1
00:00:05,080 --> 00:00:10,440
Speaker 1: Welcome to Prognosis. I'm Laura Carlson. It's day seventy six

2
00:00:10,720 --> 00:00:15,560
Speaker 1: since coronavirus was declared a global pandemic. Our main story.

3
00:00:16,720 --> 00:00:20,000
Speaker 1: The US seems to have finally gotten its testing operation

4
00:00:20,040 --> 00:00:23,880
Speaker 1: on track, but it's still hard to find data on

5
00:00:24,040 --> 00:00:28,360
Speaker 1: just how many tests are being done, so a volunteer

6
00:00:28,400 --> 00:00:32,600
Speaker 1: group of journalists took up the challenge. Now the group

7
00:00:32,640 --> 00:00:36,720
Speaker 1: produces reports that are so reliable even the White House

8
00:00:36,720 --> 00:00:48,839
Speaker 1: depends on that. But first, here's what happened today. The

9
00:00:48,960 --> 00:00:52,320
Speaker 1: US drug giant Mark revealed its plans to develop a

10
00:00:52,360 --> 00:00:57,160
Speaker 1: pill that will treat the coronavirus. The company is also

11
00:00:57,240 --> 00:01:01,480
Speaker 1: working on two vaccines. Work bought the rights to develop

12
00:01:01,480 --> 00:01:06,160
Speaker 1: a promising anti viral drug that was discovered at Emory University.

13
00:01:07,640 --> 00:01:12,280
Speaker 1: Over the past century, Mark has pioneered inoculations for diseases

14
00:01:12,400 --> 00:01:17,640
Speaker 1: from ebola to diphtheria. The company pledged that if they

15
00:01:17,640 --> 00:01:20,880
Speaker 1: are able to make a coronavirus vaccine, they will work

16
00:01:20,920 --> 00:01:24,240
Speaker 1: to make it accessible to anyone in the world who

17
00:01:24,319 --> 00:01:29,560
Speaker 1: needs it. WU Want, the epicenter of China's coronavirus outbreak,

18
00:01:29,840 --> 00:01:33,720
Speaker 1: said it tested nearly seven million people in twelve days.

19
00:01:34,920 --> 00:01:38,280
Speaker 1: That's after a handful of infections prompted fears of a

20
00:01:38,319 --> 00:01:43,039
Speaker 1: second wave and spurred a campaign to test the entire population.

21
00:01:44,560 --> 00:01:49,360
Speaker 1: The six point eight million nucleic acid tests uncovered two

22
00:01:49,480 --> 00:01:55,040
Speaker 1: hundred and six asymptomatic cases, according to Bloomberg calculations based

23
00:01:55,080 --> 00:01:59,760
Speaker 1: on daily numbers released by the local Health Commission. And Finally,

24
00:02:00,760 --> 00:02:03,600
Speaker 1: researchers may have found a small piece of the puzzle

25
00:02:03,680 --> 00:02:09,320
Speaker 1: about whether people with COVID nineteen later become immune. An

26
00:02:09,360 --> 00:02:12,400
Speaker 1: early draft of a study of hospital staff in northeastern

27
00:02:12,440 --> 00:02:16,080
Speaker 1: France found that almost all doctors and nurses who got

28
00:02:16,120 --> 00:02:21,840
Speaker 1: mild forms of COVID nineteen produced antibodies that could prevent reinfection.

29
00:02:23,280 --> 00:02:28,120
Speaker 1: The research by Institute Pasteur and University Hospitals in Strasbourg

30
00:02:28,600 --> 00:02:33,519
Speaker 1: addresses a crucial question regarding the new coronavirus, whether people

31
00:02:33,600 --> 00:02:37,640
Speaker 1: who had COVID nineteen, and especially those who didn't get

32
00:02:37,680 --> 00:02:44,000
Speaker 1: severely ill, develop antibodies against the disease. As recently as April,

33
00:02:44,919 --> 00:02:48,320
Speaker 1: the World Health Organization said that there's no evidence yet

34
00:02:48,680 --> 00:02:52,680
Speaker 1: that people who have recovered and have antibodies are protected

35
00:02:52,840 --> 00:03:04,480
Speaker 1: from a second infection, And now our main story at

36
00:03:04,480 --> 00:03:07,640
Speaker 1: the start of the coronavirus pandemic. The scathed number of

37
00:03:07,720 --> 00:03:11,760
Speaker 1: virus tests being conducted in the US was a major fiasco.

38
00:03:13,080 --> 00:03:17,000
Speaker 1: Now though most supply problems have been solved and many

39
00:03:17,080 --> 00:03:20,919
Speaker 1: more tests are being done throughout the country, but it's

40
00:03:20,919 --> 00:03:23,720
Speaker 1: still hard to get a sense of just how many.

41
00:03:25,320 --> 00:03:28,240
Speaker 1: Knowing who is being tested is essential for getting an

42
00:03:28,280 --> 00:03:31,960
Speaker 1: accurate picture of the spread of the virus, but the

43
00:03:32,080 --> 00:03:38,080
Speaker 1: government hasn't readily provided this data. Instead, experts, media outlets,

44
00:03:38,160 --> 00:03:42,440
Speaker 1: and even the Trump administration have turned to its surprising source,

45
00:03:43,440 --> 00:03:46,760
Speaker 1: a volunteer effort by a team of journalists called the

46
00:03:46,880 --> 00:03:52,200
Speaker 1: COVID Tracking Project. Bloomberg News reporter and a Court spoke

47
00:03:52,240 --> 00:03:55,800
Speaker 1: with the project's co founder, Alexis Magical, about why he

48
00:03:55,880 --> 00:03:59,000
Speaker 1: decided to chase these numbers and what they're telling us

49
00:03:59,240 --> 00:04:06,960
Speaker 1: about who with the virus is affecting. Alexis Magical started

50
00:04:07,000 --> 00:04:10,080
Speaker 1: paying close attention to the data on COVID nineteen in

51
00:04:10,160 --> 00:04:14,200
Speaker 1: early March Alexis as a reporter for The Atlantic. He

52
00:04:14,240 --> 00:04:17,359
Speaker 1: found that a month after the first confirmed case of

53
00:04:17,440 --> 00:04:20,760
Speaker 1: COVID nineteen in the US, the country's numbers on the

54
00:04:20,800 --> 00:04:24,760
Speaker 1: infections were way too low. A friend of his from college,

55
00:04:24,880 --> 00:04:28,919
Speaker 1: Jeff Hammerbacker, was also watching the subject closely, so they

56
00:04:29,000 --> 00:04:32,720
Speaker 1: joined together to create the COVID Tracking Project. It's a

57
00:04:32,839 --> 00:04:36,320
Speaker 1: volunteer led effort based out of The Atlantic magazine that

58
00:04:36,440 --> 00:04:41,680
Speaker 1: gathers state level data about testing cases, deaths, and hospitalizations.

59
00:04:42,279 --> 00:04:46,080
Speaker 1: The project also tracts race and ethnicity data when possible.

60
00:04:47,120 --> 00:04:49,880
Speaker 1: That's time consuming work. In the early days, it was

61
00:04:49,960 --> 00:04:54,240
Speaker 1: just Alexis Robinson mayor his colleague at The Atlantic, Jeff

62
00:04:54,320 --> 00:04:58,320
Speaker 1: and some other volunteers like co founder Aaron Cassane. They

63
00:04:58,360 --> 00:05:01,640
Speaker 1: were all working around the clock. I spoke with Alexis

64
00:05:01,720 --> 00:05:05,800
Speaker 1: about the project. The data collection effort, you know, is

65
00:05:05,839 --> 00:05:09,279
Speaker 1: not that difficult to do once, right, you know, you

66
00:05:09,320 --> 00:05:11,960
Speaker 1: go to all these state department state health department websites,

67
00:05:12,279 --> 00:05:15,120
Speaker 1: you write down a bunch of information and a spreadsheet. Um,

68
00:05:15,160 --> 00:05:17,880
Speaker 1: you know, doing that one time. You know, anyone can

69
00:05:17,920 --> 00:05:20,160
Speaker 1: imagine doing that. You know, lots of reporters have done

70
00:05:20,160 --> 00:05:22,800
Speaker 1: this with different kinds of COVID nineteen data, and you can.

71
00:05:22,839 --> 00:05:25,640
Speaker 1: You can do it once. But now let's say you

72
00:05:25,680 --> 00:05:28,839
Speaker 1: need to do it three times a day for a month.

73
00:05:29,440 --> 00:05:31,280
Speaker 1: You can't have the same person doing it like that.

74
00:05:31,600 --> 00:05:33,479
Speaker 1: No one person is going to survive that, No small

75
00:05:33,480 --> 00:05:36,440
Speaker 1: team is going to survive that the relentlessness of it,

76
00:05:36,560 --> 00:05:39,280
Speaker 1: you know, is just too tough. So now you've got

77
00:05:39,279 --> 00:05:42,640
Speaker 1: to create rules for how you're going to code certain

78
00:05:42,640 --> 00:05:45,120
Speaker 1: types of information. The states are all reporting things and

79
00:05:45,760 --> 00:05:48,840
Speaker 1: very very different ways, and insofar as we can standardize

80
00:05:49,000 --> 00:05:59,960
Speaker 1: those things, we try. You might imagine technology could be very,

81
00:06:00,040 --> 00:06:04,520
Speaker 1: very useful in tracking COVID nineteen data, but Alexis says

82
00:06:04,680 --> 00:06:10,120
Speaker 1: it's actually a highly manual process. Volunteers pull the numbers,

83
00:06:10,240 --> 00:06:15,080
Speaker 1: and more volunteers double check their work. This requires systems

84
00:06:15,120 --> 00:06:20,240
Speaker 1: to ensure data quality and train new volunteers. Basically a

85
00:06:20,320 --> 00:06:25,679
Speaker 1: whole organization complete with Slack and Google Docs. The data

86
00:06:25,720 --> 00:06:29,240
Speaker 1: is collected from public health authorities via official reports, as

87
00:06:29,279 --> 00:06:34,200
Speaker 1: well as news conferences, Twitter, and other sources. Jordan's Gasparay,

88
00:06:34,640 --> 00:06:37,280
Speaker 1: who produced the segment, is one of the many journalists

89
00:06:37,320 --> 00:06:41,239
Speaker 1: from various outlets who contributed to this massive data collection effort.

90
00:06:41,720 --> 00:06:45,680
Speaker 1: Alexis says that in an ideal world, the Centers for

91
00:06:45,720 --> 00:06:49,479
Speaker 1: Disease Control and Prevention would be gathering and standardizing this

92
00:06:49,600 --> 00:06:54,520
Speaker 1: crucial data, not an army of volunteers. In fact, he

93
00:06:54,640 --> 00:06:58,560
Speaker 1: likes to call their group the pirates c DC. You know,

94
00:06:58,680 --> 00:07:01,479
Speaker 1: these systems for gathering data at a you know, in

95
00:07:01,480 --> 00:07:03,280
Speaker 1: a country as large as the United States with a

96
00:07:03,279 --> 00:07:07,320
Speaker 1: healthcare system is fragmented as it is, are just remarkably difficult.

97
00:07:07,440 --> 00:07:09,640
Speaker 1: And a lot of it is voluntary reporting, which means,

98
00:07:09,960 --> 00:07:12,640
Speaker 1: you know, even if the federal government says to states like, hey,

99
00:07:12,640 --> 00:07:14,680
Speaker 1: can you please report all of your data to us,

100
00:07:15,120 --> 00:07:17,480
Speaker 1: the hospitals in that state just may not do that. Um,

101
00:07:17,520 --> 00:07:19,200
Speaker 1: they should do that, but they may not do that,

102
00:07:19,280 --> 00:07:22,200
Speaker 1: you know. And the same goes for like race and

103
00:07:22,200 --> 00:07:24,760
Speaker 1: ethnicity data and things like that. People just don't fill

104
00:07:24,800 --> 00:07:26,840
Speaker 1: it out. They don't fill out when they're ordering a test,

105
00:07:26,920 --> 00:07:29,520
Speaker 1: they just don't fill out race and ethnicity on the form.

106
00:07:29,640 --> 00:07:34,440
Speaker 1: And so the kind of data holes that begin, you know,

107
00:07:34,520 --> 00:07:37,239
Speaker 1: with small actions, you know, down at the point of care,

108
00:07:37,880 --> 00:07:40,760
Speaker 1: blow up to you know, on the national scale, to

109
00:07:40,840 --> 00:07:43,360
Speaker 1: be these massive data holes that don't allow us to

110
00:07:43,360 --> 00:07:45,960
Speaker 1: to you know, truly understand what's going on with the

111
00:07:46,000 --> 00:07:49,120
Speaker 1: outbreak or or how to respond to it. Even the

112
00:07:49,160 --> 00:07:53,160
Speaker 1: Trump administration has cited numbers from the COVID tracking project,

113
00:07:53,640 --> 00:07:56,840
Speaker 1: As my colleague Kristen V. Brown and I have reported,

114
00:07:57,840 --> 00:08:01,520
Speaker 1: the White House released a nationwide testing strategy document in

115
00:08:01,640 --> 00:08:05,680
Speaker 1: late April that credits data to Alexis and his colleagues project.

116
00:08:06,600 --> 00:08:10,240
Speaker 1: The Trump administration didn't return our request for comments about

117
00:08:10,280 --> 00:08:13,560
Speaker 1: this or answer our questions about where it's data is

118
00:08:13,600 --> 00:08:18,360
Speaker 1: coming from, but the administration has previously released numbers on

119
00:08:18,560 --> 00:08:23,280
Speaker 1: testing that roughly match up to the COVID tracking projects reporting.

120
00:08:24,480 --> 00:08:27,680
Speaker 1: I asked Alexis if the federal government is using the

121
00:08:27,720 --> 00:08:30,920
Speaker 1: project's data, and whether the White House has its own

122
00:08:30,960 --> 00:08:36,200
Speaker 1: source of COVID nineteen information. What I choose to believe

123
00:08:36,559 --> 00:08:40,640
Speaker 1: is that the federal government has data um and that

124
00:08:40,920 --> 00:08:43,600
Speaker 1: some of it is way better than ours, even but

125
00:08:43,720 --> 00:08:47,120
Speaker 1: that perhaps our data has its own kinds of utility,

126
00:08:47,200 --> 00:08:51,720
Speaker 1: like stretching back in time and standardizing in certain ways

127
00:08:51,720 --> 00:08:55,120
Speaker 1: and sort of allowing the federal government to understand how

128
00:08:55,160 --> 00:08:57,320
Speaker 1: the states are reporting this relative to how they are

129
00:08:57,360 --> 00:09:00,079
Speaker 1: seeing data come in. I don't know. I mean that

130
00:09:00,280 --> 00:09:02,120
Speaker 1: the truth is I don't know the answer to these questions.

131
00:09:02,160 --> 00:09:05,200
Speaker 1: But but you know, just having been around this testing

132
00:09:05,280 --> 00:09:07,640
Speaker 1: data and talk to enough people who are in diagnostic

133
00:09:07,679 --> 00:09:10,319
Speaker 1: labs and other kinds of reporting, I think there's a

134
00:09:10,400 --> 00:09:14,160
Speaker 1: zero percent chance the federal government has nothing. But the

135
00:09:14,200 --> 00:09:16,880
Speaker 1: fact that there's nothing public means they probably don't have

136
00:09:16,960 --> 00:09:21,119
Speaker 1: everything for whatever reason. All of this matters because widespread

137
00:09:21,160 --> 00:09:25,040
Speaker 1: testing is understood to be key to measuring the number

138
00:09:25,120 --> 00:09:29,560
Speaker 1: of COVID nineteen cases and reopening the economy as safely

139
00:09:29,760 --> 00:09:35,920
Speaker 1: as possible. Without mass testing, experts can't be confident in

140
00:09:35,960 --> 00:09:39,800
Speaker 1: the number of cases that have been reported. And while

141
00:09:39,800 --> 00:09:42,920
Speaker 1: the White House has long insisted that the US has

142
00:09:43,000 --> 00:09:47,240
Speaker 1: excelled at testing, getting a complete, up to date picture

143
00:09:47,520 --> 00:09:54,560
Speaker 1: of how it's all going has often been difficult. We

144
00:09:54,600 --> 00:09:56,560
Speaker 1: don't have a good idea of how many people are sick.

145
00:09:57,400 --> 00:10:02,640
Speaker 1: That's why. But part of what's happening is that you know,

146
00:10:02,880 --> 00:10:05,400
Speaker 1: New York, New Jersey, and Connecticut. You know, the New

147
00:10:05,480 --> 00:10:09,320
Speaker 1: York metro area had a blazing outbreak that caught the

148
00:10:09,520 --> 00:10:14,240
Speaker 1: entire nation, entire world by surprise because of the botched

149
00:10:14,679 --> 00:10:16,959
Speaker 1: early testing efforts in the United States, they were only

150
00:10:16,960 --> 00:10:18,880
Speaker 1: testing very sick people until you know, one out of

151
00:10:18,920 --> 00:10:22,920
Speaker 1: every two people was coming back positive. Now what's happening

152
00:10:23,040 --> 00:10:28,160
Speaker 1: is the outbreak is growing outside of those um three places.

153
00:10:28,640 --> 00:10:31,960
Speaker 1: But we but we have better eyes on it because

154
00:10:31,960 --> 00:10:34,120
Speaker 1: there's more testing capacity now than there was in the

155
00:10:34,120 --> 00:10:37,600
Speaker 1: beginning of March. Testing grew to become one of the

156
00:10:37,600 --> 00:10:40,560
Speaker 1: biggest crises in the US in the first months of

157
00:10:40,640 --> 00:10:46,319
Speaker 1: COVID nineteen, and even though the country's testing abilities have improved,

158
00:10:46,640 --> 00:10:53,080
Speaker 1: many problems have lingered, including around capacity. I would like

159
00:10:53,120 --> 00:10:55,600
Speaker 1: to say that we had far seeing vision and we

160
00:10:55,640 --> 00:10:57,520
Speaker 1: of course knew that this would be the crucial issue

161
00:10:57,520 --> 00:10:59,959
Speaker 1: of this entire pandemic, But it's not true. I mean,

162
00:11:00,040 --> 00:11:03,240
Speaker 1: and for me, it's not true. My reporting partner, Rob

163
00:11:03,280 --> 00:11:06,400
Speaker 1: Meyer at The Atlantic, he immediately identified. He was like,

164
00:11:06,440 --> 00:11:08,400
Speaker 1: this is the most important number in America right now,

165
00:11:08,520 --> 00:11:10,800
Speaker 1: is how many people have been tested? UM. He did

166
00:11:10,800 --> 00:11:13,319
Speaker 1: say that at the time. Uh, And so I will say,

167
00:11:13,360 --> 00:11:15,720
Speaker 1: perhaps Rob knew that this would be this big. But

168
00:11:15,800 --> 00:11:20,520
Speaker 1: to be honest, in the weeks moving through March, when

169
00:11:20,720 --> 00:11:22,480
Speaker 1: there was all this talk about the scale up and

170
00:11:22,520 --> 00:11:24,760
Speaker 1: blah blah blah blah blah, I did not think that

171
00:11:24,800 --> 00:11:27,000
Speaker 1: it would that testing would continue to be a number

172
00:11:27,080 --> 00:11:30,199
Speaker 1: that was so deeply important. I really didn't. I thought

173
00:11:30,240 --> 00:11:33,679
Speaker 1: that the project would probably wrap up UM sometime in April,

174
00:11:34,320 --> 00:11:36,440
Speaker 1: either because the CDC came or it just didn't matter

175
00:11:36,480 --> 00:11:40,800
Speaker 1: anymore because testing availability was so enormous. And then basically

176
00:11:40,800 --> 00:11:44,120
Speaker 1: every report that came out in April basically said like, actually,

177
00:11:44,120 --> 00:11:46,520
Speaker 1: we need millions of tests to safely reopen this country.

178
00:11:46,640 --> 00:11:49,280
Speaker 1: And like when every expert is to saying with something

179
00:11:49,360 --> 00:11:52,560
Speaker 1: like that, you go like, well, damn, I guess this

180
00:11:52,720 --> 00:11:59,840
Speaker 1: really is it. The US can now do about four

181
00:12:00,080 --> 00:12:03,760
Speaker 1: hundred thousand tests a day, less than half the nine

182
00:12:03,880 --> 00:12:08,679
Speaker 1: hundred thousand one group of experts says is needed. That's

183
00:12:08,679 --> 00:12:13,000
Speaker 1: the case even as states pushed towards reopening their economies,

184
00:12:13,760 --> 00:12:18,640
Speaker 1: sending people back to work in school and eating at restaurants.

185
00:12:19,840 --> 00:12:22,199
Speaker 1: There's where we are with testing right now in the US.

186
00:12:22,400 --> 00:12:25,880
Speaker 1: We tested less than two thousand people total by March six.

187
00:12:26,120 --> 00:12:28,520
Speaker 1: You know, we entered April doing you know, just about

188
00:12:28,559 --> 00:12:31,800
Speaker 1: a hundred thousand tests a day. We left April doing

189
00:12:31,880 --> 00:12:35,320
Speaker 1: you know, about two hundred thousand tests a day. But

190
00:12:35,440 --> 00:12:38,760
Speaker 1: most of April was a big long plateau actually, and

191
00:12:38,800 --> 00:12:40,320
Speaker 1: it was only really at the end of the month

192
00:12:40,520 --> 00:12:42,960
Speaker 1: um that testing started to pick up. You know, most

193
00:12:43,000 --> 00:12:46,640
Speaker 1: testing now is done by lab corps and Quest and

194
00:12:46,760 --> 00:12:50,400
Speaker 1: other you know, big sort of commercial laboratories. Alexis says

195
00:12:50,480 --> 00:12:54,480
Speaker 1: he's a believer that once the American innovation system gets

196
00:12:54,520 --> 00:13:00,920
Speaker 1: cranking along, testing numbers will eventually take off. Over the

197
00:13:01,000 --> 00:13:05,040
Speaker 1: next few months. He says we'll get there, maybe even

198
00:13:05,160 --> 00:13:10,920
Speaker 1: stand out on a global level. But Alexis also says

199
00:13:11,080 --> 00:13:15,440
Speaker 1: it's possible that by trying to bring on testing while

200
00:13:15,480 --> 00:13:20,520
Speaker 1: also reopening, there will be massive outbreaks and major damage

201
00:13:20,800 --> 00:13:23,720
Speaker 1: done along the way. That's kind of how it looks

202
00:13:23,760 --> 00:13:25,400
Speaker 1: to me. It looks like a race between sort of

203
00:13:25,400 --> 00:13:31,400
Speaker 1: our innovative capacities, you know, and are kind of reactionary

204
00:13:31,440 --> 00:13:33,840
Speaker 1: impulses to just try and get everything to go back

205
00:13:33,840 --> 00:13:36,120
Speaker 1: to normal even though it's clearly not going to happen.

206
00:13:48,920 --> 00:13:51,840
Speaker 1: That was in the court and that's our show today.

207
00:13:52,600 --> 00:13:56,080
Speaker 1: For coverage of the outbreak from one buros around the world,

208
00:13:56,520 --> 00:14:01,560
Speaker 1: visit Bloomberg dot com, slash coronavirus and if you like

209
00:14:01,679 --> 00:14:04,480
Speaker 1: the show, please leave us a review at a rating

210
00:14:04,800 --> 00:14:08,440
Speaker 1: on Apple Podcasts or Spotify. It's the best way to

211
00:14:08,440 --> 00:14:13,880
Speaker 1: help more listeners find our global reporting. The Prognosis Daily

212
00:14:14,000 --> 00:14:18,160
Speaker 1: edition is produced by Topher Foreheads, Jordan gas Pure, Magnus

213
00:14:18,160 --> 00:14:23,080
Speaker 1: Hendrickson and me Laura Carlson. Today's main story was reported

214
00:14:23,080 --> 00:14:28,240
Speaker 1: by Emma Court. Original music by Leo Sidrin. Our editors

215
00:14:28,320 --> 00:14:33,560
Speaker 1: are Francesco Levi and Rick Shine. Francesco Levi is Bloomberg's

216
00:14:33,760 --> 00:15:00,280
Speaker 1: head of podcasts, Thanks for listening. One