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Speaker 1: How the extraordinaries. Just a quick heads up that in

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Speaker 1: today's episode, we're going to be talking about cholera, which

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Speaker 1: is a particularly unpleasant disease that still claims lives today

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Speaker 1: on with today's episode. Since eighteen seventeen, there have been

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Speaker 1: seven cholera pandemics. Many of us listening to this podcast

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Speaker 1: are probably lucky enough to be blissfully unaware that the

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Speaker 1: seventh pandemic is actually not over yet and continues to

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Speaker 1: claim lives in Sub Saharan Africa and Southeast Asia. The

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Speaker 1: World Health Organization or WHO, which the US officially withdrew

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Speaker 1: from recently, to my absolute dismay, reported that in twenty

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Speaker 1: twenty three, there were over half a million cases of

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Speaker 1: cholera and over four thousand deaths across forty five countries.

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Speaker 1: The WHO suspects that this is an underestimate, in part

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Speaker 1: because some regions are hesitant to report cholera because of

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Speaker 1: how that might impact trade in their region. The WHO

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Speaker 1: estimates that the numbers are probably closer to one point

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Speaker 1: three to four million cases of cholera, resulting in somewhere

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Speaker 1: between twenty one thousand and one hundred and forty three

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Speaker 1: thousand deaths annually. We're going to look at the state

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Speaker 1: of things today in the next episode. But today we're

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Speaker 1: going to go to the past, to a time when

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Speaker 1: people had no idea how cholera picked its victims, so

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Speaker 1: there was nothing you could do to protect yourself from

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Speaker 1: death visiting you and your family, and death's visits were

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Speaker 1: often short and to the point taking away entire families

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Speaker 1: in less than a week. Doubt put into perspective what

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Speaker 1: it was like living in a time before we knew

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Speaker 1: how cholera was transmitted and how to treat it. I'm

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Speaker 1: going to read to you a short passage from Stephen

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Speaker 1: john Since two thousand and six book The Ghost Map.

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Speaker 1: This book is also a major source that I drew

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Speaker 1: from while researching today's episode, and I definitely recommend it. Okay,

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Speaker 1: here's the passage. To live in such a world was

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Speaker 1: to live with the shadow of death hovering over your

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Speaker 1: shoulder at every moment. To live was to not be dead. Yet,

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Speaker 1: from our vantage point more than a century later, it's

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Speaker 1: hard to tell how heavily that fear weighed upon the

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Speaker 1: minds of individual victorians. As a matter of practical reality,

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Speaker 1: the threat of sudden devastation your entire extended family wiped

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Speaker 1: out in a matter of days was far more immediate

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Speaker 1: than the terror threats of today. At the height of

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Speaker 1: a nineteenth century cholera outbreak, one thousand Londoners would often

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Speaker 1: die of the disease in a matter of weeks, out

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Speaker 1: of a population that was a quarter of the size

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Speaker 1: of modern New York. Imagine the terror and panic if

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Speaker 1: a biological attack killed four thousand otherwise healthy New Yorkers

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Speaker 1: over a tw twenty day period. Living amid cholera in

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Speaker 1: eighteen fifty four was like living in a world where

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Speaker 1: urban tragedies on that scale happened week after week, year

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Speaker 1: after year, a world where it was not at all

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Speaker 1: out of the ordinary for an entire family to die

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Speaker 1: in the space of forty eight hours, children suffering alone

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Speaker 1: in the arsenic lit dark, next to the corpses of

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Speaker 1: their parents. Today, we're going to take a stroll through

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Speaker 1: the history of medicine, and in particular, we're going to

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Speaker 1: chat about doctor John Snow's amazing work showing that cholera

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Speaker 1: is transmitted through drinking contaminated water. This work played a

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Speaker 1: role in finally overturning the idea that cholera and a

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Speaker 1: bunch of other diseases were something that you caught by

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Speaker 1: breathing foul smelling air. This helped usher in the germ

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Speaker 1: theory of disease. Welcome to Daniel and Kelly's germ filled universe.

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Speaker 2: Hi. I'm Daniel. I'm a particle physicist, and I know

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Speaker 2: so many biologists that I've become desensitized to poop talk.

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Speaker 1: Hello. I'm Kelly Wainer Smith. I study parasites and space

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Speaker 1: and I can't even remember a time when poop talk

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Speaker 1: used to bother me anymore. How far gone I am.

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Speaker 2: I realized this recently when we were at dinner at friends'

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Speaker 2: houses and I started bringing up some pooper related stories

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Speaker 2: and Katrina was like giving me funny looks like that's

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Speaker 2: not appropriate, And then I realized, Wow, she has context

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Speaker 2: sensitivity to this, and I've lost it.

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Speaker 1: Oh no, oh no. Do you think that being a

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Speaker 1: physicist played a role, like lack of social skills sort

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Speaker 1: of thing?

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Speaker 2: Ooh wow, oh make it personal?

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Speaker 1: Sorry?

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Speaker 2: No. I think that she's just aware that she talks

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Speaker 2: about it at dinner, she talks about at work, but

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Speaker 2: she doesn't talk about it at friends dinners or out

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Speaker 2: in public and I've just totally lost that because I

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Speaker 2: hear about it all the time.

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Speaker 1: I got invited to give a talk at this like

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Speaker 1: Smithsonian magazine event, and they brought me out for like

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Speaker 1: a fancy dinner and a bunch of people were eating

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Speaker 1: lobster afterwards. And at the end of the dinner, I

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Speaker 1: looked around the table and a bunch of people still

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Speaker 1: had the lobster in front of them, and it took

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Speaker 1: me a second. I was like, oh, was the lobster

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Speaker 1: not delicious? And I asked one of the people was

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Speaker 1: my friend, And I was like, why didn't know I

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Speaker 1: eat the lobster? And they were They said, why did

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Speaker 1: you talk about parasites all dinner?

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Speaker 2: I'm so sorry.

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Speaker 1: I mean, you guys inviting me here to do that,

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Speaker 1: That's why I did. I'm sorry.

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Speaker 2: So you were like talking about parasites while stuffing your

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Speaker 2: face with shellfish? Nice?

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Speaker 1: I mean I don't eat shellfish. So I was eating

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Speaker 1: something else and it wasn't bothering me. And so anyway,

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Speaker 1: I do need to be reminded that not every conversation

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Speaker 1: is dinner conversation.

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Speaker 2: Well, today we're having a very context sensitive conversation.

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Speaker 1: Take it away, Kelly, well, today, hey, we are talking

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Speaker 1: about cholera. And since Daniel usually asks what does that

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Speaker 1: word come from? And usually I just stare at him

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Speaker 1: blankly and then have to jump onto Google.

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Speaker 2: Look at that. You had developed a little internal Daniel

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Speaker 2: to help you in your life.

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Speaker 1: That's right, what would Daniel do? Wwdd. So cholera comes

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Speaker 1: from the Greek word for bile. Very long ago, Gallan

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Speaker 1: was the one who popularized this idea that we had

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Speaker 1: to keep in balance the four humors in our bodies

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Speaker 1: and bile, which again the Greek word for it sounded

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Speaker 1: sort of like cholera. When you were having diarrhea or vomiting,

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Speaker 1: that was interpreted as your body trying to get back

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Speaker 1: into balance the right amount of bile in your body.

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Speaker 1: And so a long time ago, anytime you had a

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Speaker 1: diarrheal disease, people would say you had cholera. And over

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Speaker 1: time cholera became the quintessential diarrheal disease.

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Speaker 2: And now it's one specific one.

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Speaker 1: Yes, it is specifically the disease caused by Vibrio cholerad.

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Speaker 2: And is it like the champion diarrheal disease? Is it

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Speaker 2: like the most diuretic of all diar reeal diseases it is.

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Speaker 1: Yeah, yeah, unfortunately it wins that race.

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Speaker 2: Yeah yeah, that's sad. So why are we talking about

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Speaker 2: cholera today? What is it about cholera that you find extraordinary?

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Speaker 1: Well, there are some stories about cholera that I was

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Speaker 1: particularly interested in for another project that I'm working on,

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Speaker 1: and as I dug in, I was like, oh my gosh,

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Speaker 1: there's so much about cholera that I didn't know. And

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Speaker 1: to be honest, I'm like kind of a huge John

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Speaker 1: Snow fangirl, and this was my operation.

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Speaker 2: John Snow from Game of Thrones.

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Speaker 1: Uh no, no, actually, fun fact, have not watched Game

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Speaker 1: of Thrones nor read the books. One day. I feel like,

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Speaker 1: if I'm between projects and I have like a month

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Speaker 1: and a half, I want to watch all of the

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Speaker 1: all of the episodes, but that day may never come.

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Speaker 1: All right, but no, doctor John Snow, physician in London

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Speaker 1: in eighteen forty that John's real person, real person. Yep,

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Speaker 1: absolutely amazing, and I thought this was a great opportunity

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Speaker 1: to talk about his amazing experiments, which helped overturn this

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Speaker 1: idea that often we get sick because we're breathing in

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Speaker 1: stinky air, and there were multiple quote great stinks end

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Speaker 1: quote in Europe. There were two I think great stinks

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Speaker 1: in Paris and at least one great stink in London

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Speaker 1: because we were not very good at containing our sewage

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Speaker 1: for a long time there, and our cities were growing

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Speaker 1: like exponentially and we were not keeping up. And at

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Speaker 1: the same time, diseases were rampantly spreading because we were

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Speaker 1: just not doing a good job at containing our sewage.

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Speaker 1: There were lots of people, diseases were just bouncing between

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Speaker 1: these people, and we thought there was this correlation between

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Speaker 1: the stankiness in our cities and the fact that we

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Speaker 1: were all getting sick.

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Speaker 2: I see fascinating. Well, we've had some great stinks over here,

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Speaker 2: usually after I eat too much garlic for dinner, but

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Speaker 2: it doesn't usually get anybody like physically ill. Yes, But

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Speaker 2: I love this story because it's the triumph of like

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Speaker 2: data science and reason and rationality and solving problems. It's

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Speaker 2: like a triumph of empiricism, you know, penetrating all these

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Speaker 2: vague ideas and showing you like the data will tell

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Speaker 2: you the answer, right, the universe speaks to you through data.

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Speaker 2: It's wonderful. Yeah, So where does this start. How long

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Speaker 2: have people been suffering from cholera?

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Speaker 1: Yeah, so it's hard to know exactly. We have very

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Speaker 1: ancient records of people having absolutely horrific diarrheal diseases which

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Speaker 1: could be cholera, but we our best records on cholera

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Speaker 1: start around eighteen seventeen and even for the first handful

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Speaker 1: of pandemics, which is essentially when this disease goes global,

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Speaker 1: that's what we mean by a pandemic. We can't be

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Speaker 1: one hundred percent sure because nobody actually like looked, you know,

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Speaker 1: at the diarrhea under a microscope to actually identify the bacteria,

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Speaker 1: because this was before we knew that bacteria was the cause.

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Speaker 1: But based on symptoms, we think that around eighteen seventeen

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Speaker 1: this disease left India. So it was common in the

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Speaker 1: Yes region of India and it was what we call endemic,

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Speaker 1: which is like an area where you find this disease

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Speaker 1: over and over again, you know, seasonally for example. And

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Speaker 1: around eighteen seventeen it started spreading. And why it started spreading,

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Speaker 1: we think is because you know the world was becoming

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Speaker 1: a lot more connected. There were pilgrimages happening, there were

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Speaker 1: importantly the East India Company was doing a lot of

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Speaker 1: trade in the region. The British Navy was in there

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Speaker 1: and they were you know, bring in their boats and

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Speaker 1: then they were traveling all over the world. And what

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Speaker 1: sources you read will impact who is getting blamed for

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Speaker 1: bringing cholera. But at the end of the day, it

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Speaker 1: is our connectedness that is spreading cholera. And there have

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Speaker 1: been seven major pandemics where cholera started in India and

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Speaker 1: started spreading way far out there. The seventh pandemic is

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Speaker 1: actually still happening today.

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Speaker 2: Oh, I know, right, We're in the middle of a

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Speaker 2: cholera pandemic right now.

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Speaker 1: Yes, yeah, Sub Saharan Africa and some parts of Asia

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Speaker 1: still have a lot of cholera issues going on right now.

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Speaker 1: The World Health Organization is you know, working on trying

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Speaker 1: to tackle these problems. But yeah, we still have a

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Speaker 1: cholera pandemic going on right now.

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Speaker 2: And in each case, is it leaving India or are

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Speaker 2: there now other pockets that can spark pandemics.

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Speaker 1: It can start in other places, like the seventh pandemic

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Speaker 1: appears to have started in Indonesia, but now it is

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Speaker 1: you know, pretty common in some parts of Africa and

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Speaker 1: it's just kind of sticking around.

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Speaker 2: Fascinating. Wow. So probably people have been suffering from cholera

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Speaker 2: in India for thousands and thousands of years, and now

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Speaker 2: in our new modern, interconnected world, you can get colera anywhere.

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Speaker 1: Yeah. Yeah, at least hundreds, maybe even thousands of years,

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Speaker 1: it's been a problem in India. Yeah, and that's right.

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Speaker 2: So this has become a common experience around the world.

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Speaker 2: So let's share with our listeners what is it like

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Speaker 2: to have cholera? Kelly walk us through the experience.

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Speaker 1: It sounds absolutely horrible. A paper that I was reading

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Speaker 1: today says the cardinal manifestation of the disease is the

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Speaker 1: voluminous outpour of fluid. Oh and so yeah, essentially that

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Speaker 1: the deal here is you accidentally consume the bacteria, usually

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Speaker 1: in water. Sometimes it can be because like the fruits

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Speaker 1: are vegetables that you're eating, didn't like they got maybe

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Speaker 1: fertilized with water that had the color of bacteria, they

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Speaker 1: didn't get rinsed off, And then you eat it, and

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Speaker 1: you happen to eat a bunch of the bacteria, and

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Speaker 1: then maybe you'll start hearing borborygmy. Have you heard this

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Speaker 1: word before, Daniel.

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Speaker 2: I've never heard of bor burugny.

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Speaker 1: I hadn't either.

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Speaker 2: Apparently this is sounds like a Polish delicacy to me.

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Speaker 1: Apparently it's the gurgling sound that your tummy makes.

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Speaker 2: Oh no, just before it's like the warning noise just

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Speaker 2: before you explode.

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Speaker 1: No, it's just like generally the gurgling noise that your

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Speaker 1: tummy makes. But when you have chlora, you tend to

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Speaker 1: get a lot of those noises before the massive diarrhea onsets.

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Speaker 1: And like just think for a second. So like if

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Speaker 1: you lived in, for example, London in the eighteen fifties,

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Speaker 1: when like food sanityation standards were like nonexistent, you probably

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Speaker 1: got barberigby all the time, you know, And like, so

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Speaker 1: can you imagine every time your stomach or your child's

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Speaker 1: stomach went But I'm making a great face right now

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Speaker 1: for everybody wondering, can you imagine wondering like is this?

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Speaker 1: Am I about to get cholera? Like? Is this is

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Speaker 1: this the end? Like that must have been petrifying.

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Speaker 2: Do you think people in eighteen fifteen London were saying

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Speaker 2: the word borberger to each other?

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Speaker 3: I doubt it, you know, Like, you know.

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Speaker 2: Person one is like, like, are you having the bord

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Speaker 2: berrig me? I can't imagine that. It doesn't seem like

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Speaker 2: an English word.

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Speaker 1: Cheerio, chep, you got to touch at the bar brigby.

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Speaker 2: All right? So stage one or brig mey gone wild?

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Speaker 1: Yeah, okay, all right? So stage two is just massive

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Speaker 1: diarrhea that that can hit with essentially like no warning.

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Speaker 1: I was reading an eighteen ninety three description written by

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Speaker 1: a physician who worked in the United States, and he

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Speaker 1: worked two different cholera outbreaks, so he had quite a

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Speaker 1: bit of experience, and his description was it often happens

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Speaker 1: that without warning, a large water stool is evacuated and

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Speaker 1: the diarrhea sets in with sudden violence.

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Speaker 2: Violence is not a word do you ever want to

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Speaker 2: associate with stool?

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Speaker 1: No, no, absolutely not. He says. There's no pain or

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Speaker 1: anything like that. But it's fluid. Evacuation pours out noisily, and.

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Speaker 2: There's rather a relief there isn't there?

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Speaker 1: Yes, right from distension. I guess your tummy was, you know,

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Speaker 1: feeling like it really needed to let go of something.

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Speaker 1: And another characteristic is what they call rice water stools.

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Speaker 1: It's got these little white things in it and it

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Speaker 1: turns out those white things are the lining of your intestine,

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Speaker 1: which is being shed off in this massive quantity of stools.

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Speaker 1: And it comes on so suddenly that you often you

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Speaker 1: don't have a chance to get to the restroom wherever

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Speaker 1: you are when it happens, that's where you are, and

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Speaker 1: so clothing and bedding are stained, and you know it's

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Speaker 1: a very unpleasant thing.

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Speaker 2: Why did you skip over the juiciest detail?

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Speaker 1: What this is a look into Daniel's psychology? Well, I

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Speaker 1: thought that maybe the quote was too long, so I jumped.

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Speaker 1: What was the juiciest detail that I skipped? Daniel?

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Speaker 2: Well, this doctor describes it as thin lighting color, yeasty

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Speaker 2: in appearance, and with a mouse like odor.

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Speaker 1: Oh okay, yeah, mouse like odor is no good. I

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Speaker 1: don't like the smell of mice.

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Speaker 2: I don't know what that means. Like, Wow, your diarrhea

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Speaker 2: smells like mice. That's fascinating.

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Speaker 1: Oh see, I used to work with a lot of snakes,

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Speaker 1: and so I know exactly what a mouse like odor is.

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Speaker 1: Do you Is it hard for you to imagine a

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Speaker 1: mouse like odor?

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Speaker 2: We had rats, and so I know a little bit

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Speaker 2: about what rats smell like. Is it related?

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Speaker 1: Uh?

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Speaker 2: Is there a rodent aroma.

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Speaker 1: I think mice have a slightly different smell.

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Speaker 2: So there are no perfumes inspired by like the rodent aroma. No, no,

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Speaker 2: not a business idea.

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Speaker 1: No, I don't think so. I'm not going into business

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Speaker 1: with you.

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Speaker 2: All right. So basically, you have massive watery diarrhea. Right,

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Speaker 2: this isn't even stool. This is just like your body's

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Speaker 2: just dumping water. That's fascinating, right, Like what's happening inside

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Speaker 2: you chemically to make your body just like eject all

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Speaker 2: of its fluids.

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Speaker 1: Yeah, I'm going to make you wait because next week

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Speaker 1: you're going to talk about the science behind what the

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Speaker 1: bacteria is doing to make this happen.

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Speaker 2: It's just chemistry of cholera.

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Speaker 1: Yeah, that's right. But like very quickly you go from

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Speaker 1: having you know, like stools the color you would expect

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Speaker 1: to as you said, just it essentially being like clear

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Speaker 1: or white with these white like splotches of bits of

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Speaker 1: your intestine, like your epithelium, which is like you know,

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Speaker 1: the skin lining your intestine that have just sloughed off. Yeah,

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Speaker 1: and at the same time you also are vomiting. There's

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Speaker 1: just so much fluid that it's you know, starts coming

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Speaker 1: out of both ends. And the main problem that you

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Speaker 1: end up having here is that your blood starts to

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Speaker 1: get very thick because there's not enough water in it,

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Speaker 1: and it gets difficult for your heart to pump it

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Speaker 1: to all the places it needs to go, so you

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Speaker 1: stop getting oxygen to all the places that you need

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Speaker 1: to get the oxygen to. So organs starts shutting down.

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Speaker 1: You start like your skin starts sort of sinking in,

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Speaker 1: your pulse starts to slow, your heart needs to work

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Speaker 1: a lot harder, and essentially, in a lot of cases,

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Speaker 1: you go into a coma as your organs shut down,

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Speaker 1: and you die. And death can happen in some cases,

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Speaker 1: like in a day, like literally, you know, at midday

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Speaker 1: you can start having this like absolutely massive diarrhea. Adults

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Speaker 1: can lose something like twenty leaders.

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Speaker 2: Twenty leaders. That's twenty leaders, wow.

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Speaker 1: And that is why you die. Yeah, like in a day,

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Speaker 1: and you know they can be dead by the end

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Speaker 1: of the day. And you know, families can get this,

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Speaker 1: and you know you read stories about like the parents

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Speaker 1: dying and you know, like just they're hoping that they

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Speaker 1: can outlive their kids by a few hours and they don't,

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Speaker 1: and it's just absolutely tragic the way this disease very quickly,

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Speaker 1: you know, destroys families essentially just by dehydrating everybody very rapidly.

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Speaker 1: There was this quote in the book that I was

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Speaker 1: looking at that, like, you know, so he's describing just

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Speaker 1: how horrible it is to have this disease. And then

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Speaker 1: there's this moment of levity where he says, he describes

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Speaker 1: what some of his patients were doing in this state

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Speaker 1: of like near death. He's describing like the end stages.

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Speaker 1: So here's a quote he says, although lying thus in

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Speaker 1: the lowest state compatible with existence, sometimes his muscular strength

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Speaker 1: is preserved to a marvelous degree. Without pulse at the wrist,

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Speaker 1: patients have been known to get up unexpectedly and wander about,

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Speaker 1: and some in bed alongside each other have been heard

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Speaker 1: to make bets regarding the distance to which they could

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Speaker 1: eject the vomit and onto the dexterity with which they

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Speaker 1: could deposit it in a given receptacle. Just I love

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Speaker 1: the idea that our species is like, all right, this

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Speaker 1: is it, this is the end for me. But in

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Speaker 1: my last shot a vomit, I'm putting twenty I can

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Speaker 1: get it in that fucket. Yeah, I'm getting in that

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Speaker 1: pot over there, and like, you know, I love the

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Speaker 1: idea that, like I'm going out with the.

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Speaker 2: Last people can find joy even in suffering.

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Speaker 1: Yes, right, so anyway, go our species.

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Speaker 2: Well, it's amazing to me that people die so quickly,

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Speaker 2: because for a disease to spread, people have to like

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Speaker 2: live long enough to spread it. So it's fascinating to

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Speaker 2: me that it kills you so rapidly. Right, So let's

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Speaker 2: take a break, but when we come back, we're going

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Speaker 2: to hear more about cholera, specifically how it gets around.

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Speaker 2: All right, we're back and we're talking about the disease cholera,

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Speaker 2: which creates an extraordinary amount of diarrhea in it's suffering patients.

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Speaker 2: And next we're going to hear about how cholera goes

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Speaker 2: from person to person. How does it all work. When

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Speaker 2: we understood cholera was that pre germ theory or post

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Speaker 2: germ theory.

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Speaker 1: So this was pre wide acceptance of the germ theory.

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Speaker 1: And so at the time, we mostly thought that diseases

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Speaker 1: were caught by you know, like as we talked about

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Speaker 1: a little bit earlier, by stinky air. But we had

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Speaker 1: some sense that there could be contagious diseases. So for

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Speaker 1: a long time we knew that this disease called leprosy

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Speaker 1: was something that could pass from person to person. So

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Speaker 1: the idea of contagious stuff was something that we could handle. Also,

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Speaker 1: Van Lewin Hook, whose name I will never say correctly,

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Speaker 1: Let's have some Dutch listeners send in a correct pronunciation

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Speaker 1: for comparison. Okay, great, and we'll wait and we'll send

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Speaker 1: them to our audio engineer. And every time I have

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Speaker 1: to say it in the future, he could just pick

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Speaker 1: one of those and cover up my my voice. It's

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Speaker 1: in the future, okay. So in sixteen seventy three, so

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Speaker 1: like two hundred years earlier, he had looked under the

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Speaker 1: microscope and noted that there's a tiny microscopic world that

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Speaker 1: you know, none of us had seen before.

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Speaker 2: Amazing.

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Speaker 1: But this connection between that tiny microscopic world could make

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Speaker 1: us sick and kill us had not been made by

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Speaker 1: a lot of people. There were some people out there

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Speaker 1: who were maybe starting to think about it, but this

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Speaker 1: idea that stinky air could make people sick was just

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Speaker 1: really hard to shake and was much much, much, much

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Speaker 1: much more popular.

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Speaker 2: That's really fascinating. Do you think that revealing that microscopic

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Speaker 2: world also connected with like existing ideas about reductionism. You

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00:21:43,280 --> 00:21:47,320
Speaker 2: know that like our world at this scale is controlled

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Speaker 2: by the behavior at a smaller scale. It's sort of

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Speaker 2: in the same century as like atomic theory, which you

404
00:21:52,119 --> 00:21:54,679
Speaker 2: know leads to reductionism in particle physics. But do you

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Speaker 2: think that sort of thinking was widespread, that we're like

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Speaker 2: looking at the internal mechanisms of the world by seeing

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Speaker 2: the microscopic view.

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Speaker 1: No, I don't think that that had really taken hold

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Speaker 1: with the biology folks yet. Unfortunately, like you know, because

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00:22:08,760 --> 00:22:11,479
Speaker 1: the miasma theory was still so big, and it wouldn't

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Speaker 1: be until like I think eighteen ninety is when Cooke's

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Speaker 1: postulates came out, and Lister's antiseptic technique doesn't come out

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Speaker 1: until like something like a decade after John snow does

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Speaker 1: this experiment we're about to talk about, Like, I think

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Speaker 1: it took a while for those connections to be made.

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Speaker 2: It seems so obvious to us now, But I think

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Speaker 2: that's an example of how much modern philosophical assumptions we're

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Speaker 2: carrying with us all the time. And so when we

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Speaker 2: look back at what people were doing two hundred years ago,

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Speaker 2: like the connections and the conclusions seem obvious to us

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Speaker 2: in hindsight, but at the time like that's a big

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Speaker 2: ingredient in understanding how the world works. So anyway, it's fascinating.

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Speaker 2: So that means that in early days of cholera, we

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Speaker 2: didn't really understand the germ based mechanism for how this

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Speaker 2: disease spread.

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Speaker 1: I do think it's tempting to paint broad straight though

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Speaker 1: for an era, and you know, assume that everybody sort

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Speaker 1: of had the same ideas, and there were some people

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Speaker 1: in the era who were, you know, sort of thinking

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Speaker 1: along the lines that John Snow would be thinking. So

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Speaker 1: for example, I'm gonna I'm gonna jump ahead a little bit.

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Speaker 1: In the same year John Snow does his experiments, there's

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Speaker 1: an Italian guy named, uh, Filipo Puccini. My apologies to

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Speaker 1: every Italian out there because I.

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Speaker 2: Probably sounded correct.

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Speaker 1: Oh yeah, nice, okay, all right, At the University of

437
00:23:27,600 --> 00:23:31,159
Speaker 1: Florence in eighteen fifty four he publishes the paper microscople

438
00:23:32,320 --> 00:23:37,359
Speaker 1: and that's where I blow it. No, I'm not gonna

439
00:23:37,359 --> 00:23:39,879
Speaker 1: I'm not gonna go back. I'm moving forward. Observations and

440
00:23:39,920 --> 00:23:45,240
Speaker 1: pathological deductions on cholera. He actually identifies the cholera bacteria

441
00:23:45,280 --> 00:23:47,680
Speaker 1: and publishes on it, but the rest of the community

442
00:23:47,800 --> 00:23:50,800
Speaker 1: was not ready to move past the miasma theory, and

443
00:23:50,840 --> 00:23:54,400
Speaker 1: so for thirty years his paper is essentially just totally ignored.

444
00:23:55,080 --> 00:23:57,240
Speaker 1: So like, there are people out there, were other people

445
00:23:57,280 --> 00:23:59,320
Speaker 1: out there, like this idea was in the air that

446
00:23:59,400 --> 00:24:02,920
Speaker 1: like germs could be causing diseases, but like as a whole,

447
00:24:03,000 --> 00:24:05,760
Speaker 1: the community was just not really ready to embrace this idea.

448
00:24:06,200 --> 00:24:08,159
Speaker 1: But you know, John Snow's paper is part of what

449
00:24:08,240 --> 00:24:09,360
Speaker 1: helps turn the tide.

450
00:24:09,840 --> 00:24:12,200
Speaker 2: Yeah, that's a really important point that there's a huge

451
00:24:12,240 --> 00:24:16,000
Speaker 2: diversity of thinking and ideas, right, and sometimes you can

452
00:24:16,040 --> 00:24:19,040
Speaker 2: go back and trace the origins of a modern idea

453
00:24:19,440 --> 00:24:23,720
Speaker 2: on the fringes of academia or you know, mainstream thought

454
00:24:23,840 --> 00:24:26,480
Speaker 2: two hundred years ago. Really cool. Yeah, right, So tell

455
00:24:26,520 --> 00:24:29,360
Speaker 2: us about John Doe and what he was doing. John

456
00:24:29,640 --> 00:24:37,560
Speaker 2: about John Snow Jeff when he wasn't killing the white walkers, Uh.

457
00:24:36,960 --> 00:24:40,640
Speaker 1: Whatever that means? All right, So John Snowe was at

458
00:24:40,640 --> 00:24:42,280
Speaker 1: the time of the outbreak we're going to be talking about.

459
00:24:42,359 --> 00:24:45,000
Speaker 1: John snow was a forty two year old physician. He

460
00:24:45,640 --> 00:24:48,960
Speaker 1: was a vegetarian and a teetotaler, so, like some would say,

461
00:24:49,000 --> 00:24:51,119
Speaker 1: maybe not a ton of fun, but he seems like

462
00:24:51,160 --> 00:24:53,720
Speaker 1: a cool guy to me. He had made his name

463
00:24:54,119 --> 00:24:58,200
Speaker 1: by doing a bunch of experiments on ether and chloroform.

464
00:24:58,280 --> 00:25:01,920
Speaker 1: So we did that episode on anesthesia and how anesthesia

465
00:25:01,960 --> 00:25:06,360
Speaker 1: sort of came along in the eighteen thirties eighteen forties,

466
00:25:06,760 --> 00:25:09,920
Speaker 1: So when it came out, there was this massive problem

467
00:25:09,960 --> 00:25:12,320
Speaker 1: with like sometimes it worked, sometimes it didn't, And John

468
00:25:12,359 --> 00:25:14,320
Speaker 1: snow realized that probably had a lot to do with

469
00:25:14,400 --> 00:25:16,760
Speaker 1: like temperature and how it was being delivered. So he

470
00:25:16,800 --> 00:25:20,720
Speaker 1: did a bunch of very careful experiments and created these

471
00:25:20,960 --> 00:25:23,160
Speaker 1: charts where like, when the temperature is this, you should

472
00:25:23,200 --> 00:25:25,239
Speaker 1: give them this much. And he created a device to

473
00:25:25,280 --> 00:25:28,680
Speaker 1: try to standardize how much ether and chloroform people got

474
00:25:28,680 --> 00:25:31,919
Speaker 1: so that they could always be anestatized the right amount.

475
00:25:32,240 --> 00:25:35,280
Speaker 1: And he actually became so famous for being great at

476
00:25:35,320 --> 00:25:38,800
Speaker 1: anestatizing people that he was able to give chloroform to

477
00:25:38,880 --> 00:25:40,840
Speaker 1: the Queen when she was giving birth to one of

478
00:25:40,880 --> 00:25:43,840
Speaker 1: her kids, and so he became sort of famous for this.

479
00:25:43,960 --> 00:25:46,760
Speaker 1: And despite being a teetotaler, he did a bunch of

480
00:25:46,800 --> 00:25:51,160
Speaker 1: experiments on himself with chloroform and ether, So he wasn't

481
00:25:51,280 --> 00:25:55,080
Speaker 1: drinking alcohol, but he was playing around with the chloroform

482
00:25:55,160 --> 00:25:57,400
Speaker 1: quite a bit, and we think that this actually might

483
00:25:57,440 --> 00:26:01,639
Speaker 1: have led to his somewhat early demise unfortunately, but this

484
00:26:01,840 --> 00:26:05,080
Speaker 1: is how he made a name for himself.

485
00:26:05,280 --> 00:26:07,879
Speaker 2: Well, I've watched this show Victoria, which is about the

486
00:26:07,920 --> 00:26:11,120
Speaker 2: life and times of Queen Victoria, and John snow makes

487
00:26:11,119 --> 00:26:14,800
Speaker 2: an appearance in that series quite prominently about the cholera outbreak.

488
00:26:14,920 --> 00:26:17,639
Speaker 2: But they also portray him in a very interesting manner.

489
00:26:18,200 --> 00:26:21,160
Speaker 2: He's like awkward. I think he has a stutter. He's

490
00:26:21,320 --> 00:26:25,680
Speaker 2: really not a very charismatic personality as portrayed by the BBC.

491
00:26:25,920 --> 00:26:27,040
Speaker 2: I don't know if that's accurate.

492
00:26:27,200 --> 00:26:30,680
Speaker 1: I don't get the sense that he's a super charismatic

493
00:26:30,720 --> 00:26:33,679
Speaker 1: person to be honest, but you know, not all of

494
00:26:33,760 --> 00:26:38,120
Speaker 1: us are super charismatic. Doesn't mean we're not making good contributions, Daniel.

495
00:26:41,040 --> 00:26:43,159
Speaker 2: I'm just saying you sort of described him in a

496
00:26:43,200 --> 00:26:45,000
Speaker 2: way that might make people think he was like, you know,

497
00:26:45,080 --> 00:26:48,440
Speaker 2: swashbuckling or extravagant in some way. But no, I think

498
00:26:48,440 --> 00:26:50,720
Speaker 2: he really was a data nerd, wasn't he.

499
00:26:50,359 --> 00:26:52,399
Speaker 1: He was a massive nerd. Yeah, which is awesome.

500
00:26:52,720 --> 00:26:54,960
Speaker 2: I mean that in the best possible way, right, it's

501
00:26:55,000 --> 00:26:56,080
Speaker 2: not highest compliment.

502
00:26:56,640 --> 00:26:59,080
Speaker 1: Yep, no, totally, yeah, No, I don't think he was cool.

503
00:26:59,080 --> 00:27:02,080
Speaker 1: I think this guy was straight up nerd. But he's

504
00:27:02,080 --> 00:27:05,520
Speaker 1: a straight up nerd that changed the world. So all

505
00:27:05,520 --> 00:27:06,280
Speaker 1: hail the nerds.

506
00:27:06,520 --> 00:27:08,800
Speaker 2: Yes, the nerds can change the world. Thank you to

507
00:27:08,800 --> 00:27:09,840
Speaker 2: all the nerds out there.

508
00:27:09,960 --> 00:27:13,200
Speaker 1: Amen. All right, So he makes this observation in eighteen

509
00:27:13,240 --> 00:27:17,600
Speaker 1: forty eight, So kolera had spread through Europe, but then

510
00:27:17,640 --> 00:27:19,040
Speaker 1: for a couple of years it had sort of like

511
00:27:19,040 --> 00:27:21,199
Speaker 1: disappeared from Europe, and then all of a sudden it

512
00:27:21,240 --> 00:27:25,920
Speaker 1: shows up in Hamburg. And then this boat leaves Hamburg

513
00:27:26,520 --> 00:27:30,879
Speaker 1: and shows up in London, and then suddenly kolera shows

514
00:27:30,960 --> 00:27:35,239
Speaker 1: up in this hotel in one room that has a

515
00:27:35,280 --> 00:27:38,000
Speaker 1: sailor that was on that ship from Hamburg.

516
00:27:38,200 --> 00:27:41,720
Speaker 2: I got to ask, is Hamburg the German pronunciation or

517
00:27:41,720 --> 00:27:44,639
Speaker 2: the Kelly pronunciation, because most Americans say Hamburg.

518
00:27:45,200 --> 00:27:48,919
Speaker 1: I don't know. I can't be like Hamburg like Hamburger,

519
00:27:48,960 --> 00:27:49,440
Speaker 1: could it?

520
00:27:49,480 --> 00:27:49,840
Speaker 2: Is it?

521
00:27:50,359 --> 00:27:51,840
Speaker 1: That just seems too American? King?

522
00:27:51,960 --> 00:27:54,120
Speaker 2: I don't know. I mean, that's what Americans usually say

523
00:27:54,160 --> 00:27:55,800
Speaker 2: when they're talking to each other. But I wasn't sure

524
00:27:55,840 --> 00:27:58,879
Speaker 2: if you were being all, you know, like correct German pronunciation.

525
00:27:59,320 --> 00:28:01,199
Speaker 1: I think I'm over compensating because I was in the

526
00:28:01,200 --> 00:28:04,160
Speaker 1: Middle East recently and I kept saying Iran and everyone

527
00:28:04,480 --> 00:28:09,240
Speaker 1: would be like Iran Americans, and I was like, oh,

528
00:28:09,480 --> 00:28:12,600
Speaker 1: I guess you guys don't say Iran over here. But

529
00:28:13,520 --> 00:28:14,560
Speaker 1: so you think it's Hamburg.

530
00:28:14,960 --> 00:28:17,119
Speaker 2: I mean, I think Americans say Hamburg. I don't know

531
00:28:17,119 --> 00:28:18,320
Speaker 2: what Germans say to each other.

532
00:28:18,440 --> 00:28:22,479
Speaker 1: All right, Well, that German city, there was a German

533
00:28:22,560 --> 00:28:26,080
Speaker 1: city that Colera popped up in, and a boat left

534
00:28:26,119 --> 00:28:29,240
Speaker 1: with sailors from that German city showed up in London.

535
00:28:29,920 --> 00:28:33,560
Speaker 1: One of those sailors had rented a room, got Calera

536
00:28:33,920 --> 00:28:35,680
Speaker 1: died in that room before.

537
00:28:35,359 --> 00:28:36,639
Speaker 2: He could invent the hamburger.

538
00:28:37,119 --> 00:28:40,440
Speaker 1: That's right, I know, no, thank goodness, somebody else did,

539
00:28:40,520 --> 00:28:43,880
Speaker 1: because what would Americans be without their Hamburgers. And then

540
00:28:43,920 --> 00:28:47,000
Speaker 1: sometime later somebody else ends up in that room and

541
00:28:47,120 --> 00:28:50,920
Speaker 1: they get Calera and they die. And that's despite the

542
00:28:50,960 --> 00:28:53,800
Speaker 1: fact that there hadn't been Colera in London for a

543
00:28:53,880 --> 00:28:56,960
Speaker 1: really long time, right, Okay, And so John Snow thinks

544
00:28:56,960 --> 00:29:01,120
Speaker 1: to himself, this is not good for the diasthma theory, right,

545
00:29:01,200 --> 00:29:08,640
Speaker 1: because why would cholera jump from that German city that

546
00:29:08,680 --> 00:29:12,320
Speaker 1: I'm not going to say again ever, and then all

547
00:29:12,360 --> 00:29:14,840
Speaker 1: of a sudden end up in London in this one

548
00:29:14,960 --> 00:29:19,600
Speaker 1: room where the German sailor was like, he didn't bring

549
00:29:19,680 --> 00:29:21,880
Speaker 1: the bad air with him, right, Doesn't it seem much

550
00:29:21,880 --> 00:29:25,640
Speaker 1: more likely that there was some other way that the

551
00:29:25,720 --> 00:29:30,120
Speaker 1: German brought the disease with him to London and then

552
00:29:30,400 --> 00:29:33,760
Speaker 1: left it behind and gave it to somebody else. And

553
00:29:33,800 --> 00:29:36,080
Speaker 1: he's like, I don't necessarily know how he gave it

554
00:29:36,120 --> 00:29:38,719
Speaker 1: to somebody else. And there was a doctor that visited

555
00:29:38,760 --> 00:29:40,760
Speaker 1: both men and he didn't get it, and so that's

556
00:29:40,760 --> 00:29:42,720
Speaker 1: a little concerning. I'm not really sure what happened there.

557
00:29:42,760 --> 00:29:45,080
Speaker 1: But also the doctor should have gotten it if it

558
00:29:45,160 --> 00:29:47,920
Speaker 1: was bad air that happened to be in that hotel room.

559
00:29:48,080 --> 00:29:49,760
Speaker 1: And so he's like, all right, I feel like the

560
00:29:49,800 --> 00:29:52,840
Speaker 1: miasma theory isn't really explaining things. We need something else.

561
00:29:53,240 --> 00:29:56,440
Speaker 2: So the thing I don't understand about the miasma theory

562
00:29:57,160 --> 00:30:00,240
Speaker 2: is if it's just in the air and and it's

563
00:30:00,240 --> 00:30:02,720
Speaker 2: not like moving around, it's that connected to the place,

564
00:30:03,280 --> 00:30:07,000
Speaker 2: how in that theory does color ever spread from India

565
00:30:07,080 --> 00:30:10,640
Speaker 2: or whatever to Europe? Right, Like, doesn't that theory fundamentally

566
00:30:10,680 --> 00:30:12,240
Speaker 2: conflict with their observations.

567
00:30:12,520 --> 00:30:14,680
Speaker 1: Oh man, Okay, this could be a really long answer.

568
00:30:14,720 --> 00:30:16,240
Speaker 1: I'll try to keep it short. So all right, So

569
00:30:16,280 --> 00:30:19,320
Speaker 1: it depends on the time and the place. So like

570
00:30:19,640 --> 00:30:22,560
Speaker 1: way back, like when Hippocrates or like you know the

571
00:30:22,560 --> 00:30:24,680
Speaker 1: many people who go by the name Hippocrates, were like

572
00:30:24,680 --> 00:30:27,880
Speaker 1: writing about disease, they would say something to the effect of, like,

573
00:30:27,920 --> 00:30:30,640
Speaker 1: if you want to understand disease, you need to understand

574
00:30:31,040 --> 00:30:34,080
Speaker 1: which way the winds are blowing and how that changes seasonally.

575
00:30:34,240 --> 00:30:38,200
Speaker 1: Because the winds were blowing diseases in from different places,

576
00:30:38,720 --> 00:30:41,560
Speaker 1: and so understanding how the winds were blowing would impact

577
00:30:41,560 --> 00:30:44,719
Speaker 1: what diseases people would get depending on what season. And

578
00:30:44,800 --> 00:30:48,640
Speaker 1: so this idea sort of morphed over time and depending

579
00:30:48,680 --> 00:30:51,840
Speaker 1: on where you were and when the exact specifics would change.

580
00:30:51,840 --> 00:30:54,920
Speaker 1: But I found a report from eighteen fifty which was

581
00:30:54,960 --> 00:30:59,160
Speaker 1: written by the health professionals living in London, reporting on

582
00:30:59,440 --> 00:31:03,040
Speaker 1: the epidemic in eighteen forty eight and eighteen forty nine.

583
00:31:03,200 --> 00:31:05,440
Speaker 1: So this is when John Snow was like starting to

584
00:31:05,480 --> 00:31:08,360
Speaker 1: collect his data, and eighteen fifty four is when he

585
00:31:08,400 --> 00:31:10,600
Speaker 1: really like collected the data. You know, the broad Street

586
00:31:10,600 --> 00:31:13,200
Speaker 1: pump data and so this is like the time and

587
00:31:13,240 --> 00:31:16,680
Speaker 1: place that's sort of relevant to your question. And they

588
00:31:16,680 --> 00:31:19,200
Speaker 1: were saying that what mattered was that like the winds

589
00:31:19,280 --> 00:31:22,680
Speaker 1: blowing out of India were like hot and moist, and

590
00:31:22,720 --> 00:31:25,200
Speaker 1: so that's what was blowing the cholera and that it

591
00:31:25,280 --> 00:31:28,120
Speaker 1: happened to be a time when you know, first the

592
00:31:28,120 --> 00:31:30,600
Speaker 1: winds that were blowing into Russia were hot and moist,

593
00:31:30,640 --> 00:31:32,600
Speaker 1: and then they were blowing into these other regions. So

594
00:31:32,680 --> 00:31:35,840
Speaker 1: essentially they were saying, hot moist air was taking cholera

595
00:31:36,120 --> 00:31:39,360
Speaker 1: on this journey. But and this is kind of amazing.

596
00:31:40,080 --> 00:31:42,560
Speaker 1: After doing this like long explanation of how the winds

597
00:31:42,600 --> 00:31:46,160
Speaker 1: had brought cholera you know, around the world, then they say,

598
00:31:46,640 --> 00:31:49,840
Speaker 1: but also, you know, Scotland had colera in January and

599
00:31:49,880 --> 00:31:53,160
Speaker 1: it was really cold moving on and then they out

600
00:31:53,960 --> 00:31:55,920
Speaker 1: and then they don't deal with it, and then they

601
00:31:56,280 --> 00:31:59,680
Speaker 1: say they start talking about how like it's complicated, right,

602
00:31:59,720 --> 00:32:02,440
Speaker 1: so you have to have the wind blowing cholera in,

603
00:32:03,120 --> 00:32:06,240
Speaker 1: but then it needs to meet a population that has

604
00:32:06,320 --> 00:32:10,320
Speaker 1: been impacted by something, and so like foul air could

605
00:32:10,320 --> 00:32:12,280
Speaker 1: be the thing. So they talk about, for example, if

606
00:32:12,320 --> 00:32:15,920
Speaker 1: people live in an area where there's lots of people

607
00:32:16,680 --> 00:32:19,280
Speaker 1: for few square feets. So for example, if you have

608
00:32:19,440 --> 00:32:22,120
Speaker 1: like twenty people in a small room, they're going to

609
00:32:22,160 --> 00:32:25,800
Speaker 1: start breathing out bad air, like decaying matter as they

610
00:32:25,800 --> 00:32:28,720
Speaker 1: put it, and that is going to start to poison

611
00:32:28,720 --> 00:32:31,120
Speaker 1: the air. And the poison air that you're breathing in

612
00:32:31,320 --> 00:32:34,960
Speaker 1: is going to make you more susceptible when cholera comes through.

613
00:32:35,720 --> 00:32:38,680
Speaker 1: And it was really interesting. There's like this motivated reasoning

614
00:32:38,680 --> 00:32:40,880
Speaker 1: that they find. So, for example, there was a girls

615
00:32:40,960 --> 00:32:43,920
Speaker 1: school where the girls got sick and a boys' school

616
00:32:43,920 --> 00:32:46,880
Speaker 1: where the boys didn't, and they claimed that that's because

617
00:32:46,920 --> 00:32:50,000
Speaker 1: the boys kept breaking the windows in their school and

618
00:32:50,120 --> 00:32:53,000
Speaker 1: they were getting better ventilation than the girls and that's

619
00:32:53,000 --> 00:32:55,320
Speaker 1: why they didn't get it, because they weren't breathing in

620
00:32:55,360 --> 00:32:57,520
Speaker 1: as much poison vapors as the girls were.

621
00:32:58,760 --> 00:33:01,400
Speaker 2: So this is fascinating because I'm trying to understand the

622
00:33:01,440 --> 00:33:04,880
Speaker 2: difference essentially in the two theories. John Snow is thinking

623
00:33:04,920 --> 00:33:09,240
Speaker 2: it's basically transmitted from person to person by something created

624
00:33:09,280 --> 00:33:11,680
Speaker 2: by the person and then transmitted through the water, and

625
00:33:11,720 --> 00:33:14,360
Speaker 2: the contrasting theory is that it's not really person a person,

626
00:33:14,400 --> 00:33:17,320
Speaker 2: it's just like comes through the air but now there's

627
00:33:17,320 --> 00:33:20,320
Speaker 2: this other part of it where people can make it

628
00:33:20,400 --> 00:33:23,040
Speaker 2: more likely that you get it from the air if

629
00:33:23,040 --> 00:33:26,680
Speaker 2: you're also breeding out poison vapors. So in the miasma theory,

630
00:33:26,720 --> 00:33:30,160
Speaker 2: it's not that they're breathing out cholera and passing it

631
00:33:30,200 --> 00:33:34,000
Speaker 2: to each other in their breath. It's that they are

632
00:33:34,000 --> 00:33:36,400
Speaker 2: making it more likely that the other girls are going

633
00:33:36,480 --> 00:33:40,080
Speaker 2: to get it by being cooped up with their poisonous vapors.

634
00:33:40,200 --> 00:33:42,360
Speaker 1: Right, that's so complicated, and it's very complicate. I mean,

635
00:33:42,400 --> 00:33:45,160
Speaker 1: you could also get those poisonous vapors by living above

636
00:33:45,360 --> 00:33:49,560
Speaker 1: assess pits, or by living too close to a graveyard

637
00:33:50,080 --> 00:33:52,040
Speaker 1: at a time when the graveyard is stinky. Like, the

638
00:33:52,040 --> 00:33:53,720
Speaker 1: graveyard has to be stinky. It can't be like a

639
00:33:53,760 --> 00:33:57,280
Speaker 1: graveyard from three hundred years ago where everybody has like

640
00:33:57,560 --> 00:34:00,080
Speaker 1: long sin skeletonized. It needs to be a stinky.

641
00:33:59,760 --> 00:34:02,800
Speaker 2: Grip or a graveyard that smells wonderful.

642
00:34:02,720 --> 00:34:05,240
Speaker 1: Yeah, right, or a graveyard with lots of flowers or something.

643
00:34:05,280 --> 00:34:07,800
Speaker 1: But but you know it's interesting because then they'll say, like,

644
00:34:08,200 --> 00:34:11,440
Speaker 1: but you know, there was this other recent color pandemic

645
00:34:11,520 --> 00:34:16,640
Speaker 1: where it seemed to only hit healthy, middle class people

646
00:34:16,760 --> 00:34:20,640
Speaker 1: who weren't living in bad conditions, moving on.

647
00:34:20,880 --> 00:34:23,400
Speaker 2: Yeah, And like, I guess this is like the epicycles

648
00:34:23,400 --> 00:34:25,680
Speaker 2: of cholera, right, It's like you have this idea, you

649
00:34:25,719 --> 00:34:27,279
Speaker 2: want to make a fit to the data. You keep

650
00:34:27,320 --> 00:34:30,279
Speaker 2: adding bits and pieces to it and whirly gigs until

651
00:34:30,320 --> 00:34:32,480
Speaker 2: eventually you just look, this isn't working and we have

652
00:34:32,560 --> 00:34:34,080
Speaker 2: to tear it down and start from something else.

653
00:34:34,160 --> 00:34:34,359
Speaker 3: Yeah.

654
00:34:34,360 --> 00:34:36,560
Speaker 1: But so so this report was written by Edwin Chadwick,

655
00:34:36,560 --> 00:34:38,840
Speaker 1: who was like the guy in charge of like public

656
00:34:38,880 --> 00:34:41,960
Speaker 1: health decisions in London at the time, and he, for

657
00:34:42,040 --> 00:34:44,279
Speaker 1: a long time, even after John Snow like came out

658
00:34:44,280 --> 00:34:47,560
Speaker 1: with his big theory, would continue to push the miasma theory.

659
00:34:47,760 --> 00:34:50,120
Speaker 1: There was even a section where the report noted that

660
00:34:50,160 --> 00:34:52,879
Speaker 1: there were people who were sharing the same air and

661
00:34:53,200 --> 00:34:56,120
Speaker 1: they differed in the water that they were drinking, and

662
00:34:56,360 --> 00:34:59,800
Speaker 1: one group was drinking water that you know, appeared to

663
00:34:59,840 --> 00:35:02,400
Speaker 1: have given them cholera because that group got cholera, and

664
00:35:02,480 --> 00:35:05,280
Speaker 1: another group drinking a different drinking water from a different

665
00:35:05,320 --> 00:35:08,200
Speaker 1: source didn't get calera. And it was as though he

666
00:35:08,280 --> 00:35:11,759
Speaker 1: was trying to support John Snow's theory, and then he

667
00:35:11,920 --> 00:35:15,680
Speaker 1: just moved on without like, you know, digging into it anymore.

668
00:35:15,680 --> 00:35:18,440
Speaker 1: And so there's all of these inconsistencies in the reports,

669
00:35:18,840 --> 00:35:20,480
Speaker 1: but at the end of the day, the guy who

670
00:35:20,520 --> 00:35:24,520
Speaker 1: writes the report continues to be a miasma person, making

671
00:35:24,920 --> 00:35:29,160
Speaker 1: massive and important public health decisions in support of trying

672
00:35:29,160 --> 00:35:32,319
Speaker 1: to get stinks out of the area, even if those

673
00:35:32,360 --> 00:35:35,200
Speaker 1: decisions result in more sewage in the Thames, which is

674
00:35:35,239 --> 00:35:36,960
Speaker 1: what is actually contributing to cholera.

675
00:35:37,200 --> 00:35:37,439
Speaker 2: Wow.

676
00:35:37,440 --> 00:35:40,359
Speaker 1: And so anyway, the miasma theory is sort of inconsistent.

677
00:35:40,719 --> 00:35:43,120
Speaker 1: Has to do with the wind and the stinks and

678
00:35:43,160 --> 00:35:46,759
Speaker 1: maybe some other stuff that we can't explain. And so yeah,

679
00:35:46,760 --> 00:35:49,920
Speaker 1: the answer is it's not a very like internally consistent theory.

680
00:35:51,400 --> 00:35:53,680
Speaker 2: Yeah. And it's tempting to judge these folks and be like,

681
00:35:53,760 --> 00:35:55,759
Speaker 2: how could you believe this when the data was saying

682
00:35:55,800 --> 00:35:58,360
Speaker 2: the opposite, But it's so easy to do that in

683
00:35:58,480 --> 00:36:01,279
Speaker 2: hindsight when we know the truth and it's clear. Yeah,

684
00:36:01,320 --> 00:36:03,160
Speaker 2: and you know, they're at the forefront of human knowledge.

685
00:36:03,160 --> 00:36:04,880
Speaker 2: They don't understand. They were doing their best to make

686
00:36:04,960 --> 00:36:07,480
Speaker 2: sense of the data and nobody's perfect, so it's easy

687
00:36:07,480 --> 00:36:09,320
Speaker 2: to criticize, but who knows what we would say in

688
00:36:09,320 --> 00:36:09,719
Speaker 2: their shops?

689
00:36:09,760 --> 00:36:12,000
Speaker 1: Yeah, I mean absolutely, And this was an era, you know,

690
00:36:12,120 --> 00:36:14,600
Speaker 1: before the germ theory really was established. And you know,

691
00:36:14,640 --> 00:36:17,239
Speaker 1: you get a school where the windows were broken and

692
00:36:17,280 --> 00:36:19,719
Speaker 1: the kids were fine, and another where they weren't. Like

693
00:36:19,760 --> 00:36:23,560
Speaker 1: I can totally get thinking like, okay, maybe maybe ventilation

694
00:36:23,719 --> 00:36:26,880
Speaker 1: did matter. And I don't know. I don't judge them

695
00:36:26,880 --> 00:36:28,919
Speaker 1: too harshly, but it is a bummer that they didn't

696
00:36:28,920 --> 00:36:31,040
Speaker 1: listen to John Snow because people died.

697
00:36:31,280 --> 00:36:35,000
Speaker 2: So then basically John Snow's big idea is that somehow

698
00:36:35,040 --> 00:36:38,160
Speaker 2: the disease is moving with the person, right that it's

699
00:36:38,200 --> 00:36:40,920
Speaker 2: coming with him, He's carrying something with him, and it

700
00:36:41,040 --> 00:36:44,279
Speaker 2: can then go from person to person. And his hypothesis

701
00:36:44,320 --> 00:36:45,920
Speaker 2: is maybe it's water based.

702
00:36:46,239 --> 00:36:48,319
Speaker 1: Yes, cool. And he had also had a little bit

703
00:36:48,320 --> 00:36:51,719
Speaker 1: of prior experience with cholera because he in the prior pandemic,

704
00:36:51,719 --> 00:36:53,400
Speaker 1: he had been training as a physician, and he had

705
00:36:53,440 --> 00:36:57,920
Speaker 1: worked with some coal miners and they weren't experiencing the

706
00:36:58,040 --> 00:37:00,440
Speaker 1: right kind of bad air that was popped is an

707
00:37:00,480 --> 00:37:04,040
Speaker 1: idea for transmitting cholera at the time, and they happened

708
00:37:04,080 --> 00:37:06,319
Speaker 1: to be working in an area where their food and

709
00:37:06,400 --> 00:37:08,759
Speaker 1: their water was mixing with human waste while they were

710
00:37:08,800 --> 00:37:11,239
Speaker 1: down in the coal mines. And that was getting his

711
00:37:11,360 --> 00:37:14,959
Speaker 1: mind on this idea of like cantamination as a way

712
00:37:15,080 --> 00:37:18,320
Speaker 1: of transmitting cholera. And so at this point he starts

713
00:37:18,360 --> 00:37:21,600
Speaker 1: to get water on the brain, and so to follow

714
00:37:21,680 --> 00:37:24,440
Speaker 1: up on this idea, he collects two sets of data

715
00:37:24,480 --> 00:37:27,480
Speaker 1: in eighteen forty nine to start getting this water borne

716
00:37:27,480 --> 00:37:28,280
Speaker 1: idea of rolling.

717
00:37:28,600 --> 00:37:30,440
Speaker 2: I think this is a really important moment here actually,

718
00:37:30,480 --> 00:37:34,120
Speaker 2: because he doesn't have enough evidence to conclude that it's

719
00:37:34,160 --> 00:37:36,279
Speaker 2: water based or it's a contaminant. He just kind of

720
00:37:36,320 --> 00:37:38,360
Speaker 2: got a hunch, right, and turns out he's right, and

721
00:37:38,400 --> 00:37:41,120
Speaker 2: he does the experiments which prove it. But your hunch

722
00:37:41,200 --> 00:37:43,680
Speaker 2: doesn't have to be but your hunch doesn't always have

723
00:37:43,760 --> 00:37:46,800
Speaker 2: to be like based in evidence. Right. Science is about

724
00:37:47,040 --> 00:37:49,680
Speaker 2: taking these leaps and asking questions and then backing it

725
00:37:49,760 --> 00:37:51,840
Speaker 2: up with data. Right. But there are important moments of

726
00:37:51,960 --> 00:37:53,360
Speaker 2: just like kind of guesswork.

727
00:37:53,600 --> 00:37:57,160
Speaker 1: Yeah, Well, in this first moment was just him thinking, well,

728
00:37:57,200 --> 00:37:59,799
Speaker 1: wait a minute, the prominent idea of the time right

729
00:37:59,840 --> 00:38:03,080
Speaker 1: now now just doesn't make sense with this observation, So

730
00:38:03,200 --> 00:38:06,840
Speaker 1: what else might make sense? And so like these first

731
00:38:06,880 --> 00:38:09,319
Speaker 1: two mini experiments that he does that we're not even

732
00:38:09,320 --> 00:38:11,960
Speaker 1: talking about the big Broad Street pump experiment. Yet these

733
00:38:12,000 --> 00:38:14,839
Speaker 1: are just like little places where he dips his toe

734
00:38:14,920 --> 00:38:17,920
Speaker 1: in to be like, could something else make sense? And

735
00:38:18,000 --> 00:38:20,080
Speaker 1: so the first thing he did was he went to

736
00:38:20,239 --> 00:38:24,040
Speaker 1: essentially like a slum in London in eighteen forty nine,

737
00:38:24,080 --> 00:38:26,759
Speaker 1: and he finds this row of cottages that are all

738
00:38:26,760 --> 00:38:30,160
Speaker 1: connected and they all share a well and there's like

739
00:38:30,239 --> 00:38:34,319
Speaker 1: a crack and that allows essentially sewage to seep into

740
00:38:34,400 --> 00:38:37,840
Speaker 1: the well when it gets too rainy, and everybody in

741
00:38:37,880 --> 00:38:41,640
Speaker 1: those cottages had died of cholera. And then right nearby

742
00:38:41,760 --> 00:38:45,080
Speaker 1: he finds like the exact same setup of cottages, but

743
00:38:45,280 --> 00:38:48,439
Speaker 1: their well is more sound and when rain comes they

744
00:38:48,520 --> 00:38:51,520
Speaker 1: don't get sewage in their will and only one person

745
00:38:51,760 --> 00:38:55,760
Speaker 1: in that row of cottages got cholera. And so he's like, well,

746
00:38:55,800 --> 00:38:58,000
Speaker 1: it looks like you get sewage in the well, and

747
00:38:58,040 --> 00:38:59,720
Speaker 1: then you drink from the well, you get colera.

748
00:39:00,080 --> 00:39:02,479
Speaker 2: So he's solid theory, poopy water kills you.

749
00:39:02,440 --> 00:39:05,759
Speaker 1: Poopy water death, and so he's so he collects those

750
00:39:05,840 --> 00:39:07,640
Speaker 1: data and then the next set of data that he

751
00:39:07,680 --> 00:39:10,920
Speaker 1: collects looks at the Thames. So the Thames is the

752
00:39:10,920 --> 00:39:12,560
Speaker 1: big river that flows through London.

753
00:39:12,760 --> 00:39:14,759
Speaker 2: Correctly, the way.

754
00:39:14,840 --> 00:39:20,680
Speaker 1: I've been to London I'm sure if I hadn't, I

755
00:39:20,719 --> 00:39:22,320
Speaker 1: would have said Sames or something.

756
00:39:22,440 --> 00:39:27,359
Speaker 2: But if you had, we would make you famous for it.

757
00:39:27,400 --> 00:39:30,080
Speaker 1: Ah. Thanks. Uh yeah, No, I definitely saw it on

758
00:39:30,120 --> 00:39:31,960
Speaker 1: a map and then heard someone say it and had

759
00:39:31,960 --> 00:39:35,080
Speaker 1: a like had a moment where id I was like,

760
00:39:35,080 --> 00:39:38,040
Speaker 1: I'm going to be quiet until I convinced myself that

761
00:39:38,040 --> 00:39:40,839
Speaker 1: these are the same things. And anyway, okay, so so

762
00:39:41,120 --> 00:39:44,280
Speaker 1: uh based on where the sewage was being emptied into

763
00:39:44,320 --> 00:39:47,560
Speaker 1: the Thames, and the Thames ended up being the source

764
00:39:47,600 --> 00:39:50,480
Speaker 1: of the Great Stink years later because so much sewage

765
00:39:50,560 --> 00:39:53,319
Speaker 1: was emptied into it that it like killed the fisheries

766
00:39:53,440 --> 00:39:56,160
Speaker 1: and stuff like that. So anyway, based on where the

767
00:39:56,160 --> 00:39:59,839
Speaker 1: sewage was being emptied into the Thames, John Snow hypothesized

768
00:39:59,880 --> 00:40:03,560
Speaker 1: that that casualties from cholera should be much higher and

769
00:40:03,680 --> 00:40:06,160
Speaker 1: the South part of the Thames for people that are

770
00:40:06,200 --> 00:40:08,040
Speaker 1: getting the water from the south part of the Thames,

771
00:40:08,440 --> 00:40:10,160
Speaker 1: then if you're getting it from the north, And what

772
00:40:10,239 --> 00:40:12,359
Speaker 1: he found was that if you are getting your water

773
00:40:12,400 --> 00:40:14,680
Speaker 1: from the South part of the Thames, eight out of

774
00:40:14,680 --> 00:40:17,520
Speaker 1: a thousand people are dying there, and if you get

775
00:40:17,520 --> 00:40:19,480
Speaker 1: it from the north or the west, one out of

776
00:40:19,480 --> 00:40:21,040
Speaker 1: a thousand people are dying.

777
00:40:20,800 --> 00:40:23,040
Speaker 2: The idea being that the north has less poop in

778
00:40:23,080 --> 00:40:24,920
Speaker 2: it because it's like upstream.

779
00:40:24,880 --> 00:40:28,839
Speaker 1: Yep, exactly. But if you live on the East End

780
00:40:29,040 --> 00:40:32,160
Speaker 1: of London, the East End is an area that was

781
00:40:32,239 --> 00:40:35,400
Speaker 1: like super destitute and super stinky, right, So if you

782
00:40:35,400 --> 00:40:39,000
Speaker 1: believe in the miasma theory, that's where most people should

783
00:40:39,000 --> 00:40:42,800
Speaker 1: be dying, but they should maybe have an intermediate amount

784
00:40:42,840 --> 00:40:45,959
Speaker 1: of sewage in their water. And they only had four

785
00:40:46,040 --> 00:40:48,719
Speaker 1: out of a thousand people dying from cholera, and so

786
00:40:48,880 --> 00:40:52,840
Speaker 1: he's arguing that like, this is actually pretty good data

787
00:40:53,000 --> 00:40:56,279
Speaker 1: suggesting that this it's that cholera is coming from the

788
00:40:56,320 --> 00:40:57,839
Speaker 1: water and not coming from the air.

789
00:40:58,080 --> 00:41:00,520
Speaker 2: Because the death rate follows the river. The more downstream

790
00:41:00,560 --> 00:41:02,080
Speaker 2: you are, the more likely you are to.

791
00:41:02,040 --> 00:41:05,960
Speaker 1: Die, exactly right, So it follows that like the sewage gredients.

792
00:41:06,560 --> 00:41:09,920
Speaker 1: So he publishes these results. The reviews are pretty positive,

793
00:41:10,800 --> 00:41:12,959
Speaker 1: but they're also pretty skeptical, and so there are people

794
00:41:12,960 --> 00:41:16,200
Speaker 1: who are saying, like, look, you found a correlation, but

795
00:41:16,280 --> 00:41:17,760
Speaker 1: you haven't actually found a cause.

796
00:41:18,200 --> 00:41:20,800
Speaker 2: Good points, solid critique, that's.

797
00:41:20,600 --> 00:41:23,680
Speaker 1: True, and so the London Medical Gazette in particular says,

798
00:41:23,719 --> 00:41:26,200
Speaker 1: you know what we'd really love to see we would

799
00:41:26,280 --> 00:41:29,880
Speaker 1: love to see you find a case where somebody takes

800
00:41:30,040 --> 00:41:33,960
Speaker 1: water that's contaminated and goes way far away and then

801
00:41:34,040 --> 00:41:36,400
Speaker 1: drinks it and then gets sick, like way far away

802
00:41:36,400 --> 00:41:39,000
Speaker 1: from what could have been the source of contaminated air,

803
00:41:39,520 --> 00:41:42,040
Speaker 1: and then get sick and dies. Yeah, and we'd also

804
00:41:42,160 --> 00:41:44,560
Speaker 1: like to see some people who are in an area

805
00:41:44,600 --> 00:41:47,200
Speaker 1: where there could be bad air, but they didn't drink

806
00:41:47,239 --> 00:41:50,360
Speaker 1: the water and they don't die. That kind of stuff

807
00:41:50,440 --> 00:41:53,200
Speaker 1: might convince us that it's not miasma that happens to

808
00:41:53,200 --> 00:41:56,120
Speaker 1: be in the same area that's actually causing the problem.

809
00:41:56,400 --> 00:41:58,879
Speaker 2: I agree these are all insightsive experiments, but they feel

810
00:41:58,920 --> 00:42:01,959
Speaker 2: so unethical to me because we're talking about people dying.

811
00:42:02,080 --> 00:42:04,879
Speaker 1: Right, But he's not doing these experiments. They're like, if

812
00:42:04,880 --> 00:42:08,080
Speaker 1: you could get observational data that eat these criteria, And

813
00:42:08,160 --> 00:42:10,160
Speaker 1: so John Snow is like, oh my gosh, where am

814
00:42:10,160 --> 00:42:12,040
Speaker 1: I going to get these observational data? Right? This is

815
00:42:12,040 --> 00:42:15,120
Speaker 1: going to be really hard and so and this is amazing.

816
00:42:15,120 --> 00:42:18,640
Speaker 1: This is like two geeks in the night meeting together

817
00:42:18,719 --> 00:42:22,320
Speaker 1: in magic happening. So he goes to the London Registrar General.

818
00:42:22,960 --> 00:42:24,759
Speaker 1: This is like the guy who keeps track of the

819
00:42:24,760 --> 00:42:28,280
Speaker 1: birth and death stats, and this guy is a stats nerd,

820
00:42:28,719 --> 00:42:32,160
Speaker 1: and so nerd another nerd, and his job was just

821
00:42:32,160 --> 00:42:33,800
Speaker 1: to keep track of like when people are born and

822
00:42:33,840 --> 00:42:35,719
Speaker 1: when people die. But he was like, you know what,

823
00:42:35,800 --> 00:42:38,400
Speaker 1: these data could be so much more useful if I

824
00:42:38,480 --> 00:42:41,600
Speaker 1: knew more stuff, And so he started encouraging doctors to

825
00:42:41,600 --> 00:42:44,479
Speaker 1: not just tell him when someone died, but why they died,

826
00:42:44,760 --> 00:42:47,440
Speaker 1: how old they are, what was their occupation, where did

827
00:42:47,440 --> 00:42:50,720
Speaker 1: they live. And after talking to John Snow, he starts

828
00:42:50,800 --> 00:42:54,280
Speaker 1: also collecting information on where they were getting their water,

829
00:42:55,360 --> 00:42:58,839
Speaker 1: which was amazingly useful because Londoners were getting their water

830
00:42:59,000 --> 00:43:02,120
Speaker 1: from like a hand of different companies that had pipes

831
00:43:02,160 --> 00:43:05,640
Speaker 1: that were like crisscrossing their way under London, and so

832
00:43:05,800 --> 00:43:08,279
Speaker 1: you know, you could live in a house and get

833
00:43:08,280 --> 00:43:10,440
Speaker 1: your water from one company and next door could get

834
00:43:10,480 --> 00:43:13,800
Speaker 1: their water from a totally different company. And these companies

835
00:43:13,840 --> 00:43:17,040
Speaker 1: had very different standards for how they treated their water

836
00:43:17,080 --> 00:43:19,439
Speaker 1: where the water came from. But there were two main

837
00:43:19,520 --> 00:43:21,719
Speaker 1: companies that were servicing an area that was kind of

838
00:43:21,800 --> 00:43:25,880
Speaker 1: near near where Snow lived. One of the companies had

839
00:43:26,080 --> 00:43:28,719
Speaker 1: upgraded where they were sucking the water from so that

840
00:43:28,760 --> 00:43:31,200
Speaker 1: it was less likely to be sucking in sewage and

841
00:43:31,280 --> 00:43:35,520
Speaker 1: the other one hadn't done that yet. And these companies

842
00:43:35,560 --> 00:43:38,400
Speaker 1: also differed in how saline the water was, and so

843
00:43:38,440 --> 00:43:41,120
Speaker 1: you could tell the difference between the water by doing

844
00:43:41,120 --> 00:43:43,080
Speaker 1: a water test. And one of them was a little

845
00:43:43,160 --> 00:43:46,200
Speaker 1: saltier than the other, and so it was possible to

846
00:43:46,400 --> 00:43:49,399
Speaker 1: know who was getting what kind of water, either by

847
00:43:49,440 --> 00:43:51,680
Speaker 1: asking who do you pay your water bill to or

848
00:43:51,800 --> 00:43:53,799
Speaker 1: by taking a water sample and checking to see how

849
00:43:53,880 --> 00:43:58,240
Speaker 1: much salt was in it. And with this setup, John

850
00:43:58,320 --> 00:44:04,080
Speaker 1: Snow five years later, when a massive outbreak came a

851
00:44:04,120 --> 00:44:07,080
Speaker 1: couple blocks away from his home, he was prepared to

852
00:44:07,120 --> 00:44:10,840
Speaker 1: collect the data that would show that cholera was a

853
00:44:10,880 --> 00:44:11,840
Speaker 1: water board disease.

854
00:44:12,120 --> 00:44:14,319
Speaker 2: And when we come back from break, we'll hear all

855
00:44:14,480 --> 00:44:17,120
Speaker 2: about how nerds save the world.

856
00:44:17,400 --> 00:44:34,760
Speaker 3: Nerds.

857
00:44:37,880 --> 00:44:40,640
Speaker 2: All right, we're back, and we're hearing the story of cholera,

858
00:44:40,960 --> 00:44:44,040
Speaker 2: how it kills you, how it spreads, and how nerds

859
00:44:44,080 --> 00:44:47,200
Speaker 2: figure it all out. So is John Snow at this

860
00:44:47,320 --> 00:44:49,839
Speaker 2: moment when he has his idea and he's waiting for

861
00:44:49,880 --> 00:44:52,840
Speaker 2: his data, is he still like a real outlier in

862
00:44:52,880 --> 00:44:55,760
Speaker 2: the community. Nobody's really taking it seriously because it feels

863
00:44:55,800 --> 00:44:59,240
Speaker 2: like if this is a possibility, even then somebody should

864
00:44:59,280 --> 00:45:01,960
Speaker 2: like do something about it, you know, work hard on

865
00:45:02,000 --> 00:45:05,120
Speaker 2: reducing the poopy water that people are drinking, because it's

866
00:45:05,160 --> 00:45:06,520
Speaker 2: we're talking about lots of lives.

867
00:45:07,000 --> 00:45:08,960
Speaker 1: Yeah, he's still kind of an outlier, like the main

868
00:45:09,000 --> 00:45:11,520
Speaker 1: people who are in charge of, like the public health

869
00:45:11,640 --> 00:45:14,920
Speaker 1: are much more concerned about the stinkiness. So at the moment,

870
00:45:15,239 --> 00:45:17,920
Speaker 1: the way waste is sort of managed in London is

871
00:45:18,040 --> 00:45:20,160
Speaker 1: a lot of people have what are called cess pits

872
00:45:20,280 --> 00:45:24,080
Speaker 1: underneath their houses, and so you know, poop in a

873
00:45:24,080 --> 00:45:26,120
Speaker 1: bowl and then you dump it in a pit underneath

874
00:45:26,120 --> 00:45:28,479
Speaker 1: your house, and when that pit fills up, you pay

875
00:45:28,520 --> 00:45:31,920
Speaker 1: somebody to come scoop it out and haul it away,

876
00:45:32,160 --> 00:45:34,600
Speaker 1: which is part of why everything stinks a lot. And

877
00:45:34,680 --> 00:45:37,040
Speaker 1: so to try to get rid of that stink, people

878
00:45:37,120 --> 00:45:40,000
Speaker 1: are starting to create a sewage system that pipes it

879
00:45:40,040 --> 00:45:43,640
Speaker 1: all into the Thames. And that if you believe that

880
00:45:43,800 --> 00:45:48,720
Speaker 1: stink causes cholera, dumping the waste into the Thames sounds

881
00:45:48,760 --> 00:45:54,400
Speaker 1: like a good idea. But if water borne contamination causes cholera,

882
00:45:55,160 --> 00:45:59,520
Speaker 1: you are killing people by pumping waste into the Thames. Yeah,

883
00:45:59,640 --> 00:46:03,000
Speaker 1: And so at the moment they are changing the system

884
00:46:03,080 --> 00:46:06,200
Speaker 1: and essentially dumping the waste into the Thames instead of

885
00:46:06,320 --> 00:46:11,040
Speaker 1: using this cesspit system. So yes, snow is pushing against

886
00:46:11,040 --> 00:46:12,080
Speaker 1: the tide at the moment.

887
00:46:12,680 --> 00:46:13,279
Speaker 2: Fascinating.

888
00:46:13,560 --> 00:46:15,879
Speaker 1: All right, all right, So it turns out that this

889
00:46:16,040 --> 00:46:19,560
Speaker 1: giant outbreak happens at broad Street Pump. So Broad Street

890
00:46:19,600 --> 00:46:22,520
Speaker 1: Pump is this very popular source of water. It's well

891
00:46:22,560 --> 00:46:25,600
Speaker 1: known because it's a very hot summer in eighteen fifty four,

892
00:46:25,640 --> 00:46:28,640
Speaker 1: and this pump produces cool water that is supposed to

893
00:46:28,640 --> 00:46:31,840
Speaker 1: taste way better than the nearby pump. People really like it.

894
00:46:31,880 --> 00:46:34,319
Speaker 1: So a lot of people come to this pump. An

895
00:46:34,320 --> 00:46:37,960
Speaker 1: outbreak starts at this pump, and John Snow runs to

896
00:46:38,000 --> 00:46:41,480
Speaker 1: his friend, the statistician, and he says, all right, you've

897
00:46:41,480 --> 00:46:44,120
Speaker 1: got data on the people who are dying. Can I

898
00:46:44,160 --> 00:46:47,560
Speaker 1: have that data? And his friend shares the data and

899
00:46:47,960 --> 00:46:50,680
Speaker 1: John Snow looks at the data and he says, all right.

900
00:46:50,719 --> 00:46:53,040
Speaker 1: These people are like, they're near the broad Street pump,

901
00:46:53,080 --> 00:46:54,799
Speaker 1: and he starts knocking on their doors. He's like, where

902
00:46:54,800 --> 00:46:56,120
Speaker 1: you get in your water? Where you getting your water?

903
00:46:56,120 --> 00:46:58,360
Speaker 1: Where you're getting your water? And he realizes that the

904
00:46:58,400 --> 00:47:01,439
Speaker 1: broad Street pump seems to be the sore of the death,

905
00:47:01,480 --> 00:47:04,200
Speaker 1: the source of the cholera, and so he starts like

906
00:47:04,239 --> 00:47:06,480
Speaker 1: following up on all of the deaths whenever he can,

907
00:47:06,560 --> 00:47:09,000
Speaker 1: but sometimes he's too late and everyone in the family

908
00:47:09,040 --> 00:47:10,799
Speaker 1: has passed away and he can't get the data that

909
00:47:10,840 --> 00:47:13,480
Speaker 1: he needs. But by following up, he starts to find

910
00:47:13,520 --> 00:47:16,759
Speaker 1: some sort of interesting situations that are interesting and that

911
00:47:16,840 --> 00:47:20,879
Speaker 1: they help fulfill the criteria that folks had set forth

912
00:47:20,920 --> 00:47:23,440
Speaker 1: for him previously, which is to say, we want you

913
00:47:23,480 --> 00:47:26,120
Speaker 1: to give us examples where water was taken from the

914
00:47:26,120 --> 00:47:29,160
Speaker 1: pump far away from the area where there might be

915
00:47:29,200 --> 00:47:33,000
Speaker 1: stinky air, and then consumed and then kill people. And

916
00:47:33,080 --> 00:47:36,040
Speaker 1: so there were these brothers called the Eli Brothers. They

917
00:47:36,040 --> 00:47:39,080
Speaker 1: had a factory in town. The factory made percussion caps.

918
00:47:39,120 --> 00:47:40,600
Speaker 1: These are caps that make it so that when you

919
00:47:40,640 --> 00:47:42,919
Speaker 1: shoot a gun, if you shoot it even in the rain,

920
00:47:43,360 --> 00:47:46,000
Speaker 1: it's going to go off anyway. So it essentially like

921
00:47:46,040 --> 00:47:49,319
Speaker 1: protects the gun under wet conditions, which is important because

922
00:47:49,360 --> 00:47:52,800
Speaker 1: the Crimean war was happening. They had brought Broad Street

923
00:47:52,800 --> 00:47:55,359
Speaker 1: water pump to give to their factory workers, because again

924
00:47:55,400 --> 00:47:58,040
Speaker 1: this is supposed to be like the best water around,

925
00:47:58,360 --> 00:48:01,239
Speaker 1: and a bunch of their factory workers are dying. They

926
00:48:01,440 --> 00:48:03,840
Speaker 1: also brought it to their mother, who lived in a

927
00:48:03,880 --> 00:48:07,960
Speaker 1: town farther out, and the mother then shared it with

928
00:48:08,280 --> 00:48:11,560
Speaker 1: their niece, and both their mother and their niece died.

929
00:48:11,840 --> 00:48:14,239
Speaker 2: Oh my gosh, they're basically poisoning everybody they know.

930
00:48:14,440 --> 00:48:16,359
Speaker 1: All these people that they love and care for, right,

931
00:48:16,480 --> 00:48:20,080
Speaker 1: And so that ended up being pretty good data for them, unfortunately.

932
00:48:20,120 --> 00:48:23,480
Speaker 2: And why again, is this water so famous and popular

933
00:48:23,520 --> 00:48:25,799
Speaker 2: like that they're carrying across town to share with people.

934
00:48:25,880 --> 00:48:28,239
Speaker 1: So the other water in a nearby pump came from

935
00:48:28,280 --> 00:48:31,040
Speaker 1: a different source, and it was notoriously kind of like

936
00:48:31,080 --> 00:48:33,880
Speaker 1: stinky and gross and maybe a little bit warmer.

937
00:48:34,160 --> 00:48:36,799
Speaker 2: But this broad Street water must have had poop in it, right,

938
00:48:37,080 --> 00:48:40,480
Speaker 2: That's why it's killing people, But it's also famously delicious.

939
00:48:40,760 --> 00:48:42,480
Speaker 2: This is the thing I'm struggling to reconcile.

940
00:48:42,880 --> 00:48:45,080
Speaker 1: Well, so you know, probably at this time, Daniel, everything

941
00:48:45,120 --> 00:48:48,280
Speaker 1: was gross, and this was just had a good reputation

942
00:48:48,360 --> 00:48:51,040
Speaker 1: for being less gross, I see. And also I think

943
00:48:51,080 --> 00:48:52,680
Speaker 1: it came from a little bit deeper, so it was

944
00:48:52,719 --> 00:48:56,520
Speaker 1: cooler during a particularly hot summer. And also if it

945
00:48:56,560 --> 00:48:58,000
Speaker 1: was the one that's like right in front of your house,

946
00:48:58,040 --> 00:48:59,839
Speaker 1: you probably just you know, go for it.

947
00:49:00,600 --> 00:49:02,600
Speaker 2: No offense to all of our listeners in London. I

948
00:49:02,600 --> 00:49:05,960
Speaker 2: was just curious what flavor of poopy water Londoner's perceh.

949
00:49:05,960 --> 00:49:08,960
Speaker 1: Well, I hope that things have gotten better, you know,

950
00:49:09,480 --> 00:49:09,879
Speaker 1: all right.

951
00:49:09,920 --> 00:49:12,239
Speaker 2: So he figures out that there's a cluster connected to

952
00:49:12,320 --> 00:49:15,080
Speaker 2: this one pump, and then he finds this example of

953
00:49:15,160 --> 00:49:19,120
Speaker 2: people carrying that water from that pump two places far away,

954
00:49:19,239 --> 00:49:20,479
Speaker 2: and those people also dying.

955
00:49:20,600 --> 00:49:22,759
Speaker 1: Yes, And then he finds some exceptions to the rules.

956
00:49:22,800 --> 00:49:25,680
Speaker 1: So he finds a workhouse that is very close to

957
00:49:25,719 --> 00:49:27,960
Speaker 1: the pump, and so he would have expected a bunch

958
00:49:28,000 --> 00:49:30,279
Speaker 1: of the people working at the workhouse to be dying

959
00:49:30,320 --> 00:49:33,040
Speaker 1: of cholera, but pretty much no one is dying of

960
00:49:33,080 --> 00:49:35,160
Speaker 1: cholera there. So he goes to the workhouse and he's like,

961
00:49:35,440 --> 00:49:37,279
Speaker 1: what's going on here, and they're like, oh, actually, we

962
00:49:37,360 --> 00:49:39,840
Speaker 1: have our own private supply of water and we have

963
00:49:39,880 --> 00:49:42,240
Speaker 1: our own well, so we don't go to Broad Street pump.

964
00:49:42,320 --> 00:49:44,640
Speaker 1: They're in the same area, breathing in the same bad air,

965
00:49:45,120 --> 00:49:47,120
Speaker 1: but they're drinking different water, so they're fine.

966
00:49:47,239 --> 00:49:49,200
Speaker 2: So the color is following the water and not the.

967
00:49:49,160 --> 00:49:52,040
Speaker 1: Air exactly, yes, And then something similar is happening at

968
00:49:52,160 --> 00:49:55,600
Speaker 1: a local brewery. Turns out the broad Street brewery guys

969
00:49:55,600 --> 00:49:59,360
Speaker 1: are mostly just drinking malt liquor and not drinking Broad

970
00:49:59,400 --> 00:50:04,840
Speaker 1: Street pump water, so they're okay. So he's got this

971
00:50:05,000 --> 00:50:08,239
Speaker 1: list of eighty three people who had died. Seventy three

972
00:50:08,280 --> 00:50:10,319
Speaker 1: of them are really close to the pump, and so

973
00:50:10,440 --> 00:50:12,799
Speaker 1: he's thinking, like, Okay, probably these are people who drank

974
00:50:12,840 --> 00:50:14,840
Speaker 1: from the pump because it's the closest pump. Why wouldn't

975
00:50:14,840 --> 00:50:17,279
Speaker 1: they have drank from it. Sixty one of them he's

976
00:50:17,320 --> 00:50:21,000
Speaker 1: able to confirm that they definitely drink from broad Street pump,

977
00:50:21,120 --> 00:50:24,600
Speaker 1: so they almost certainly died from that. Six of them

978
00:50:24,719 --> 00:50:28,520
Speaker 1: say they're definitely not Broad Street pump drinkers, so maybe

979
00:50:28,560 --> 00:50:31,799
Speaker 1: they got it from somewhere else. Six were mysteries he

980
00:50:31,880 --> 00:50:35,120
Speaker 1: just couldn't track down, you know, any living members or

981
00:50:35,160 --> 00:50:37,799
Speaker 1: anything like that. And then he had ten cases that

982
00:50:37,880 --> 00:50:40,640
Speaker 1: were far enough away from the broad Street pump that

983
00:50:40,719 --> 00:50:42,760
Speaker 1: he's like, well, like, you maybe went to a different

984
00:50:42,800 --> 00:50:45,080
Speaker 1: there was probably another pump that was a little bit closer.

985
00:50:45,520 --> 00:50:47,719
Speaker 1: But he still ended up thinking that those people had

986
00:50:47,760 --> 00:50:50,760
Speaker 1: probably drank from the broad Street pump because, for example,

987
00:50:51,239 --> 00:50:54,520
Speaker 1: it looked like they frequented a coffee house that used

988
00:50:54,560 --> 00:50:57,480
Speaker 1: the Broad Street pump water to make a special thing

989
00:50:57,520 --> 00:51:00,000
Speaker 1: that they called Sherbert, where they had mixed broad stret

990
00:51:00,120 --> 00:51:03,000
Speaker 1: pump water with some other stuff, and then their customers

991
00:51:03,040 --> 00:51:03,880
Speaker 1: had started dying.

992
00:51:04,200 --> 00:51:06,960
Speaker 2: You say a special thing like Sherbert, as if nobody's

993
00:51:07,000 --> 00:51:07,680
Speaker 2: heard of Sherbert.

994
00:51:08,120 --> 00:51:10,840
Speaker 1: They called it Sherbert. It wasn't like Sherbert like we

995
00:51:10,880 --> 00:51:14,120
Speaker 1: would call it today. They were mixing like a effervescent

996
00:51:14,200 --> 00:51:16,000
Speaker 1: I don't remember exactly what it was. Maybe I even

997
00:51:16,000 --> 00:51:18,160
Speaker 1: shouldn't have used the word Sherbert. They called it Sherbert,

998
00:51:18,200 --> 00:51:21,719
Speaker 1: but it wasn't the description. Wasn't any Sherbert that I recognized.

999
00:51:24,600 --> 00:51:27,440
Speaker 1: So he's essentially pulled together this list of people who

1000
00:51:27,480 --> 00:51:30,480
Speaker 1: have definitely died of cholera. A very high percent of

1001
00:51:30,480 --> 00:51:33,160
Speaker 1: them had drank from the pump. He's got an extra

1002
00:51:33,239 --> 00:51:37,240
Speaker 1: list of people who have like drank cholera far away

1003
00:51:37,280 --> 00:51:39,520
Speaker 1: from the area where it could have been explained by

1004
00:51:39,600 --> 00:51:41,880
Speaker 1: just bad air, and they also died from the water.

1005
00:51:42,160 --> 00:51:45,279
Speaker 1: You've got people who are close by the pump were

1006
00:51:45,400 --> 00:51:47,520
Speaker 1: breathing in the air, but they didn't die. So he's

1007
00:51:47,560 --> 00:51:50,319
Speaker 1: pulling all these data together, but there's still some people

1008
00:51:50,320 --> 00:51:52,600
Speaker 1: who are missing that he can't really figure out like

1009
00:51:52,680 --> 00:51:55,000
Speaker 1: they left town. He wants to know if they died

1010
00:51:55,120 --> 00:51:57,400
Speaker 1: or not. And he's really trying to track down like

1011
00:51:57,480 --> 00:52:00,680
Speaker 1: the first case that started everything, but he just doesn't

1012
00:52:00,680 --> 00:52:03,520
Speaker 1: have the like knowledge of the town that he needs

1013
00:52:03,719 --> 00:52:06,840
Speaker 1: to do this. But he starts talking to this reverend,

1014
00:52:07,440 --> 00:52:12,359
Speaker 1: Reverend Whitehead, and this reverend knows this community intimately. This

1015
00:52:12,400 --> 00:52:15,720
Speaker 1: is his flock. He's been actually like in the houses

1016
00:52:15,719 --> 00:52:17,879
Speaker 1: with a bunch of these people as they've been passing away.

1017
00:52:17,960 --> 00:52:20,560
Speaker 1: He's been, you know, holding their hands. He knows everybody

1018
00:52:20,560 --> 00:52:22,880
Speaker 1: here really well. So he's able to track down a

1019
00:52:22,880 --> 00:52:25,880
Speaker 1: bunch of the cases that are missing and confusing because

1020
00:52:25,920 --> 00:52:27,719
Speaker 1: he knows everybody. He's like, oh, you know, yes, that

1021
00:52:27,800 --> 00:52:30,360
Speaker 1: person left and they went to you know, this town,

1022
00:52:30,400 --> 00:52:32,080
Speaker 1: and I know their grandma, so I'll try to figure

1023
00:52:32,080 --> 00:52:34,800
Speaker 1: out where they went. And by going through old records,

1024
00:52:34,880 --> 00:52:38,400
Speaker 1: he's able to figure out the first case that brought

1025
00:52:38,440 --> 00:52:40,960
Speaker 1: colera into town. And it turned out there was a

1026
00:52:41,000 --> 00:52:44,600
Speaker 1: baby that got colera and the mom was washing the

1027
00:52:44,640 --> 00:52:48,160
Speaker 1: diapers out and she dumped the pails of dirty water

1028
00:52:48,320 --> 00:52:51,240
Speaker 1: into the cess pit in the bottom of their house.

1029
00:52:52,080 --> 00:52:54,680
Speaker 1: And then they had the cesspit examined and it turned

1030
00:52:54,680 --> 00:52:57,800
Speaker 1: out there was a crack and the water was seeping

1031
00:52:57,880 --> 00:53:02,160
Speaker 1: into the well that served Broad Street pump. And so

1032
00:53:02,400 --> 00:53:06,600
Speaker 1: usually Broad Street pump did have pristine water, but this

1033
00:53:06,719 --> 00:53:10,680
Speaker 1: one cess pit was leaking and when colera got dumped

1034
00:53:10,680 --> 00:53:13,480
Speaker 1: into it. For a very short period of time, calera

1035
00:53:13,680 --> 00:53:16,239
Speaker 1: was introduced into the pump, but it was just this

1036
00:53:16,320 --> 00:53:19,279
Speaker 1: one case. And so actually the cases of cholera were

1037
00:53:19,280 --> 00:53:22,319
Speaker 1: starting to go down when John Snow went and talked

1038
00:53:22,320 --> 00:53:23,960
Speaker 1: to like the public Health board and was like, you

1039
00:53:23,960 --> 00:53:26,560
Speaker 1: guys have to remove the handle to Broad Street pump

1040
00:53:26,760 --> 00:53:29,680
Speaker 1: because this is what's giving everybody cholera. And they listened

1041
00:53:29,680 --> 00:53:31,640
Speaker 1: and they took the handle off, and so usually this

1042
00:53:31,719 --> 00:53:35,200
Speaker 1: story goes John Snow saved everybody by removing the broad

1043
00:53:35,239 --> 00:53:38,840
Speaker 1: Street pump. At this point, the pandemic was already wrapping

1044
00:53:38,840 --> 00:53:41,360
Speaker 1: itself up because the baby had died and there wasn't

1045
00:53:41,640 --> 00:53:46,320
Speaker 1: more colera being introduced. But the dad ends up getting calera,

1046
00:53:46,960 --> 00:53:49,759
Speaker 1: and presumably his waist would have also been put into

1047
00:53:49,760 --> 00:53:51,959
Speaker 1: the cess pit, and that might have started up another

1048
00:53:52,040 --> 00:53:54,439
Speaker 1: round of cholera. So he could have saved the town

1049
00:53:54,480 --> 00:53:56,640
Speaker 1: from another round of cholera. But the rest of the

1050
00:53:56,680 --> 00:53:59,520
Speaker 1: people in the house, and this is disgusting. Instead of

1051
00:53:59,520 --> 00:54:02,120
Speaker 1: throwing their waste into the cesspit, they were throwing it

1052
00:54:02,120 --> 00:54:04,520
Speaker 1: into the backyard because they didn't want to, like they

1053
00:54:04,520 --> 00:54:08,480
Speaker 1: didn't have access to the downstairs cesspit. London was super gross.

1054
00:54:10,000 --> 00:54:14,880
Speaker 1: So anyway, with this information and the extra information that

1055
00:54:14,920 --> 00:54:18,320
Speaker 1: the reverend gave him by having this like amazing social

1056
00:54:18,360 --> 00:54:21,080
Speaker 1: network that he was able to use to like fill

1057
00:54:21,120 --> 00:54:23,480
Speaker 1: in the gaps that John Snow wasn't able to fill

1058
00:54:23,480 --> 00:54:26,400
Speaker 1: in on his own, John Snow was able to publish

1059
00:54:26,400 --> 00:54:30,759
Speaker 1: this data and make a really good case that it's

1060
00:54:30,800 --> 00:54:35,400
Speaker 1: not foul air, it's waterborne contamination. And so what do

1061
00:54:35,440 --> 00:54:38,279
Speaker 1: you think, Daniel, do you think after that everybody was like,

1062
00:54:38,640 --> 00:54:40,840
Speaker 1: we will never speak of the miasthma theory again.

1063
00:54:42,680 --> 00:54:44,399
Speaker 2: I think it takes a long time for a new

1064
00:54:44,440 --> 00:54:46,799
Speaker 2: idea to become mainstream, doesn't it.

1065
00:54:47,480 --> 00:54:50,520
Speaker 1: Yeah, it does yep. So it wasn't immediate. There was

1066
00:54:50,560 --> 00:54:53,319
Speaker 1: still pushback. There were you know, even in the city

1067
00:54:53,360 --> 00:54:56,480
Speaker 1: of London, there were people like health officials who were like,

1068
00:54:56,560 --> 00:55:00,239
Speaker 1: it's my asthma. You didn't you didn't really show anything all.

1069
00:55:00,920 --> 00:55:03,400
Speaker 1: But by like eighteen sixty six, which is about a

1070
00:55:03,440 --> 00:55:06,560
Speaker 1: decade later, the Lancet Is Final, which is a huge

1071
00:55:06,560 --> 00:55:09,719
Speaker 1: medical journal was finally saying something like, all right, yeah,

1072
00:55:09,760 --> 00:55:13,480
Speaker 1: John Snow he was probably onto something with that theory,

1073
00:55:13,600 --> 00:55:18,160
Speaker 1: and it's getting broader acceptance. And in eighteen sixty seven

1074
00:55:18,280 --> 00:55:21,239
Speaker 1: Lister is coming up with his antiseptic technique. So this

1075
00:55:21,400 --> 00:55:23,360
Speaker 1: is where you like, you know, if you're going to

1076
00:55:23,440 --> 00:55:25,680
Speaker 1: do an amputation, you should probably clean it off to

1077
00:55:25,680 --> 00:55:27,879
Speaker 1: make sure there's no bacteria in there before you close

1078
00:55:27,960 --> 00:55:30,880
Speaker 1: up the wounds, so you don't kill people with infections.

1079
00:55:31,480 --> 00:55:34,200
Speaker 1: And then in eighteen seventy six Robert Coke does this

1080
00:55:34,280 --> 00:55:37,279
Speaker 1: amazing work showing that anthrax is caused by a bacteria,

1081
00:55:37,640 --> 00:55:39,480
Speaker 1: and then we're really off to the races with the

1082
00:55:39,520 --> 00:55:40,600
Speaker 1: germ theory of disease.

1083
00:55:40,960 --> 00:55:43,520
Speaker 2: Yeah, that's really fascinating to see how long it takes

1084
00:55:43,520 --> 00:55:46,080
Speaker 2: a new idea to be accepted. And you know, some

1085
00:55:46,120 --> 00:55:49,040
Speaker 2: people hear this and then they say, bl academa is

1086
00:55:49,080 --> 00:55:51,400
Speaker 2: too slow and it takes forever, And that's probably true,

1087
00:55:51,640 --> 00:55:54,480
Speaker 2: but it doesn't mean that every idea that hasn't been

1088
00:55:54,520 --> 00:55:58,000
Speaker 2: adopted is valid and should be taken seriously. Right, The

1089
00:55:58,080 --> 00:56:00,680
Speaker 2: lesson is to follow the data. Yeah, right, The data

1090
00:56:00,680 --> 00:56:02,920
Speaker 2: should speak. The data will tell you the truth about

1091
00:56:02,920 --> 00:56:06,600
Speaker 2: the universe, and it sometimes takes academics a while to

1092
00:56:06,719 --> 00:56:09,120
Speaker 2: come around to the data because they have other theories

1093
00:56:09,120 --> 00:56:12,080
Speaker 2: in their minds. But every new idea needs data to

1094
00:56:12,160 --> 00:56:12,719
Speaker 2: support it.

1095
00:56:12,840 --> 00:56:14,279
Speaker 1: And the other thing here is, you know, like John

1096
00:56:14,320 --> 00:56:18,319
Speaker 1: Snow was able to get moderately convincing data, yeah, in

1097
00:56:18,400 --> 00:56:21,040
Speaker 1: eighteen forty nine during one pandemic, and then he had

1098
00:56:21,080 --> 00:56:23,759
Speaker 1: to wait another five years for like the you know,

1099
00:56:23,920 --> 00:56:26,600
Speaker 1: the right set of conditions to come about. Unfortunately, the

1100
00:56:26,680 --> 00:56:30,319
Speaker 1: right set of conditions was people lying, was people dying.

1101
00:56:30,480 --> 00:56:32,120
Speaker 2: That baby killing all those people?

1102
00:56:32,600 --> 00:56:35,520
Speaker 1: Yeah, babies, Shake my fist at babies.

1103
00:56:35,800 --> 00:56:38,600
Speaker 2: The other fascinating thing there is that the pandemics end

1104
00:56:38,680 --> 00:56:40,520
Speaker 2: on their own. Is that a feature of the thing

1105
00:56:40,520 --> 00:56:42,839
Speaker 2: we were talking about earlier, that color kills you so

1106
00:56:42,960 --> 00:56:47,759
Speaker 2: quickly that it's not optimized for spreading continuously.

1107
00:56:48,080 --> 00:56:50,640
Speaker 1: I don't think it's always self limiting. So in this case,

1108
00:56:50,640 --> 00:56:52,920
Speaker 1: it was self limiting because there was only one family

1109
00:56:52,960 --> 00:56:56,080
Speaker 1: that had access to the cesspit that could leak into

1110
00:56:56,080 --> 00:56:59,080
Speaker 1: the well that fed Broad Street pump. I think if

1111
00:56:59,080 --> 00:57:02,680
Speaker 1: the entire commuit unity had their waist emptying into the

1112
00:57:02,719 --> 00:57:05,720
Speaker 1: broad Street pump, maybe it would have figured it out faster,

1113
00:57:05,760 --> 00:57:07,480
Speaker 1: because then it would have been really obvious that there

1114
00:57:07,520 --> 00:57:09,960
Speaker 1: was poop in the pump. But I mean, in general,

1115
00:57:10,160 --> 00:57:12,719
Speaker 1: I think I read that when one person gets sick

1116
00:57:12,760 --> 00:57:17,080
Speaker 1: with cholera, they can produce a trillion cholera bacteria that

1117
00:57:17,160 --> 00:57:19,680
Speaker 1: can then get into the water. And so, you know,

1118
00:57:19,760 --> 00:57:22,200
Speaker 1: I think as long as cholera is getting into the

1119
00:57:22,240 --> 00:57:25,400
Speaker 1: water and people continue to drink that water, and you've

1120
00:57:25,440 --> 00:57:29,040
Speaker 1: got more people who are you know, contributing more bacteria,

1121
00:57:29,480 --> 00:57:31,120
Speaker 1: I think it can keep going for a while.

1122
00:57:31,720 --> 00:57:35,000
Speaker 2: All right, Well, tune in next week for more details

1123
00:57:35,080 --> 00:57:38,560
Speaker 2: on color. If you haven't had enough twenty liters of

1124
00:57:38,680 --> 00:57:41,960
Speaker 2: cholera details, it's not satisfying you. We're going to learn

1125
00:57:41,960 --> 00:57:44,480
Speaker 2: more about how cholera works, how we can treat it,

1126
00:57:44,720 --> 00:57:48,640
Speaker 2: and the biological and chemical mechanisms underlying all that diarrhea.

1127
00:57:49,040 --> 00:57:59,080
Speaker 1: YY Nerds, see you next time. Daniel and Kelly's Extraordinary

1128
00:57:59,160 --> 00:58:02,360
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1129
00:58:02,400 --> 00:58:02,880
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1130
00:58:03,000 --> 00:58:05,920
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1131
00:58:06,120 --> 00:58:08,760
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1132
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00:58:15,520 --> 00:58:19,040
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1136
00:58:19,080 --> 00:58:22,320
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