 Hello, everybody. Good morning. Good afternoon wherever you are in the world and thank you for joining today's webinar on the DHS to climate data app. I just like to let everybody know that we are recording this webinar. It's also being live stream. I welcome everybody that is joining to be able to introduce yourselves in the chat and also you can use the chat to ask questions throughout the presentation. We will have a session at the time at the end for questions and answers. And we'll also use our community of practice to address any questions that were not answered during this webinar. And please feel free to contribute to the community of practice as well. My name is El Galloreno. I'm the project manager for the DHS to climate and health project. And I'm joined today by my colleagues, Patrick Omeo, the climate and health regional implementer based in his Buganda and Andrew from Ministry of Health Rwanda that will be speaking today on climate data app as well as the Rwanda experience of installing the app on the National HMIS. Next slide, please, Patrick. And I just like to say that this climate app is made possible by an investment from Welcome Trust into the HISP center in order to build on existing DHS to systems for the help of developing climate and health analytical and modeling tools and data visualizations and dashboards. It's a very exciting initiative that we are diving into. Next slide, please. And as we've said before, in the past, DHS2 is very widely used from 70 ministries use DHS2 as a national information system as well as another 10 countries using it at a subnational level. So we're looking at how countries are already routinely monitoring health outcomes that are influenced by climate change, as well as information that can measure impact on facilities and health systems. And we're looking today at how climate and health integration can help provide a holistic view of how climate factors contribute to health risks and enabling a deeper understanding of the relationship between environmental conditions and health outcomes. Next slide, please. And I'd just like to touch on some of the benefits of using climate data in DHS2 to help health programs can be looked at planning interventions and resource allocation, analyzing and using climate data with routine disease surveillance data for near real time monitoring as part of an integrated HMIS, as well as responding to localized climate informed early warnings with targeted interventions to maximize effectiveness and costs. Integrating and making visualized integration and visualizations between epi data entomological data vector control data and routine data coming from a variety of programs. And as well as tailoring data visualizations for subnational use and automating the distribution of surveillance bulletins. So with that, I would like to hand this over to my colleague Patrick for next slide to talk about our climate data app. Thank you. Thank you so much, Elle and welcome everyone. Elle already introduced me and Patrick O'Mean, based at East Uganda, but supporting the regional implementation of this DHS2 for climate health. So, so the whole the gist of this webinar is really to focus on this application that is really centered around integrating climate data into DHS2. And so, so the overall picture is, if we are to leverage on DHS2 platform for climate informed health programs, then there are probably two things that we need to focus on. And part of this webinar is to focus on one of the products that has been developed. So we are looking at the first one of course is data integration, where we have to make sure that we're able to integrate data within DHS2. And then the other one would be that analysis and with analysis, we know DHS2 has done a lot with terms of if you're building the analytics. But for this climate and health work, there's the component of modeling that also needs to come in. So, so the focus will be on these two products. But for now, the focus is on the first one that allows for us to integrate data within the DHS2 platform. So as you can see this illustration, DHS2 within the DHS2 core platform, we have health data. And so the application that we are introducing today is just to try and integrate this climate data into this core platform, so that you can go ahead and do the analysis. And then the second part will be now, how do we do modeling and prediction. And for that, we are looking at extending the DHS2 to be able to support that function. That may not be possible within the core, but could be within the server extension of DHS2. And the way that would work then would be if you get climate data into the core DHS2, and you have that already with health data, then you could then pull that into the modeling tool, be able to do the modeling and prediction, and then be able to push that back to DHS2. So this part two will be coming later, but for now the focus is this part where we've been able to integrate climate data and specifically global data into the DHS2 platform. So how does this, what does this application do? So two main things that this application does, it helps to explore data, climate data, and then the second one does is to import this climate data, and I'll be demonstrating that shortly. Specifically, we are starting with three variables. The first one is temperature precipitation and humidity, but as need arises, this will be reviewed from time to time, and that needs to maybe be built upon. But for these three, we've been able to demonstrate that yes, it's possible you can do the integration. So the new component that has been added is the weather focus, the 10 day weather focus that can also be, you can also be able to have a 10 day weather focus. But this should be when you're looking at a health facility or a point location and we shall be demonstrating this so that you can appreciate how this works. But in the process of integrating this data, as you learn later that the data we're dealing with is greeted data. And if you understand the structure of DHS2, most of our organization structures are based on, of course, polygons and also point locations. So there has to be a process of getting this data that's greeted to be able to get a value that you can have for, say, district or health facilities. So there's that process of computing values that befits our DHS2 kind of structure. So the data we're importing is daily, but of course, as you analyze this data, you can come up with weekly monthly or even quarterly or you could also have an annual kind of aggregation. But the beauty is that once you've imported this data into DHS2, you can then go and use the core DHS2 features to do the analysis. And here you can use the data visualizer, you can use the maps and all the other features that allows you to analyze data within DHS2. And there you can do triangulation or any other analysis that you can quickly do within the core features of DHS2. And maybe just to mention again that the data set that we are integrating is called ERA5, and I will show you that shortly. This ERA5 land data is the most accurate and complete global climate data set that's available. And being a global data set doesn't mean that we are not interested in local data. There's a great need for local data and for reasons that, you know, plates ownership of this work, but also it helps to validate what we are getting from the global. So it's very important that we consider bringing local data. So again, this app, we are still taking it as pilot because there's a lot of updates that are ongoing to make it better. But it works, it's able to integrate air climate data, but there's a lot of feedback that we still need to get. So there will be a lot of updates and we would want that you frequently go look into the app hub to be able to get the latest updated version. And again, to say that this is not part of the core feature of DHS2. It's an application, but as need arises, some of the features may be considered to be integrated into the core DHS2. Yeah, so this is really how we are able to source this data. So the data source that we are getting is from this source called ERA5. And this source comes from the European Center for Mid-Range Weather Focus that goes through a process called Reanalysis Version 5. We will take you through that quickly. But basically the way they source this data is that when weather stations that come from national meteorology report this data to the World Meteorological Organization. This data is then accessed by the ECMW and then it goes through a real analysis process. So that real analysis process basically combines this data that comes from weather stations with satellite data. And then it's made available through the Google Earth Engine and then also the Norwegian Meteorological Institute that provides a web API. So for these two sources, we mean with these two endpoints, then this application that sits within the DHS2 is able to draw data through the Google Earth Engine and then also through this API. So those are the sources, those are the points where we are able to get this data. So one does pass weather data and then this one does the weather focus. So this is just a bit of a video that explains the process of reanalysis so that you understand I'll play it shortly. But just to indicate that this is a global source. And the resolution is a nine by nine grid and we will again explain that shortly and your variables. I mean you have data from 1950 to date and this comes hourly but we've been able to aggregate on a daily basis. We're able to get for an average for a day and you have up to 50 variables that you can actually generate. So I'll play this video and then you will be able to appreciate this process of reanalysis. Let me just take it back a bit. Okay. So let me just play for a moment. Today more vital than ever that we have Patrick we don't have any audio. I don't know if we can't hear anything. Patrick, can you hear me? Patrick, we can't hear the video. I don't know if you can hear me. Okay. So I hope you were able to get the video. Patrick, you were not able to hear the video. Oh, sorry. How do I play it then? Sorry, because I was playing under. Okay. Yeah. I think maybe we just move on and we can add a link to it later. This is an external video. We can show the link. Apologies for that. Yeah. So basically that was trying to explain that process of reanalysis that helps us to generate this data. That's a process that I go through. But from the reanalysis, this is the kind of data that you end up getting screened, meaning that you have this grid of nine by nine kilometer. So meaning for given area of nine by nine kilometer, you can get a value for temperature for precipitation or humidity. And now we have to go through a process of, because our districts, as you can see, are usually they're not in a square. They're polygons. And so we have to get an average for a given district to be able to generate a value that would be that we can store within DHS to and for a health facility to where you have a point. So if that point falls within a given grid, that is a value that we take. So that's really how this data is is calculated and made available within the DHS to. So for you to be able to set this up within the DHS to is quite simple to say the first thing is making sure you have the right version. And the version we are working with here is version three point I mean 2.37 and above should be able to work for you. Key to note is that your organization unit should be able to have the should be georeferenced, meaning that your district health facilities, whatever administrative level that you have your DHS to that you want to explore the data or actually import the data for you should be able to have the geocordinates of those locations. And then also you should be able to have your DHS to instance connected to the Google Earth engine, because that is what will help you do the connection to the Google Earth engine. And then lastly, it is important that you have to set up a data set or a form that would help data. I mean, that would help when you're importing data into a DHS to and so you have to create the right data elements, the data sets and then also the necessary metadata grouping. Yeah, so with that, then you should be able to import, explore the data and then be able to import data. So for exploring data and I'm going to demonstrate this shortly. So I will be a bit quick with this. The first thing is you definitely need to select your organization unit at whatever level that you would want to look at, and then you start the process of exploring. So the first thing you'll be looking at is the, the temperature. So with the temperature, these are the kind of visualization that you'd be seeing. And again, I just want to mention that we are going to demonstrate this shortly. But just to quickly highlight that for temperature, we shall be looking at both monthly and daily temperature. And also, we are able to look at what we call normal temperature, which one would sometimes call it expected temperature for a given month versus an average or a mean for a given period of time. And the difference will definitely help you to understand the change in temperature over time. We shall be demonstrating that shortly. And then the other parameter we are looking at is the precipitation. And again, precipitation, you should be able to look at it on monthly and daily, and also be able to look at the normal, which is the same as this, which is the same way we compute for the temperature. We are also looking at an average for 30 years and be able to compare, get an average value that would consider as your normal for a given month. And you should be able to also look at trends for a period of time. Again, we're looking at both monthly and daily. And then also relative humidity. We're able to look at relative humidity, but this one will look at it alongside dew point temperature, air temperature, but also we're able to compare the normal and the relative humidity. Again, for the daily we're able to just look at the, the average is for a given a day. And then lastly, no, not lastly, there's one more with this temperature change or what one would say at temperature normally. And here again, we're able to look at the normal is in temperature for a period of time, starting from way back in 1970 to the most current year. And here you can look at for a given month and see whether over time for a given month you've had some anomalies or changes in temperature that needs to be observed. And you can look at these two reference periods we shall demonstrate also that one shortly. And then lastly, we have a new functionality that has been introduced. And this one helps with 10 day weather focus. You can look at weather focus for a point location. And specifically here we could look at for a health facility and you're able to look at a 10 day weather focus. This one we are able to get it from the, the, the, the metro, the med web API that is provided by the Norwegian meteorological Institute. So this one you're able to have the tender weather focus and to focus of course on the this. You need to be kind of familiar with these icons to be able to understand this but I'm sure most of you are familiar with that but alongside that you should be able to have the maximum temperature, the mean and maximum temperature, the precipitation, the relative humidity and the wind speed. But just to say that we are yet exploring how this can be useful to the users. So we are not importing it into the DHIS to for now. But as and when need arises, we definitely see if this can also be imported but we definitely need to hear from you to get feedback from you. So then we also have a guide on how to be able to configure this to be able to import data. As I explore the application, you will be able to see this, but it's as simple as creating a few data elements, a data set, and then creating a group for that, and then be able to import. But while setting up the data elements, it's important to pay attention to the, the data aggregation, because what we don't want is data to be imported say at a health facility level, and able to aggregate that for district that would be wrong or that will not be accurate. So at each level we have to import data, and then be able to analyze it at that level. This is just a screen that shows the data import, but key to note is that we cannot import more than 50,000 values and I'll be showing you that you can only import data less than 50,000. But we are also in the process of making this much better, based on again feedback but also looking at how best we can make this work for your own experience. This is just a screenshot of how we are starting to look at the data. So here it was just some graphs and a map that was bring together malaria and climate data. Again, I'll be showing you that shortly. So without taking a lot of time, let me switch to the demo so that you can appreciate. Then I'll come back to this later. So, so for you to be able to again have this in your DHS to, of course, you need to consider the things I mentioned earlier the right versions and making sure DHS to is properly has the right metadata that allows for the organization you need to be able to set that up I mean to be able to stream and also import the data. So here we have the demonstration instance that I'm going to use to demonstrate this. So this is a demonstration instead that's provided by loud. So let me just log in. So when you log into the DHS to instance, and say first time this is going to be set up you need to definitely install the application. You come to this apps menu and then be able to look at apps management. This is the tool that allows to install apps within DHS to so when I click on it, it should be able to take me to a place or connect me to the app hub. So this is where we have all the apps that are built for DHS to you can explore this, but the one that I'm interested in is climate climate data, this is application and this is the latest version. So to be able to install that simply click on that and then be able to to install of course there's information here that different versions. So always look out for the latest version to be able to install. So if I click install, it shouldn't take long. Of course I'd already installed but still just to demonstrate that so already that is installed. So I'll come back to my apps menu and then be able to look for climate data. So this is the application. So when I click on that application, it is able to provide the different tabs that helps to manage I mean helps to bring out the features that we want. So the first one is the home tab that basically provides information about the data set that we are using. And you can see the video that I was playing shortly. So when you install this, you will take time and play this video. And then also it also talks about how the data is calculated. So it's very important for you to go through that. And then before I go into exploring the data, you have this provision to change the settings. This is very important to make sure that your time zone is right for you to be able to do the weather focus. So this is very important to make sure that you set the right time zone. So we have all the different time zones here. You select the one that is appropriate. I've selected this because the demo is the demo instance that I'm using is based on that time zone. We also have the guide that talks about how you can configure the app, but specifically how you can set the metadata in your system to be able to import this data in the VHS too. And then I'll go to the one that helps to explore the data. And this is the most exciting one. So basically when you're exploring data, at that point you would not get imported data. You're just looking at how climate data is looking like for a given location. And so what this application does, it pulls all your organization unit structure that is set up within your DHS too. So for this demo instance, we have this is at national level. And these are now the province, this provisional level. So if I go down, these are the districts. And if I go down, I'm able to get the health facilities. So if I'm exploring this data, I could look at on any of these levels as long as those levels have the organization, I mean, the geo coordinates configured, it should be able to help me explore the data. So let's start at a facility level. And the reason I'm starting at facility level is because I just want to quickly show you how you can start with a with a with a 10 day weather focus because we are not doing weather focus at the district level we are looking at it at a point location, which is typically a health facility now a scenario. So if I click on this health facility, you can see right away, I'm able to get the 10 day weather focus. And again, try and familiarize yourself on how to interpret this, so that you can make a use of it. So this allows you to then look at the 10 day weather focus. Today is Tuesday, you can look at that until next week on Thursday. The next of course you would want to look at temperature again we are still within the same health facility. You can then start looking at the temperature for that health facility. And again here we have monthly and daily. Yeah. So, so for the monthly you if we look at, let me try and expand this a bit to give a bit of. So if I want to look at say the trends from January over 2022 to date, and then update, then you should be able to have a period spanning for that the different months spanning from January of 2022 to the current month of March. And here there are things I need to explain our first turn of others not cause confusion. So I'll first turn off the range, and then turn off the, the mean temperature. So, I want first explain this normal temperature so this normal temperature is basically generated as an average for this reference period. So, and that is generated for each of the months. So, so if you look at the month of the January, the normal temperature or I would say expected temperature for month of January, which is really an average for this reference period that we have 1991 to 2022 is 20 is 22 degrees So that is the January normal that is expected each time you look at January, you should be able to have that. Now, as data comes in, as the post time goes, you now need to compare that again is the, the, the mean temperature. And here you are then able to see how does the mean temperature or an average for a given month measure with this reference period. And here you can clearly see that sometimes it's below, but for this health facility you can see largely that most of them were above the normal temperature, and this gap is really the anomalies that we're always talking about. And that one we shall talk about it again at the end here. So this is how you're able to explore your temperature for a given period of time. And then when you bring in the range, this gives you the max and minimum for average for a given month. And so you can have that chart that has all those three in one for the monthly we're basically looking at just the the mean and then the range for again, for this period of time that we've been able to display. So that is how you explore temperature. And it's very similar to precipitation. The only thing is just the graphs a bit different. The other one was like a line graph, but here we've used this bar. But again, here we can look at precipitation for a month. And again, also bring in this normal precipitation, which is based on a given reference period. And then also you can do the monthly and again here you can have this ability to kind of zooming using this. And so you're able to kind of understand this in more detail. When you're done, you can just reset and be able to get back. And then last, not lastly, but we also have now, we have relative humidity and relative humidity, you can also look at it again monthly. And then also daily for monthly there are a lot more variables that we're looking at. We are looking at relative humidity alongside a ton of this for. So we could look at it alongside the normal relative humidity, which is again based on a reference period. But you could also add in the due temperature, which I'm told is a key variable for to be able to understand the relative humidity and also the air temperature. So this is what you have for relative humidity. And then the last one is the the temperature normally or temperature change over time. And again here we're able to explore month by month, you can see, if you look at say the month of March that we're that we've just passed. You can look at an anomaly from 1970 up to 2024, only the month of March to see how is the temperature changing over time, based on this reference period of about 30 years. So you can see that that is high, but you could even go back, we go way back in period, I mean, in the period to start from 1961 to 1990. And then you'll see that the normal anomaly is even much higher, and you'll be seeing much of the rate. And the rate here means that it went above the normal that is expected and that difference is what we're looking at here. And that's what is being measured by the change this for each of these you can use this to actually full view full screen. You can also download this if you need it for your presentation. So that is how you explore data. And this one was at a facility level. We'll take it high up at district level. And so for that, if I click on the district where that help facility belongs, I should also be able to explore data but here, the only missing pieces the 10 day weather forecast that we're not able to do for now for districts or provision level, we have put it only for help facilities. So here are the same things that we've been looking at. So this is how you explore your data. And once you've been able to explore your data, the next thing you would want to do is to import this data because what we are doing here is basically streaming this data through the Google Earth engine. It's not stored within your DHS to but the DHS to climate data app is just streaming this getting that through the Google Earth engine and being able to display for you. But now for you to be able to analyze this data hand in hand with the health data, you definitely need to import it into your DHS to. So when I click on this tab import data, then it will give me a provision here but there's instruction that you need to follow here. But to understand how this works, but the easiest thing is just to make sure that you're able to select the variables that are available for import. So if I select say temperature, I can then specify a period that I want to import for. So here I could change to keep it short. Maybe just starting January of this year to end 231st of March, that period of about three months. And then I select a level here. Again, here you can select one of these provinces to import data for that province, or you can say province and then be able to select all districts under that province. For this demonstration, I can just pick one of these. One of the provinces here, and then I could choose a district level or health facility level, but to keep it short, I will leave it at a provisional level. And then here is where now you map this variable that we specified here with the data element within your DHS to. So meaning that it should have created this data element within your DHS to. But when you're creating this data element, there's a provision for you to create codes. And once the code matches what is pre configured, then it will automatically help you to select at the right data element. So let's quickly import this. So here what it's what's happening is it's importing data for 91 days. 91 days is from this period. Start here and end there. And that's why you're having 90 days. Now this is being ignored because I think there's already some data. So let me switch to another. So let me try that. So if I run that. Yeah, so you see this one has no data. So it's been about to to actually import data for this period of time. So if you want to report, say if I came here and clicked Laos, and I say give me data for all health facilities of Laos. You see you'll get this kind of warning because the data you're trying to import is a lot. And so you'll get this warning. So you may have to consider then importing maybe a province by province for this to be able to achieve importing for health facilities in the country. So that is how you import the data. And once you've been able to import this data, the next you'd want to do is of course then check whether the data has been imported just to confirm. You could run analysis and then go go use the data visualizer and analytic tools to be able to start running some people tables or any visualization. But if that is not something you can do immediately you can just go into a data entry screen. And I'll just open one that I've already created. So how do you get there you simply go into your data entry. Use the data entry up. That's what we used to enter because this is aggregate data and then peak the province where we imported data. I think we did import for one of these I could try. And then we did for a period within that. So, so you can see that temperature data has already been inserted into a DHS and this is a simple data set that we've created and when you're doing that. And then you have your data within the DHS to so after confirming that you have data in the DHS to then you can go and start using some of the analytic features within the DHS to analyze your data. For learning for demonstration we've created some small dashboard here, which is still actually just for demonstration. We hope we can get more input from the content experts to be able to improve the analysis. But what we've done here is you can see we have created a graph aligned graph, which is a door chart that is able to look at relative humidity and malaria cases. And this one you're looking at a trend over January to should have been December. So like a two years trend, just to try and see if we can see any, any pattern in the data. Yeah, so here we are just trying to run malaria and relative humidity. And you can see some similar pattern in in these two variables. But again, we just want to portion that we are still seeing the best way this can be presented and analyzed. We have also been able to do a map here that can help you look at again relative humidity. Again, with malaria cases. And again, for this you can have a core plate map, and also with a bubble map to be able to show that. Yeah, so we also did a split map, just to show the different months, and also how you can have the relative humidity for the different months varying so you can see here, the relative humidity is much higher in October, and the match there's not my I mean it's much less in March in March. This is just I think to help people appreciate the temperature trends, say for a given a country for the different districts. So with that, I will then turn back to the last few slides before we end the. So, so the things to now think of is really the quality of data because remember we are we are we are looking at for start we're looking at global data, but with the time you're going to get local data. So now when you have these two data points, you have to do some validation. Yeah, but things to note out for is the more weather stations you have the better because even when we go through the process of reanalysis. Some of the data comes from, and actually data comes from the weather stations, and then they combine that with satellite data to be able to go through that process of reanalysis. So, for countries where they have dense weather stations, you definitely would expect good quality data or the data that's closely comparison comparable with the with the global data. It's also important that you pay attention to areas that are mountainous or coastal areas because again when you're doing these observations. Most of those areas may not be clearly observed but also some of those areas may not have these the stations available. And so some of those areas may be missed out. And so you need to find a way of how do you then get data from some of those areas that are missed. Maybe you need to think of a proxy or if the nearest district. That's closer to that island, you could use that that as a proxy to be able to use for analysis. And then also the most important thing here is always look out for the trends in the data. And also when you're comparing the local, the global data, they may not be exactly the same, but overall you will see a pattern that is similar. And then that will give you an indication that probably the data working with has some level of validity. And this is a good example of data that was compared for Laos. And here they were looking at the era five data versus the local data and they were looking at aggregations for weekly. And if you look at this, you can clearly see that the average temperature, the temperature, the average temperature is both local and local data and the global data were quite close. Two lines up. But when as you go down and you look at the maximum temperatures, you can see there's a bit of variations. But even then you're still able to see very similar a pattern. And then lastly, if you look at the mean and the max, you can see that there was a bit of a wider gap. And later they realized that the problem was with, you know, for local data, they able to do readings from during the day basically between 7am and 7pm. But with the global data, this process is continuous, you're able to get this all the time. So when you compare the two, you will see that gap, but largely you'll see the pattern similar. This was just to explain the areas that could be missed out the coastal areas and this was for Mozambique. It was about to show some of the areas that were missed out. Yeah, so, so as we end, I think the most important thing is that we've indicated that this gap is still going through a lot of development. And so it's very important that you try out and give us feedback. So try to make sure you set it up within your DHS to instance, and then be able to see if you can do what I've just done in terms of exploring the data. And then also see if you're able to see most of the variables that we've been able to that is so far provided, but also try and take the next step of importing this data into your DHS to instance, and then also do the analysis that we've been able to do. But also we still want to get a lot of feedback from you on how the app is working for you, but also things that we could improve to make this application much better. And so you can use this contact to be able to get in touch, but also you could get more information within that on this link that's provided. So, so what is next, as you can see this application is working, but there's still some things that we keep making improvements on so these updates will continue. There's also a very strong need to have local data integrated. And that one we're already talking to countries that have local data really available to be able to integrate that. But also most critically we need to think of the second product. And that is modeling and prediction it modeling to that can also further enhance analysis. Of course, with this already we are starting the process of capacity building, including this webinar, and also for us, the his groups who are in country, we are on standby to support countries that are interested to implement this application. So with that, I'll stop there and wait for questions later as I turn it over to Andrew to take us through the next few slides over to you, Andrew. Thank you. Yeah. Thank you. Thank you, Patrick. And thank you for the good presentation. I think it is a use case from Rwanda actually we we've been working with Universal Oslo and Patrick, and then his Rwanda to see how we can set up this climate up in our health management information system, because there was a push and high demand of having data connection between health and climate data. So what we have done actually the app is really not complicated is the user friendly and easy to set up. So what we did actually on having this app installed in our health management information system which is a centralized system is a first of all, we configured the Google Earth engine here in our instance server. And then after that one we configured all created a few data elements like five, which is temperature, relative humidity, precipitation, and others, and also created the data sets to ensure that these data elements are assigned to the data sets. What we did was that as soon as we created these data set to realize that for us to be able to create to do analysis, we realize that our Ministry of Health System does not have the country polygons. So we had also to upload the country polygons. After having the app, what you do is just importing the data, it's automated, you just pick the data element, then you just pull and you pick the frequency, then you just import the data from the global registry. So this one was really straightforward, we managed to pull daily data for three years. Then from there at least we can be able to analyze and have the dashboard that shows the climate global data and the clinical data. So the beauty of this app is that as soon as you pull the data, it really you take advantage of existing analytics in the HS2 analytics, which is data visualizer, pivot table and others, they all use those existing analytics in the HS2. So currently what we did as Patrick said, we said, okay, since we have these data, let us create a few dashboards analytics just to allow program people to use them just to visualize these two data sets that are translated between climate and health. Again, what we have seen, there's a correlation or there's a relationship between the two. But again, as Patrick said, there's more research needed to really come up with some significant relationship. That's why we are pushing for these modeling and also predictions. So next one, actually, it's not a one, it's not one installation because currently we are also working on the local data import. We've been working with our meteorological, rather meteorological agency to ensure that also the local data is imported in our HMI system. So we have reached a stage of where they have agreed to give the data for almost 30 years ago from, I think, not even 30 years from 1981. Those are the data that they have. So you can understand that these are very good data for predictions. So as a way forward, given that this app is new, we are continuing working with the University of Oslo teams that are developing the app, giving them feedback to ensure that the app is much more improved. We are also working with the University of Oslo to see how we can explore the modeling and prediction tools to ensure that we have like early warning systems. We are also trying to continuously uploading this data from the global data storage. In short, at least we have more long or multiple years data into our system. So again, we are working with the meteorological agency to do the data integrations to ensure that at least we will be having some more integration where the data will be moving from their site to our site. So for the data sources, as I said, of course, local data is very critical on what we are doing. But again, also we are combining it with action research to ensure that at least we are contributing to all sides of the project. So for the local integrations, as I said, I will not repeat it. We have really worked with the meteor and now currently we have already started the integration process and pulling data across the two systems. So probably in the near future, we'll be able to present the three data sources together into our health management system. This is the end of my slides. I think much have been said by Patrick. Thank you. Back to Ellen. Wonderful. Thank you, Patrick, for that wonderful demo and Andrew for for talking about the Rwanda Ministry of Health use case. It was really wonderful. And to everybody in the chat, it's been wonderful to see your questions. Thank you, Bjorn, for responding to the vast many of them. We just have a few minutes before ending the webinar. So one of the questions. So we have some time for Q&A and I welcome you guys to keep dropping those questions in the chat. We'll use this last few minutes to respond to them and also continue the discussion on community of practice. And another thing that we're also very interested in is understanding what implementations you're interested in running as well. I saw a question from Robbie asking if there are any plans to integrate climate and health data for other diseases apart from malaria and cholera. And really in response to your question, Robbie, that is something that would be determined by your health program. The examples we have right now are with malaria and cholera because that was driven by the interest of the ministries of health and national stakeholders. And then also I'd just like to remind everybody that registration for the DHIS to annual conference is open and we have a slide up here with the QR code. There will be some wonderful presentations on climate and health work and chances for lots of discussion and interaction. So we hope to see you there as well. Jumping over, there's a question from Alinda about what are the next steps regarding the modeling component and is there an estimated timeline for that. This is something that we are prioritizing working on. We're hoping to, I don't have a timeline to share with you but we are hoping to be able to share updates within these coming next quarters about the modeling component. So please stay tuned for for that. And I will also once again plugging for the annual conference will be will be diving deeper into that modeling piece there as well. I'm sorry, my my way out. I'm sorry for mispronouncing your name. Is there any plan to automate the importation processes like weekly or monthly. I'll hand that over to Patrick or beyond to answer. Beyond can answer but yeah. We're really looking at something that can quickly help us get started. But that is something that has been already pointed out. So at some point I guess that should be considered but for now, we're able to do it manually. I can assure you that for small smaller countries few organization unit, it shouldn't be very tedious but if you have a bigger country with so many organization unit that can be a bit slow, but that is being considered. Thank you, Patrick. Another question here from Cheryl that is in case it is possible to correlate health and climate data to what extent is it possible to embed dashboards into web pages. I think the question is to what extent is it possible to embed the dashboards into web pages. I don't have the full overview of the current possibilities. I think there are a bit limited with the with the current analytics app. So I think the best option now is to show this on the dashboard, but we are working on solutions for for getting this at that you can export these elements, at least as images that you can can then embed on other pages. Thanks, Bjorn. And I also want to say that in terms of what we're doing we're able to bring in the climate and health data within DHIS too. But in terms of looking at correlations, it's still very important to be working with your statisticians and with your, your technical advisors in terms of really analyzing the data to identify correlations. DHIS too is not able to identify those correlations, but we're able to bring the data together to help with your analysis. Okay, well I think that we have addressed all the questions in the chat and thank you Alice for putting the link in the chat to continue the discussion on the community of practice we really thank you for your time today and once again this recording will be shared broadly. As well as we will also have this same presentation available on April 25 in Spanish and on April 30, it will be done in in French as well. So thank you everybody, and hope you have a wonderful day. Thank you.