1 00:00:03,160 --> 00:00:07,800 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,039 --> 00:00:13,119 Speaker 2: Earlier this year, Bloomberg data reporter Jinshawn Honk got hung 3 00:00:13,200 --> 00:00:16,280 Speaker 2: up on this question. It was something she kept noticing 4 00:00:16,640 --> 00:00:20,840 Speaker 2: at work, at home, everywhere she went. She couldn't shake it. 5 00:00:21,079 --> 00:00:24,639 Speaker 1: Why does it seem like everyone everywhere like seems to 6 00:00:24,680 --> 00:00:26,480 Speaker 1: be getting sick all the time. 7 00:00:27,120 --> 00:00:30,920 Speaker 2: Jinshawn's colleagues in Hong Kong and their family members all 8 00:00:30,960 --> 00:00:34,720 Speaker 2: seemed to be getting sick. Jinhawn kept catching things too. 9 00:00:34,840 --> 00:00:36,279 Speaker 2: Everybody was talking about it. 10 00:00:37,120 --> 00:00:41,360 Speaker 3: I'm a person that rarely gets sick. I had pink eye, 11 00:00:41,840 --> 00:00:44,920 Speaker 3: my broat was swollen. I couldn't breathe through my nose. 12 00:00:45,200 --> 00:00:48,080 Speaker 3: It was the worst two months of my life. 13 00:00:48,560 --> 00:00:51,000 Speaker 2: Here at the Big Take, we've been hearing similar laments 14 00:00:51,000 --> 00:00:52,760 Speaker 2: from our family and friends all year. 15 00:00:53,400 --> 00:00:57,800 Speaker 1: My husband had COVID in September. My daughter and I 16 00:00:57,920 --> 00:00:59,840 Speaker 1: had stepped throat in October. 17 00:01:00,520 --> 00:01:00,960 Speaker 3: RSB. 18 00:01:01,320 --> 00:01:05,039 Speaker 2: In November, it seemed like everybody had their own sickness story. 19 00:01:05,400 --> 00:01:07,880 Speaker 2: I had pneumonia. It turns out I went to urging 20 00:01:07,920 --> 00:01:12,640 Speaker 2: care twice. It probably took a little about three weeks 21 00:01:12,680 --> 00:01:14,319 Speaker 2: before I started feeling human again. 22 00:01:14,600 --> 00:01:17,839 Speaker 3: In March, I had shingles, at the age of twenty nine. 23 00:01:18,040 --> 00:01:20,120 Speaker 1: At first I thought it was allergies, but now I 24 00:01:20,160 --> 00:01:23,840 Speaker 1: think I'm sick. My nose is running uncontrollably and I 25 00:01:23,880 --> 00:01:25,119 Speaker 1: feel achy and tired. 26 00:01:27,560 --> 00:01:31,560 Speaker 2: Even Olympic athletes are getting sick. This week, Team USA 27 00:01:31,640 --> 00:01:34,200 Speaker 2: sprinter Noah Lyles won a bronze medal in the two 28 00:01:34,319 --> 00:01:37,600 Speaker 2: hundred meter dash, only to disclose afterwards that he had 29 00:01:37,680 --> 00:01:41,760 Speaker 2: tested positive for COVID a few days before. Olympic authorities 30 00:01:41,840 --> 00:01:44,480 Speaker 2: new Liles had COVID, but he was allowed to compete anyway. 31 00:01:45,000 --> 00:01:48,280 Speaker 2: In fact, dozens of athletes have reportedly tested positive for 32 00:01:48,320 --> 00:01:50,920 Speaker 2: COVID at the Games, but most people seem to have 33 00:01:51,000 --> 00:01:54,760 Speaker 2: accepted that that's just our new reality. From the sniffles 34 00:01:54,800 --> 00:01:58,560 Speaker 2: to shingles to our heart to shake nemesis COVID this year, 35 00:01:58,720 --> 00:02:03,160 Speaker 2: sickness is showing up everywhere. But Jinshan is a data reporter. 36 00:02:03,680 --> 00:02:07,720 Speaker 2: She knows talk is cheap and noticing a trend isn't enough. 37 00:02:08,120 --> 00:02:11,280 Speaker 1: We decided to look into this and find out whether 38 00:02:11,320 --> 00:02:14,600 Speaker 1: it's just a perception issue, is there something really going 39 00:02:14,639 --> 00:02:16,880 Speaker 1: on that we should figure out for the public. 40 00:02:17,400 --> 00:02:21,000 Speaker 2: It was a mystery, so Jinhn and her team went 41 00:02:21,160 --> 00:02:25,760 Speaker 2: into detective mode. Working with disease forecasters to gather case counts, 42 00:02:26,200 --> 00:02:30,040 Speaker 2: calling up doctors, combing through research from all over the world, 43 00:02:30,600 --> 00:02:36,919 Speaker 2: and what they found was truly eye opening. Today on 44 00:02:36,960 --> 00:02:39,760 Speaker 2: the show, grab your hand sanitizer and your N ninety 45 00:02:39,840 --> 00:02:43,919 Speaker 2: five's for a data detective story, we joined Jinshan as 46 00:02:43,919 --> 00:02:48,639 Speaker 2: she scours the research for clues, culprits, correlations, and causations 47 00:02:49,200 --> 00:02:52,480 Speaker 2: as she takes on the case of why everybody seems 48 00:02:52,520 --> 00:02:55,960 Speaker 2: to be getting sick all the time. This is the 49 00:02:56,000 --> 00:03:05,480 Speaker 2: big take from Bloomberg News. I'm Sarah Holder. Tracking how 50 00:03:05,560 --> 00:03:09,360 Speaker 2: sickness spreads is a massive data undertaking and to begin 51 00:03:09,440 --> 00:03:13,800 Speaker 2: to understand how often it's spreading post COVID, Bloomberg's Jinshawn 52 00:03:13,880 --> 00:03:16,600 Speaker 2: Honk first had to narrow down a list of illnesses 53 00:03:16,639 --> 00:03:19,360 Speaker 2: to look at, so she enlisted the help of a 54 00:03:19,400 --> 00:03:24,120 Speaker 2: London based firm that forecasts diseases worldwide called Airffinity, and 55 00:03:24,160 --> 00:03:28,880 Speaker 2: the help of her colleague Buma Shrivastova. Together, they analyzed 56 00:03:28,919 --> 00:03:32,680 Speaker 2: data from sixty public health agencies and organizations like the 57 00:03:32,760 --> 00:03:36,840 Speaker 2: WHO and UNICF and came up with a grim list. 58 00:03:37,440 --> 00:03:41,000 Speaker 1: We will to vote to identify at least thirteen communicabo 59 00:03:41,120 --> 00:03:45,040 Speaker 1: diseases the dah surging in paths of the world that's 60 00:03:45,280 --> 00:03:48,880 Speaker 1: above pre pandemic waves and in some cases is surpass 61 00:03:48,960 --> 00:03:51,800 Speaker 1: the pre pandemic peak by a significant margin. 62 00:03:52,520 --> 00:03:58,840 Speaker 2: These diseases included cholera, measles, tuberculosis, RSV, dengay, and the flu. 63 00:04:00,080 --> 00:04:03,800 Speaker 2: Jahn also wanted to know where these diseases were spiking. 64 00:04:03,680 --> 00:04:06,160 Speaker 1: So before the pandemic we were able to find out 65 00:04:06,240 --> 00:04:09,840 Speaker 1: the peak of every disease in every country between twenty 66 00:04:09,880 --> 00:04:13,560 Speaker 1: seventeen to twenty nineteen, and after COVID we have twenty 67 00:04:13,640 --> 00:04:16,560 Speaker 1: twenty two to twenty twenty four, also three year and 68 00:04:16,640 --> 00:04:19,520 Speaker 1: we find out a peak and compare the two. Whenever 69 00:04:19,600 --> 00:04:22,440 Speaker 1: we see a spike. Then we market on a map, 70 00:04:22,680 --> 00:04:25,560 Speaker 1: and with that we were able to identify regions where 71 00:04:25,560 --> 00:04:28,880 Speaker 1: certain diseases are searching more profoundly. 72 00:04:29,720 --> 00:04:33,679 Speaker 2: Now, this data wasn't completely exhaustive, but it did show 73 00:04:33,800 --> 00:04:37,560 Speaker 2: some notable trends. All thirteen of the diseases they tracked 74 00:04:37,600 --> 00:04:40,920 Speaker 2: had surged above post pandemic levels somewhere. 75 00:04:41,160 --> 00:04:43,880 Speaker 1: It may not be higher in every country, but then 76 00:04:44,080 --> 00:04:46,640 Speaker 1: we do see every one of them seem to be 77 00:04:46,720 --> 00:04:51,440 Speaker 1: showing up in a variety of geographies at higher levels. So, 78 00:04:51,480 --> 00:04:56,080 Speaker 1: for example, dn GEY is making a very strong resurgence 79 00:04:56,400 --> 00:05:00,560 Speaker 1: in Americas. We also have like nisos like spreading to 80 00:05:01,400 --> 00:05:05,239 Speaker 1: about twenty states in the US and other countries in Europe. 81 00:05:05,680 --> 00:05:09,880 Speaker 1: And we are also seeing tuberculesis is like really making 82 00:05:10,000 --> 00:05:13,960 Speaker 1: a lot of spikes in the developing world. And with that, 83 00:05:14,279 --> 00:05:18,800 Speaker 1: everyday common diseases like cold and flu and RSV are 84 00:05:18,839 --> 00:05:21,839 Speaker 1: also reported above pre pandemic levels. 85 00:05:22,080 --> 00:05:25,360 Speaker 2: Some of the surges are especially dramatic and more than 86 00:05:25,400 --> 00:05:28,280 Speaker 2: forty places at least one of these diseases has seen 87 00:05:28,360 --> 00:05:31,960 Speaker 2: case counts leap tenfold or more from their pre pandemic baselines. 88 00:05:32,720 --> 00:05:35,880 Speaker 2: Influenza was up forty percent in the US during the 89 00:05:35,960 --> 00:05:39,960 Speaker 2: last two flu seasons compared to pre COVID levels. Besides 90 00:05:40,000 --> 00:05:42,760 Speaker 2: the health impacts and the strains to the medical system 91 00:05:42,839 --> 00:05:47,040 Speaker 2: this can create, there are also other economic impacts. All 92 00:05:47,080 --> 00:05:49,280 Speaker 2: those sick days are starting to add up. 93 00:05:49,480 --> 00:05:54,680 Speaker 1: We were looking through workplace research reports from places like 94 00:05:54,839 --> 00:05:59,760 Speaker 1: UK and the US and there is more absenteesan with that, 95 00:05:59,800 --> 00:06:03,240 Speaker 1: we seeing people reporting more sick days or taking longer 96 00:06:03,279 --> 00:06:07,400 Speaker 1: sick leaves from work. Maybe in the COVID years when 97 00:06:07,400 --> 00:06:10,080 Speaker 1: you were a little sick, you'll still go out and 98 00:06:10,520 --> 00:06:12,640 Speaker 1: have a drink. It's like, Ah, I'm sick, but I'm 99 00:06:12,640 --> 00:06:15,160 Speaker 1: not sick to a degree that I cannot function. I'll 100 00:06:15,160 --> 00:06:17,800 Speaker 1: still go to work. But now, like I think, we 101 00:06:17,880 --> 00:06:21,039 Speaker 1: are also more aware that, oh I feel a bit 102 00:06:21,120 --> 00:06:23,320 Speaker 1: sick today, maybe I shouldn't go to work. 103 00:06:23,800 --> 00:06:26,680 Speaker 2: That's one helpful lesson to come out of COVID. That's 104 00:06:26,680 --> 00:06:30,680 Speaker 2: staying home when you're sick and curb infections. But illnesses 105 00:06:30,720 --> 00:06:35,560 Speaker 2: are spiking anyway, and that's concerning, especially because Ginhan reminded 106 00:06:35,600 --> 00:06:39,520 Speaker 2: me the COVID nineteen pandemic was unprecedented, and so is 107 00:06:39,560 --> 00:06:41,040 Speaker 2: our post pandemic reality. 108 00:06:41,640 --> 00:06:46,719 Speaker 1: According to who's chief scientist Jeremy Farral, we are really 109 00:06:46,760 --> 00:06:52,040 Speaker 1: in a new place because the last devastating major pandemic 110 00:06:52,200 --> 00:06:55,839 Speaker 1: we had was in nineteen eighteen, which was so called 111 00:06:56,080 --> 00:06:59,880 Speaker 1: the Spanish Flu, and back then we were not having 112 00:07:00,160 --> 00:07:05,919 Speaker 1: as many vaccinations, diagnosis, or even treatment. At that time. 113 00:07:06,000 --> 00:07:08,520 Speaker 1: It was really a different stage. So what we are 114 00:07:08,600 --> 00:07:13,560 Speaker 1: facing right now is really an unparallel situation that scientists 115 00:07:13,600 --> 00:07:15,000 Speaker 1: are raising to understand. 116 00:07:15,760 --> 00:07:19,520 Speaker 2: Even though it was a massive data lift, Jinshan says 117 00:07:19,560 --> 00:07:22,280 Speaker 2: that figuring out that we were all getting sick more 118 00:07:22,280 --> 00:07:26,440 Speaker 2: often was actually the easy part coming up after the break, 119 00:07:26,840 --> 00:07:30,679 Speaker 2: unraveling the mystery of what's causing this spike in global 120 00:07:30,680 --> 00:07:41,160 Speaker 2: illness If you think about this story as a big 121 00:07:41,200 --> 00:07:45,080 Speaker 2: global health mystery at this point, Bloomberg's Jinshawn Hank has 122 00:07:45,120 --> 00:07:48,960 Speaker 2: identified the say victims, those of us who are getting 123 00:07:48,960 --> 00:07:53,200 Speaker 2: sick more often all around the world. But who or 124 00:07:53,600 --> 00:07:57,800 Speaker 2: what in this case is the culprit what's making everyone 125 00:07:57,880 --> 00:08:02,200 Speaker 2: sick in this post pandemic era. Well, Jinhehn told us 126 00:08:02,320 --> 00:08:06,080 Speaker 2: there are a few major theories floating around. I asked 127 00:08:06,120 --> 00:08:15,480 Speaker 2: her to introduce some of the prime suspects. So first, 128 00:08:15,880 --> 00:08:19,000 Speaker 2: there's this idea that we all lost our immunity because 129 00:08:19,040 --> 00:08:22,360 Speaker 2: we stayed home during the pandemic, there were quarantines, we 130 00:08:22,360 --> 00:08:25,600 Speaker 2: weren't being exposed to as many diseases. How much of 131 00:08:25,640 --> 00:08:27,080 Speaker 2: that is at play here? 132 00:08:28,600 --> 00:08:32,720 Speaker 1: That theory, which was at one point very leading theory 133 00:08:33,240 --> 00:08:37,920 Speaker 1: during the pandemic, is that it's called immunity debt, where 134 00:08:38,040 --> 00:08:44,120 Speaker 1: people became more susceptible to various infectious respiratory diseases because 135 00:08:44,160 --> 00:08:48,280 Speaker 1: they were not exposed to the pathogen during the lockdown years. 136 00:08:48,520 --> 00:08:52,760 Speaker 1: But that's still quite controversial among scientists that we talked 137 00:08:52,760 --> 00:08:56,320 Speaker 1: to some of them think there's not enough data yet 138 00:08:56,320 --> 00:08:59,000 Speaker 1: to prove it, and some other thing. Even if they 139 00:08:59,040 --> 00:09:03,440 Speaker 1: make surgeons, they are not supposed to be the size 140 00:09:03,559 --> 00:09:05,880 Speaker 1: of the spikes that we actually see today. 141 00:09:08,960 --> 00:09:13,000 Speaker 2: While this immunity debt theory is contested, experts told Jinshn 142 00:09:13,040 --> 00:09:15,880 Speaker 2: that lockdowns could have contributed to the current spikes in 143 00:09:15,920 --> 00:09:20,360 Speaker 2: a different way. Babies who avoided catching respiratory diseases during 144 00:09:20,360 --> 00:09:25,320 Speaker 2: COVID quarantines and school closures maybe getting exposed and sick 145 00:09:25,600 --> 00:09:27,520 Speaker 2: for the first time as toddlers. 146 00:09:28,760 --> 00:09:32,640 Speaker 1: It's more like a delayed education to their immune system. 147 00:09:33,240 --> 00:09:37,520 Speaker 2: Delayed education. In other words, in the years since lockdowns ended, 148 00:09:37,720 --> 00:09:40,240 Speaker 2: more kids might now be getting sick all At the 149 00:09:40,240 --> 00:09:44,360 Speaker 2: same time, these kinds of COVID related delays are also 150 00:09:44,400 --> 00:09:47,439 Speaker 2: showing up in some countries mortality rates. 151 00:09:47,600 --> 00:09:51,400 Speaker 1: Some countries that used to control COVID very well during 152 00:09:51,440 --> 00:09:55,480 Speaker 1: the pandemic years seem to have higher O course mortality 153 00:09:55,559 --> 00:09:59,480 Speaker 1: rates right now. So one theory they presented was that 154 00:10:00,040 --> 00:10:04,400 Speaker 1: because those countries were able to keep frail elderly people 155 00:10:04,760 --> 00:10:09,400 Speaker 1: live longer and keep them away from regularly circulating disease 156 00:10:09,520 --> 00:10:13,320 Speaker 1: that are usually common in the communities. So with that 157 00:10:13,360 --> 00:10:15,600 Speaker 1: they are now facing a higher death burden. 158 00:10:16,040 --> 00:10:19,360 Speaker 2: Another mystery doctors and scientists are trying to investigate is 159 00:10:19,400 --> 00:10:23,000 Speaker 2: the effect of COVID infections on people's longer term health. 160 00:10:23,400 --> 00:10:27,319 Speaker 2: Did you look into long COVID. Has COVID itself made 161 00:10:27,400 --> 00:10:30,079 Speaker 2: people more susceptible to other illnesses? 162 00:10:30,400 --> 00:10:34,200 Speaker 1: Yeah, that is also a very heated topic that scientists 163 00:10:34,240 --> 00:10:39,360 Speaker 1: are looking into, because COVID definitely has changes on some 164 00:10:39,440 --> 00:10:43,760 Speaker 1: people that's much more than the general public, But that's 165 00:10:43,800 --> 00:10:47,720 Speaker 1: still like relatively a smaller population compared to the general 166 00:10:47,880 --> 00:10:51,920 Speaker 1: public in terms of everybody. I think there's no proof 167 00:10:51,960 --> 00:10:55,360 Speaker 1: at this point, according to our interviews, that we are 168 00:10:55,440 --> 00:10:57,560 Speaker 1: becoming much weaker than before. 169 00:11:00,080 --> 00:11:03,440 Speaker 2: Suspect in the rise of illness across the world. The 170 00:11:03,520 --> 00:11:05,200 Speaker 2: anti vaccine movement. 171 00:11:05,200 --> 00:11:08,680 Speaker 1: For example, one thing that went very rampant during COVID 172 00:11:09,240 --> 00:11:14,960 Speaker 1: was the vaccine misinformation, the social media and how the 173 00:11:14,960 --> 00:11:18,880 Speaker 1: information got spread to many many people, and the mistrust 174 00:11:19,000 --> 00:11:21,040 Speaker 1: of vaccination seems to continue. 175 00:11:21,280 --> 00:11:25,360 Speaker 2: Vaccine hesitancy and misinformation, along with supply chain issues, have 176 00:11:25,480 --> 00:11:29,600 Speaker 2: led to a steep drop in childhood vaccination rates in Europe. 177 00:11:29,840 --> 00:11:33,360 Speaker 2: Musles case suspect thirty fold last year after a few 178 00:11:33,400 --> 00:11:37,080 Speaker 2: years where nearly two million infants missed their shots. And 179 00:11:37,120 --> 00:11:38,840 Speaker 2: it's not just musles vaccines. 180 00:11:39,160 --> 00:11:44,199 Speaker 1: Basic vaccinations for children such as DTP had declined and 181 00:11:44,240 --> 00:11:47,199 Speaker 1: that's resulting in a lot of the surges right now. 182 00:11:48,320 --> 00:11:52,080 Speaker 2: Meanwhile, COVID exacerbated other issues that can keep people sick. 183 00:11:52,280 --> 00:11:58,400 Speaker 1: There's also the social inequality caused indirectly by COVID policies. 184 00:11:58,880 --> 00:12:04,439 Speaker 1: So there are increasingly poorer communities living in crowded environments 185 00:12:04,760 --> 00:12:10,920 Speaker 1: and that prompts and potentially fuel disease circulating in the areas. 186 00:12:11,480 --> 00:12:14,079 Speaker 2: So those are all the culprits that are potentially related 187 00:12:14,080 --> 00:12:17,480 Speaker 2: to the pandemic and its aftermath. But as any mystery 188 00:12:17,520 --> 00:12:21,480 Speaker 2: fan knows, oftentimes the real villain is completely unrelated to 189 00:12:21,520 --> 00:12:25,520 Speaker 2: the obvious suspect. So I asked Jinshawn, were there any 190 00:12:25,640 --> 00:12:29,400 Speaker 2: other plot twists or culprits unrelated to the pandemic. 191 00:12:30,360 --> 00:12:32,720 Speaker 1: When we talked about climate change, we tend to think 192 00:12:32,720 --> 00:12:38,199 Speaker 1: about economic losses or risks to different kinds of countries 193 00:12:38,320 --> 00:12:41,480 Speaker 1: and people. But like in terms of diseases, now we 194 00:12:41,520 --> 00:12:45,880 Speaker 1: are seeing it playing out in multiple aspects, with more flooding, 195 00:12:46,200 --> 00:12:50,320 Speaker 1: with more extreme weather, with more warm weather. We are seeing, 196 00:12:50,400 --> 00:12:55,000 Speaker 1: for example, like Dean Gay, which relies on mosquitoes to 197 00:12:55,080 --> 00:12:59,160 Speaker 1: spread the disease is getting to more places because a 198 00:12:59,280 --> 00:13:04,360 Speaker 1: mosquitos where able to survive in previously colder environments. 199 00:13:05,160 --> 00:13:08,600 Speaker 2: So Jinchon, out of everything that we've talked about, what 200 00:13:08,880 --> 00:13:11,880 Speaker 2: did the experts tell you is the most likely reason 201 00:13:12,320 --> 00:13:14,319 Speaker 2: why sicknesses are surging? 202 00:13:14,840 --> 00:13:18,760 Speaker 1: They tell us it's a perfect storm, and it is. 203 00:13:18,920 --> 00:13:22,680 Speaker 2: A puzzle a perfect storm. What they mean is there's 204 00:13:22,720 --> 00:13:25,599 Speaker 2: not just one bad guy here. There's the disruption to 205 00:13:25,640 --> 00:13:29,520 Speaker 2: our immune systems, a rise in global poverty, climate change, 206 00:13:29,559 --> 00:13:33,520 Speaker 2: and a dip in childhood vaccination rates. It's that last one, 207 00:13:33,760 --> 00:13:38,160 Speaker 2: vaccines that many scientists and health researchers agreed is most compelling. 208 00:13:38,600 --> 00:13:42,000 Speaker 2: In the meantime, I asked Jineon what can we do 209 00:13:42,280 --> 00:13:45,920 Speaker 2: to stay healthier? I've been sick, You've been sick. Nobody 210 00:13:46,160 --> 00:13:49,800 Speaker 2: likes being sick. How can people at home buck the 211 00:13:49,840 --> 00:13:53,000 Speaker 2: global trend and stop getting sick all the time? 212 00:13:53,600 --> 00:13:57,720 Speaker 1: That's something I think people have been trying to find 213 00:13:57,720 --> 00:14:01,080 Speaker 1: a balance with. Do we need to continue a lot 214 00:14:01,120 --> 00:14:04,480 Speaker 1: of the measures that we started with COVID, for example, 215 00:14:04,480 --> 00:14:08,880 Speaker 1: wearing masks on public transport and buses when you feel unwell. 216 00:14:09,280 --> 00:14:12,480 Speaker 1: The answer from some experts that we talked to is 217 00:14:12,600 --> 00:14:18,560 Speaker 1: probably yes, because there's kind of a public fear for 218 00:14:18,920 --> 00:14:23,400 Speaker 1: doing those measures again because they make them look weird, like, 219 00:14:23,720 --> 00:14:27,040 Speaker 1: you know, COVID is over, why are you still wearing masks? 220 00:14:27,320 --> 00:14:29,640 Speaker 2: But actually, right, you're living in the past. 221 00:14:29,960 --> 00:14:32,280 Speaker 1: Yeah, are you living in the past. What are you 222 00:14:32,400 --> 00:14:35,440 Speaker 1: afraid of? But if you really feel sick, that might 223 00:14:35,600 --> 00:14:40,880 Speaker 1: help to spare your colleague from this particular disease that 224 00:14:40,920 --> 00:14:41,800 Speaker 1: you are going through. 225 00:14:42,840 --> 00:14:46,640 Speaker 2: So after months of research and data collection and creating 226 00:14:46,680 --> 00:14:49,440 Speaker 2: her map, Jinchon did not end up being able to 227 00:14:49,520 --> 00:14:53,240 Speaker 2: name any one offender. There was no kernel mustard in 228 00:14:53,280 --> 00:14:56,720 Speaker 2: the library with the candle to really hammer home the metaphor. 229 00:14:57,200 --> 00:15:01,880 Speaker 2: He likely had some accomplices. The culprits seem to include 230 00:15:01,920 --> 00:15:04,720 Speaker 2: all of us have slightly wonky immune systems after being 231 00:15:04,800 --> 00:15:07,720 Speaker 2: isolated and inside for a long time, though that might 232 00:15:07,760 --> 00:15:10,080 Speaker 2: be a little bit of a red herring. There's the 233 00:15:10,120 --> 00:15:14,560 Speaker 2: effects of the COVID virus itself, fewer people are getting vaccines, 234 00:15:15,320 --> 00:15:19,320 Speaker 2: and climate change the wild card that's causing disease spreading 235 00:15:19,320 --> 00:15:22,880 Speaker 2: agents like mosquitoes to move to different places. But my 236 00:15:23,040 --> 00:15:26,240 Speaker 2: main takeaway from Jinshan's research is that even as we 237 00:15:26,320 --> 00:15:28,960 Speaker 2: try to move our minds away from the days of COVID, 238 00:15:29,480 --> 00:15:32,240 Speaker 2: in a lot of ways, our bodies have not moved on, 239 00:15:32,840 --> 00:15:39,640 Speaker 2: at least not yet. This is the Big Take from 240 00:15:39,680 --> 00:15:43,280 Speaker 2: Bloomberg News. I'm Sarah Holder. This episode was produced by 241 00:15:43,320 --> 00:15:47,200 Speaker 2: Adriana Tapia. It was edited by Stacy Vanicksmith and Rachel Chang. 242 00:15:47,720 --> 00:15:50,560 Speaker 2: It was mixed by Veronica Rodriguez. It was fact checked 243 00:15:50,560 --> 00:15:54,360 Speaker 2: by Thomas lu Special thanks to Arafat Jolasho Perry. Our 244 00:15:54,400 --> 00:15:57,920 Speaker 2: senior producers are Kim Gittleson and Naomi Shadan. Our senior 245 00:15:58,040 --> 00:16:01,520 Speaker 2: editor is Elizabeth Ponso. Old Beamster bor Is. Our executive 246 00:16:01,520 --> 00:16:05,160 Speaker 2: producer Sage Bauman is Head of podcasts. Thanks so much 247 00:16:05,200 --> 00:16:08,000 Speaker 2: for listening. Please follow and review The Big Take wherever 248 00:16:08,040 --> 00:16:10,720 Speaker 2: you get your podcasts. It helps new listeners find the show. 249 00:16:11,280 --> 00:16:12,360 Speaker 2: We'll be back next week.