1 00:00:04,200 --> 00:00:09,039 Speaker 1: Welcome to Prognosis. I'm Laura Carlson. It's day one and 2 00:00:09,160 --> 00:00:14,400 Speaker 1: forty since coronavirus was declared a global pandemic. Today's main 3 00:00:14,480 --> 00:00:19,279 Speaker 1: story the virus is surging in the US, but we 4 00:00:19,520 --> 00:00:24,520 Speaker 1: still don't have good accessible data about the outbreak. That's 5 00:00:24,600 --> 00:00:27,800 Speaker 1: left the public in the dark about how bad things 6 00:00:28,360 --> 00:00:41,040 Speaker 1: really are. But first, here's what happened in virus news today. 7 00:00:48,640 --> 00:00:52,239 Speaker 1: The United States is about to reach one fifty thousand 8 00:00:52,280 --> 00:00:57,320 Speaker 1: coronavirus deaths, and things continue to get worse in COVID hotspots. 9 00:00:58,560 --> 00:01:01,640 Speaker 1: Florida posted a wreck gird number of virus deaths for 10 00:01:01,680 --> 00:01:07,920 Speaker 1: a second straight day, Arizona's infection rate jumped, and in Texas, 11 00:01:08,319 --> 00:01:12,000 Speaker 1: Hurricane Hannah came ashore Saturday and knocked out power to 12 00:01:12,080 --> 00:01:17,600 Speaker 1: more than three hundred thousand homes and businesses. New coronavirus 13 00:01:17,640 --> 00:01:22,400 Speaker 1: guidelines for line crews, along with flooding are complicating efforts 14 00:01:22,480 --> 00:01:25,800 Speaker 1: to turn the lights back on. Some in the region, 15 00:01:26,200 --> 00:01:31,440 Speaker 1: maybe without power for a week. Russia plans to register 16 00:01:31,480 --> 00:01:36,480 Speaker 1: are coronavirus vaccine as soon as August ten. That would 17 00:01:36,520 --> 00:01:39,160 Speaker 1: clear the way for what would be the world's first 18 00:01:39,400 --> 00:01:44,880 Speaker 1: official approval of an inoculation The drug, developed by Moscow's 19 00:01:45,000 --> 00:01:49,120 Speaker 1: Gamalaya Institute and the Russian Direct Investment Fund, may be 20 00:01:49,240 --> 00:01:53,080 Speaker 1: approved for civilian use within three to seven days of 21 00:01:53,160 --> 00:01:58,360 Speaker 1: registration by regulators. The information comes from a person familiar 22 00:01:58,360 --> 00:02:02,200 Speaker 1: with the process who asked not to be identified because 23 00:02:02,200 --> 00:02:07,880 Speaker 1: the information is in public. Finally, in most places, so 24 00:02:07,960 --> 00:02:12,120 Speaker 1: called herd immunity won't be achieved before a vaccine is 25 00:02:12,200 --> 00:02:17,680 Speaker 1: widely available. But in some of India's largest slumps, around 26 00:02:17,919 --> 00:02:22,320 Speaker 1: six and ten people have antibodies for the novel coronavirus, 27 00:02:23,120 --> 00:02:28,200 Speaker 1: indicating they had the virus and recovered. That means the 28 00:02:28,320 --> 00:02:32,800 Speaker 1: country's poorest areas have one of the highest population immunity 29 00:02:32,919 --> 00:02:43,200 Speaker 1: levels worldwide. And now for today's main story, more than 30 00:02:43,240 --> 00:02:46,560 Speaker 1: a month into a resurgence of the novel coronavirus that 31 00:02:46,639 --> 00:02:51,000 Speaker 1: has besieged Sun Belt states, flooded hospitals, and strained public 32 00:02:51,040 --> 00:02:55,720 Speaker 1: health infrastructure, the US still lacks a complete picture of 33 00:02:55,760 --> 00:03:00,360 Speaker 1: the reality on the ground. That's because the US doesn't 34 00:03:00,400 --> 00:03:04,640 Speaker 1: have any official real time system to track the virus 35 00:03:04,680 --> 00:03:09,280 Speaker 1: is spread. At times, even the federal government has had 36 00:03:09,320 --> 00:03:13,720 Speaker 1: to rely on third party databases. I talked to reporter 37 00:03:13,880 --> 00:03:17,000 Speaker 1: and a court who explained the danger of a COVID 38 00:03:17,120 --> 00:03:24,480 Speaker 1: data black hole. What are some of the problems we're 39 00:03:24,480 --> 00:03:30,360 Speaker 1: seeing with the US specifically with regard to tracking virus data. 40 00:03:31,040 --> 00:03:34,360 Speaker 1: The reality is that the United States doesn't have a 41 00:03:34,400 --> 00:03:41,280 Speaker 1: good national, real time public picture of COVID nineteen in 42 00:03:41,320 --> 00:03:45,040 Speaker 1: the country even today. It's sort of staggering when you 43 00:03:45,160 --> 00:03:48,080 Speaker 1: say it out loud, but for the people who are 44 00:03:48,200 --> 00:03:52,480 Speaker 1: monitoring this crisis every day, this is just a reality 45 00:03:52,560 --> 00:03:57,000 Speaker 1: of the situation. There isn't a accessible real time system 46 00:03:57,040 --> 00:04:00,240 Speaker 1: to track the virus is spread in this voy. A 47 00:04:00,360 --> 00:04:03,920 Speaker 1: lot of other tools have cropped up from third parties 48 00:04:03,920 --> 00:04:06,360 Speaker 1: and things like that. You know, JOHNS Hopkins has become 49 00:04:06,360 --> 00:04:10,080 Speaker 1: a big source of a lot of coronavirus data. But 50 00:04:10,120 --> 00:04:13,200 Speaker 1: the real issue, and you know, there's been a lot 51 00:04:13,280 --> 00:04:17,119 Speaker 1: of attribution of failures during this crisis to the Trump administration, 52 00:04:17,560 --> 00:04:19,960 Speaker 1: and they've certainly played a role in this. But I 53 00:04:20,080 --> 00:04:24,200 Speaker 1: see this as confluence of different factors. We've had decades 54 00:04:24,240 --> 00:04:28,640 Speaker 1: of neglect of the technological public health infrastructure in this country, 55 00:04:29,080 --> 00:04:32,320 Speaker 1: and there's this history of federalism, of states taking their 56 00:04:32,360 --> 00:04:36,720 Speaker 1: own approaches to different things, including public health data, and 57 00:04:37,040 --> 00:04:41,520 Speaker 1: the result is this sort of crisis that's been exacerbated 58 00:04:41,560 --> 00:04:44,640 Speaker 1: in many ways by the strategy taken by the Trump administration, 59 00:04:44,680 --> 00:04:47,279 Speaker 1: which has been each state is going to do their 60 00:04:47,320 --> 00:04:50,760 Speaker 1: own thing, and data is such an essential component of 61 00:04:51,160 --> 00:04:55,320 Speaker 1: whether you can reopen, whether it's advisable to reopen. The 62 00:04:55,400 --> 00:04:58,960 Speaker 1: government admits that they have big gaps in data reporting. 63 00:04:59,440 --> 00:05:03,120 Speaker 1: A lot of experts have turned in the void of 64 00:05:03,720 --> 00:05:08,000 Speaker 1: really reliable federal system to the different state dashboards. So 65 00:05:08,040 --> 00:05:10,120 Speaker 1: I think many of us have become familiar with this. 66 00:05:10,279 --> 00:05:12,440 Speaker 1: You can log on I'm in New York. You can 67 00:05:12,480 --> 00:05:15,159 Speaker 1: log on and see what New York's numbers are that day, 68 00:05:15,200 --> 00:05:19,040 Speaker 1: how things are changing. But each state also reports data 69 00:05:19,160 --> 00:05:23,560 Speaker 1: very differently, and so you can't necessarily cobble together all 70 00:05:23,640 --> 00:05:27,119 Speaker 1: of these fifty different state dashboards, for instance, and get 71 00:05:27,160 --> 00:05:31,880 Speaker 1: a national picture that is really comprehensive, because everyone talks 72 00:05:31,920 --> 00:05:33,960 Speaker 1: about the numbers a little differently, they measure it a 73 00:05:34,000 --> 00:05:37,720 Speaker 1: little differently, and there's just so much variation in this data. 74 00:05:38,160 --> 00:05:42,600 Speaker 1: You know, what are some of these variations or differences 75 00:05:42,640 --> 00:05:46,960 Speaker 1: in reporting that make it so difficult to get a 76 00:05:47,080 --> 00:05:51,440 Speaker 1: comprehensive national picture, especially when we're looking at how the 77 00:05:51,520 --> 00:05:55,800 Speaker 1: states are reporting things differently. What's interesting is this nonprofit 78 00:05:55,839 --> 00:05:59,520 Speaker 1: called Resolved to Save Lives actually took a deep dive 79 00:05:59,560 --> 00:06:01,560 Speaker 1: into the US and they looked at all the different 80 00:06:01,640 --> 00:06:04,480 Speaker 1: state dashboards and they looked at they basically came up 81 00:06:04,480 --> 00:06:07,320 Speaker 1: with this list of fifteen essential data points and how 82 00:06:07,360 --> 00:06:10,279 Speaker 1: ideally you would want to be tracking these measures like 83 00:06:10,360 --> 00:06:14,440 Speaker 1: new cases, deaths, and hospitalizations. And after they went through 84 00:06:14,640 --> 00:06:17,720 Speaker 1: the ways each state did this, they found what they 85 00:06:17,720 --> 00:06:21,480 Speaker 1: described as an information catastrophe. They found that even these 86 00:06:21,920 --> 00:06:25,360 Speaker 1: metrics people are often turning to, like new cases, hospitalizations, 87 00:06:25,360 --> 00:06:29,680 Speaker 1: and deaths, weren't reported in a standard manner. An example 88 00:06:29,720 --> 00:06:33,280 Speaker 1: of this is, for instance, in Florida, until recently, they 89 00:06:33,320 --> 00:06:38,159 Speaker 1: weren't reporting new hospitalizations each day. They were reporting cumulative 90 00:06:38,200 --> 00:06:41,320 Speaker 1: hospitalizations each day. And that may not seem so difficult. 91 00:06:41,320 --> 00:06:43,400 Speaker 1: I mean, you can look at the numbers from before, 92 00:06:43,440 --> 00:06:46,159 Speaker 1: and you can do the math. But the result is 93 00:06:46,200 --> 00:06:49,679 Speaker 1: that you can't easily grab fifty sets of state data 94 00:06:49,760 --> 00:06:52,120 Speaker 1: and combine them and get a really good picture of 95 00:06:52,160 --> 00:06:55,880 Speaker 1: what's going on. And also, you know, it takes time 96 00:06:55,920 --> 00:06:59,240 Speaker 1: to dive into these numbers and understand what their implications are. 97 00:06:59,640 --> 00:07:02,560 Speaker 1: And so if each state is reporting numbers slightly differently, 98 00:07:02,960 --> 00:07:06,039 Speaker 1: that makes it harder to understand what's happening in that state, 99 00:07:06,120 --> 00:07:08,840 Speaker 1: just off the top. And the big issue here is 100 00:07:09,000 --> 00:07:13,040 Speaker 1: the public knowledge. Right, public health officials in these states 101 00:07:13,320 --> 00:07:17,560 Speaker 1: have access to data. The problem is, we've seen so 102 00:07:17,680 --> 00:07:20,560 Speaker 1: many errors be made that it's important to have this 103 00:07:20,880 --> 00:07:25,280 Speaker 1: public accountability so the public can understand, for instance, how 104 00:07:25,320 --> 00:07:29,720 Speaker 1: the state is managing the local conditions, but also understand 105 00:07:29,840 --> 00:07:32,280 Speaker 1: is it safe for me right now to be going 106 00:07:32,360 --> 00:07:36,000 Speaker 1: out in my community. And these are important things that 107 00:07:36,040 --> 00:07:38,880 Speaker 1: have major implications for how people lead their lives, what 108 00:07:39,080 --> 00:07:43,000 Speaker 1: level of risk they expose themselves to, and it matters. 109 00:07:43,600 --> 00:07:46,520 Speaker 1: I want to turn now, maybe to the federal level. 110 00:07:46,680 --> 00:07:50,960 Speaker 1: It seems that hospitals have been told not to send 111 00:07:51,240 --> 00:07:54,640 Speaker 1: virus data to the Centers for Disease Control and Prevention 112 00:07:54,680 --> 00:07:58,840 Speaker 1: the CDC, but instead to the U. S. Department of 113 00:07:58,920 --> 00:08:01,720 Speaker 1: Health and Human Service. Is now, I was hoping you 114 00:08:01,800 --> 00:08:05,560 Speaker 1: might explain why would there be this change in the 115 00:08:05,600 --> 00:08:09,200 Speaker 1: middle of the pandemic of where this data was sent. Yeah, 116 00:08:09,200 --> 00:08:12,640 Speaker 1: so in some ways, the reason this change was made 117 00:08:13,160 --> 00:08:16,480 Speaker 1: really has to do with these data issues. So you know, 118 00:08:16,880 --> 00:08:19,280 Speaker 1: when they made this change a couple of weeks ago, 119 00:08:19,800 --> 00:08:22,920 Speaker 1: the agency said the idea was to streamline the system 120 00:08:23,160 --> 00:08:26,120 Speaker 1: and better present what they what data they had, and 121 00:08:26,120 --> 00:08:29,080 Speaker 1: what data they didn't have. UH. And this is data 122 00:08:29,120 --> 00:08:32,080 Speaker 1: that has really important implications. That helps you know, the 123 00:08:32,120 --> 00:08:35,240 Speaker 1: agencies decide where to send things like medical supplies. Right. 124 00:08:35,280 --> 00:08:39,800 Speaker 1: It helps inform the government's response. But the concern was 125 00:08:40,280 --> 00:08:44,360 Speaker 1: the CDC is the public premier public health agency in 126 00:08:44,400 --> 00:08:48,880 Speaker 1: this country. Their job is to maintain public databases like this. 127 00:08:49,080 --> 00:08:51,880 Speaker 1: They have worked with data like this for years. You know, 128 00:08:51,960 --> 00:08:54,880 Speaker 1: this is what they do. And the concern is are 129 00:08:54,880 --> 00:08:57,559 Speaker 1: you taking the data away from the agency that knows 130 00:08:57,600 --> 00:09:00,079 Speaker 1: how to deal with the data and is there some 131 00:09:00,480 --> 00:09:04,680 Speaker 1: politics involved here? The CDC is viewed as being having 132 00:09:04,679 --> 00:09:07,600 Speaker 1: been largely cut out of the us is response to 133 00:09:07,640 --> 00:09:10,760 Speaker 1: COVID nineteen, And the question is is this data is 134 00:09:10,760 --> 00:09:13,120 Speaker 1: still going to be available to the public. Are we 135 00:09:13,120 --> 00:09:15,520 Speaker 1: going to get a better system here or is the 136 00:09:15,559 --> 00:09:18,679 Speaker 1: system going to become even less transparent than it was before. 137 00:09:19,320 --> 00:09:22,760 Speaker 1: Uh And I think it's important to know. The HHS 138 00:09:22,880 --> 00:09:25,839 Speaker 1: perspective on this is this is a way of being 139 00:09:25,880 --> 00:09:30,160 Speaker 1: more transparent. We're making even more data available UH. And 140 00:09:30,200 --> 00:09:32,480 Speaker 1: there are gaps in our data reporting, but we are 141 00:09:33,120 --> 00:09:38,080 Speaker 1: sharing what the gaps are through this new data system. 142 00:09:38,160 --> 00:09:40,880 Speaker 1: Let's let's look at this in a broader international context. 143 00:09:41,160 --> 00:09:44,720 Speaker 1: How does the US is UM approach to tracking virus 144 00:09:44,840 --> 00:09:48,640 Speaker 1: data compare to what some other countries are doing worldwide. 145 00:09:49,160 --> 00:09:52,959 Speaker 1: What's really striking about the patchwork system we have here 146 00:09:53,000 --> 00:09:57,240 Speaker 1: in the United States is that other countries have made 147 00:09:57,280 --> 00:10:02,120 Speaker 1: their data more accessible in one place. So right now 148 00:10:02,160 --> 00:10:05,400 Speaker 1: I could log on to South Korea's c d C 149 00:10:05,720 --> 00:10:09,679 Speaker 1: and see, you know, information about their testing efforts, information 150 00:10:09,720 --> 00:10:13,439 Speaker 1: on a daily basis about what's happening in that country. 151 00:10:13,800 --> 00:10:17,000 Speaker 1: Australia is also a very large country, like the US, 152 00:10:17,080 --> 00:10:20,280 Speaker 1: has a similar system set up as well, and to 153 00:10:20,360 --> 00:10:23,600 Speaker 1: be sure, in many parts of the world there are 154 00:10:23,640 --> 00:10:26,839 Speaker 1: issues with how they've handled the COVID response, But in 155 00:10:26,880 --> 00:10:30,160 Speaker 1: some ways what's happened in the US is very surprising 156 00:10:30,200 --> 00:10:33,160 Speaker 1: from an international perspective. You know that we're a country 157 00:10:33,160 --> 00:10:35,920 Speaker 1: that's gotten involved in public health all around the world. 158 00:10:36,320 --> 00:10:39,520 Speaker 1: We have the pharmaceutical companies are that are developing all 159 00:10:39,559 --> 00:10:42,320 Speaker 1: these COVID vaccines, many are based here in the United States. 160 00:10:42,760 --> 00:10:45,040 Speaker 1: So in some ways, the US is thought to have 161 00:10:45,120 --> 00:10:49,080 Speaker 1: been a world leader in health crisis specifically, and it's 162 00:10:49,080 --> 00:10:52,640 Speaker 1: not just data where our reputation is suffering, but it's 163 00:10:52,640 --> 00:11:02,840 Speaker 1: certainly a part of it. That was Emma Cord And 164 00:11:02,920 --> 00:11:05,400 Speaker 1: that's it for our show today. For coverage of the 165 00:11:05,440 --> 00:11:08,600 Speaker 1: outbreak from one and twenty bureaus around the world, visit 166 00:11:08,640 --> 00:11:13,280 Speaker 1: bloomberg dot com slash Coronavirus and if you like the show, 167 00:11:13,640 --> 00:11:16,080 Speaker 1: please leave us a review and a rating on Apple 168 00:11:16,120 --> 00:11:19,280 Speaker 1: Podcasts or Spotify. It's the best way to help more 169 00:11:19,320 --> 00:11:23,920 Speaker 1: listeners find our global reporting. The Prognosis Daily Edition is 170 00:11:23,960 --> 00:11:28,600 Speaker 1: produced by Toph Foreheads, Jordan Gaspore, Magnus Hendrickson and me 171 00:11:29,000 --> 00:11:33,040 Speaker 1: Laura Carlson. Today's main story was reported by Emma Court. 172 00:11:33,720 --> 00:11:37,880 Speaker 1: Original music by Leo Sigrian. Our editors are Rick Shawn 173 00:11:38,120 --> 00:11:42,839 Speaker 1: and Francesca Levi. Francesca Levi is Bloomberg's head of podcasts. 174 00:11:43,440 --> 00:12:08,199 Speaker 1: Thanks for listening, l