 Good and we have two persons. We don't need to introduce them. So that's for more than sorrow. It's also in the new Okay, so today on this last session of Day two we have multiple presentation. We talk about climate health and one health It's perfect. Yes. So to start with we have a Mr. Peter from Ministry of Natural Resources and energy. So he will environment. Yes Monterey, that's what we call for short Affirm so if we can come around and present that what Ministry of Environment is doing in law Hello Good afternoon So I'm Peter Mokamprao from Department of Meteorology and Meteorology in Laos So I would like to present on how to met data monitoring and transmission in my country So mostly in Monterey, so they have like many sector we live into the climate, but for The information is based on the image. So it just focus on The image to do tea and adjust briefly and I'm not going in many details So the online I will brief about the team is organization and the way function of the damage and also the monitoring and system and monitoring and transmission and the last one is focusing So the image is one of the organizations if you belong to the Ministry of Nature really saw as a recall in Monterey so It consists of a six division like an initiative financing division and climate and hydrology division hydrology division and hydrology network and the quick division variation division and weather forecast and the one is division so I'm working with hydrology division. So mostly my duty is like a tool modeling and data analysis for water level forecasting and so like a platform So the function of the DMH DMH I usually In part of the So where installation like an extended hydrology and methodology and network Station and also management and maintenance of the station and improve on the methodology direction if it is some place or some station is broken or Some equipment is shot down and DMH can go to the field and to the station and it still looks nice Equipment and also monitoring collection the data from the station and you evaluate the design tool like a tool to do data Control quality and then we insert all the data to the database system and for the cooperation and with the others organization so we Like a coordinate and cooperate with the agency organization like for us example like a water leak source Department and climate change department and at the other research and then especially we Coordinated with the MRC because of we got some budget from the RC and we monitored all the station a lot and we got river And if you also exchange the data and my information in hydrology and methodology skill to the other Education Institute especially the university and college or like a secondary school Sometimes they came to DMH and they would like to see I'll look up the hydro material equipment and you can share them and explain to them Yes Yeah, these are so now deep age operate about 190 station of the hydrology station The climate station But we still have a no one hundred and nine station like a manual station. So the station will have to use people To manage every day, so we just update about 84 station to be the automatic recall For the reform also update 66 main one station and also just a little bit of a station about and For the data collection method so every day we have to Collect the data from the local so decent from the main one station By what's a proof if some time solely get a data at the 7 a.m. And 7 p.m. So some of them in like a We had the conditions not very good and you cannot share by bus app We have to call them right to the local office and to get it And then after we got a data we made a data quality control and check the data quality and then we can Say that it up to the I don't want that company that like a Brigades and department by email and then we after you got a data So data belong to the maker river To the hammock that we can share these data to MSE and then after we got all the time We make analysis and check the data quality and if some time Maybe Heavy rain or some strong will Hit some part of the law country that you can share the information to the social media like a tv or radio and So like if you also have so this like a real term station like auto meditation So we have several Don't know to support us like jica korean chinese and word bang and so on And then they have their own like a web page blogging and to access to the data So now it's not integrated yet And so this is just so only the page of the word bang So like I saw station from the support from High government to see And also we have like a flash flood monitoring system So but now we just finish only Installation only for Spacing in loud like a tweeter upper part of the country between the middle And then the system consider for station and water level and the warning post If The heavy rain came and the water levels again higher and higher until the reach the water level So then the system will allow to the community to know that Maybe the water level will get Higher and higher until it can flood and then the community can move to the safety place so after that Then the last one is for forecasting after we got all the data from local area And then we set to the statistical method to analyze it to make forecasting and we also I use the model. This is like a film model This is our model is support from MRC and then we run this model and model and combine with the weather information come on from Japan and from Vietnam and from Korea also from Thailand to combine together and And then we can after that if you are someday they make Imperial public in the country Then we can report this information to the government and the government can issue the official later sent to the local community community and after that we issue the Weather information to the government and we can share the information that by to the social media like a radio It's made by a TV network and so we have been so close on the watchable place to also I say like I like forecasting by youtube and Yeah, that's all for my presentation. Thank you very much Yeah, so we take sorry we take the question after the presentation Now like I'll invite Dr. Achala to Talk about what's happening with the the climate health initiative in law. Thank you, John Good afternoon everyone. So I'm going it's going to be very quick. I'm going to explain the activities we have planned by the WHO country of peace in collaboration with other stakeholders Over the next five years recently we received grant from the climate fund For a project to be continued over the next five years this in collaboration with Save the Children so this project overall help the law government to achieve this sustainable development for goals overall like three seven six and thirteen. I'm not going in to details So in this project we have four outcomes I think for us, I think the second outcome is the most important and But let me start with the first outcome that is the the health systems governance and leadership is climate resilient So with the government of laws we develop health strategies policies that incorporate the threats presented by climate change and increase increased climate climate resilience So this this includes the developing training materials and the training and trainings at both national and sub national levels Then when it comes to the strengthening health information systems Integrating climate and weather data into the the routine health information systems As you have heard from Dr. Chancellery this morning in law us we have 10 years old well established HIS2 based national health management information system I'm not going into details because John will explain in in his presentation more technical details currently we have integrated rainfall and temperature data into the national HMIS and already we have disease data for example dengue, diarrhea, etc So basically we are trying to integrate this data into one system and uh using Integrating a system called EVOS early warning and early warning system and try to predict diseases using RR codes so and make the health system resilient to climate-sensitive diseases prevention, diagnosis and better management of climate-sensitive diseases using disintegrated data So that that is basically the main outcome Then we are we are trying to integrate other indicators like such as wash water and water sanitation and hygiene for example So this is the basic overall overall plan for the outcomes for next five years So I'm not this project will be starting next year I'm not going into details because we are still like in the the planning process for the the detailed activities and I think John will present how we did this With the integration between the HIV to N EVOS Thank you Okay, um now like we heard about the The what ministry of uh environment is doing so how they are collecting the data even though like it's only focusing on the hydrology But they have multiple different divisions um in many countries we will have many different departments and divisions who are collecting Weather data, which is not only when we just talk about weather data climate Okay, the temperature that's fine and then the rainfall. Okay. What about the air quality? But the problem is air quality is in other division So we need to first identify which are all the key stakeholders Are there so that like we can try to negotiate with them work with them And to see like how best we can get the data and also coordinate what we think are I'm stealing a few of the slides from uh chancellor This is the the slide which is showed in the morning Where we just say in law we have multiple systems where they're now trying to integrate All the different health programs in the ministry of health But now they are moving ahead not only focusing on only on the health But like the what are other things are related So this is the something like what we've been trying to work with with the ministry of health and also with the WHO law team to try to build some kind of modeling first thing is is The impact of climate change is it like we all know there is climate change is happening, right? But does it affect the health? So that's why like we we can always see like in the context of plow the rainy season is getting shorter and more Intensive we can see here every day. We are getting rain But in Sri Lanka, you can like I guess like you can also support me if it is right or wrong The rainy season should have been ended This is a dry season, but we are still having rain So there is something happening around and then how does it affect the all the The our health side prediction and other things That's something which we want to try to look at and when it is dry season It's really really hot and warmer and humid. So that's also like something which we need to try to to work with So First we just saw what we have In in dhi store in any countries in law. We had all the disease surveillance data from long time We had by the villages and all different things. I'll explain to you I'll show a few of the demo when I'm starting with it But like here like we had a good disease surveillance data in dhi Which is collected at the village level So the health worker is collecting and they're also collecting. Okay. This this is happening in this particular village In law each and every village is mapped to the gps point so that we can analyze those data Not only by the health facility, but also by the villages So saying which villages has more dango cases or which villages has more diarrhea or more white So they have this is surveillance form, which has been Imported or worked with multiple departments like psi NCLE department and also with WHO so they're all collaborating together to Implement one system which is in dhi as to but training the support looking at the data quality Is not only belongs to ministry of health, but also with other departments That's how we can try to make the good data for disease surveillance. That's as been established has been working through So What we try to do like just to show is there any correlation between weather data and Diseases so we took dengue as one of the things because like it's been spreading quite a lot Last year the dengue cases was in In windchill capital because like even in like in hanoi and all most of the capital has dengue But this year it was very different then could didn't happen in a in the windchill capital It was in the neighboring province. I just say why it was supposed to we know that there is a dengue season We all know So these are these are the months for the dengue season, but the places were changing So these are something which we can try to we're trying to predict And try to just see this one up So what we did was is to work with Unions of Gothenburg in sweden. So they were created a model called evan. They have developed their researchers So they developed a tool based in our language where if you feed in all the disease surveillance data Yeah, plus the climate data the rainfall and the min and max the temperature and then it will predict these are all the Possibility what happens? So you might get dengue in these particular places and all So we wanted to try to test that model. So for doing that one first we went to Monterey and say can you please give us all your weather data? So then they gave us the all the csv file with list of all the different things for their weather station Then we imported all the data into DHS to in DHS to we already had the The daily cases of the dengue and everything. So then we matched By day every day we match the the cycle and all the things I'll show you that one up And then we just see like what is the core relationship we can derive or not derive So for doing that So what we did was DHS to health workers collecting all the disease surveillance data And then like we took the metrological data and imported into DHS to And then from DHS to we send this file to the e1 system Which does the all the prediction modeling and other things and then we put it back the output of that The system into DHS to so that like we can analyze not only we but like many people can analyze the data Because one of the main thing it should not be only at the higher level But also at the local level where the people can able to analyze different types of data So this is what we've been trying to do and then also created some kind of prediction Like in DHS to we have this notification all the things like if you have that we can try to deal with So these are some of the charts. It does not look so good here But like during the the demo like it looks fine. Odumsa is one of the province So it is based on by different provinces what we created And this was like rainfall temperature and Dengue data in the same screen And this is what's the one of the output from the e1. So then like we get like all these things Model and then like we can just see how best we can try to include and where are all the different places Dengue outbreak can happen The this model is not we are not saying this is the only model Then like when we showed this one, there was also one other group called Haya They were using the Google satellite and other things and they were also developing their own predicting model So when we come around to to this place So DHS has all the data, but we can have not one model but multiple model From different different places. The model is just the prediction, right? So sometimes this model might be working sometimes that model will be working So we need to work with the universities and the places to find multiple models And maybe this one works for Dengue but not for Sari So we need to make sure like how are the different diseases are working and which model is good in predicting the the accurate the data Okay, just going very quick This was the test thing So how this will help So we can try to just say whenever we have some kind of this diseases and all things we can actually Tell them give more supplies We take more ORS just especially for the heat waves or like the the air quality and all different things we can try to include So and also with the diarrhea we can the health work can be prepared well So those are different things what we can try to do and also with the education people we can try to do So now let me do a quick demo Hopefully it works The first thing what we have to do when we go to dhs2 is to check whether we have logged in or not Okay, so logged in perfect Oh What's happening now? Okay, can you see my screen? Okay So I was just like checking if I was logged in. Yes So this is the the output what we have Through in dhs2 So like if we just like see around here, this is for the whole law pdr Where like the lines are the the rainfall And the top one is the temperature So we took it from like 19 till 222 So 19 20 20 20 20 24 years data. So we try to put it in and then the all the lines are the Dengu cases So this was like when we started collecting the data and all different things and if you see Then goes not so much in 2021, but 2022 started really increase So that's also something which we can try to just see so that's what's for the law for the overall and then we try to drill down to different Problems. So this is cinema which in capital and then we go around. So it's the same in Um, Samana keith and Urum Sai if you see in Urum Sai oops Urum Sai there were Dengu cases many years But like if you go for capital and all the it was not So that's why like it can keep on changing. So we need to this is only at the province level, but If you have good data Even the weatherfall data and other things at the health facility or the district So then we can do much more So right now this was just the proof of concept that like there is a core relationship And there are all the different things and how best we can try to work to Okay, this is the other things so in dh is to like I guess like Tomorrow Austin will also show a few of the things about the maps How do I drag this one down? Okay So I want to show you to you this one What's happening in law? So these are all the Dengu cases What's there? So like this is not the location of the health facility but location of where it happened because we mapped all the villages So when the people are reporting what the reporting is they are selecting the where the the person is from Which province which district and which villages when they select the villages those points are linked around here And these are are not where the health facilities are but actually where the cases are and then In dhs too like you know that we can always link with the google earth engine, right? So when we try to do so like here if we just see we have Elevation temperature land cover And the population so all those things there we can try to overlay on our things So right now what the map you are seeing around is Is the law and then the Dengu cases and these are all the Uh the perspiration Yeah, so now let me just say We don't want to do that. So I'll just like close And I'll just see the temperature Okay Just a bit drill down a bit more So like here when you right click for any place, let's just go much more detail Let's just see here Show the temperature So like here like you can practically just see what was the temperature on that particular day when the Dengu happened we need to also Improve our analysis techniques, but like at least like we have some things But like the units the faster the global team are working very closely to to have more modeling and more easier way to do the cross-correlation Okay, so if you want we can also it will gives you all this all this data is not in dhs It is taking directly from the the google So when you try to in your dhs too you can set up the google the The url, which austin will show tomorrow Then like you can get all this data for free and if it's ministry of health, so you get it for completely free And then you can use all different kind of analysis out you can also see latitude longitude land cover you can also include like say a perspiration So like here you can also see the the whole data So all those things are coming from other places. So we have this one like Two three years back already, but like we need to start trying to use these features Not only just collect the data, but also try to just see these are all the tools what we have in our ham But like we need to like just like yon was saying, how do we use the data? This is also the tools to use the data for our own purposes Okay So let me You can also include elevation That's also something which we can try to do especially for if it's too high That like there is no dengue cases or easier cases. You can also use the land cover like for example This is when we are talking about dengue, right? So then like we can just say So these are all the places. I'll hide this I'll put on the land cover So these are all the data which is you are getting it from the place So then we can just say let's just see this area south So if you have a very good internet then you can get like even more details You can also just see build on where right Close You get the the whole details. What are all the different things? What's the hectares and everything for that particular place? I This one is also something which you already have the organ profile where you can just see what kind of facilities So and all things you have And let's show the the data also So these are all the different details which you can try to just see So and these all these details you can get it more more easily. Okay So i'm not going to go around too too much details, but You can try to add on and try to see how best we can try to analyze this data Okay, so there are two things one way is Import all the climate data into dhs too so that like we can try to have this modeling Then the other one is use the the google one which we are showing around here So that like you can try to deal with all the In the raster way image itself So there are two different variation this one. We don't know how to predict yet maybe beyond The person who's developed this app can like he's been working very closely try to see How best we can try to use different types of data so that like we can make better decision Yeah, so with this one like i'm done Now i'll have one more thing before i leave So law has got funding 2.8 million something 2.8 million dollar for including climate and health 28 sorry 28 million 28.2 million Small grant according to the things Yeah, so these are all this is already online. So now like what we were trying to do is to work very closely with ministry of Environment and the health so that like we can try to work on so there are needs to improve and collaborate But like we've been writing we've been doing this one for the last two years Showing them there is working and then we need to have an mou between the two different departments Also, maybe food and the agriculture department so that like we can collaborate very good I also want to show you one other issue like which Was very nice when we visited the montreal office So these are all the weather station which already we got from In the morning So what they have done in law is to How this this is for everyone anyone can in law can download this app Which gives the weather forecast and just say what is good for agriculture or farming So many countries have already been done that one. So it's very good way So this app like what we are thinking if we can identify key things then we can just say this is a dengue season coming please make sure Have the mosquito nets and all different things. Please don't open the or don't leave them the water things So here like they are giving up the advisory for farming So we can also give advice saying that this particular time there will be heat wave Make sure that you drink more water So and this is for the livestock the animal and all things based on so they work very closely with the agriculture department and they have made Co-relationship between climate and agriculture So now what we need to try to also do is to include help Agriculture if it's affected then like we also have Nutrition and other things what you can try to include up So these are some kind of factors what we've been trying to just see and see then how best we can try to include all things Okay, so now I hand over to to ula to explain what unicef oslo is Is doing on the climate change Thanks, John. Yeah So I think we've seen Through the screen the climate front. There's a lot of interest. We were approached by Another foundation welcome trust in the UK, which is a big charity foundation about a year ago They had seen the impact of the HS2 and the scale of the HS2 across so many ministries of health in the world And wanted to invest in climate and health and saw that there's an opportunity To work with the HS2 platform to see how you can integrate very you know very similar objectives to what we saw from lao If you can integrate with the climate data in the HS2 then you can reach health workers and health managers That can make decisions on on climate and health Um So this is an eight year project. They grant us funding for two years So we have basically two years to prove that this is a good concept Uh, and that means that we need results at country level. Um, so we are working quite actively with all the his groups with potential countries um to try to innovate and and see how we can support these objectives Uh through the HS2 platform We also work with a lot of partners that can add value to the platform You saw the example from lao where they worked with the university in Gothenburg on this modeling we have uh Experts on on data modeling and and the climate and health modeling in in Barcelona super computing center We have a machine learning group at the university of Oslo that will be engaged And we'll try to to bring all these experts together to see how we can develop tools That can be applicable across countries and also then bring all this learning from all these pilot countries together And then try to come up with uh, something that's generic Unshareable and kind of follow the same principles as we do with HS2 So this will include of course to make climate data available in the HS and you know, there is some there already But I think we can do a lot more I think we need to find out What are the needs and john mentioned, you know, we need to Find out what this what data and what information products decision makers need at different levels to be able to To act on this data, and I think that's something we need to do with all of you with the countries By working with the local experts So I think we've identified a few different use cases. I think most of them have been mentioned already and the the idea is to overall look at You know the different diseases Health programs that are affected by climate change of this one is around the early warning systems for climates and stiff infectious diseases We saw dengue mentioned in law There's a similar project that's been running for a few years in musambique looking at malaria But but very similar kind of type of approach where you you bring the birth data and all the climate data together with the Historical malaria cases you run models and then you can predict and send back the details to Information on districts where there are potential outbreaks of malaria in the coming month, for example And I think that's those are kind of proof of concept that we need to take further We need to see how we can improve it Make it more readily available for for other countries another use case is to look at Droughts and how that affects farming So we mentioned agriculture But then of course there's a link between the food security food production and the nutrition programs Which also affects the health programs. So that's a notable and then At the bottom area where we listed a few other kind of examples that we mentioned in the proposal to welcome from You can look at early warning systems for floods heatwaves Cyclones is extreme weather events that will affect Health programs one way or another and can help to prepare To meet these challenges air pollution and respiratory diseases. It's another kind of link. That's potential to look into Basically, it's quite open in this welcome trust project. What what the linkages are, but it's about Supporting the ministries of health and health managers to to make their health system more resilient to climate change Yeah, so what what does this mean? We need to to work together with multiple countries Uh, we need to understand the use cases better their information needs better We need to engage stakeholders not just in the ministries of health, but also in other Government entities and local universities. We need to work with, you know, local Institutes of meteorology or other government bodies that manage the climate data and then we need to pilot innovative solutions There's no way that Austin and his team can develop a lot of core functionality and release them in time for this, you know, rapid innovations, but there are a lot of customizations that we can do locally and then eventually we can bring this into the core Software and then more available to across all countries that are So this means really kind of working on the action research approach that Jörn talked about yesterday linking researchers implementers and developers locally across countries and also bringing in expertise from various global parties And then rapidly share what we learn across Across the his groups across countries so that we can uh move fast together. That's basically Thank you. I don't know Austin if you want to add anything more from the technology perspective what the plans are Do we have like I don't want to add that I think it really Mentioned there's a lot of ways that we can kind of build technical parts of this around And here's a We're just trying to bring in a digital source in there to be able to It's a And then also to get some of that health information into models Find a very high resolution find a data from local other states And then during the back of the The predict things to do identify potential stock outs for health warnings So really really bad opportunity and it's not It's not like many many years down the road for the agi two-component areas using what's already there software and Having a lot of evictions surrounded The And then maybe you should mention that In terms of kind of announcing this work is a cop 28 in the boy December in the boy Welcome we'll um digital square We launched this project and there's uh for the first time. I think dedicated health day Linked to the cop meeting So a lot of emphasis on this also from double h or global Yeah Good Yeah After The last thing I mean Uh So Not all of these cases are Of course And I Listen Uh But You Together This was my Some other Yeah Yes, they were okay, especially in our bunny areas where the blue blasting Very good By an audience I think we also need to try to include some of the media players I don't think in some decisions that people are trying to also do that one, but not not in a holistic way And then like it's also where the data is staying one place a place if we share across different departments in the health We can still get all the data Right now like the my department they are collecting about the Their health workers or their staff it's going down kind of checking for the Vector phone thing. They have them all just they are also collecting Whether they know But that is not shared to them so that I can use that data in other place That's again like if there are it was happening And then if we should not we should like actually just see what's happening and then we can share the data and then We can try to just see like what's going to happen in this particular small area where there are so much spaces So it should not be We are in the human behavior data for the whole country then we are overburdening our health workers So we can try to identify in this area. It could be due to the current at this specific This area we don't have to do that one because there is no real threat on that area so but even after when we have an actual level or The reason that we are telling the ministry of health We just say you should try to state that then they say they will include in their registers and all the things so that All the health workers in the whole country are collecting that that doesn't make any sense, right? So those are some things we think they can practically work with So So this happens and we know that there is a demand source of the data source for our whole family and that some national levels are in the district and deep and I know of others coming into the solidarity demand list and there should be a lack of data and on that we can do more of this. That's the. I mean, I have been. So Ishael and some of the people, some of the audience is a bit of an actor that's acting. We do have a lot of other things. Some of them are extra, extra, some of them are just to be done by the police. And it's just a matter of how key people are going to be able to do that. I have reached the dynamic of the program. I have taught Ishael to get some feedback. We thought kind of the safety area and the size and more of the learning process. We all know that the reason that you are a people is not because of what you're doing, but because of what you're doing. You're going to be able to take your life and stick to what you're doing. But it's a matter of how we connect that data if you want to be and make a contribution. That will be the future of the program. That will be the future to see research on how to do the best you can. Any questions? Okay. Before we move to the one health, I wanted to ask how many people are using DHR students? Yeah, using DHR students? Yes. So how many DHR students instance has these layers? The population density, the elevation, which you are subscribed to? So all these things are coming from the Google, not storing DHR students at all. But what you have tried to do is to subscribe. Just one email. They quickly reply within a day that you will get all the things. Okay. You have a bigger group. It's fine. Anyway, I'm not going to show how to do it. That Austin will just say. So tomorrow, if you want to see how you want to configure, attend Austin session. So I'm promoting your session now. Please make sure that these are all the things. The first thing is you have these two. Generate two included. The second one is this modeling one. That's different. You have to talk to your different people, get the actual data that requires a bit of effort. This one is just couple of email and configuration. That's all one person can sit down for a few minutes and fix all the things. Then you can get all this data and then you can try to analyze how things are. Right click and see, see on the visualization. What's happening? And then like we can ask, okay, this is not working. That is not working because beyond, we have this map guy, which Austin will just say. Some we can also give. I want to see this kind of data, this kind of data, this kind of visualization. Yeah. Austin. I just want to say that one of the maps that we've been given to us, the big part of. And obviously because. Yeah. Yeah. But one of the areas that we're really looking into now is. These are very powerful layers. They're just for the, the type of the iceberg is much more fun and fun, but for what. What. So much data out there. And so much processing and modeling capabilities. And that we can tap into, if in the future, maybe for that data, that's the ability to be accessible to people that are using. This is going to be fun. I'll talk more about this. I mean, it's like a full plan. expert tool. That's how we're trying to, but try to get the data in front of the. People that are actually thinking about these things. So that they can make decisions. Based on. More layers with different sources, more. Using. Bringing in additional sources of. Localize and global data products for. Climate data. And for that as well. More. More course. Temporal evolution. Yeah. Layers here. And I'm looking at how do you, what do you want to know? What is it? This is a little problem. And. And. Water. They shall. There's a lot I think we can do without a lot of. It doesn't take a ton of work. To get that point. To bring some of that. Into. People are. Thanks. Good. So now we go to. One health. Any questions. Any questions on online. Let me just quickly just see. From Nepal. So this is from the other Nepal. Let's say common things. Okay. So now like we are touching into. A different area. Not on the climate, but also on animal. Because like in many countries, like you know them. The health gets. Most of the funding right. And the animal side. They get very less funding. But they are also collecting all the data. They also have the workers and all the things going around. Collecting all the different places. Different data and all different things. The only thing data, which we used to collect. Is rabies. Right. So that's what like they're usually in the health system. We always have rabies as one of the key things. And then like we just say how many dog bites. And all different things. And all. But here, like what we've been trying to work with. With the CDC. And you know, Steve Osler. They've been. Actually just trying to just see how what are the. Coalition between the human and animal health. We're not only just talking only about like what is the coalition. But like how best we can try to integrate the data. Between. A human side and the animal side. So any human side. In the countries where they're using DHS to. We have a proper structure. Where every. Every day or every month we are collecting the data from the. Health facilities to. Distrait and everywhere in. In the other side. It is basically dealing with. Different ways. I just see. First, like what we talked about. Let's just identify some of the key. Scope what we want to try to integrate. And how best we can try to. Co relate and work together. And see what data. Can be exchanged between. The animal health side. And the human health side. And then like we can just see how that works just like. Like yesterday when you on was mentioning. Okay. So. So CDC actually funded like. This project and they have identified. Three countries. One is in Tanzania. One in. Liberia. And one in Cambodia. To. To see like how best we can try to learn from these pilot sites. And what are all the key indicators that we can. Say at the global level at the national level. National level. Will be useful to collect and. Share the data between both the places. Okay. So one is. And we are still at the also learning phase. They have done a very extensive study. In. Tanzania and Zanzibar. Have the multi. Lateral agreement between sharing the data. Using the data. From one particular place at all. And I know that in Indonesia. You're also doing something on. Collecting the zoonotic data. In DHS to. Similarly in DRC. Which one will explain them a bit later. Be prepared on. Okay. I'm just going to go quickly through. These are just the same thing. How best we can try to. Configure in DHS to. I'll share this slide. And also it's we can try to look at it later. Yeah. So these are the different alerts and notifications. We already know in DHS to we have. All the notifications alerts. Predictor where we can try to send. Like then how does this both system works. So. We always collect this IDSR that this is surveillance. Park in DHS to where. Health facilities are reporting. That's fine. But when we come down to the. The other side. Which most of the country use. To collect. The animal side data. To send to the global level. So you press is a system where you log in online. Which is a one system you don't have to. In a country, but like you need to have your. Access. So then like they enter all the different data there directly. And then. Use it wrong. It's also depends on which department or organization are using it. Usually it is FAO or. Food and Drug Administration. Especially in law also is FDD. Which try to use this one. So. Here what we try to just say if there are. Some kind of like they're also going and testing all the. Animals in their farms. And then like if the. If the chicken or things are like sick and all things that. Also transfers to things. When do we have to notify. That we have in this particular area. There are like so many chickens are dying or the bird flu and all things. So then like we can try to talk with. The health department people. Or the other way around. Where we can try to set. Send some. Information back and forth together. So these are the few of the things. We just go through. Me. Yeah. So like here. This is the one instance where we're trying to collect. This is from the integrated surveillance data. No. First thing what we want to try to use was. Just the case base not collecting any. Any names or anything. Trying to enter the data and. And all. And we just get aggregate data. I logged in on the. Things. They have done the pilot study. So what they did in thing is to. What system currently exists in the country. And who are the key actors. So that's what the one thing is what we try to. They try to find out. On the both on the health side. And as well as on in the animal side. So where the data is coming from. Those are all the different things. And then is the first aggregation level. In. Health side our first aggregation level is district. In for example, in law. In other countries it might be sub district. Where we are aggregating the data. Not the health facility health facilities where we are. Giving the treatment and recording. The first aggregation level. We need to identify in the animals. What is their reporting. The same in most of the countries. This was the one of the things what we've been. Trying to just. Some places that. Let me just. So if you how many people have. Heard about Empress. Only Oslo only us. Okay, so how many people. Yeah, food and drug. Department FFO for example in Bangladesh. FFO. I was in Bangladesh for FFO right. So where we are trying to deal with. That's where also them. In law the food and drug department. They're also dealing with AFI. That was even following immunization. So they are the one who is responsible for dealing with all the. And the agriculture side is FFO. Yeah. So the FFO. And the HR. There are a couple of. Issues. Yeah, and. At the, the goal of this one is. Unicef Oslo and the team who've been working the global team. They want to try to create a package which can be installed. In your. And also data collection. So those who are the different use cases. But right now. We are just still starting to. Understand how the country works. Based on this three country pilot. We will try to. That's already been started. This year. To working on doing the initial study. Good for your own country decision. Okay. So these are some of the. The analysis what we're trying to do. Very quickly. I will ask you to explain. Since he. Is here. And he. I know he worked. He. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Oh. Yeah. Yeah. So we will try to put most of the documents around here. The reason why we are talking about one hell. Like in the climate. We cannot just be blind. And like focus only on the health. So we need to include. and like focus only on the health. So we need to include climate. We also need to look at the animal health and see how do the data cross-relate. Yes, like there are different structure. It is not our department, it's our things, but we need to be aware and like willing to integrate the data and see what's happening because everything's happening in our country. So we can work together and see like how best we can try to get the data across. That's the whole point, the things, the idea behind it. I can also say that the sample is very different. The dry areas, like in Africa, and also in the Asia, where we live from animals. So it's a big problem. And that creates a problem in the nutrition and we're starting to achieve it. And then more and more and more happens. So it is a centralized hour in a little bit. So we can talk about climate health thinking now. So with this CDC funding, we've just been identified as four things, but it's also like something which we can try to think through, find out how things are, find, do some more research and all different things, try to get more information and like have a way, like how best we can try to integrate. Maybe in your country, you already have a very strong empress or the animal health system, which can be benefit, which can benefit the health. So those are different things which you can try to work through cross-sectorally. Good. Any questions or are we all tired? Since it's the last thing, we can like close at 5.25. No, no, no. It's okay. Yep, good. So enjoy the city tour.