1 00:00:04,280 --> 00:00:08,840 Speaker 1: Welcome to Prognosis. I'm Laura Carlson. It's day three hundred 2 00:00:08,880 --> 00:00:12,840 Speaker 1: and sixty two since coronavirus was declared a global pandemic. 3 00:00:13,720 --> 00:00:17,520 Speaker 1: Today's main story Israel has been a model for how 4 00:00:17,560 --> 00:00:21,800 Speaker 1: to quickly vaccinate millions of people. Now the country has 5 00:00:21,840 --> 00:00:24,360 Speaker 1: come out with one of the best studies yet on 6 00:00:24,440 --> 00:00:29,520 Speaker 1: the effectiveness of the vaccine, and the news is very good. 7 00:00:30,840 --> 00:00:49,159 Speaker 1: But first, here's what happened in virus news today. The 8 00:00:49,280 --> 00:00:52,880 Speaker 1: US Centers for Disease Control and Prevention is finally letting 9 00:00:52,920 --> 00:00:56,040 Speaker 1: people know what they can and can't do after they've 10 00:00:56,080 --> 00:01:01,080 Speaker 1: been vaccinated. According to new CDC guidelines, innoculated people can 11 00:01:01,200 --> 00:01:05,240 Speaker 1: visit indoors without masks, but must still wear them in 12 00:01:05,319 --> 00:01:10,320 Speaker 1: public and avoid large gatherings around unvaccinated or vulnerable people. 13 00:01:12,120 --> 00:01:16,160 Speaker 1: Germany will drastically speed up its vaccination campaign to as 14 00:01:16,160 --> 00:01:20,760 Speaker 1: many as ten million weekly inoculations by late March. The 15 00:01:20,760 --> 00:01:24,560 Speaker 1: country's finance ministers set on German television that in April, 16 00:01:24,800 --> 00:01:28,920 Speaker 1: May and June, vaccination centers and doctors will have to 17 00:01:29,000 --> 00:01:34,200 Speaker 1: handle millions of doses every week. Germany has administered a 18 00:01:34,240 --> 00:01:39,520 Speaker 1: total of seven point three three million doses since inoculation 19 00:01:39,600 --> 00:01:45,759 Speaker 1: started ten weeks ago, according to Bloomberg's Vaccine Tracker. Finally, 20 00:01:46,400 --> 00:01:50,920 Speaker 1: India's COVID nineteen vaccination drive has jumped nearly fourfold. After 21 00:01:50,960 --> 00:01:55,160 Speaker 1: a sluggish start, The country's program, one of the world's biggest, 22 00:01:55,440 --> 00:01:59,200 Speaker 1: sped up after it expanded eligibility and got a crucial 23 00:01:59,240 --> 00:02:03,760 Speaker 1: public endorsed it from the inoculation of Prime Minister Norrendra Moody. 24 00:02:05,280 --> 00:02:08,000 Speaker 1: Almost twenty one million shots have been administered in India 25 00:02:08,080 --> 00:02:11,600 Speaker 1: so far, up from five point eight million a month ago, 26 00:02:11,960 --> 00:02:15,679 Speaker 1: according to data compiled as of Monday by Bloomberg and 27 00:02:15,800 --> 00:02:25,800 Speaker 1: Johns Hopkins University and Now for today's main story. Israel 28 00:02:25,880 --> 00:02:29,359 Speaker 1: has had one of the world's most successful vaccination efforts 29 00:02:29,440 --> 00:02:33,160 Speaker 1: yet now a new study from the country shows the 30 00:02:33,280 --> 00:02:38,880 Speaker 1: Fiser vaccine was overwhelmingly effective against the virus. Public health 31 00:02:38,960 --> 00:02:44,280 Speaker 1: experts say the Israeli studies shows that immunizations could end 32 00:02:44,440 --> 00:02:49,120 Speaker 1: the pandemic. Naomi Krasy reports on what makes the Israeli 33 00:02:49,200 --> 00:02:53,120 Speaker 1: study so significant and why at might point to an 34 00:02:53,120 --> 00:03:19,920 Speaker 1: eventual way out of the pandemic. Art These are the 35 00:03:19,960 --> 00:03:24,200 Speaker 1: sounds of a busy vaccination center in Israel. Early this month, 36 00:03:25,120 --> 00:03:30,880 Speaker 1: the country's inoculation campaign started on December. By now more 37 00:03:30,919 --> 00:03:33,760 Speaker 1: than half its nine million residents have had at least 38 00:03:33,760 --> 00:03:38,160 Speaker 1: a first dose, largely of the Messenger RNA vaccine developed 39 00:03:38,160 --> 00:03:42,080 Speaker 1: by Fiser and its German partner by On Tech. That's 40 00:03:42,120 --> 00:03:45,560 Speaker 1: the highest COVID vaccination rate in the world. And as 41 00:03:45,640 --> 00:03:48,360 Speaker 1: part of a deal for quick vaccine shipments that Israeli 42 00:03:48,480 --> 00:03:52,720 Speaker 1: Prime Minister Benjamin Netanyah, who made with Fiser, he agreed 43 00:03:52,760 --> 00:03:57,480 Speaker 1: to share the data on the real world results. Netanyah 44 00:03:57,520 --> 00:04:00,680 Speaker 1: who explained the project at a World Economic Forum meeting 45 00:04:00,800 --> 00:04:04,040 Speaker 1: in January, and we offered to share that with Fiser 46 00:04:04,400 --> 00:04:08,560 Speaker 1: and with all humanity to understand what the effects of 47 00:04:09,120 --> 00:04:12,680 Speaker 1: mass inoculations are. The most important thing, I think the 48 00:04:12,720 --> 00:04:18,520 Speaker 1: most pressing thing is the question of what real degree 49 00:04:19,520 --> 00:04:24,599 Speaker 1: not only of personal protection you get from vaccines, but 50 00:04:24,800 --> 00:04:29,839 Speaker 1: what is the level of preventing infections when you receive 51 00:04:29,960 --> 00:04:34,640 Speaker 1: the inoculation. That's critical question obviously, as you want to 52 00:04:34,640 --> 00:04:38,080 Speaker 1: open the economy and restore life to normal. Netania Who's 53 00:04:38,160 --> 00:04:43,080 Speaker 1: deal vaccines for data, did raise some questions within Israel 54 00:04:43,200 --> 00:04:48,680 Speaker 1: about safeguarding patient information, but the country is uniquely positioned 55 00:04:48,760 --> 00:04:53,360 Speaker 1: for this type of experiment. Four large HMOs manage its 56 00:04:53,440 --> 00:04:58,240 Speaker 1: universal health care system, and about of citizens have digital 57 00:04:58,279 --> 00:05:01,359 Speaker 1: health records that go back as many as twenty years. 58 00:05:02,560 --> 00:05:05,680 Speaker 1: Ben Rice, who directs the Predictive Medicine Group at the 59 00:05:05,680 --> 00:05:11,120 Speaker 1: Boston Children's Hospital Computational Health Informatics Program and Harvard Medical School, 60 00:05:11,680 --> 00:05:16,120 Speaker 1: told me Israel's system enables radical integration of health data 61 00:05:16,520 --> 00:05:20,159 Speaker 1: into one place. Since d H and MOS in Israel 62 00:05:20,160 --> 00:05:24,720 Speaker 1: provide the healthcare, provide the vaccination services, and the coronavirus 63 00:05:24,760 --> 00:05:28,039 Speaker 1: testing services, all this data could be integrated into one 64 00:05:28,120 --> 00:05:33,200 Speaker 1: anonymized record to track the real effects of vaccines. Researchers 65 00:05:33,279 --> 00:05:37,359 Speaker 1: needed to use this extremely detailed data on a massive scale. 66 00:05:38,279 --> 00:05:41,680 Speaker 1: Rice and a team of other researchers from Harvard and Klalite, 67 00:05:41,880 --> 00:05:45,200 Speaker 1: the policy arm of Israel's biggest HMO, set out to 68 00:05:45,200 --> 00:05:53,440 Speaker 1: solve the problem. Fiser wasn't involved with their project. So 69 00:05:53,520 --> 00:05:56,440 Speaker 1: in order to conduct such a study, you need a 70 00:05:56,520 --> 00:06:01,320 Speaker 1: very specific set of circumstances that will happen. It's Noah Taken, 71 00:06:01,480 --> 00:06:05,039 Speaker 1: director of Data and AI driven Medicine at Kluit and 72 00:06:05,080 --> 00:06:07,800 Speaker 1: one of the lead researchers on the study. First, you 73 00:06:07,880 --> 00:06:11,719 Speaker 1: have to have information about a community based cohort of 74 00:06:11,720 --> 00:06:17,720 Speaker 1: individuals for which you know all background medical information, and 75 00:06:17,760 --> 00:06:19,840 Speaker 1: for the same cohort of individuals, you need to know 76 00:06:19,960 --> 00:06:22,960 Speaker 1: several things. You need to know which of these are 77 00:06:23,040 --> 00:06:27,440 Speaker 1: being tested for COVID nineteen, the PCR results. You'll have 78 00:06:27,520 --> 00:06:30,800 Speaker 1: to know the results of those tests. You have to 79 00:06:30,839 --> 00:06:35,479 Speaker 1: know who got vaccinated at at what date, and you 80 00:06:35,600 --> 00:06:37,720 Speaker 1: have to know what happened to them. And that means 81 00:06:37,760 --> 00:06:40,839 Speaker 1: that you have to have an integrated data source that 82 00:06:40,920 --> 00:06:45,720 Speaker 1: takes all of these different resources together, because usually hospital 83 00:06:45,800 --> 00:06:50,160 Speaker 1: data is collected in one electronical medical record, and laboratory 84 00:06:50,200 --> 00:06:54,120 Speaker 1: results are collected in another setting, and the community electronical 85 00:06:54,360 --> 00:06:57,400 Speaker 1: medical record is the third setting, and all these together 86 00:06:57,560 --> 00:07:00,120 Speaker 1: are needed in order to do that. In Israel, the 87 00:07:00,160 --> 00:07:04,160 Speaker 1: Ministry of Health created a reporting system that asks everyone 88 00:07:04,200 --> 00:07:08,760 Speaker 1: to record COVID nineteen test results, hospitalizations, and degrees of 89 00:07:08,880 --> 00:07:13,360 Speaker 1: severity of hospitalization. That gave Noah and the team two 90 00:07:13,480 --> 00:07:16,720 Speaker 1: data sets to work with, the h m MOS decades 91 00:07:16,760 --> 00:07:21,760 Speaker 1: of health data and the government's detailed pandemic data. And 92 00:07:22,000 --> 00:07:25,120 Speaker 1: when we cross these two resources together, we can actually 93 00:07:25,160 --> 00:07:28,520 Speaker 1: know where each patient is and what happened to them 94 00:07:28,720 --> 00:07:31,600 Speaker 1: in order to tell how well the vaccine was working. 95 00:07:32,000 --> 00:07:35,960 Speaker 1: The team needed as unbiased a comparison between the vaccinated 96 00:07:36,280 --> 00:07:40,679 Speaker 1: and unvaccinated as possible. They needed to match up each 97 00:07:40,720 --> 00:07:45,400 Speaker 1: inoculated person with someone as similar as possible who hadn't 98 00:07:45,480 --> 00:07:49,280 Speaker 1: yet gotten a shot. So as an example, for you, 99 00:07:49,400 --> 00:07:52,320 Speaker 1: to be matched in the study, you have to find 100 00:07:52,320 --> 00:07:55,360 Speaker 1: someone was very similar to you. So a seventies six 101 00:07:55,480 --> 00:08:00,280 Speaker 1: year old ultra Orthodox Jewish male from specific neighborhoods from 102 00:08:00,280 --> 00:08:03,640 Speaker 1: Tel Aviv who received four influenza vaccines in the last 103 00:08:03,680 --> 00:08:06,680 Speaker 1: five years and has two common abilities that are known 104 00:08:06,760 --> 00:08:09,960 Speaker 1: risk factors for severe corby nineteen will only be matched 105 00:08:09,960 --> 00:08:12,680 Speaker 1: in the study if we can find a similar ultra 106 00:08:12,840 --> 00:08:16,840 Speaker 1: Orthodox Jewish mail from the same neighborhood who's aged seventy 107 00:08:16,920 --> 00:08:19,760 Speaker 1: six to seventy seven years and received three to four 108 00:08:19,800 --> 00:08:25,160 Speaker 1: influenza vaccine and has two common abilities. It was a 109 00:08:25,160 --> 00:08:30,760 Speaker 1: computing challenge. Every day from December twentie to February one, 110 00:08:30,920 --> 00:08:36,240 Speaker 1: the team matched each newly vaccinated person with an unvaccinated control. 111 00:08:36,679 --> 00:08:39,440 Speaker 1: So I can tell you that the first iteration of 112 00:08:39,520 --> 00:08:43,320 Speaker 1: code that we've written took four or five days just 113 00:08:43,400 --> 00:08:46,600 Speaker 1: to run to run it, not to write it. And 114 00:08:46,640 --> 00:08:50,120 Speaker 1: we wrote that piece of code in different languages and 115 00:08:50,160 --> 00:08:54,720 Speaker 1: with different algorithms again and again until if we reduced 116 00:08:54,720 --> 00:08:58,000 Speaker 1: the running times from five days to one day, and 117 00:08:58,040 --> 00:09:00,760 Speaker 1: from one day to four or five hours, and in 118 00:09:00,800 --> 00:09:03,760 Speaker 1: the final version to ten or fifteen minutes, and that 119 00:09:03,760 --> 00:09:06,120 Speaker 1: that's the current version that we're using now, and we 120 00:09:06,160 --> 00:09:09,160 Speaker 1: are rerunning every few days to see what what's the 121 00:09:09,200 --> 00:09:14,280 Speaker 1: status with the information that is gradually building. Ultimately, the 122 00:09:14,320 --> 00:09:18,520 Speaker 1: team was able to compare five hundred and ninety six thousand, 123 00:09:18,600 --> 00:09:24,120 Speaker 1: six hundred and eighteen people vaccinated between December and February 124 00:09:24,280 --> 00:09:29,800 Speaker 1: one with their unvaccinated counterparts added together almost one point 125 00:09:29,840 --> 00:09:34,880 Speaker 1: two million people in all. Published on February in the 126 00:09:34,880 --> 00:09:39,199 Speaker 1: New England Journal of Medicine, their results were overwhelmingly positive. 127 00:09:40,000 --> 00:09:45,720 Speaker 1: Two doses of the vaccine prevented of symptomatic COVID nineteen cases. 128 00:09:46,679 --> 00:09:49,960 Speaker 1: Once the team counted people who hadn't had symptoms but 129 00:09:50,080 --> 00:09:54,120 Speaker 1: tested positive for the virus anyway, they found two doses 130 00:09:54,200 --> 00:10:00,079 Speaker 1: prevented of the documented infections, and importantly, it showed the 131 00:10:00,160 --> 00:10:04,000 Speaker 1: vaccine was also extremely effective for people who are older 132 00:10:04,480 --> 00:10:08,960 Speaker 1: or who have other diseases. Here's Ron Ballaser, director of 133 00:10:09,000 --> 00:10:13,160 Speaker 1: health policy planning for Klalite, and we've been able to 134 00:10:13,200 --> 00:10:16,720 Speaker 1: demonstrate that the vaccine is exceedingly effective as it was 135 00:10:16,760 --> 00:10:20,439 Speaker 1: in the clinical trial. This study that was performed in 136 00:10:20,480 --> 00:10:23,920 Speaker 1: Israel at Kalite, would be able to demonstrate for decision 137 00:10:23,920 --> 00:10:27,960 Speaker 1: makers and the public worldwide that mass vaccination campaigns have 138 00:10:28,080 --> 00:10:32,840 Speaker 1: a huge potential in controlling the illness globally, as well 139 00:10:32,880 --> 00:10:37,839 Speaker 1: as curbing the detrimental impact of disease dissemination on human lives. 140 00:10:38,520 --> 00:10:41,280 Speaker 1: Ron told me that he hopes at some point the 141 00:10:41,360 --> 00:10:44,800 Speaker 1: study won't be able to continue because they'll run out 142 00:10:44,840 --> 00:10:49,640 Speaker 1: of unvaccinated people to use as control comparisons, But for now, 143 00:10:50,040 --> 00:10:54,559 Speaker 1: the team is continuing as their study population grows. They 144 00:10:54,600 --> 00:10:57,880 Speaker 1: hope to answer questions about how specific groups of people 145 00:10:58,000 --> 00:11:02,000 Speaker 1: respond to the vaccine and help CLAD and other health 146 00:11:02,040 --> 00:11:06,959 Speaker 1: providers know how to handle COVID nineteen immunizations for those groups. 147 00:11:08,040 --> 00:11:11,319 Speaker 1: Here's no one part of clear It's head of epidemiology 148 00:11:11,320 --> 00:11:16,960 Speaker 1: and research, so we constantly gather data so we have 149 00:11:17,080 --> 00:11:22,280 Speaker 1: the biggest possible result pool with which to to inform 150 00:11:22,360 --> 00:11:26,560 Speaker 1: decisions within the organization. Also for subgroups. So for example, 151 00:11:26,559 --> 00:11:30,959 Speaker 1: what we do have a huge sympathize for the overall population, 152 00:11:31,440 --> 00:11:34,439 Speaker 1: maybe we specifically want to know what is happening with 153 00:11:34,640 --> 00:11:37,880 Speaker 1: people who are immuno compromise with people who have certain 154 00:11:38,160 --> 00:11:42,160 Speaker 1: remote with conditions. So we do continue to gather these 155 00:11:42,280 --> 00:11:47,240 Speaker 1: data daily to have better and better hands. But for now, 156 00:11:47,760 --> 00:11:51,119 Speaker 1: at the end of this long dark winter of seemingly 157 00:11:51,440 --> 00:11:56,520 Speaker 1: unending pandemic anxiety, we were finally given something really solid 158 00:11:56,679 --> 00:12:01,040 Speaker 1: to cheer about, real world evidence that the vaccine is 159 00:12:01,120 --> 00:12:05,600 Speaker 1: working and perhaps even better than people thought, because it 160 00:12:05,640 --> 00:12:09,520 Speaker 1: seems to prevent not just infection with COVID, but also 161 00:12:09,600 --> 00:12:22,920 Speaker 1: transmission of the virus. That was Naomi Kraski. And that's 162 00:12:22,960 --> 00:12:24,959 Speaker 1: it for our show. To day for coverage of the 163 00:12:25,000 --> 00:12:28,360 Speaker 1: outbreak from one and twenty bureaus across the world, visit 164 00:12:28,400 --> 00:12:33,120 Speaker 1: Bloomberg dot com slash coronavirus and if you like the show, 165 00:12:33,360 --> 00:12:35,640 Speaker 1: please leave us a review and a rating on Apple 166 00:12:35,720 --> 00:12:38,839 Speaker 1: Podcasts or Spotify. It's the best way to help more 167 00:12:38,920 --> 00:12:43,400 Speaker 1: listeners find our global reporting. The Prognosis Daily edition is 168 00:12:43,400 --> 00:12:48,000 Speaker 1: produced by Tophor foreheads Magnus Henrickson and me Laura Carlson. 169 00:12:48,600 --> 00:12:53,079 Speaker 1: Today's main story was reported by Naomi Kresky. Original music 170 00:12:53,080 --> 00:12:57,000 Speaker 1: by Leo Cedrin. Our editors are Rick Shine and Francesca Levi. 171 00:12:57,600 --> 00:13:02,080 Speaker 1: Francesco Levi is Bloomberg's head of punk casts. Thanks for listening.