Today Pouya is speaking with Tyler Martin, Physical and Mathematical Specialist with extensive research into Mathematical modeling of COVID-19 data and String Theory.
The article discussed in the talk: NYT article on modelling paths to herd immunity in the USA
Episode Transcript...
SUMMARY KEYWORDS
herd immunity, people, models, vaccination, assumptions, lockdowns, mathematical modeling, vaccinate, talking, deterministic, masks, number, means, mathematical models, strategies, politicians, frontline workers, account, restrictions, predict
SPEAKERS
Pouya LJ, Tyler Martin
Tyler Martin 00:16
Hello, ladies and gentlemen, welcome back to yet another episode of The BGP podcast. I'm here joined today by my good friend and colleague, Tyler Martin. He is okay. Why don't I hand him I'll hand it over to him to tell. Tell him tell you guys about himself. Hey, Tyler, how are you? Hey, I'm good. How are you?
Pouya LJ 00:36
Very good.
00:36
Thank you for having me on podcast. I've always heard your podcast. So.
Tyler Martin 00:43
Thank you for being here. It's a pleasure. to to to be talking to you. Now. Okay, so why don't you tell us a little bit about yourself? What do you do? What are you? What are your likes and dislikes in this? crazy world? Okay, um, a little bit about me. I'm doing physical mathematical specialist utsc with, with you, we're in a lot of the same classes. And so my dislikes COVID right now is a big dislike. Yeah, yeah. But I love physics and math. That's my go to. Yeah. No, it's great stuff. I do agree with you in that. In that sense, we share
Pouya LJ 01:26
the love of physics and math. I so I don't know if you remember, Mr. He was on the podcast A while back. Now, now we got you. And hopefully bunch of other people join in the cohort. But yeah, today, actually, we're going to talk about something very relatively timely to all the COVID stuff. And so you have done some research and studies on, you know, mathematical modeling, modeling, which I think it's I guess people are getting tired of hearing it. That is surprising, because now politicians are talking about it, right? Like, oh, yeah, these are the numbers and mathematical models based on these, we're making these decisions. Never, you could you could never get politicians to pay so much attention to science, I suppose. As we do now. So now, all of that is about the spread, mostly, that's what they were talking about. But all of that also applies to vaccination strategies, which is probably the most timely because those are the decisions that politicians and then the world leaders around the world are making decisions on, right, all of these numbers that are jumped out. So now, for the audience, we are trying today, with the help of Tyler to make sense of all of this. What are these? You know, modeling that they talk about? What What is it behind the scenes, and to simplify it to a degree basically. So why don't you Why don't you go ahead and like started us start us off with? What was the what was the starting point of your research early on? And what were your, you know, thoughts going into it? And immediately after you started reading some papers and articles.
Tyler Martin 03:10
Yeah, definitely. So first, like, you watch the news. And you see all these politicians, like you said, talking about mathematical modeling, and then you go, what is even mathematical modeling in the first place. So then you have to do a little bit of research if when it first started. First off, we haven't even done a ton of research and mathematical modeling. It only started around, like going up around 40 years ago. And recently, there's two big definitions of mathematical modeling we can use today. One is the deterministic, and the stochastic, those are big fancy words. But they pretty much mean stochastic as in random randomness. So we can capture the randomness of humans, because no one can actually predict human behavior or human psychology. deterministic is a little less complex. It just kind of puts humans as a person with no emotion, no thoughts of what they're gonna do. They're just there. And then we can judge how a disease reacts from these two different types of models.
Pouya LJ 04:22
Right. Right. And do you know, so I mean, that's a natural, we're gonna delve into what they are in a second, maybe in further depth. But do you know, what are the when people talk about these models? Is it is it mainly stochastic or deterministic? Or sometimes just sometimes that are a combination of both? What are they usually talking about? Or what are the most effective
Tyler Martin 04:46
perhaps I don't know, the most effective from my point of view from what I found is stochastic modeling is most effective, although it's more complex, meaning we have to have big Fancy computers to run all our simulations, it's more effective in actually grasping our results and accurate results. What to when you compare it to deterministic modeling? I would say, for deterministic modeling is more better for handing paper. So if you're wanting to do a model by hand and paper, like we all do in class, then that's a good way. But stochastic modeling is definitely the way to go. When you have the time. Yeah.
Pouya LJ 05:30
Right. So so the deterministic model doesn't take into account just to clarify, right, it doesn't take into account human behavior. So for example, if you're supposed to be social distancing, your social this, that's absolute state, like, it doesn't consider that, you know, if you're on a lockdown, you're going into grocery, and you might happen to you remove your mask to unlock your phone. So none of these is accounted for. I mean, I guess it's not specifically accounted for in this
Tyler Martin 05:58
model, either.
Pouya LJ 06:00
But it basically treated as absolute steady state, meaning that it's everything being perfect. Is it? Well, I guess it depends on the assumptions you make to right you can also be assumption that, right? So so but whatever assumption you make is a fixed one in this in the, in the, what do you call it? The deterministic modeling? Right? Yeah, everything is fixed. For the stochastic model, we actually have a probability, right? So like, if you're more probable to go outside, or if you're more probable to stay inside, so it's not like you're fixed to do one certain thing. We have a probability density. Right? So instead of means 01, it's somewhere between zero and one. Exactly. Potentially. And our deals models like this stochastic ones. Are these probabilities dynamic, maybe changing in time? time?
Tyler Martin 06:51
Yeah, for sure. They change in time because people's reactions to a pandemic changes with time as well. So like, like we saw when the pandemic first started, a lot of people were outside and about not really caring. But as soon as the stay at home orders and stuff came along in the lockdowns, then we had to stay inside. So then our model has to account for that as well. Yeah.
Pouya LJ 07:17
Yeah, that's, that's fair. Is there anything specific you want to talk about in either of the two? In the technicality? So what are the factors that we're looking at? When we're saying probability of, for example, you mentioned human behavior, but what other factors are relevant here,
Tyler Martin 07:35
because actually, it depends on how complex you want to make your model. So if you want to make a super complex model, then you could take in a ton of factors like not only just human behavior, but like traveling around the world, and which planes travel to which countries and which are bringing back stuff. Or another common thing is for Western societies, we like to shake hands. And so for other societies, we don't have that type of contact. Like in Asian societies, it's normal to bow. So just like even the smallest things, just like that you can take into account into our model. And but as the more you take into account, the more complex it gets, so it's kind of like a trade off. Right, right.
Pouya LJ 08:23
Yeah. And then you did mention the, we get to be practicing soon enough, I suppose. But you didn't mention like, it is really deep. It's all these these models all started with some sort of assumption, right? And that assumption, determines what the what so let me take actually a couple of steps back for people who are not maybe thinking about so the idea is that you want to see, you want to model meaning try to predict what will happen given a certain guesses like so you you, you say okay, if there's no lockdowns, right, I'm correct me if I'm wrong here or if I'm slightly off, or you can add a caveat to it. But the idea is that if we make certain assumptions, meaning for example, there's no lockdowns, everybody's behaving like they would there's no pandemic at all right? What is the number what are the numbers are going to look like? What are the number, the number of people who are getting sick or who are dying, what demographics what you know, geographical neighborhoods, perhaps are the country, the city etc. And based on that, and then you combine and then you create different models with different sets of set of assumptions and find out what you want to do depending on what you want to achieve. So for example, you want so what is that absolutely no restrictions What? at all, one with minimal restrictions, maybe just socially distancing, and mask but then do whatever you want. Or maybe to 20% capacity, restaurants, whatever or absolutely locked out. So you create certain, you take certain assumptions, and you model these and you see, try to see into the future, essentially, and then try and then politicians come up and based on those predictions, if you will make certain decisions about what to do, what restrictions they were they want to impose on the population and whatnot. Is that Is that a fair summary of what what is the point of these models? in the first place?
Tyler Martin 10:25
Yeah, yeah, that was a great summary. And the big point is, is the relationship between the politicians and the scientific researchers, so if they don't have a good relationship, and they're not constantly communicating over what they're finding from these models, then the politicians will have a harder time making decisions on health policy issues, right. So that you have to have that constant communication going back and forth. So you can make those good decisions. Exactly. Now, that's
Pouya LJ 10:57
a very fair, fair point, actually. And so, now, I said all of this, to clarify all of this, but the beef I have with these models at some point, not not all the time. But first of all, they're not the so this is the this is the idea that some people talk about, actually, my dad always talks about this, he's like, the carpenter only cares about the wood, or the shoemaker cares about his shoes, and the electrician cares about his wires. At the end of the day, when you're talking to somebody whose job is to save lives, the only thing they're going to care about is to save lives. And yeah, the save lives doesn't comprehensively and take into account everything. It just takes into account saving lives who are being lost due to COVID. Period. Yeah, you know, like, if it if it. I mean, I'm not saying those people actually thinking like this, but that's their priority, because that's their job. The same way My job is, I don't know what it is. But right now to talk. So all I'm gonna focus on this fucking right.
Tyler Martin 12:00
So
Pouya LJ 12:03
my point is that, okay, all these things are getting done, I guess, supposedly, the politicians job is to take into account all of these models from the, you know, the, the scientific community from the, from the health community first in the first place. And then similar models are going to be done slightly different, obviously. But similar mathematical models are going to be done in on the economical side by the economist, or what is what are the impacts are going to be based on different assumptions, again, to the economy, and then eventually politician is going to be a general person, taking all of these into account, that's at least the idea, and then make some some decisions. Anyways, let's back up. So the beef that I have is that there, there, the there is no caveats, by when when you're talking about me, and you know it, the scientists know it, but when you're communicating this to the public, there's no caveat that all these modern things, though, they predict into the future, they have, they highly depend on your assumptions. And as you mentioned, ultimately, they're completely probabilistic. Like I, some of these models I have seen specifically restricted to Ontario is where we are in Canada. So and, and some of these don't take in taking don't take into account at all that they're, in fact, travelers coming from different countries. And I'm not saying they shouldn't, right. And, and they're, they're their only variable is human behavior due to lockdowns or restrictions or whatnot. And sure, that changes the numbers. But But let's let's let's toy around with no travels whatsoever, or where are these? You know, where are these outbreaks actually coming from? Is it is it because of travelers? Or is it not? Or is it because people are going to restaurant or is not? So I think this is very last Sunday? Again, and it portrays outside to the public so much that, you know, these are God given things, which I think and Would you agree with that they're they're very, very varying, depending on your assumptions.
Tyler Martin 14:12
Definitely assumptions is like, probably one of the biggest things like you can have a model that is almost exactly the same. But if you vary one thing, they can go completely different directions, like you can be off by if you're calculating the number of deaths, you can be off by quite a lot. So our underlying assumptions of our model are particularly important that we make accurate assumptions from what we actually perceive in the world.
Pouya LJ 14:45
Perfect. Now, I just wanted to make sure that I'm on track. They're not just spewing nonsense out there. Now, obviously now, the more interesting subject today has become the vaccination and vaccination. strategies, how are you vaccinated when vaccinate, who which population to vaccinate, which geographical location to vaccinate, etc. And so all of these are very good questions. And again, similar models are being done. And I know you were talking about before we started this conversation live recorded. You were talking about this new york times article, which was looking at different vaccination strategies. And essentially, they were trying so this is the title of the article, if I let me read it out, when when could the United States reach herd immunity? Well, question question mark. And the answer is, it's complicated. And hey, answer this. So first of all, let's define herd immunity. What is hurting herd immunity for those who don't know it?
Tyler Martin 15:43
Okay, I have to define one more thing before I define herd immunity. Okay, fair enough. Oh, first, there's a reproduction number. So a reproduction number basically just says, If I had the virus, how many people on average, would I pass the virus on to? So say, I have a reproduction number of two, that means me having the virus on average, I pass it on to two more people. So a herd immunity says that our reproduction number is less than less than one. So when we have less than one, then there's no chance of an outbreak or epidemic happening. And this means that there's less risk of the situation getting more serious. So herd immunity, basically just says, um, let me get a good definition that the state of the population where the fraction protected is sufficient to prevent outbreaks. And so herd immunity kind of just is basically what we want to reach from vaccination efforts. Yeah.
Pouya LJ 16:53
vaccination and the fact that people already some people already got and and recovered, right. And supposedly they can't get reinfected.
Tyler Martin 17:00
Exactly. So they're like, we have to take into account or remove population when we're doing these, the removed population is basically people who've gotten it and can't get it again, or people who have tragically passed away from it, or people who have immunity to it COVID. We don't know if it's any immunity to it yet. Like underlying immunity, but there are other diseases with immunity.
Pouya LJ 17:25
Right? Exactly. So so then that, and that, because there's a certain portion of the population whatever that number may be, that is removed, then they are not, which is the reproductive number drops below one which ends up and over time this virus decays, because it cannot. So if I get it, if my r naught is one mean, means that if the average is means that if I get it, I can only give it to one more person, so I'm only replacing myself, I'm not growing. And if it's less than one, on average, it means that I'm not even replacing myself. So over time, this is gonna vanish, because that's exactly okay. So yeah, right. So So in that sense, it's a combination of these, whether you're vaccinated and your immune or your so if I got it, and I come to contact with you, I'm assuming you're vaccinated, then you can't possibly get it. Whereas if you were not vaccinated, I would give it to you. And my Arnott would be at least plus one, because you're not you. You are not vaccinated. You're not Yeah. Yeah, that immune not being immune, or whatever. So either that person has passed. So it doesn't even exist to, you know, contract it, or they already got it. So there they have immunity because they cannot be reinfected. At least for a period of time. We don't know what the period of time is exactly. But let's just say for now, for the purpose of this argument, let's just say it's indefinite, and or persons vaccinated. Again, same idea. Now, now, let's go back to the article, I suppose and you can take the range from there, but I'm going to reiterate the question. So they were trying to researchers were trying to see when, you know, says reaches this herd immunity, meaning that the reproductive number will be less than one. So eventually the virus will die out over a period of time, and it definitely cannot grow. And their conclusion in one sentence was this complicated. So why did they say that on what, what what were they looking at? What they find what happened? Go ahead.
Tyler Martin 19:31
Yeah, it is actually very complicated. I think as a Canadian to looking at what the states is doing is definitely beneficial for us. Because we don't vary a ton from them. Some of the states have a lot more relaxed. laws as in like, they can walk around without masks and stuff, but we're actually fairly the same. So just looking at this is very interesting. One thing they want to look at was They sped up the rate of vaccination. So on average, the US is administering about 1.7 vaccination shots a day. So if they continue to do this, their reach herd immunity by July, and around 100,000 people pass. However, if they sped it up, it would increase to around 13 million shots per day, then they reach herd herd immunity By May, and 90,000 people with pass. And if they increased it even more, which is very improbable to 5 million day, that's kind of insane. They reach herd immunity by enpro. And 80,000, people would pass. I think the more interesting part of this article is looking up is looking how herd immunity and vaccination along with with relaxing social distancing measures comes into effect. So, if you actually keep 1.7 million shots per day, and then look at relaxing your social distancing measures, they return her to me by July, like I said before, 100,000 people would pass. But if you lift restrictions, when 15% of the population is vaccinated, then you reach herd immunity by June, so a little earlier than July and 17. Or say 170,000 people have passed. So that's a big jump from 100,000. And then even more interesting, if they end all restrictions right now. Then they reach herd immunity by May. But in that case, 3200 or 320,000 people who pass so these jumps to me are just like, insane. When you look at how many people would pass if you just relax the restrictions on social distancing?
Pouya LJ 22:15
Mm hmm. Right. And, yeah, that isn't saying the same thing we were talking about. The initial assumptions can change a lot. And the same thing happens in the vaccination strategies and social distance. So I think so. Now, I don't know if I got it. So with currently with the with, what do you call it, their current rates of vaccination? And the if we don't, if we keep the measures in place, like the social distancing, or at least the basic measures, such as the social distancing and the masks, yeah, now, that number of deaths in the United States until the herd immunity is achieved is 100,000. People. Yeah. Right. So if so let's let's pick this again. So if the same rate of giving vaccine to the US population is continued, not increased, not decreased, which is 1.7 million per day, which is impressive, by the way, is a lot. Yeah. Do they have a lot of big population too? Yeah. Bigger than Canada. I mean, so anyways, so 1.7 million per day until the next foreseeable future, like unless next few months, and then you still do social distancing, you still do wear masks? Maybe not no major parties or anything. And then the estimated number of deaths from COVID until July which is the time that they reach herd immunity is 100,000. But if they don't take the if they ease up the measures, meaning don't wear masks, maybe don't social distance, maybe throw away some parties but not a lot then then that then that number jumps by almost twice and 171 point seven 170 1000 also, which is and and and and now let's say we keep this social distancing measures and hold on a second. Let me see if I get this article. Right. Excuse me. Now if you do increase the supply to 3 million a day, yeah, but that but then they didn't do any investigation as to what happens if you do measures or don't do measures today? No. If you increase the supply, but also keep the measures
Tyler Martin 24:39
Oh, no, they didn't. They didn't do that. Okay,
Pouya LJ 24:41
yeah, okay, okay. Okay. They didn't do that. Okay, but it is very interesting and okay, but if they do increase the however if they do almost double the shots, although they reach herd immunity much sooner, still number of deaths is like 10,000 people. That's 490 Yeah. That doesn't make a lot of sense. How's that? You know,
Tyler Martin 25:03
I'm not too sure. That would also depend on what we're talking about before their underlying assumptions. Right. Right. So
Pouya LJ 25:08
they didn't talk about those assumptions, I suppose.
Tyler Martin 25:12
Right? And yeah, they Yes, they do. Put it in the beginning a little bit, but not too
Pouya LJ 25:19
long. Because,
Tyler Martin 25:20
yeah, they also do cover the different types of variants. Oh, interesting. So with the current variant, like I said before, 100,000 by July. So 100,000, people would pass, and they'd return immediately by July. But for the more contagion, like, very more contagious variants with precautions, and they in the states gets all of those variants, they would have around 200,000 people pass, and they reach herd immunity by July. But if they have the most contagious variants with no precautions at all, they reached an insane number of 530,000 people. And the herd immunity by April.
Pouya LJ 26:16
So more number of people in short amount of time, basically. Mm hmm.
Tyler Martin 26:20
Exactly. Yeah, it's it's quite a number to look at. The death toll at that point would be just insane.
Pouya LJ 26:29
So in a way, the the the immunity due to getting the virus and recovering from it as actually acting much faster than the vaccination process, basically. So that's why they're getting to the herd immunity earlier, because the virus is infecting everybody and whoever survives just as immune. So the immunity increases fast moving, but, but obviously, a lot of people are
Tyler Martin 26:53
doing cost. Yeah,
Pouya LJ 26:54
yeah. Well, that's, that isn't, like these numbers that that is looking at these numbers is actually quite, we will, by the way, I should say this, we will put the link a link to this article in the show notes. So if anybody wants to go and look at these numbers, themselves, feel free to do so. Okay, let's, let's now move move forward. Unless you want to talk about this article more. I don't know if there's anything left? No, no. Okay. Yeah. Okay. So let's move forward a little bit and talk about what are the discussed around the table, if you will, the different strategies of vaccinations? And what is the argument for each of them in terms of who to vaccinate, which areas to vaccinate, why and why not? Etc.
Tyler Martin 27:40
Yeah, there's a couple different methods of vaccination. One very promising one is called a focus method of vaccination. That's where you focus in on a certain group of people. Give them all the vaccination that we have. And then once there are not basically as getting better, then you move out to a little more like diverse, further out rural areas and start vaccinating back. Oh, so
Pouya LJ 28:09
it's mostly thinking geographically, right? Yeah. If you're, if you're in our big, if you're in a big, congested populated city, for example, let's say Toronto, New York, whatever, then you focus on that and leave the rest of the state under province a lot, right? That's the idea. Okay. Okay. Go ahead.
Tyler Martin 28:26
And so like that strategy is actually one of the more promising strategies. So we actually, as Canadians, we see this happening now. Nova Scotia is giving up some of their vaccination to other places in Canada, so that we can actually get to a herd immunity for Canadians as a whole faster. Yeah. Interesting.
Pouya LJ 28:51
And do you know it is now across Canada? One story, but within Ontario, do you know if they're using this strategy or not? Like the focus wrench?
Tyler Martin 29:00
I'm not too sure. From what I know, it's more of just everyone gets to or the most people. important people get a first as in the people who are doctors who are Yeah, doctors, frontline workers, or people who need it, like the elderly need it. So people like that would get it first. So it's more than not focusing on a particular area. They're just trying to get the people who are who
Pouya LJ 29:34
can't think of the word more vulnerable, maybe Exactly. Yeah. Yeah. Look, I get I get the I think the doctors and nurses is a bit clear to me because we want them to be healthy to take care of all of us, not just for COVID for everything really. So that's even from a very selfish point of view, not acknowledging their sacrifices, complete selfish point of view. You still want them to get it first. I think the frontline worker Especially doctors and nurses, and the rest of the frontline workers, perhaps to paramedics, police officers, etc. So those are because there's a very important, like they have to be able to function. And at the same time, like very urgently be able to function at the same time, they're much higher at much higher risk. So that I think it makes complete sense. But after that, although there are there really are most vulnerable, but they're not mixing as much. I'm not so convinced that the focus after that prioritizing those people, the focus approach will not be more successful. But they still also, by the way, so they still do these similar modeling, get taking different assumptions, right, for example, assuming that you give it to elderly and assuming that you give it to like a focus strategy to give it to parts of the population that are mixing and mingling more. Maybe there are denser neighborhoods, for example, the basically the places that have the highest numbers, geographical places, right? Yeah. So get them. You know, as soon as they get contained, they can't really move it on either. Right? as much. What do you think on that? That would be your thoughts? personally.
Tyler Martin 31:16
I think mathematical modeling wise, it's a, it's a better way to get down to death toll to vaccinate the elderly first, like if you think about it, our death toll will go down. If we vaccinate the elderly first compared to everyone else, because the elderly are dying the most Sure. So if you're just looking at it, from a mathematical modeling point of view, it's like if we want to get down the death toll, vaccinate the elderly. In general, though, I'm not too sure. I would say the focus strategies, probably the better strategy to go to, but, um, after the frontline workers get their vaccination, I don't know. I'm not too sure. Who should get it after that. Right. Really not. It's a very complicated story, obviously. Yeah. And you don't get on people's feet by saying that you shouldn't get vaccinated. Yeah, so yeah, no, I
Pouya LJ 32:17
mean, I'm definitely not saying that I think focus is the right way to go. I'm just saying it's not really clear which one is? And maybe there is no one right answer or one wrong, and maybe both answers are wrong, or both of them? Yeah. So it's just a slightly better, I think they're actually at the end of the day in the long margins of things, depending on what you're looking at. Yeah, sure. Maybe if you're here, purely looking at death tolls due to COVID specific because on the other hand, look, there have been reports and studies done on the the side effects of this whole COVID thing like not, you know, not just the deaths and despair from the COVID, but also that loss of job economic distress, you know, suicide rates, people who couldn't get their scans and for cancer, etc, their operations. And that all of that is obviously costly as well, we cannot just ignore that, although the forefront is to COVID disaster, but it has, you know, side effect that is rippling through our societies and communities as well. Right. So we definitely would like to, if we do like to look at it comprehensively, I think, at the end of the day that the approaches are not really clear cut. And that is what you were mentioning, like in terms of, if you want to introduce more variables, it just keeps getting more complicated. Exactly, yeah. And perhaps even impossible to to predict anything with any good amount of good measure of accuracy. So yeah, I guess I bought my way complicated. It's not as easy as this is the right way to go. So maybe we can relinquish that arrogance, I suppose to a degree, because it is a complicated problem. So yeah. Is there anything we left on the vaccination fund that you wanted to talk about that we
Tyler Martin 34:14
didn't? I think we pretty much covered it all. By no means am I also a vaccination expert. No, we're just
Pouya LJ 34:22
discussing our own, you know, experience with these articles.
Tyler Martin 34:27
Yeah,
Pouya LJ 34:28
yeah. That's good. Because I think, two to a high degree because you actually did study these matters to a degree. I mean, again, I'm not quite we're not claiming to be experts, neither of us but because you have done specifically the math, math, mathematical modeling. I'm sure you have more understanding than many including myself. So it's good to. We don't need to listen to the greatest experts to increase our knowledge. I think it doesn't. That's as long as you know more than me. I can learn from you. That's it. Yeah.
Tyler Martin 35:00
Yeah, exactly.
Pouya LJ 35:02
Right. Okay, yeah. So I think it's a good place to stop, like, end that conversation. I'm gonna give you a few moments after this to, you know, gather your thoughts. final words, if you want to save, but before that, I think it's a good point. I think this was a good. Good understand, I think you had this epiphany, I suppose. And I definitely did that it is, in fact, a complicated matter. It's not as easy as 123 go. It's a bit requires in depth contemplation. And at the end of the day, there are going to be mistakes, there are going to be errors, there are going to be things that are not going to get to the right answer. Or what is even the right answer. Right. So all of these are subject to a lot of assumptions. And I think that was that was the some of the most important epiphany of all in this in this journey today. Exactly. Do you have any final thoughts that you want to add to it?
Tyler Martin 36:07
Maybe one last thought, and that is that no mathematical model can accurately predict the future. Like no matter what if we take in the as many complex variables as we can, we can predict the exact amount of people who will die. So take every everything these politicians say about mathematical modeling with a grain of salt when they're saying it, but some of them are actually fairly accurate at the same time.
Pouya LJ 36:34
Yeah. They're the best worst thing we have. Exactly.
Tyler Martin 36:39
Exactly. Okay.
Pouya LJ 36:40
Fair enough. Okay. Thanks. Thanks, Tyler. It was a pleasure talking to you today.
Tyler Martin 36:46
It was pleasure. Thank you so much for having me on.
Pouya LJ 36:48
No problem. I'm thank you all for tuning in and listening to yet another episode and I hope you enjoyed it. Leave your comments, suggestions, questions below there are there's going to be shownotes as I mentioned, which we're going to include the New York Times article in it and until later episode, have a good one. Take care
