 Okay, so I'm very pleased to introduce our speaker today, Dr. Jacob Barhawk. Okay, Jacob Barhawk is an independent computational disease modeler focusing on machine comprehension of clinical data. The reference model for disease progression was self-developed by Dr. Barhak. The reference model is the most validated diabetes model known worldwide and also the first COVID-19 multi-scale ensemble model. His efforts include standardizing clinical data through clinicalunitmapping.com, and he is the developer of the micro simulation tool or MIST. Dr. Barhak has a diverse international background in engineering and computing science. He is active within the Python community and organizes the Austin evening of Python coding meetup. For more information, check back onto the meetup page and he has a link. Okay. Okay, can I start? Yeah, okay, I'll pass it off to Dr. Barhak and then, yeah, any questions you can feel free to interrupt. Yeah. So I'm going to talk about the reference model for disease progression, but now for COVID-19. The presentation itself is interactive. You can actually go and if you want, you can, there's a QR code that will lead you to it. You will see it says chronic disease and infectious diseases in Paris, France last month. Well, this was the keynote there in that conference um and i'm going to repeat more or less the same presentation but hopefully it will be more interactive and you can ask questions and i will add a few more things just to absorb what what it's the work is all about so this is the model and it explains what it is it's in cmtk.org this is where modelers one of the sites where models put the models on or advertise their models and there's a lot of information about it and historically i'm going to go from back this is how the reference model started. What is the reference model? I was modeling diabetes and noticed there are many, many models and many populations, and the models are not always good for each population. So I started by making them compete. And this matrix kind of shows models against populations and shows you what's good, what's bad. This was 2014, and then it's actually the reference point start 2012, but the first 2014 or 16 are still doing the matrix. In 17, I started making the models collaborate. This is how diabetes model looks like the diagram I'm going to show it later. And within time, the diabetes model grew up to the point that in 2020 it was the most validated diabetes model that was ever made. I started adding more and more information into it. And then came the pandemic. 2019, I presented in the Condacon. You can see there's a good presentation that shows what it is today. And more or less, this is where things were in 2020. And then the pandemic hit. You can see Harry. He's the head of Midas these days my this is a group that organizes all the people the deal model infectious diseases and you can see in 2021 I already presented something there and this is what I'm going to tell you about what I present I actually created the model for COVID so let me show you how things were before this is diabetes model this is what was there before this is before and then uh this is how diabetes model results look like clinical trials where you can see the names of names or acronyms of clinical trials if all clinical trials almost have acronyms. Sometimes they become words, those acronyms. And then you can see some results. So this is the diabetes model that was the reference model. And what it does, it makes many, many models like MI models, stroke models, and mortality models compete. This is what was in 2020. Actually, this was supposed to be presented at the NIH, but then pandemic hit and all was canceled, meaning all conferences were canceled, everything became online. So this is a remnant of what was supposed to be a poster there and never and it became something online. And now when the pandemic hit people started asking me oh you're a computational disease model what you have to do with the say about COVID and at that point not much because chronic diseases are kind of different than infectious diseases. In chronic diseases the person has a disease and basically interaction with other people doesn't change anything. It's like the disease progresses in that person. So the modeling is kind of different because there are interactions between people. In COVID or infectious diseases, one person infects another. Still the disease progresses within that person but still there can be reinfection and stuff like that oh my tools were not ready for that it took me a few months to actually change the tools to be able to adapt to uh infectious diseases and today and then i created uh i published in curious the initial use case this is the publication you can find it this was in july in july 2020. then later i got some more uh information i got some support for computing power my model takes a lot of computing power we'll talk about it a little bit later and uh i published an interactive paper again i'm using holovist technology for all the interactive stuff you'll see so this is an interactive paper you can find it either through this eoi at the top or you can just google it's on my github account here and uh this is where uh the reference model was created the multi-scale ensemble model so there aren't that many ensemble models from a covenant team but none of them is multi-scale i'm going to talk about it in this during the talk why is it a little bit different and it is different than other ensembles you found in the past, because in this ensemble, the idea is for the model to actually understand what's going on. My purpose in working, and this is what the talk will be today, is I'm trying to apply the reference model to explain the data that I saw in the US. Many models try to forecast what will be in the future, they call it predict, but you cannot really predict and forecasting is very hard, especially if you don't understand all the things that you're modeling. So what I'm trying to do is I'm trying to take intelligible models, models that you can explain what they are and merge them together and using machine learning techniques and a lot of computing power put them together into something that you can later explain someone what's going on with covet 19 in ways that humans can understand it and i'm showing it visually i'm using all of this technology to show this. So let's dive into COVID. Unless you have questions by now, which you just interrupt me. If we're talking about the activity, if you hover in this presentation, you can press tabs, you can hover over things all of this is only written in Python everything I do is Python fuel Python so eventually it becomes an HTML file but it's all Python so you can actually find references and see exactly what information I use and if you click on them it will take you to that reference. But let's talk about COVID. This is a diagram of COVID. And I'm trying to explain COVID in the US, the numbers that were reported by the COVID tracking project. If someone remembers in the beginning, there was a project that reported infections and deaths in each US state, and they were pretty good and they were pretty good about it. They had the best data I could find at the beginning of the pandemic. So when people started bugging me about what about COVID, then I started looking for data. So I'll give them a good recommendation, the COVID tracking. i can actually still probably find something online they allowed me to use their data and what i'm trying to do is i'm trying to figure out how people move from covered to infected and from covered to recovered or death using all sorts of models and each one of those words here in purple this is a whole group of models infectiousness i have multiple models transmissions i have multiple models response responses response to the pandemic it's behavioral models recovery well notice the few things about recovery i'm not modeling the error back of people who recover get reinfected. Why? Because it's still early in the pandemic. I'm modeling April 2020. There aren't that many recovered people that you can actually – that this error will make sense, the back error from recovered to infected. But later on we can add this. But still I'm trying to explain the beginning of the pandemic. And there are, of course, mortality models models there can be multiple moving things and beyond everything there's an observer model an observer model is the model that corrects all the inaccuracies that or actually it kind of distorts all the things like they were supported in real life like people were claiming that there were claiming that there is infections are not reported wrong or the deaths are not reported correctly. So the observer model distorts things the same way that they were distorted in reality. And there are multiple of those models because there are assumptions. One basic thing is that the model is not always true. The model is always an assumption and we're trying to figure out the best best set of assumptions that actually fits the data and the data comes from multiple sources project i told you about but there's also uh census data from the us there is also papers that extract the information from either data or models. And finally, I even added temperature data of the US states I took from NOAA. But there's many, many models that came from different sources and throughout the presentation, you'll see people contributing this or that model and you'll see a link to wherever they came from. So let's start talking about infectiousness. But before we start talking about infectiousness, let's tell you why am I modeling infectiousness. So at the beginning of the pandemic, maybe even now, I didn't check lately, but the Department of Homeland Security, DHS, released a document called the Master Question List about the pandemic. You can actually find it probably in archives. It was a government document that looked over what they know or think they know about the pandemic. They were quite accurate about what they think they know and they had categories of we know this, we think we know this, and these are the questions that we want to ask one of the questions and this is from a document from may 26 this document updated this question later disappeared they probably either figured out or figured out this question it's not what they want but in may 26 they asked the following question what is the average infectious period during which individuals can transmit the disease so So think about it. In the end of May, a few months into the pandemic, the government still didn't know the answer for that. A very good work. If you want the document that explains the pandemic, go and look at this document. They really did great work. I only can compliment them, but they still didn't know the answer. And partially because the answer is more complicated than the question they asked for. Here are some models that you can find on the Internet of people trying to model infectiousness. And I took the references you can find out here. Some of those models, I made some liberties with them. I made some corrections. Some of them i had to copy by hand but you can see the different people or different models sometimes it's like they took information about one infection sort of person sometimes it's models or models of disease and you see the models become different all of them here are normalized so so you will see if it actually they count according to the maximum shedding of the virus, which is considered one. You see the relative shedding of the virus for each day since infection. Day zero is their own infection, then you can see later how it progresses. So you can see if i change the infections as different people have different infectious curves which is one the correct or which is the average one that's the question the model will attempt to answer but what it will actually do it will take all of those into account because it's an ensemble it allows you to take multiple models and model them all together then um the next type of category of models or transmission models which ask you uh if you encounter a person what's the probability of actually uh and the person has covered what's the probability of you actually contracting that parent counter this is the number in percentages it may seem low and i'm actually well it was very surprised about it however um and in the beginning when the first publication i found that it was like 0.6 percent but but the numbers there were a little bit uh but the numbers there were a little bit underestimating the pandemic. So later on, I found out it's more about 2%, the numbers you see here. If you fit in higher numbers, then the numbers don't fit. The epidemic spreads too fast. So this has to do with multiple factors. This is only for one encounter, but remember, This has to do with multiple factors. This is only for one encounter, but remember, there are other things that affect the disease, like number of encounters, which is not part of the transmission model, but population density is. Some states have higher population that may affect the transmission. There's random elements like people coming out of state, and therefore they were not part of the state population, that may affect the transmission. There's random elements like people coming out of state and therefore they were not part of the state population, but they still affect people. So it's a random elements that you add. And finally, at some point I noticed that the temperature, sorry, some states had numbers that were way off and we figured maybe it was something to do with temperature. So we had the temperature because both biosurveillance and the human behavior affected by temperature, some colder states, some other states. So models two and five here, each row here is a model. So models two and five have elements that have to do with temperature effect. By the way, they contradict each other. So if they have the same influence, they should cancel each other out. So these are assumptions about how transmission happens. And we'll see later how those behave. But one thing, the element that is missing within the transmission model is the response model. A response model is human response to the pandemic. This means behavior, human behavior. So I'm modeling the response model, how many interactions people have compared to what they had before, how the number of interactions changes. How do you do this? Well, you can take estimators like Apple Mobility data. So Apple Mobility looks at how much people look at their phone and they click on the map app. And they record this and for the entire population of states, they provide you the information for each day so this is one estimator and i took two models that changed the variable that used that some changes model number three is for people and i believe you know people like this who ignored the pandemic said no i'm gonna not gonna change my behavior because of the pandemic i know a few people like that um and i heard the other people also saying so there are some people like this so this is model number three model number four and five this is eric ferguson uh actually this is me taking eric ferguson data eric ferguson montclair university he published information he did analysis of one state it's here in this reference what i did is uh later he sent me information from all states and say when did the state close whereas calls and bars and restaurants open or close a non-essential shop and so on and so forth and each state had different measures so this affects the behavior of people in different states so model four and five say people comply with those measures 50 percent of the time or 90 percent of the time those are two different behaviors and this will change the number of people you interact with to people only in your household or only or the amount of people you usually interact with so all of those are variations of how people behave and just like in society people behave differently each one of those models will represent a part of society when we model things. Finally, if we have infectious transmission and response model, we can actually calculate how many people get infected. But after they get infected, some people die. So mortality model is kind of complicated, how they are constructed. I'm going to show it to you visually. So this is how mortality models look like. This is a probability of dying according to age. According to your age, this is the person will die, but we don't know when the person will die. This is important when we start comparing numbers because the person starts getting infected, eventually they will die at some point if they die. I gathered some numbers, did some calculations and assume this is Gaussian and or seven or some variation of Gaussian and this is when people will die so this is for multiple sources however this is not a good enough model I want actually to know at any day what's the probability of a person dying in this case this is where it came filipo castiglione actually made my model uh not only him but his example was very good made my model and multi-scale ensemble model what's a multi-scale ensemble what's multi-scale multi-scale is when you model what happens at the cell level or even if you all this before this at the molecular level in the cell level at the organ level then in the human level one the person level and then at the population level so you have different scales multi-skills and you cross scales so he actually crossed scales. He was modeling how cells would die. And then the organ will die at some point at some cutoff level and then the person dies. And he was basically modeling the propagation of the virus within the cells. And he can give me, because he was modeling this, he was actually able to give me an equation that looks like this, that for a certain age, for a certain time, you can find a probability. So a person at age 60, at day 20, has about 0.4% dying on that specific day, if they have COVID, of course. This is according to his model. At 90, it's about 1%. It's day 21, something like that. But remember, a person may die, there's a percentage of them dying each year, and you can see that there's a probability density function. This was a very good model to plug in, and it made my model a multi-scale ensemble, because I now incorporate models that will operate on different scales finally once you have the mortality model you can calculate how many people are infected and how many people died or you can think you can calculate then you come to the point that you compare those results with the actual results that you obtained from the COVID tracking project. And I have those results already. I'm not trying to predict the future or forecast the future. What I'm trying to do is I'm trying to make the results match all of those models that make sense and we can understand what they are. I want the best combination of them that will match the data. So this is where it becomes tricky because the data is dirty. We don't know how dirty it is sometimes, so we have to throw away assumptions or experts that will tell you how well it is and this is the observer model. The observer model models things as if the difference between what actually happened and what was observed what the observers saw so observer model number one the simplest one says for infections or mortality the numbers reported are correct meaning whatever cover copy tracking project told you about how many positive tests there were and how many deaths. This is the true numbers. This is what you should match to. However, Modelo number two, and most people think that those numbers are skewed. You can multiply the number of infections by five. You don't have to change the number of deaths because deaths are kind of harder to tweak. If someone died, then yeah, you know they died. But, you know, tests, if you test someone with COVID, you don't always test correctly, you don't test everyone. So, there's more variability. So, models number two and three says, say, multiply the number of positives you have by five or by 20 model number three to actually model the correct number model number four came from uh lucas botcher he he said that the number of deaths is something that has to be corrected and he has a publication about it he actually sent me a table of correction he also said multiply the infections by seven point fifteen and the number of deaths well it's very tired per state someday states were more accurate some you were less accurate he did the work I just took his words for granted model number five well this one is interesting this one i said you know what after we did one simulation we know what the numbers we got and we know numbers which you should have got let's create a correction that by average will correct it and we'll just plug it in just to make the numbers right this is kind of like cheating because i i already know what I'm supposed to be and I'm trying to fix my number. So apparently, if I'm correct and everything I'm saying is correct, the model number five should give me the actual, the best model because I already know what I should correct to correct it for. happens. Real life is actually much more interesting than this. Let's look at the results. So the results that you see here were computed, and by the way, I have many sets of them. I have more than 100 simulations by now, even more than 120, and I have 58 versions of the model. I've been working on this for about two years. Each simulation I do takes weeks, days, depends on what it is. This simulation took about three weeks to compute these days is how much it takes because i'm running heavy simulations uh think about it it's like three and a half years of computation on a single core but i'm using 64 cores to actually run this uh why because what i'm doing is i'm running a simulation of 100,000 individuals for each state. Think about it, there are 50 states, I'm running like 5 million people. And actually in the simulation I'm running 48 states. I won't go into details why, some of them don't have some missing data. So, but then you have to repeat the simulations like many times because each simulation is different just like in the world. If you repeat the same process, it doesn't, it's random, it will be different. So I do it, I do it more and more and more. And then I have to run it for all the models and I have to optimize it, meaning running multiple iterations. So all of this takes time. One iteration takes about two days for each simulation i'm running it's like five minutes so the five minutes go to like a huge number because i'm running so many of them i use python tools to do this i use dusk i use very good tools let's look at the results so the results uh look like this at the end. I gather everything in so I can show it's not wait. What happens? This is what the slide here does. If you look at this plot on the upper left, this represents what's going on each circle here you see there are many circles sometimes they overlap each circle here represents one time stamp on the simulation so you are started from april 1st 2020 and this is day 21 meaning this is april 22nd 2020 in new york this is new jersey let's look at New Jersey for a second. The height of the circle represents the error. I want all of those circles to be near zero. This means I have a perfect model. But tell me how to get there. It's far and it's random data. It will never be perfect. But we please want to improve it as much as possible to get insight on how the disease behaves so the let's look at the number called observed infected this is the number that i'm trying to match to the number after the slash the model is the number before the 323 is number before the 10th the 1076 is the number after this is what was 7 1076 is what was reported by the carbon tracking project out of 100 000 people for that day in uh in new jersey i'm trying to match it and you see it's way off at the beginning like 300 now it's a bad model number of observed deaths also it's like way off so this is why the circle is very high because the errors in both of them are high and what i'm trying to optimize or the error i call it the fitness score is kind of a combination of both of these it's almost addition but not exactly the equation is a little bit both of these it's almost addition but not exactly the equation is a little bit uh more complicated so what i'm trying to do is i'm trying to get all those circles to zero and how do i do it i change the mixture of the models at the beginning of the simulation all models have the same influence we know what the models are we know how the world should behave and or assume how we behave. And now we're at the beginning, we don't know all the experts, they told us things, we don't know which one's better or not. So what we do is say, okay, we believe you the same. And this is why you see all those blues, the blues are infection models, they have all the same influence, all about 20%. This means about 20 population behaves like one of those infectious scores like this 20 like this 20 and so on and so forth so then there are transmission models the response models the deaf models are the high one they are combined complicated this is why they are high. I won't go into details. And the observer models are pinkish in the end. What I do is I ask when I run the simulation for one, each one say, okay, what if you were more influential? What will happen? And then I run the simulation and run gradient descent algorithm. And it tells me, oh, you know, if this model was a little bit better, then the results would be like this. algorithm and it tells me oh you know if this model was a little bit better then the results will be like this and notice the circles have dropped so results became a little bit better and then a little bit better and now intuition then a little bit more better and you can see the average in the plot here or the bottom this shows you all of the components this is kind of like the average of whatever you see here the weighted average of what you see here considering the size of the state and several other factors sometimes and if you continue simulation you find around single iteration nine you get the best results and notice new jersey now now is much, much better. We're like 866,000 infections, not that bad. And the number of deaths is like, well, six off. It's much better than it was before. This is what the reference model does. Now let's look at all of those models again, and you can see the infectiousness model that had a long set covered is long and takes a long time to to infect a lot well it was very influential while models that are short infection not very influential this means probably covered is infectious for a long time this is the answer for the DHS the transmission model that is dominant the one about point something percent but if you combine them all the transmission is like two percent per person a parent counter not per person per encounter ah and then models two and five they seem to be not very influential, but also they're not very different than each other. This means temperature doesn't have a lot to do with COVID. And we tried, we tried to model C, but it might have some influence, but it's very mild. People apparently, the estimator of Apple mobility data seem to be pretty good because it actually, the models that says oh people behave like a couple mobility is very good only about 10 percent of the population uh ignore ignore it covered and some people but not that much uh followed the instructions or they gave by uh they gave in the uh they gave by this for each state but it seems that the and remember some of those models kind of like overlap in a sense so they will not eliminate each other very quickly but it seems that apple mobility data is a good estimator of how much people interact with each other. The Castiglione mortality model seemed to be very dominant. This is good news. This means we might want to have more models like this in the future, maybe more multi-scale models to compute the mortality better. And you remember the story I told you about the observer model that I added that should be perfect? This is model is model number five no it's the same as everyone else apparently observation observer models don't have much influence here and i cannot explain it fully now i've been to working on two years i have a hunch and the hunch is what we're modeling is something exponential in nature, almost exponential. The beginning, the spread of the disease. Well, those observer models are just correct by something linear. They multiply something. Maybe there's something wrong with them and maybe this is why they're like you're trying to. Model something exponential with something linear, maybe this is why I don't think pressure but maybe something else it may be even the bug and I found bugs in the what to use of working so and then I've eliminated many of them so whatever I'm telling you here you have to take with a grain of salt this is the best I could tell you about after two years of modeling it doesn't mean it's ground true because I'm still working on this and ask me in a year how well or how well it looks like um this is you remember infectious and vhs what was asking is by dhs apparently iteration number nine this is the best combination of the models that i created that probably explains best according to all the data and all the computations i put in the model so far this curve is actually the answer the ths would have gotten if i had those tools ready or in the future if there's a future pandemic and we want to figure out how infectious it is, this tool can be used by only gathering statistics and figuring out this curve from those elements. And at this point, I won't bore you too much. You weren't asking too much questions, but I want to conclude. So everything I told you has to be taking the grain of salt. I've been modeling this for two years and I've made mistakes in the past. Like I thought that the transmission was 0.6% in my first publication. Now I found it's more like 2%. Talk to him in a few months, I might change what I'm saying, but these results that you're seeing has been stable for a few months now. So I'm probably onto it, but I still have to make sure that the numbers match not only April, they match May, they match June, and so on and so forth. This means more simulation, it means a lot more computing power, a lot more work, then we'll know more. So the more information you have, the better it is. But I'm trying to figure out what would the government or someone wants to know about the pandemic will know after about a month of data. And if we know that and it is helpful, then the next pandemic will have a tool to help. There is some information about what I use, the Python tool I use and some some reproducibility information. I have to acknowledge many people who helped by providing help, giving me models, giving funding for also institutions, giving me funding for computing power. There are so many of them. And this work, maybe I'm signed on it, but many people did contribute and did help this make happen. Even by hosting my server, it takes a lot of electricity. Even that was helpful. And since I'm a sole proprietor, I have to add a conflict of interest statement. So you'll know where the money came from for this. So I've been funding this project most of the time i do have two two uh two patents on it uh so anyone is interested in using this technology in the future uh and there in the us they better talk to me and uh i would love to have this technology used by someone big entities smaller uh i would like more people so i'll conclude at this point and open up for questions if you have any um yes i i was thinking on with covet 19 we have have like many many um i don't know the name like variants i mean we have omicron and so how that impact on this model so this model is talking about april 2020 even if there are variants it wasn't it wasn't dominant or there was basically one covered at that point also remember i i'm not modeling reinfections because there were not that many people infected. So it's a simple case. The idea is that in the future, I can complicate this case more and more and more and more. And with more and more computing power, I will be able to eventually explain the disease. And here are some caveats. Remember, the data I have is dirty. The assumptions are not always correct. There are many things that I may not be able to do. I'll actually show you one of them. This is a result set that I got a few days ago, I think a week ago, something like this. This is examining the same April, but here I'm looking at the different fitness score. Now I'm almost, I'm saying almost because it's not entirely correct, I'm almost ignoring infection data. I'm only looking at death data. And I'm trying to optimize just to match almost only death data with very slight influence of infection. And when I do this, you can see the system almost doesn't optimize anything. And the results are slightly different. So does it indicate that the death data isn't correct? Remember, I'm talking April, so all of those numbers are small. There was not enough to accumulate by at that point. So there isn't a lot to model in a sense. Maybe this is something that also have to be taking into account but i'm trying and i'm showing you that well it doesn't really converge it's kind of like fluctuates on the same thing it does some changes here and you see the models are slightly different than what we had before but but this simulation doesn't seem to improve anything so the now get to really improve maybe it's because i didn't have enough computing power maybe there's something else blocking it in the past i had a bug that would block this by the way i removed that bug and now it's better but like but now it's not converging so why is it not converging well maybe it's because the numbers are too small and at that point in time when i try to model it with the amount of data that it's it's insufficient maybe we'll see in the future i'll try when i add more and more and more data little by little will become better and better and better so but do i answer your question yes yes thank you your question yes yes thank you any more questions uh well also about the fitness um yes for some cities like New York and LA yeah so here New York let's take this point so the fitness score here it became better this is 54 but it should be twice so it's like way off the model at the beginning the assumptions i added the beginning to try to explain what's going on didn't didn't add up correctly the model was terrible but later when i improved it by optimization got to the point that's like an iteration nine i think it's the best results it got it almost point on compared to what it started but 1400 a little bit overestimating out of but it should be 1300 and the number of deaths is uh 90 again a little bit overestimating should be 78. so it started underestimating and overestimating, but if we go and look at one more iteration, you see it goes up again. You see the model at this point starts fluctuating. It says that there's a conflict between New Jersey and New York and some of the others. I will show you. You can see when one goes up, the other one goes up. So it's trying to fit in all of those models to exactly fit it, but all of those changes are relatively small compared to what we saw in the beginning. If I had much more computing power, then maybe we can run it for much more to be much more accurate. But remember, all of those simulations are random and we're modeling a random occurrence. a random and we're modeling a random occurrence. What we saw, it's like if the pandemic will hit again, it will may not be the same thing as we saw before. We saw during the pandemic, different states starting behaving differently all around the world. So I'm trying to find some sort of average in something that's fluctuates all the time. So putting more computing power, I'm not sure I'm even modeling something which is accurate, like the numbers I'm getting are dual representative phenomena. Maybe it's some sort of error that I'm trying to model at this point. So I'm not sure. I usually stop after a few iterations. But even in iteration four, you can see, you already see, this is where things are stabilizing more or less. Duration four is almost the same as nine. You can see New York and New Jersey are much better than they were before. Here you can see New York is overestimating again, but it's much better before. Do I make sense? Yes. I was also thinking of maybe this is actually a problem with the source probably, I mean how they were reporting the cases because as far as I remember the cities were the most impacted by COVID so maybe they didn't record properly the cases. This is where the observer models should kick in. You remember I told you the observer should correct between the number that is actual and what is reported. The observer model should do that. But the observer models don't budge. You see, it's like they don't, as if they don't influence anything. I'm still, I'm still baffled about it. This is currently what I have I have talk to me in a few months maybe I'll figure out why but like currently my hunches that it even if you give it here I had models though that you have to move to the number of infections by 20 it is the same as influences the model that says no the numbers are correct so something there is off I don't know why like it doesn't influence anything maybe because the things that really influence the pandemic is the transmission. This is the models, the infectiousness, like how infectious it is. They have much more influence and we just didn't get there. I'm not sure I'm flying hunches at this point, but you'll baffle as much as I am. But this is the best I could explain the pandemic after about two years of work. So with the best tools, I think they are the best, but that's me saying. Okay, thank you. Thank you. Thank you. Thank you. If there are no more questions, maybe we should adjourn. Yeah, I think that sounds about right. Just have a little thing to read at the end. Okay. Okay, well, thank you for coming tonight and we'll have another meeting coming up. I believe in this. Oh yeah, no, next year. This is the last. This is the last meeting of the year. Yeah, well, thank you for coming and thank you for giving such a interesting talk um yeah it was really nice meeting you yeah the same here um it was a pleasure thank you for the nice audience good questions okay have a good one bye-bye you upload this video, make sure to edit the part in the middle. Yes. Just take it out. Okay. Bye-bye. Bye-bye.
PyData Chicago: The Reference Model for COVID-19 attempts to explain USA data, by Dr. Jacob Barhak
For more details about the talk and the speaker, pleaser refer to https://www.meetup.com/pydatachi/events/289899473/

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