1 00:00:03,160 --> 00:00:07,880 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:09,000 --> 00:00:13,080 Speaker 2: Earlier this year, Bloomberg data reporter Jinshawn Honk got hung 3 00:00:13,160 --> 00:00:16,240 Speaker 2: up on this question. It was something she kept noticing 4 00:00:16,640 --> 00:00:20,799 Speaker 2: at work, at home, everywhere she went. She couldn't shake it. 5 00:00:21,040 --> 00:00:24,599 Speaker 1: Why does it seem like everyone everywhere like seems to 6 00:00:24,640 --> 00:00:26,439 Speaker 1: be getting sick all the time. 7 00:00:27,080 --> 00:00:30,920 Speaker 2: Jinshahn's colleagues in Hong Kong and their family members all 8 00:00:30,960 --> 00:00:34,680 Speaker 2: seemed to be getting sick. Jinshawn kept catching things too. 9 00:00:34,840 --> 00:00:36,239 Speaker 2: Everybody was talking about it. 10 00:00:37,080 --> 00:00:41,320 Speaker 3: I'm a person that rarely gets sick. I had pink eye, 11 00:00:41,800 --> 00:00:44,880 Speaker 3: my broat was swollen. I couldn't breathe through my nose. 12 00:00:45,159 --> 00:00:48,040 Speaker 3: It was the worst two months of my life. 13 00:00:48,520 --> 00:00:50,960 Speaker 2: Here at the Big Take, we've been hearing similar laments 14 00:00:51,000 --> 00:00:52,720 Speaker 2: from our family and friends all year. 15 00:00:53,360 --> 00:00:57,760 Speaker 3: My husband had COVID in September, my daughter and I 16 00:00:57,880 --> 00:01:00,920 Speaker 3: had stepped throat in October, RSV. 17 00:01:01,280 --> 00:01:05,000 Speaker 2: In November, it seemed like everybody had their own sickness story. 18 00:01:05,400 --> 00:01:06,240 Speaker 2: I had pneumonia. 19 00:01:06,520 --> 00:01:08,560 Speaker 1: It turns out I went to urging care twice. 20 00:01:08,920 --> 00:01:12,959 Speaker 2: It probably took a little about three weeks before I 21 00:01:12,959 --> 00:01:14,279 Speaker 2: started feeling human again. 22 00:01:14,560 --> 00:01:17,800 Speaker 3: In March, I had shingles at the age of twenty nine. 23 00:01:18,000 --> 00:01:20,080 Speaker 1: At first I thought it was allergies, but now I 24 00:01:20,120 --> 00:01:23,800 Speaker 1: think I'm sick. My nose is running uncontrollably, and I 25 00:01:23,840 --> 00:01:25,080 Speaker 1: feel achy and tired. 26 00:01:27,520 --> 00:01:31,240 Speaker 2: Back in Hong Kong, jinchn was hearing similar things. But 27 00:01:31,319 --> 00:01:34,679 Speaker 2: talk is cheap, and Jinchon is a data reporter, so 28 00:01:34,760 --> 00:01:37,280 Speaker 2: when she has a question, she doesn't just wonder about it, 29 00:01:37,640 --> 00:01:39,559 Speaker 2: she starts collecting data. 30 00:01:39,880 --> 00:01:43,000 Speaker 1: We decided to look into this and find out whether 31 00:01:43,040 --> 00:01:46,000 Speaker 1: it's just a perception issue or is there something really 32 00:01:46,040 --> 00:01:48,600 Speaker 1: going on that we should figure out for the public. 33 00:01:49,160 --> 00:01:52,760 Speaker 2: It was a mystery, so Jinchon and her team went 34 00:01:52,880 --> 00:01:57,520 Speaker 2: into detective mode, working with disease forecasters to gather case counts, 35 00:01:57,960 --> 00:02:01,280 Speaker 2: calling up doctors, combing through re search from all over 36 00:02:01,320 --> 00:02:05,320 Speaker 2: the world, and what they found was truly eye opening. 37 00:02:08,280 --> 00:02:11,040 Speaker 2: Today on the show, Grab your hand Sanitizer and your 38 00:02:11,120 --> 00:02:14,800 Speaker 2: n ninety five's for a data detective story, we joined 39 00:02:14,880 --> 00:02:19,400 Speaker 2: Jinshan as she scours the research for clues, culprits, correlations, 40 00:02:19,440 --> 00:02:23,120 Speaker 2: and causations as she takes on the case of why 41 00:02:23,240 --> 00:02:27,440 Speaker 2: everybody seems to be getting sick all the time. This 42 00:02:27,520 --> 00:02:30,359 Speaker 2: is the big take from Bloomberg News I'm Sarah Holder. 43 00:02:36,600 --> 00:02:40,680 Speaker 2: Tracking how sickness spreads is a massive data undertaking, and 44 00:02:40,720 --> 00:02:44,080 Speaker 2: to begin to understand how often it's spreading post COVID, 45 00:02:44,440 --> 00:02:47,679 Speaker 2: Bloomberg's Jinshawn Hang first had to narrow down a list 46 00:02:47,720 --> 00:02:50,840 Speaker 2: of illnesses to look at, so she enlisted the help 47 00:02:50,919 --> 00:02:54,440 Speaker 2: of a London based firm that forecast diseases worldwide called 48 00:02:54,560 --> 00:02:59,600 Speaker 2: air Affinity, and the help of her colleague Buma Shrivastava. Together, 49 00:03:00,000 --> 00:03:04,000 Speaker 2: we analyzed data from sixty public health agencies and organizations 50 00:03:04,080 --> 00:03:07,760 Speaker 2: like the WHO and UNSUFF and came up with a 51 00:03:07,840 --> 00:03:08,560 Speaker 2: grim list. 52 00:03:09,200 --> 00:03:13,400 Speaker 1: We were able to identify at least thirteen communicable diseases 53 00:03:13,520 --> 00:03:17,359 Speaker 1: that are surging in parts of the world that's above 54 00:03:17,440 --> 00:03:20,760 Speaker 1: pre pandemic waves and in some cases it surpassed the 55 00:03:20,800 --> 00:03:23,560 Speaker 1: pre pandemic peak by a significant margin. 56 00:03:24,280 --> 00:03:30,600 Speaker 2: These diseases included cholera, measles, tuberculosis, RSV, denay, and the flu. 57 00:03:31,400 --> 00:03:35,520 Speaker 2: But Jinjohn also wanted to know where these diseases were spiking. 58 00:03:35,400 --> 00:03:37,960 Speaker 1: So before the pandemic we were able to find out 59 00:03:38,000 --> 00:03:41,600 Speaker 1: the peak of every disease in every country between twenty 60 00:03:41,640 --> 00:03:45,320 Speaker 1: seventeen to twenty nineteen, and after COVID we have twenty 61 00:03:45,360 --> 00:03:48,320 Speaker 1: twenty two to twenty twenty four, also three year and 62 00:03:48,360 --> 00:03:51,240 Speaker 1: we find out a peak and compare the two whenever 63 00:03:51,360 --> 00:03:54,160 Speaker 1: we see a spike. Then we market on a map, 64 00:03:54,440 --> 00:03:57,280 Speaker 1: and with that we were able to identify regions where 65 00:03:57,320 --> 00:04:00,600 Speaker 1: certain diseases are surging more profoundly. 66 00:04:01,440 --> 00:04:05,400 Speaker 2: Now, this data wasn't completely exhaustive, but it did show 67 00:04:05,520 --> 00:04:09,280 Speaker 2: some notable trends. All thirteen of the diseases they tracked 68 00:04:09,320 --> 00:04:12,680 Speaker 2: had surged above post pandemic levels somewhere. 69 00:04:12,920 --> 00:04:15,600 Speaker 1: It may not be higher in every country, but then 70 00:04:15,840 --> 00:04:18,279 Speaker 1: we do see every one of them seem to be 71 00:04:18,480 --> 00:04:23,200 Speaker 1: showing up in a variety of geographies at higher levels. So, 72 00:04:23,240 --> 00:04:27,800 Speaker 1: for example, den gey is making a very strong resurgence 73 00:04:28,160 --> 00:04:33,440 Speaker 1: in Americas. We also have likenisos like spreading to about 74 00:04:33,440 --> 00:04:37,040 Speaker 1: twenty states in the US and other countries in Europe. 75 00:04:37,440 --> 00:04:41,600 Speaker 1: And we are also seeing tuberculesis is like really making 76 00:04:41,760 --> 00:04:45,680 Speaker 1: a lot of spikes in the developing world. And with that, 77 00:04:46,000 --> 00:04:50,520 Speaker 1: everyday common diseases like cold and flu and RSV are 78 00:04:50,560 --> 00:04:53,600 Speaker 1: also reported about pre pandemic levels. 79 00:04:53,839 --> 00:04:57,120 Speaker 2: Some of the surges are especially dramatic, and more than 80 00:04:57,160 --> 00:04:59,960 Speaker 2: forty places at least one of these diseases has seen 81 00:05:00,120 --> 00:05:03,720 Speaker 2: case counts leap tenfold or more from their pre pandemic baselines. 82 00:05:04,440 --> 00:05:07,640 Speaker 2: Influenza was up forty percent in the US during the 83 00:05:07,720 --> 00:05:11,680 Speaker 2: last two flu seasons compared to pre COVID levels. Besides 84 00:05:11,720 --> 00:05:14,520 Speaker 2: the health impacts and the strains to the medical system 85 00:05:14,600 --> 00:05:18,800 Speaker 2: this can create, there are also other economic impacts. All 86 00:05:18,839 --> 00:05:21,040 Speaker 2: those sick days are starting to add up. 87 00:05:21,240 --> 00:05:26,400 Speaker 1: We were looking through workplace research reports from places like 88 00:05:26,600 --> 00:05:31,520 Speaker 1: UK and the US, and there is more absenteesan with that, 89 00:05:31,560 --> 00:05:34,599 Speaker 1: we are seeing people reporting more sick days or taking 90 00:05:34,640 --> 00:05:38,880 Speaker 1: longer sick leaves from work. Maybe in the pre COVID years, 91 00:05:38,920 --> 00:05:41,640 Speaker 1: when you were a little sick, you'll still go out 92 00:05:41,720 --> 00:05:44,159 Speaker 1: and have a drink. It's like, Ah, I'm sick, but 93 00:05:44,200 --> 00:05:46,320 Speaker 1: I'm not sick to a degree that I cannot function. 94 00:05:46,680 --> 00:05:49,560 Speaker 1: I'll still go to work. But now I think we 95 00:05:49,600 --> 00:05:52,760 Speaker 1: are also more aware that, oh I feel a bit 96 00:05:52,880 --> 00:05:55,080 Speaker 1: sick today, maybe I shouldn't go to work. 97 00:05:55,520 --> 00:05:58,400 Speaker 2: That's one helpful lesson to come out of COVID. That's 98 00:05:58,440 --> 00:06:02,640 Speaker 2: staying home when you're sick curb infections. But illnesses are 99 00:06:02,680 --> 00:06:07,440 Speaker 2: spiking anyway, and that's concerning, especially because Ginean reminded me 100 00:06:07,839 --> 00:06:11,440 Speaker 2: the COVID nineteen pandemic was unprecedented, and so is our 101 00:06:11,440 --> 00:06:12,800 Speaker 2: post pandemic reality. 102 00:06:13,400 --> 00:06:18,480 Speaker 1: According to who's chief scientist Jeremy Farral, we are really 103 00:06:18,520 --> 00:06:23,800 Speaker 1: in a new place because the last devastating major pandemic 104 00:06:23,920 --> 00:06:27,599 Speaker 1: we had was in nineteen eighteen, which was so called 105 00:06:27,839 --> 00:06:31,680 Speaker 1: the Spanish Flu, and back then we were not having 106 00:06:31,880 --> 00:06:37,640 Speaker 1: as many vaccinations, diagnosis, or even treatment like at that time. 107 00:06:37,720 --> 00:06:40,320 Speaker 1: It was really a different stage. So what we are 108 00:06:40,360 --> 00:06:45,320 Speaker 1: facing right now is really an unparallel situation that scientists 109 00:06:45,320 --> 00:06:46,760 Speaker 1: are raising to understand. 110 00:06:47,520 --> 00:06:51,239 Speaker 2: Even though it was a massive data lift, Jinshan says 111 00:06:51,279 --> 00:06:54,000 Speaker 2: that figuring out that we were all getting sick more 112 00:06:54,040 --> 00:06:58,200 Speaker 2: often was actually the easy part coming up after the break, 113 00:06:58,600 --> 00:07:02,800 Speaker 2: unraveling the mystery of what's causing this spike in global illness. 114 00:07:10,960 --> 00:07:13,400 Speaker 2: If you think about this story as a big global 115 00:07:13,400 --> 00:07:17,560 Speaker 2: health mystery at this point, Bloomberg's Jinshawn Hank has identified 116 00:07:17,640 --> 00:07:20,920 Speaker 2: the say victims those of us who are getting sick 117 00:07:20,960 --> 00:07:25,400 Speaker 2: more often all around the world. But who or what 118 00:07:25,920 --> 00:07:29,880 Speaker 2: in this case is the culprit what's making everyone sick 119 00:07:30,000 --> 00:07:34,240 Speaker 2: in this post pandemic era. Well, Jinhawn told us there 120 00:07:34,280 --> 00:07:38,000 Speaker 2: are a few major theories floating around. I asked her 121 00:07:38,040 --> 00:07:47,840 Speaker 2: to introduce some of the prime suspects. So first, there's 122 00:07:47,880 --> 00:07:50,880 Speaker 2: this idea that we all lost our immunity because we 123 00:07:51,200 --> 00:07:54,360 Speaker 2: stayed home during the pandemic, there were quarantines, we weren't 124 00:07:54,400 --> 00:07:57,480 Speaker 2: being exposed to as many diseases. How much of that 125 00:07:57,920 --> 00:07:58,840 Speaker 2: is at play here? 126 00:08:00,360 --> 00:08:04,360 Speaker 1: That theory, which was at one point very leading theory 127 00:08:04,960 --> 00:08:09,640 Speaker 1: during the pandemic, is that it's called immunity debt, where 128 00:08:09,760 --> 00:08:15,880 Speaker 1: people became more susceptible to various infectious respiratory diseases because 129 00:08:15,920 --> 00:08:20,000 Speaker 1: they were not exposed to the pathogen during the lockdown years. 130 00:08:20,240 --> 00:08:24,600 Speaker 1: But that's still quite controversial among scientists that we talked to. 131 00:08:25,600 --> 00:08:28,160 Speaker 1: Some of them think there's not enough data yet to 132 00:08:28,280 --> 00:08:32,319 Speaker 1: prove it, and some other thing. Even if they make resurgence, 133 00:08:32,559 --> 00:08:35,600 Speaker 1: they are not supposed to be the size of the 134 00:08:35,640 --> 00:08:37,600 Speaker 1: spikes that we actually see today. 135 00:08:40,720 --> 00:08:44,720 Speaker 2: Well, this immunity debt theory is contested. Experts told Jinshn 136 00:08:44,800 --> 00:08:47,640 Speaker 2: that lockdowns could have contributed to the current spikes in 137 00:08:47,679 --> 00:08:52,080 Speaker 2: a different way. Babies who avoided catching respiratory diseases during 138 00:08:52,120 --> 00:08:57,040 Speaker 2: COVID quarantines and school closures maybe getting exposed and sick 139 00:08:57,360 --> 00:09:00,480 Speaker 2: for the first time as toddlers. 140 00:09:00,520 --> 00:09:04,400 Speaker 1: It's more like a delayed education to their immune system. 141 00:09:05,000 --> 00:09:09,240 Speaker 2: Delayed education. In other words, in the years since lockdowns ended, 142 00:09:09,480 --> 00:09:11,959 Speaker 2: more kids might now be getting sick all At the 143 00:09:12,000 --> 00:09:16,079 Speaker 2: same time, these kinds of COVID related delays are also 144 00:09:16,160 --> 00:09:19,160 Speaker 2: showing up in some countries mortality rates. 145 00:09:19,360 --> 00:09:23,120 Speaker 1: Some countries that used to control COVID very well during 146 00:09:23,160 --> 00:09:27,199 Speaker 1: the pandemic years seem to have higher or couse mortality 147 00:09:27,280 --> 00:09:31,240 Speaker 1: rates right now. So one theory they presented was that 148 00:09:31,679 --> 00:09:36,160 Speaker 1: because those countries were able to keep frail elderly people 149 00:09:36,520 --> 00:09:41,120 Speaker 1: live longer and keep them away from regularly circulating disease 150 00:09:41,280 --> 00:09:45,040 Speaker 1: that are usually common in the communities, so with that 151 00:09:45,080 --> 00:09:47,319 Speaker 1: they are now facing a higher death burden. 152 00:09:47,800 --> 00:09:51,120 Speaker 2: Another mystery doctors and scientists are trying to investigate is 153 00:09:51,120 --> 00:09:54,720 Speaker 2: the effect of COVID infections on people's longer term health. 154 00:09:55,120 --> 00:09:59,120 Speaker 2: Did you look into long COVID. Has COVID itself made 155 00:09:59,120 --> 00:10:01,800 Speaker 2: people more duptible to other illnesses? 156 00:10:02,160 --> 00:10:05,960 Speaker 1: Yeah, that is also a very heated topic that scientists 157 00:10:06,000 --> 00:10:11,120 Speaker 1: are looking into because COVID definitely has changes on some 158 00:10:11,200 --> 00:10:15,480 Speaker 1: people that's much more than the general public, But that's 159 00:10:15,520 --> 00:10:19,439 Speaker 1: still like relatively a smaller population compared to the general 160 00:10:19,640 --> 00:10:23,680 Speaker 1: public in terms of everybody. I think there's no proof 161 00:10:23,720 --> 00:10:27,120 Speaker 1: at this point, according to our interviews, that we are 162 00:10:27,200 --> 00:10:29,320 Speaker 1: becoming much weaker than before. 163 00:10:31,280 --> 00:10:33,960 Speaker 2: Our next suspect in the rise of illness across the 164 00:10:33,960 --> 00:10:36,560 Speaker 2: world the anti vaccine movement. 165 00:10:36,960 --> 00:10:40,400 Speaker 1: For example, One thing that went very rampant during COVID 166 00:10:41,000 --> 00:10:47,200 Speaker 1: was the misinformation the social media and how the information 167 00:10:47,280 --> 00:10:50,760 Speaker 1: got spread to many many people, and the mistrust of 168 00:10:50,880 --> 00:10:52,760 Speaker 1: vaccination seems to continue. 169 00:10:53,000 --> 00:10:57,120 Speaker 2: Vaccine hesitancy and misinformation, along with supply chain issues, have 170 00:10:57,240 --> 00:11:01,400 Speaker 2: led to a steep drop in childhood vaccination rate in Europe. 171 00:11:01,600 --> 00:11:05,480 Speaker 2: Musle's case suspect thirtyfold last year after a few years 172 00:11:05,559 --> 00:11:09,080 Speaker 2: where nearly two million infants missed their shots. And it's 173 00:11:09,120 --> 00:11:10,520 Speaker 2: not just Musles vaccines. 174 00:11:10,920 --> 00:11:15,920 Speaker 1: Basic vaccinations for children such as DTP had declined and 175 00:11:16,000 --> 00:11:18,960 Speaker 1: that's resulting in a lot of the surges right now. 176 00:11:20,040 --> 00:11:23,839 Speaker 2: Meanwhile, COVID exacerbated other issues that can keep people sick. 177 00:11:24,040 --> 00:11:30,160 Speaker 1: There's also the social inequality caused indirectly by COVID policies, 178 00:11:30,600 --> 00:11:36,160 Speaker 1: So there are increasingly poorer communities living in crowded environments 179 00:11:36,520 --> 00:11:42,600 Speaker 1: and that prompts and potentially fuel disease circulating in the areas. 180 00:11:43,200 --> 00:11:45,800 Speaker 2: So those are all the culprits that are potentially related 181 00:11:45,840 --> 00:11:49,240 Speaker 2: to the pandemic and its aftermath. But as any mystery 182 00:11:49,280 --> 00:11:53,200 Speaker 2: fan knows, oftentimes the real villain is completely unrelated to 183 00:11:53,240 --> 00:11:57,280 Speaker 2: the obvious suspect. So I asked Jinshan, were there any 184 00:11:57,400 --> 00:12:01,120 Speaker 2: other plot twists or culprits on r related to the pandemic. 185 00:12:02,080 --> 00:12:04,440 Speaker 1: When we talked about climate change, we tend to think 186 00:12:04,480 --> 00:12:09,959 Speaker 1: about economic losses or risks to different kinds of countries 187 00:12:10,040 --> 00:12:13,200 Speaker 1: and people. But like in terms of diseases, now we 188 00:12:13,240 --> 00:12:17,599 Speaker 1: are seeing it playing out in multiple aspects, with more flooding, 189 00:12:17,920 --> 00:12:22,080 Speaker 1: with more extreme weather, with more warm weather. We are seeing, 190 00:12:22,120 --> 00:12:26,760 Speaker 1: for example, like den Gay, which relies on mosquitoes to 191 00:12:26,840 --> 00:12:31,600 Speaker 1: spread the disease, it's getting to more places because mosquitoes 192 00:12:31,600 --> 00:12:36,000 Speaker 1: were able to survive in previously colder environments. 193 00:12:36,920 --> 00:12:40,360 Speaker 2: So turn, out of everything that we've talked about, what 194 00:12:40,600 --> 00:12:43,600 Speaker 2: did the experts tell you is the most likely reason 195 00:12:44,040 --> 00:12:46,040 Speaker 2: why sicknesses are surging? 196 00:12:46,600 --> 00:12:50,600 Speaker 1: They tell us it's a perfect storm, and it is. 197 00:12:50,640 --> 00:12:54,439 Speaker 2: A puzzle a perfect storm. What they mean is there's 198 00:12:54,440 --> 00:12:57,360 Speaker 2: not just one bad guy here. There's the disruption to 199 00:12:57,400 --> 00:13:01,280 Speaker 2: our immune systems, a rise in global poverty, climate change, 200 00:13:01,320 --> 00:13:06,080 Speaker 2: and a dip in childhood vaccination rates. It's that last one, vaccines, 201 00:13:06,320 --> 00:13:09,880 Speaker 2: that many scientists and health researchers agreed is most compelling. 202 00:13:10,320 --> 00:13:13,760 Speaker 2: In the meantime, I asked Jinshawn, what can we do 203 00:13:14,040 --> 00:13:17,680 Speaker 2: to stay healthier? I've been sick, You've been sick. Nobody 204 00:13:17,880 --> 00:13:21,560 Speaker 2: likes being sick. How can people at home buck the 205 00:13:21,559 --> 00:13:24,720 Speaker 2: global trend and stop getting sick all the time? 206 00:13:25,360 --> 00:13:29,440 Speaker 1: That's something I think people have been trying to find 207 00:13:29,440 --> 00:13:32,840 Speaker 1: a balance with. Do we need to continue a lot 208 00:13:32,880 --> 00:13:36,199 Speaker 1: of the measures that we started with COVID, for example, 209 00:13:36,240 --> 00:13:40,600 Speaker 1: wearing masks on public transport and buses when you feel unwell. 210 00:13:41,040 --> 00:13:44,240 Speaker 1: The answer from some experts that we talked to is 211 00:13:44,320 --> 00:13:50,320 Speaker 1: probably yes, because there's kind of a public fear for 212 00:13:50,640 --> 00:13:55,160 Speaker 1: doing those measures again because they make them look weird. Like, 213 00:13:55,480 --> 00:13:58,760 Speaker 1: you know, COVID is over, Why are you still wearing masks? 214 00:13:59,080 --> 00:14:01,360 Speaker 2: But actually, right, you're living in the past. 215 00:14:01,720 --> 00:14:04,760 Speaker 1: Yeah, are you living the pause? What are you afraid of? 216 00:14:05,240 --> 00:14:07,800 Speaker 1: But if you really feel sick, that might help to 217 00:14:08,320 --> 00:14:12,920 Speaker 1: spare your colleague from this particular disease that you are 218 00:14:12,960 --> 00:14:13,520 Speaker 1: going through. 219 00:14:14,600 --> 00:14:18,400 Speaker 2: So, after months of research and data collection and creating 220 00:14:18,440 --> 00:14:21,200 Speaker 2: her map. Jinhan did not end up being able to 221 00:14:21,280 --> 00:14:24,960 Speaker 2: name any one offender. There was no Kernel Mustard in 222 00:14:25,040 --> 00:14:28,480 Speaker 2: the library with the candle to really hammer home the metaphor. 223 00:14:28,960 --> 00:14:33,600 Speaker 2: He likely had some accomplices. The culprits seem to include 224 00:14:33,680 --> 00:14:36,520 Speaker 2: all of us have slightly wonky immune systems after being 225 00:14:36,560 --> 00:14:39,440 Speaker 2: isolated and inside for a long time, though that might 226 00:14:39,480 --> 00:14:41,840 Speaker 2: be a little bit of a red herring. There's the 227 00:14:41,840 --> 00:14:46,320 Speaker 2: effects of the COVID virus itself, fewer people are getting vaccines, 228 00:14:47,080 --> 00:14:51,040 Speaker 2: and climate change the wild card that's causing disease spreading 229 00:14:51,080 --> 00:14:54,640 Speaker 2: agents like mosquitoes to move to different places. But my 230 00:14:54,800 --> 00:14:57,960 Speaker 2: main takeaway from Jinshan's research is that even as we 231 00:14:58,080 --> 00:15:00,320 Speaker 2: try to move our minds away from the day of 232 00:15:00,320 --> 00:15:03,440 Speaker 2: COVID in a lot of ways, our bodies have not 233 00:15:03,600 --> 00:15:11,000 Speaker 2: moved on, at least not yet. This is the big 234 00:15:11,040 --> 00:15:14,560 Speaker 2: take from Bloomberg News. I'm Sarah Holder. This episode was 235 00:15:14,600 --> 00:15:18,040 Speaker 2: produced by Adriana Tapia. It was edited by Stacy Vanicksmith 236 00:15:18,120 --> 00:15:21,600 Speaker 2: and Rachel Chang. It was mixed by Veronica Rodriguez. It 237 00:15:21,640 --> 00:15:24,840 Speaker 2: was fact checked by Thomas lu Special thanks to Arafat 238 00:15:24,880 --> 00:15:28,880 Speaker 2: Jolasho Perry. Our senior producers are Kim Gettelson and Naomi Shaven. 239 00:15:29,200 --> 00:15:32,720 Speaker 2: Our senior editor is Elizabeth Ponso. Nicole beemsterbor is Our 240 00:15:32,760 --> 00:15:36,680 Speaker 2: executive producer. Sage Bauman is Head of podcasts. Thanks so 241 00:15:36,760 --> 00:15:39,240 Speaker 2: much for listening. Please follow and review The Big Take 242 00:15:39,360 --> 00:15:42,120 Speaker 2: wherever you get your podcasts. It helps new listeners find 243 00:15:42,160 --> 00:15:44,160 Speaker 2: the show. We'll be back next week.