Pretah smo zvoje dobro gotovali. To je nekajto, da sem se pravda nekajte načinne ljudne urablje. Danes splala mi nici. Da je bilo več in razpravljalo. TheAz At the World is a big set in the Blackboard is artificial intelligence, so the Blackboard is artificial intelligence and we don't care about everything that is outside this circle, this circle is machine learning. Zelo. Zelo je tudi tudi v 3 kategoriji. Tudi tudi, ki je to početno, če je supervisljerno, če je in supervisljerno, če je reenforcment. In sem tukaj o supervisljerničnih, v prvih različnih različnih različnih različnih različnih različnih različnih. Lekasno različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različnih različni are neural networks. So there are various forms of neural networks. The most simplest form is the feed forward neural network. Then there are other type of networks we are going to talk about, convolutional neural networks, or there are more complex networks in which the information goes back and forth between the neurons. These are called, for example, recurrent neural networks. So this should give you a bit the idea of how the field is categorized. Actually the distinction, the boundaries between what is supervised, what is unsupervised, what is reinforcement learning are not so strict. But this is just to give you an idea. And essentially in supervised learning, as I was saying, you have a set of data with associated labels. What distinguishes it from unsupervised learning is that in supervised learning you just have the data. You don't have the labels. And I'm going to talk about that now. And in reinforcement learning is a bit different. You have some information coming to, let's say the agent, but not in the form of data. And we will talk about that even later on. Oh, yes. Deep learning is nothing else than feed forward neural networks in which the number of hidden layers is relatively large. I will explain it better later on. It's supervised learning. It's feed forward neural networks. Let's say. Or whenever you add the word, the adjective deep in front of any of the neural networks means that there are more neurons. So, other question? OK, so let's continue with unsupervised learning. So as I was saying, this category of machine learning tools are characterized by the fact that there are no labels in your data, just data. And the goal is to essentially find informative patterns in your data. By this, I mean things like the example over there. So you might be given images of the type left and side there, so apple, dogs, bats, whatever. And you might hope that giving just those images to your machine learning algorithm, it will end up in somehow dividing them in clusters. Of course, there are no correct clusters here. The point is very tricky. But you might think that, OK, in all the pictures in which there are animals, there are two spots that are not too far apart, one from the other, OK? The eyes. So at some point, you might think that the algorithm might detect the fact that there are eyes in certain images and there are no eyes in other images. So you might divide these two sets, one from the other. And it's useful to have this type of classifications then because when then you have a new image, like, for example, this one, the algorithm might recognize it immediately without, as an animal, without needing to test whether it is an animal or not. So without needing to test whether it breathes or not. For example, if you have to feed it, et cetera. And this, of course, can be useful. You can imagine in whatever application you like, but for example, in medicine. So there are various methods here. So let me list, since I've listed just now this one for supervised learning. Let me list some of them here. One that we're going to see in detail is principal component analysis, abbreviated as PCA. But then there are others of which are pretty common. One that is, if you want it, you can think about it as being inspired from statistical physics. So these are called restricted Boltzmann machines, RBM. There are also here, you can use also for unsupervised learning neural networks. And in particular, what they're called generative adversarial networks. So you can already see that the boundaries between these sets become quite blurry because I'm using neural networks to do something in unsupervised learning. So you can use neural networks essentially in everything of this. Autoencoders that finds, if I'm going to explain some of the details of this, it's going to be too long, so let me skip it and let me go directly to the one that I wanted to explain, which is principal component analysis. One thing that we can use for, I think for which we can use unsupervised learning is for what is called dimensionality reduction. What is dimensionality reduction is that essentially reducing the, so your data comes with very large vectors in general, but not all of the information that you need, not all the components of your vectors encode interesting information for you, maybe. So you might want to reduce essentially the dimension of your vector. You want to find the important degrees of freedom there in case you want to use a more physical language. And there are various ways of doing it. For example, autoencoders can do that, but we're going to see how we do dimensionality reduction with PCA. Principal component analysis is something that, say it like that, might sound, at least to me at the beginning, sounded with a physicist's formation, quite strange in the end is diagonalizing a matrix and truncated for the eigenvalues with small values. That's all what it is. So I can explain it pretty easily here. So we have, as usual, our set of data in the form of vectors, XI, and each of these I, sorry, each of the components of my vector might be p real components. This p can be called features, are usually called features. So those are features of your vectors. So you can imagine some data about me. One feature can be how tall I am, one other feature my weight, et cetera, et cetera. And you can have many people, not just me, and this index I denotes the various people. And you can store all this data in a large matrix, let's call it Z, whose columns are given by my vectors X1, and say X2, et cetera. And I have n of them. So the column represents the single samples, so the single individuals, like me, and the rows represent single features. So in the first row you have the height in centimeters of all the people in my example. In the second row you have the weight, et cetera. And the main goal of PCA is that it returns the most informative basis, in a sense. What do we mean by basis here? Well, this is a matrix. So a basis is associated to it. And this is called the one that is given to you directly by your data is called naive basis. And the most informative basis will be given by a combination, so in the easiest case a linear combination of the most informative features here. So how does it work? Well, let's see first maybe an example. Ah, that is it. This is a nice example because it's very much related to Northern Ireland. So you can see that here there are let's say four individuals, the four nations of the UK. So one nation is England, one is Wales, Scotland, and the other one is Northern Ireland. And you have the average food consumption on average per person per week in each of these four nations divided in various categories of food. So first I'll call it drink, then beverage, et cetera. And okay, you see this data and you have to start wondering, okay, what's the difference between here and there? What's the difference between Northern Ireland and England? So in this case it might not be too difficult, let's say to spot that for example in Northern Ireland the consumption of fresh fruit is pretty tiny compared to the consumption of fresh fruit in England, in Scotland and in Wales. And you can see that of course the other way around is when you talk about potatoes, because Ireland is famous for potatoes. So in fact there is a high consumption of potatoes in Northern Ireland, higher, sensibly higher than the rest. So for this type of very few data it might be extremely easy to, let's say, find important features. And in general that is surely not the case. So if you use PCA, which I'm going to describe in a second for this set of data, you will find a principal component that is composed essentially of potatoes, fruit, cheese and alcohol in different weights, such that it's a linear combination of these four components essentially with different weights. And this is now going to be one single axis and if you plot the four nations on this axis you can immediately see that the cluster appears. So weights England and Scotland are cluster together whereas Northern Ireland is far apart. So this is the axis, the basis on which the largest variance can be found in this set. So the largest variance in which sense, in a specific sense. So let's give, to immediately understand this, let's give again a silly but physical example in which you have a spring moving and you have a camera taking a snapshot of the spring moving. Now the camera is not positioned perfectly perpendicular in a perpendicular position with respect to the spring. So if you plot it in this naive basis given by the camera, the various points in which the ball is you will find something like this. Which of course we are physicists, we recognize that this is a one-dimensional motion so there must be just one important degrees of freedom, one important feature. And it's simply the one that you might have if you rotate your camera. Well performing principle component analysis for this type of easy problem means actually to do a rotation. And this is the rotation. Those are exactly the same points, but rotated. And we say that this horizontal one is our principal component and you can see that there is a large variance on the data on this principal component much larger than the variance that you have in the second component. OK, let's stop here and let's explain the mathematics behind it. And you will do a tutorial exercise on this so on the computer with some real data actually coming from photonics. And so here we have our matrix here. So essentially we want to remove redundancy from this matrix. And so one first point is that the data have to be centered. So they have to be centered to their mean. So first center the data for each feature such that the mean of each feature is zero. And then once you do that you can construct immediately a mean free correlation matrix related to matrix Z. So we can construct a correlation matrix let's say we divide by the number of data that we are given. And if you construct this matrix you can immediately convince yourself that the i jth element is the average of the feature i times the feature jth so just a correlation between the features. So if you want to find the most relevant the combination of most relevant features what you do is that you diagonalize this matrix. So when you diagonalize it you might plot the values of your eigenvectors and you might see that some of them are let's say particularly large and then others are relatively smaller. And if you are lucky enough you find a significant gap. When you find that you can say okay this in this case for eigenvalues are the ones that will correspond to what we can call the most relevant eigenvectors and in this field these most relevant eigenvectors are called principal components.  Question was I... Yes. So the question is that my data can have different units can have very different you have to take that into account. So you... I would say that one can normalize this data apart from centering them you can use their own variance within each feature. Yeah, you can use that but I think that one really needs to put the hand on the problem and maybe also have some initial intuition of what can actually be the most important. Because if you normalize everything with the variance then you screw a lot. So you might think from the very beginning OK, my... the consumption of neo-Zelandis fruit in the whole UK is not going to be relevant in any case maybe it's all consumed in ways. Yeah, it needs some subjective initial filtering and interpretation of that. Other questions? I'm going to give you again a couple of examples from quantum information similar to the ones that I gave you before. So for example here we have performed... it's another silly example you can take a Werner state same as before of course there is only one parameter but if you rotate it like I did here by... this single parameter is not immediately there from the data. You diagonalize your matrix and you see that in this case there is a gap of scale one of length one from the first eigenvalue to the other significant eigenvalues. And if you add a bit of noise to this data so alpha... sorry, you add some white noise to all the components of this matrix then this is not going to be exact and these are going to be slightly blurred and once you plot your matrix on the first and second principal component so the first two eigenvectors corresponded to eigenvalues of largest value you still can see a high variance on the first principal component and a much smaller variance on the second principal component please note the scale the scale of the horizontal axis is much larger than the scale in the vertical axis. And here I just plot so these two different colors are not obtained with PCA but it's just to give you an idea that once you have found a certain... once you have found your principal component then you can further analyze your data so for example you can distinguish in this case whether the state is entangled or not and it's going to be much easier than to use for example other supervised learning method to distinguish the state if you have found just one or two degrees of freedom that are relevant rather than the full matrix and this is shown in this picture a more complex example again slightly more complex is when we use two qubits so if I take again the x states you can see that as I said before the x states three degrees of freedoms and if you plot the x states in the axis determined by these three degrees of freedom you plot for example here is the expectation value of sigma x times sigma x, here you have y and vertically you have z you can see that these x states die in this tetrahedron and here I distinguished the inner part in which the states are separable and the outside part in which they are entangled just to say that there is a certain structure here we know that there is a certain structure once you have identified these three axes but if you rotate these qubits these structures is again blurred and you cannot see it however if you first do principal component analysis and you see this is an example again of these rotated x states in which there is some noise added so these three eigen values are not exactly the same but still one can see a clear gap and if one therefore rotates accordingly or plots let's say the original data accordingly to these new bases these principal component bases the structure that I was mentioning here so this geometric structure reappeared now here it's a bit difficult to see maybe yes so here it's a bit easier because you move it and again I colored in order to make it more clear to your eyes I colored in blue the entangled states and in these other colors that I don't know what color is it I'm slightly color blind the separable ones so at the beginning these x states they have I don't know 3 plus 4 plus 4 so 11 degrees of freedom but once you find the three relevant ones then you can use them to further analyze your data in easier way so that is a possible application of principal component analysis and other examples are for general for quantum tomography you will see Luca giving to you an example of this in the tutorial or for example entanglement detection and things like that questions? if not I can start with reinforcement learning so this is the last of the three main categories the one that I was mentioning here so you can think of reinforcement learning as a set of machine learning methods in which for example you want to teach to a robot how to work in an unknown environment so you cannot really use the other methods because the environment will be unknown so you cannot train the robot on an environment for which you have a set of data a set of labels like don't go there because there are stairs and you will fall down I'm not aware of that it might be exactly the same I really don't know the question was if this is the same PCA used in social science thanks so I was saying that you can use supervised learning to teach your robot to work here in this environment but it will tell nothing to your robot if you now put it in Adriatico guest house so you cannot really use these tools so the generic setting is the following so there is an environment will be in a certain state and it will indicate this state as s and then there will be an agent your robot for example that can or let's say the brain into your robot that can observe the environment there will be some observation and based on that observation can perform certain actions to the environment means action for example to the robot itself the robot itself is part of the environment this is the agent is the machine learning algorithm that teach what to do to whatever is inside the environment and the agent usually gets information not just from the observations but also from some rewards which can simply be I managed to walk around an unknown room without falling down for a certain amount of time the largest amount of time possible so you can give a reward for every 10 seconds that you stay up and ok the setting therefore is very different with respect to the setting of the other two cases because you start with no labels and no data the environment itself can be dynamical in particular your robot can move or someone can move the objects in the room and the agent can observe the environment can perform actions and can get rewards and the goal here is to learn the best sequence of actions that maximizes the rewards the rewards can be very sparse so for example the rewards can be just at the end of your actions so the question that I was asked before about how to treat situation in which you have really small amount of information this can be a setting so this is a typical example of this setting so the state here the state S of the environment is given by the full map of the position of a player that wants to this red thing that wants to go around and get more treasures this green spots as possible moving around and gets more treasure so the action will be moving and the rewards are these treasures collected that you can imagine at self driving cars medical applications etc there are various methods for reinforcement learning some of the most used one are policy gradient that I am going to explain with some details Q learning Q learn there is also another one that I want to mention projective simulations and I am measuring it because it was not invented by computer scientists it was invented by Briegel, Hans Briegel and collaborators who is a theoretical physicist working on quantum optics etc. quantum information science and they first developed it in the classical setting because they wanted to apply to the quantum setting so there is already a quantum a full quantum variation of this but is this strict because this would be a nice moment to stop us so questions? see you then at half thirty


T F N N headline news update Good afternoon folks Steve Rhodes coming to you live from the shores of sunny Del Ray Beach, Florida This is your 1 p.m. Update coming of all the usNC's trading to the upside You've got the Dow up 772 points two and four tenths percent the S&P's up 114 two and seven tenths as well Nasdaq 100 three and three tenths percent 441 points nearly 3% for the Russell that's 57 points the some eyes up 123 Nearly 4% there trainees up 450. That's just a little over 3% you've got gold trading down 53 bucks That's off 2.6 percent 1990 is the print lights we crude. She's trading down at 110 90 and change sitting right at support. It's off 12 bucks We'll go take a look at that during the Trader Zed show natural gas is flat in the 30-year Treasury is down 18 ticks She's traded 156 16 as we take a look at our nine panel market update chart We begin with the ES many nipper left-hand corner. You're gonna see it's a big rally The price is still just trading with insight that no these February 24th swing point We just this is this is just a sideways ish move at this stage here No may get a little different picture of price can close about 42 90 get outside that February 24 swing point And what that would do is likely say that price would go target Well, there'd be about three different areas the bottom of its weekly profile and that's at the 43 31 level the center of its the center of its Daily profile and at descending trend line spot follow tunics is still well above its 50 day exponential moving average Is any kind of rug pull now you can see a big swoosh to the downside the NQ is also a big rally But just trading inside that February 24 swing point out here, so it's basically done nothing We just have this little sideways move out here in order for it to get Semi-bullish price would have to close above 13 983 US dollar index is pulling back I can't remember the top that it has out there. I believe it's a TD 9 count top But price is forming a new daily profile It's attempting to form one and so what the US dollar should do is find support at the 9773 level we won't know till tomorrow if that a new profile forms gold is pulling back It is above the top of its daily profile out there silver pulling back is above the top of its weekly profile lights We crew there's a new daily profile. It's also trying to form here The center is the area where it found support and that's at the one oh eight 86 Natural gas point back to test support that's its daily profile 442 and the 30-year treasury needs to hold 156 16 otherwise if targets 153 30 say tool for the traders and show up you have to start George Wednesday have a wonderful


Welcome back to the breakfast on plus TV Africa, our next major conversation is going to be talking about, you know, simply review of the Nigerian health sector. We currently are in a pandemic. The government has announced that we, you know, are in the fourth wave. And of course, in the news this morning, we spoke about the poor vaccination figures that we currently have in Nigeria, and also the very, very poor level of testing with regards COVID-19. But that's not the only health challenge Nigeria currently has. There's still La Saffiva and a couple of others. This morning, we're going to be speaking with Dr. Tui Mabawondu, who of course is a regular on the breakfast. Good morning, Dr. Tui. Thanks for joining us. Good morning. Thanks for having me. We also have Dr. Babatunde Ipaya, he's a former health commissioner in Ogun state. Good morning. Thanks for joining us, Dr. Ipaya. Thank you for having me. Good morning, Nigerians. Good to have you. All right. So I'm going to start with my Zoom guest, Dr. Ipaya. Could you really help us paint the clearest picture possible of what Nigeria's health sector currently is and what it currently looks like across board? Well, thank you just to just re-echo what Nigeria leaders admitted for the first time during COVID is that many of the leaders admitted that they never know that their health sector is that bad. I could remember the secretary to the federal government of the federation and the chairman of the presidential tax force then on COVID said he never knew that the SESM is this bad. And so what COVID actually was to put on the front burner the appropriate status of the Nigeria health sector to describe the Nigeria health sector is just to say it's a sector that is underfunded, a sector where the best of the brains are lost to other climes after we have subsidized training with a system where efficiency is not the all mark of the services and that where we say something as a priority and then do something else. The national health policy that was first written in 1988 and the latest version reviewed in 2016 says in the primary care is the cornerstone for health care delivery in Nigeria, but that is in a level of care that is grossly underfunded. So we say that is that should be our priority, primary care everywhere, but of course it is a list that is funded by the federal government and the state government. And so the challenge is first to to to mean what we say and then put our money where we think our priority should be. Okay, well, it's scary when it's described that way, you know, remembering that we currently are dealing with COVID-19, you know, and nobody knows what 2022 is going to bring. But I'm going to go to Dr. Mabawon do now, you know, from what he's described, I don't know if you would agree with, you know, his description of Nigeria's health sector. But you know, can you also share with, you know, us, we have over time had conversations about improved budgeting percentage for health in Nigeria, but that doesn't seem to have worked.  So is it because of a complete lack of interest or does the Nigerian government maybe feel that there's really no need to spend so much money in health care? We're okay. Well, I want to reiterate again, like Dr. Iqaya said, what he said to the government of federation, what he said, April 2020, he went round on a tour, or he held facilities to assess the capability for response to emergency. And instead of listening, he saw a hospital with our beds. He saw emergency room with that ambulance. He saw an ICU with that power supply. And he saw hospital without doctors, without health workers. He was scandalized. And then he came out and told the National Assembly that our situation is actually pretty bad, worse than he actually thought. Yes, he will say that because most of them don't actually get treatment here. And if you do a cross-section and try to look at what the health is in Nigeria, our life experience has got stagnated. We're still at 54 or there, but maybe for women 55, a bit more than that. And then Malaya kills as much as the whole burden of Malaya in the world. 25%, one quarter, Malaya deaths in the world is in Nigeria. Martina deaths in the world, 20%. One out of every five of them died in the world because of pregnancy. It's in Nigeria. Then under five mortality, one out of every six died in this country. And then we now come to the budget issue. That is the country we're facing. And you saw doctors, about three doctors, to 10,000 people. Far, far. And then you now come to the budget. In 2001, Nigeria hosted the whole Africa, the whole world, in Abuja, and said that we're going to commit 50% of our budget to health. Rwanda has achieved that. South Africa has achieved that through incremental improvement in their health budgeting. Nigeria is still at 4% at the best, 4%. And most of this 4% goes to where recurrent expenditure. So there's no clear cut thing. And then look at it now. Recently, I see about 8,000 Nigerian doctors are now registered in US. Everybody's emigrating. And they are emigrating internally or they are draining externally. So you see a whole system, corruption everywhere, poor performance, monetary, and indices of health everywhere. And now COVID came. Last half of it is ravaging. Colour is always there. Management is there. And then we have the chronic illnesses, diabetes, hypertension, typhoid, all sorts in a country where we have now seen that health is not just a social concern, it's not just a social concern. It's a strategic security issue. And we're not getting that. See, we are under investing. Corruption is not really the place. And then doctors are on strike, at least this year, twice. We saw doctors strike in April. We saw another one in August. In one of the doctors' strikes, the president traveled out to go and assess health care. Two months, doctors were on strike from August till October. So here we are. It's a very precarious situation in such a way that if you're sick now, you just have to end up in a church or with one babala to help you get well. OK, so let's bring in Baba Tundepeye right now. Let's talk about the structure of the health care system in Nigeria. Of course, you know that so we can actually have quality health care being decentralized. You have the primary health care, the secondary and the tertiary. How far do you think that we're fairing with the structure? Well, thank you. I'm actually a community specialist. And the primary care is the baby in the tertiary. And you recall that at a time in Nigeria, there was a minister called Uliko Duransomukuti that committed enough federal resources to primary care and the International Coverage was as high as 80% to 90% one of the best in the world, you know, and then you could feed primary care everywhere in entire hospitals, no matter wherever you live and be able to get quality medications and so on. And so today Uliko has been celebrated. And so the problem is what has taken us away from that because the health care needs, you know, my colleague has spoken about, you know, the malaria border in Nigeria and how Nigeria constitutes almost 25% a quarter of deaths due to malaria globally. Of course, it is also said that 11% of maternal deaths in Nigeria is due to malaria and, you know, maternal mortality of about 586, 400,000 lives, but it's nothing to be proud of, right? So if you look at the structure, as you have said, the primary level, which is the health care of the majority, deserves to be funded by the three tiers of government. And unfortunately, for some or two reasons, everybody assumes that primary care government, the least resource level government should take over that. And I kept asking everybody, including myself, where was this written? Who said and who gave the impression that primary care government that, and we also know how local government and location is being managed in Nigeria. And even beyond the payments of local government workers, hardly do you see anything happens at the level of local government. And how can you now, you know, give the responsibility for, you know, delivering primary care in the hands of the local government for every sake? How? If you look at the national health account, OK, the local government financing for health is also problem for 2% of health care financing in Nigeria. And if you say 80% of Nigeria needs primary care, so even the little that is committed to health in terms of budget, we need to do far, far, far, far more primary care. And if you now take that side by side with secondary level care, which is usually seen as responsibility of the state governments, you see, you see secondary facility structure everywhere. And the, and many governors who want to build, you know, general hospital and award contracts. And at the end of the construction, what happens? So why don't we make the few ones functional? And if you develop, you know, a hub and spoke system, where the primary caretists, you know, care of the health care need of the majority, then the referral to the secondary care will be minimum. And we need less secondary facilities as we have in Nigeria. You also have seen recent time that everybody wants to build, you know, federal medical center teaching us to everywhere and this teaching us to likely manage malaria, you know, prevention, treatment, sexual and things that should be ordinarily be managed in the, in the, in the, in the primary centers. And so why should professionals and consult and sit in the teaching of the two, we create the kind of money they have made and they are, they are treating malaria and other primary care level diseases. It's, it's, it's seen efficiency. And so for me, what we need to do is that here's the street here, we need to actually get to a point. And if you go to small countries, Rwanda, my dear colleague at National Care, you will see that every drone you see in every city, every major city, the quality, the ice of quality, you will see in the remotest part of those countries. So if I have the most, the high quality anti-malaria in the rural part of Nigeria, why should anybody get to Shagamo teaching us to, to get to check for malaria? And so if you, if we must, if we must, you know, you know, bring efficiencies into our system, we need to reorder priority for investments and we need to bring further resources to support the primary care. We need to bring state resources to support primary care. And more importantly, like my colleague has said, there must be supportive supervision because even when a little is given to that level and, and, and I'm privileged to know because I've supported, you know, the control of HIV, TB, and malaria over the years in Nigeria and I see that even some of the commodities that get to that level also get into private sector market and they don't deliver, you know, the care for the poor and the middle. And so there must be a significant level of supervision which is lacking. And if that level of supervision is provided, of course we will see a system that everybody takes advantage of. Lastly, yes, I agree with my colleague in the studio that financing is, is, is poor and the Abuja declaration 50% never has ever met. I know that Nigeria has gotten to the eyes of some, some percent of healthcare financing that is less than half of what you committed to. But even when you compare Nigeria funding to some other countries, and I'll give you an example, per capita investment in Nigeria, between $100 per capita, okay? And then if you go to Rwanda and country like Kenya, it's about $39 per capita but their system is more functional. So the question is, why are they spending less per capita and getting, you know, you know, better at? Markdown mortality is like in Nigeria and of course mortality is like half 50% of that of Nigeria. So, so the question is, we are spending so little and the little that we are spending is not efficient, it's not effective.  All right, Dr. Mabon, the final question to you. It's pretty embarrassing. Some of the statistics that you mentioned with maternal and infant mortality, the fact that the giant of Africa is still dealing with Lassa fever. You know, we've only just got over our polio not long ago, I think sometime this year. I don't know if we are completely free from polio but once again, embarrassing. Was Nigeria just simply lucky with COVID-19 or do we still not have actual clarity on what our COVID-19 situation is and we're just, you know, braving it? Your first and foremost, the figure we imagine around the COVID-19 is a suspect. Obviously you can't test 3 million or 26 million and be using that as your statistics. It's not, it's not proper. If we are going to look at the major intervention that we say that this is top notch in terms of COVID, the first is testing. Okay, because the testing tells you, it's not short, this where I am. This how many people I'm having with the COVID, the severity and then this got the response. Now, okay, now you've just done how many? 3 million or just about 3.7 million testing all over. I am not improving on that, that one is stuck, fascination is stuck. So I don't want to actually look at the COVID-19 figure. But again, the fact that we're not seeing mortality, you know, as seen on the other parts of the world may actually be subdued to the other factor which we might come, you know, to realize later as research advanced. So but again, we cannot be trained our luck and say, okay, you know what, God is so faithful to us. We won't have cash royalty because even if this Omicron variant is just 1% later, if you take 1% of 20 or 6 million, it's about 2.6 million people. That's a lot. And you don't have the system that's gonna cope with that. So I mean, when we bandaged those people, I just look at it that we're not talking sincerely, we're not talking from the point of science. That aside, you know, what has happened to the health system in Nigeria is what you are seeing is silos, everything's in silos. You know, the tertiary is in silos like this operating, the second is in silos, and the primary is in silos. And then the public health, the private health system, I just also, everybody's just operating. We must find a way of integrating all these health systems to maximize the health worker we're having. I don't see why we cannot have as an ambitious program, every blessed Niger with NIN should be linked to a primary health center, 10,000 primary health center, barely 10% of them functioning, functioning. So how are you gonna deliver it? Health system is not building. So how could we not pay attention, you know, to the primary health care, you know, which is very, I mean, closer to the people like Dr. Babatili mentioned. He talked about the fact that there are some cases that should be treated, you know, at the primary level. For instance, you have had a malaria and what have you. In most cases, you find that, you know, being attended to at, you know, the tertiary or maybe even, you know, the secondary health system. We see what happened. You see, people already cleared from their leaders, okay? Have you ever even seen any of your leaders attend your secondary or talk of your primary? Is any of your family attending it? There's a student in remote areas in the corner or in the corner, packed somewhere. And there's no attention to read. You enter the place. The place is not properly equipped. We have to look at our funders, you know, to help prop up primary health system. The workers are not motivated. People are not working there. The place is looking anyhow. So how do you now think that people will be, you know, kind of happy to enter that place? Sometimes you get there, you queue forever. I did a study, you know, delay systems, you know, when I was in Sweden, working on delay systems in health systems.  But you've studied the whole day, trying to assess simple care in the center. It's not, that's not it. We must be able to leverage on technology and rule every Nigeria under one doctor or one health system as it were. So that in a way, there's a triangulation. You talk to that one. That one can find a way to refer you to the same system. And then also, let's use virtual hospital because now that is becoming the key issue. Because first of all, we're short in human resources. We're short in equipment and all those things. We must be able to use virtual things that can take people away. And then we have to push our prevention aggressively. If you don't do all these things, we'll be here and we'll be recording bad health statistics. And poor health is poor economy, is poor security. All right, we of course would have to end that here. It's not a very, very bright picture, beautiful picture that has been painted from our two guests this morning concerning Nigeria's health care. But of course, it's not breaking news. A lot of people know these things and have also been somehow, some way victims of a poor health system. But we'd love to speak with Dr. Babatunde Pae once again. He was the former health commissioner from Minogu State. We'd love to have another and of course an extended conversation with you again sometime soon. Thank you very much for joining us and for sharing with us this morning. We wish you a very beautiful day ahead. Thank you. And also Dr. Tui Babatunde, thank you all. So good to see you again. Always a pleasure. Interesting, thank you. Well, that's so much we can take. Thank you so much for being part of the conversation. We hope that you enjoy every beat of it. We'll definitely come through tomorrow. And in the meantime, if you missed out on any part of the show, it's all right to follow us on Facebook. We're at Plus TV Africa and Instagram at Plus TV Africa. Now subscribe to our YouTube channel at Plus TV Africa lifestyle.  If you have yourself a Merry Christmas. And I am also getting a ball. See you tomorrow.