> 00:02:11,723 and the team ran those important molecular tests 47 00:02:11,747 --> 00:02:15,582 and they identified the first confirmed case of Ebola in Sierra Leone. 48 00:02:16,107 --> 00:02:18,156 This was an exceptional work that was done. 49 00:02:18,180 --> 00:02:20,440 They were able to diagnose the case immediately, 50 00:02:20,464 --> 00:02:22,777 to safely treat the patient 51 00:02:22,801 --> 00:02:25,819 and to begin to do contact tracing to follow what was going on. 52 00:02:25,843 --> 00:02:27,815 It could've stopped something. 53 00:02:27,839 --> 00:02:30,686 But by the time that day came, 54 00:02:30,710 --> 00:02:33,068 the outbreak had already been breeding for months. 55 00:02:33,092 --> 00:02:36,732 With hundreds of cases, it had already eclipsed all previous outbreaks. 56 00:02:36,756 --> 00:02:40,400 And it came into Sierra Leone not as that singular case, 57 00:02:40,424 --> 00:02:41,716 but as a tidal wave. 58 00:02:42,120 --> 00:02:44,395 We had to work with the international community, 59 00:02:44,419 --> 00:02:48,125 with the Ministry of Health, with Kenema, to begin to deal with the cases, 60 00:02:48,149 --> 00:02:50,220 as the next week brought 31, 61 00:02:50,244 --> 00:02:53,918 then 92, then 147 cases -- all coming to Kenema, 62 00:02:53,942 --> 00:02:57,243 one of the only places in Sierra Leone that could deal with this. 63 00:02:57,610 --> 00:03:01,009 And we worked around the clock trying to do everything we could, 64 00:03:01,033 --> 00:03:03,994 trying to help the individuals, trying to get attention, 65 00:03:04,018 --> 00:03:05,965 but we also did one other simple thing. 66 00:03:06,544 --> 00:03:10,333 From that specimen that we take from a patient's blood to detect Ebola, 67 00:03:10,357 --> 00:03:12,412 we can discard it, obviously. 68 00:03:12,436 --> 00:03:16,052 The other thing we can do is, actually, put in a chemical and deactivate it, 69 00:03:16,076 --> 00:03:18,806 so just place it into a box and ship it across the ocean, 70 00:03:18,830 --> 00:03:19,981 and that's what we did. 71 00:03:20,005 --> 00:03:22,101 We sent it to Boston, where my team works. 72 00:03:22,724 --> 00:03:26,561 And we also worked around the clock doing shift work, day after day, 73 00:03:26,585 --> 00:03:30,454 and we quickly generated 99 genomes of the Ebola virus. 74 00:03:30,478 --> 00:03:33,535 This is the blueprint -- the genome of a virus is the blueprint. 75 00:03:33,559 --> 00:03:34,718 We all have one. 76 00:03:34,742 --> 00:03:36,687 It says everything that makes up us, 77 00:03:36,711 --> 00:03:38,620 and it tells us so much information. 78 00:03:38,644 --> 00:03:41,821 The results of this kind of work are simple and they're powerful. 79 00:03:42,396 --> 00:03:44,866 We could actually take these 99 different viruses, 80 00:03:44,890 --> 00:03:46,337 look at them and compare them, 81 00:03:46,361 --> 00:03:49,207 and we could see, actually, compared to three genomes 82 00:03:49,231 --> 00:03:52,067 that had been previously published from Guinea, 83 00:03:52,091 --> 00:03:55,802 we could show that the outbreak emerged in Guinea months before, 84 00:03:55,826 --> 00:03:57,668 once into the human population, 85 00:03:57,692 --> 00:04:00,364 and from there had been transmitting from human to human. 86 00:04:00,388 --> 00:04:01,932 Now, that's incredibly important 87 00:04:01,956 --> 00:04:04,324 when you're trying to figure out how to intervene, 88 00:04:04,348 --> 00:04:06,414 but the important thing is contact tracing. 89 00:04:06,438 --> 00:04:09,840 We also could see that as the virus was moving between humans, 90 00:04:09,864 --> 00:04:11,121 it was mutating. 91 00:04:11,145 --> 00:04:13,296 And each of those mutations are so important, 92 00:04:13,320 --> 00:04:15,640 because the diagnostics, the vaccines, 93 00:04:15,664 --> 00:04:17,149 the therapies that we're using, 94 00:04:17,173 --> 00:04:20,531 are all based on that genome sequence, fundamentally -- 95 00:04:20,555 --> 00:04:21,776 that's what drives it. 96 00:04:21,800 --> 00:04:24,682 And so global health experts would need to respond, 97 00:04:24,706 --> 00:04:25,903 would have to develop, 98 00:04:25,927 --> 00:04:28,480 to recalibrate everything that they were doing. 99 00:04:29,079 --> 00:04:32,260 But the way that science works, the position I was in at that point 100 00:04:32,284 --> 00:04:33,435 is, I had the data, 101 00:04:33,459 --> 00:04:36,095 and I could have worked in a silo for many, many months, 102 00:04:36,119 --> 00:04:38,318 analyzed the data carefully, slowly, 103 00:04:38,342 --> 00:04:41,802 submitted the paper for publication, gone through a few back-and-forths, 104 00:04:41,826 --> 00:04:44,955 and then finally when the paper came out, might release that data. 105 00:04:44,979 --> 00:04:47,183 That's the way the status quo works. 106 00:04:47,207 --> 00:04:49,778 Well, that was not going to work at this point, right? 107 00:04:49,802 --> 00:04:51,410 We had friends on the front lines 108 00:04:51,434 --> 00:04:54,688 and to us it was just obvious that what we needed is help, 109 00:04:54,712 --> 00:04:55,868 lots of help. 110 00:04:55,892 --> 00:04:57,289 So the first thing we did is, 111 00:04:57,313 --> 00:04:59,998 as soon as the sequences came off the machines, 112 00:05:00,022 --> 00:05:01,451 we published it to the web. 113 00:05:01,475 --> 00:05:04,311 We just released it to the whole world and said, "Help us." 114 00:05:04,335 --> 00:05:05,670 And help came. 115 00:05:05,694 --> 00:05:06,856 Before we knew it, 116 00:05:06,880 --> 00:05:09,216 we were being contacted from people all over, 117 00:05:09,240 --> 00:05:11,684 surprised to see the data out there and released. 118 00:05:11,708 --> 00:05:13,960 Some of the greatest viral trackers in the world 119 00:05:13,984 --> 00:05:16,063 were suddenly part of our community. 120 00:05:16,087 --> 00:05:18,417 We were working together in this virtual way, 121 00:05:18,441 --> 00:05:21,422 sharing, regular calls, communications, 122 00:05:21,446 --> 00:05:24,197 trying to follow the virus minute by minute, 123 00:05:24,221 --> 00:05:26,442 to see ways that we could stop it. 124 00:05:27,027 --> 00:05:30,785 And there are so many ways that we can form communities like that. 125 00:05:31,182 --> 00:05:35,507 Everybody, particularly when the outbreak started to expand globally, 126 00:05:35,531 --> 00:05:39,121 was reaching out to learn, to participate, to engage. 127 00:05:39,788 --> 00:05:41,382 Everybody wants to play a part. 128 00:05:41,406 --> 00:05:44,186 The amount of human capacity out there is just amazing, 129 00:05:44,210 --> 00:05:45,933 and the Internet connects us all. 130 00:05:45,957 --> 00:05:49,208 And could you imagine that instead of being frightened of each other, 131 00:05:49,232 --> 00:05:51,089 that we all just said, "Let's do this. 132 00:05:51,113 --> 00:05:53,636 Let's work together, and let's make this happen." 133 00:05:53,660 --> 00:05:56,402 But the problem is that the data that all of us are using, 134 00:05:56,426 --> 00:06:00,463 Googling on the web, is just too limited to do what we need to do. 135 00:06:00,487 --> 00:06:03,138 And so many opportunities get missed when that happens. 136 00:06:03,162 --> 00:06:05,643 So in the early part of the epidemic from Kenema, 137 00:06:05,667 --> 00:06:08,409 we'd had 106 clinical records from patients, 138 00:06:08,433 --> 00:06:11,267 and we once again made that publicly available to the world. 139 00:06:11,291 --> 00:06:14,961 And in our own lab, we could show that you could take those 106 records, 140 00:06:14,985 --> 00:06:18,603 we could train computers to predict the prognosis for Ebola patients 141 00:06:18,627 --> 00:06:20,404 to near 100 percent accuracy. 142 00:06:20,428 --> 00:06:22,525 And we made an app that could release that, 143 00:06:22,549 --> 00:06:25,319 to make that available to health-care workers in the field. 144 00:06:25,343 --> 00:06:28,602 But 106 is just not enough to make it powerful, 145 00:06:28,626 --> 00:06:29,777 to validate it. 146 00:06:29,801 --> 00:06:32,455 So we were waiting for more data to release that. 147 00:06:32,479 --> 00:06:34,523 and the data has still not come. 148 00:06:34,547 --> 00:06:37,079 We are still waiting, tweaking away, 149 00:06:37,103 --> 00:06:39,941 in silos rather than working together. 150 00:06:39,965 --> 00:06:42,197 And this just -- we can't accept that. 151 00:06:42,221 --> 00:06:46,025 Right? You, all of you, cannot accept that. 152 00:06:46,049 --> 00:06:47,731 It's our lives on the line. 153 00:06:47,755 --> 00:06:49,466 And in fact, actually, 154 00:06:49,490 --> 00:06:52,033 many lives were lost, many health-care workers, 155 00:06:52,057 --> 00:06:53,951 including beloved colleagues of mine, 156 00:06:53,975 --> 00:06:57,722 five colleagues: Mbalu Fonnie, Alex Moigboi, 157 00:06:57,746 --> 00:07:01,757 Dr. Humarr Khan, Alice Kovoma and Mohamed Fullah. 158 00:07:01,781 --> 00:07:04,307 These are just five of many health-care workers 159 00:07:04,331 --> 00:07:06,095 at Kenema and beyond 160 00:07:06,119 --> 00:07:09,155 that died while the world waited and while we all worked, 161 00:07:09,179 --> 00:07:11,039 quietly and separately. 162 00:07:11,063 --> 00:07:13,096 See, Ebola, like all threats to humanity, 163 00:07:13,120 --> 00:07:17,004 it's fueled by mistrust and distraction and division. 164 00:07:17,028 --> 00:07:20,801 When we build barriers amongst ourselves and we fight amongst ourselves, 165 00:07:20,825 --> 00:07:22,645 the virus thrives. 166 00:07:22,669 --> 00:07:24,461 But unlike all threats to humanity, 167 00:07:24,485 --> 00:07:27,131 Ebola is one where we're actually all the same. 168 00:07:27,155 --> 00:07:29,035 We're all in this fight together. 169 00:07:29,059 --> 00:07:31,693 Ebola on one person's doorstep could soon be on ours. 170 00:07:32,177 --> 00:07:34,979 And so in this place with the same vulnerabilities, 171 00:07:35,003 --> 00:07:37,416 the same strengths, the same fears, the same hopes, 172 00:07:37,440 --> 00:07:40,649 I hope that we work together with joy. 173 00:07:42,427 --> 00:07:45,497 A graduate student of mine was reading a book about Sierra Leone, 174 00:07:45,521 --> 00:07:47,855 and she discovered that the word "Kenema," 175 00:07:47,879 --> 00:07:51,322 the hospital that we work at and the city where we work in Sierra Leone, 176 00:07:51,346 --> 00:07:55,527 is named after the Mende word for "clear like a river, translucent 177 00:07:55,551 --> 00:07:57,138 and open to the public gaze." 178 00:07:57,439 --> 00:07:58,984 That was really profound for us, 179 00:07:59,008 --> 00:08:01,102 because without knowing it, we'd always felt 180 00:08:01,126 --> 00:08:04,310 that in order to honor the individuals in Kenema where we worked, 181 00:08:04,334 --> 00:08:08,621 we had to work openly, we had to share and we had to work together. 182 00:08:09,074 --> 00:08:10,251 And we have to do that. 183 00:08:10,275 --> 00:08:14,036 We all have to demand that of ourselves and others -- 184 00:08:14,060 --> 00:08:16,934 to be open to each other when an outbreak happens, 185 00:08:16,958 --> 00:08:18,608 to fight in this fight together. 186 00:08:18,632 --> 00:08:21,541 Because this is not the first outbreak of Ebola, 187 00:08:21,565 --> 00:08:23,013 it will not be the last, 188 00:08:23,037 --> 00:08:26,192 and there are many other microbes out there that are lying in wait, 189 00:08:26,216 --> 00:08:27,641 like Lassa virus and others. 190 00:08:27,665 --> 00:08:29,174 And the next time this happens, 191 00:08:29,198 --> 00:08:32,394 it could happen in a city of millions, it could start there. 192 00:08:32,418 --> 00:08:35,117 It could be something that's transmitted through the air. 193 00:08:35,141 --> 00:08:37,288 It could even be disseminated intentionally. 194 00:08:37,312 --> 00:08:40,293 And I know that that is frightening, I understand that, 195 00:08:40,317 --> 00:08:42,971 but I know also, and this experience shows us, 196 00:08:42,995 --> 00:08:46,315 that we have the technology and we have the capacity 197 00:08:46,339 --> 00:08:47,934 to win this thing, 198 00:08:47,958 --> 00:08:50,804 to win this and have the upper hand over viruses. 199 00:08:50,828 --> 00:08:53,100 But we can only do it if we do it together 200 00:08:53,124 --> 00:08:54,321 and we do it with joy. 201 00:08:54,871 --> 00:08:56,392 So for Dr. Khan 202 00:08:56,416 --> 00:09:00,582 and for all of those who sacrificed their lives on the front lines 203 00:09:00,606 --> 00:09:03,006 in this fight with us always, 204 00:09:03,030 --> 00:09:05,837 let us be in this fight with them always. 205 00:09:05,861 --> 00:09:07,736 And let us not let the world be defined 206 00:09:07,760 --> 00:09:09,879 by the destruction wrought by one virus, 207 00:09:09,903 --> 00:09:12,684 but illuminated by billions of hearts and minds 208 00:09:12,708 --> 00:09:13,916 working in unity. 209 00:09:13,940 --> 00:09:15,114 Thank you. 210 00:09:15,138 --> 00:09:22,007 (Applause)