 Hi, I'm Idris. I'm working at the Manson Unit. I'm the mapping guy. So I'm going to present a dashboard to support almost effortless spatial epidemiology. In other words, we use existing datasets and we make it easily understandable. Have you ever seen that? Quite a lot, huh? Do you like it? No, it's difficult to get what's behind, especially when you get on a date every week you need to redo the whole job with Excel and so on. So why is that? It's because what we're looking at is raw data, okay? And therefore it's difficult to read. But what we want is to understand the information and to understand it fast. So the core idea is to move from data towards information. The data is the necessary first step. But what we want as an output is something that is helping us to have an informed decision making. So something which takes the data and make it visual, interactive, which adapts to you and not the other way around. So let me introduce you to the Tonkoli dashboard for surveillance. Tonkoli is in Sierra Leone, that's one district. Routine epidemiological passive surveillance help us detecting outbreaks at an early stage. But it's a lot of data collected and sometimes we struggle to understand and to respond to outbreaks on time. So this is what the dashboard answers and this is actually the data you just seen earlier. So what is it? It's a tool to visualize data and extract information. You click, it reacts, it talks to you, it's designed to work offline. It's a perfect fit for fieldwork, particularly in emergencies where more robust systems are difficult to put in place on time. So what it is not? It is not going to replace a standard epidemiological analysis, no health information system with dedicated software. But that's a way to facilitate and improve our routine work at local level. So we have at the moment three dashboards deployed and I will only demo the one in Sierra Leone. So there you go, that's how it works. So you load the dashboard, there is a quality check on the dashboard, on the data that you've got, and then when you hover over the map you can see the number of cases, but in that case, CHIVDAM, this is Malaria cases. We are looking at, you can see the evolution and the trends for each week. The number of cases on top and the death below is under and above five years old. You click, it reacts, it selects automatically and adapts the graphs and the map as well. Then we change the disease, we look at diarrhea for the last week, over five only, you reset the settings, you reset all the filters and you have the total data for the diarrhea and then you run the animation to see what is happening during that given period of time. And if you're interested, you can see also the data per year, so you will have the two curve that will adapt to each year. And then you can zoom at the health center level, you can click on each health center, each round is a health center and then you can investigate at the health center level what has happened. So development started at the British Red Cross. It is an open source and available freely on GitHub. The codes can be easily reused to create another dashboard, especially if we are using the same kind of dataset. In that case, this is the WHO IDSR integrated, this is surveillance and response. The cost is decreasing as the deployment and the development goes. So you will say, great, another tool until the next one, of course. But actually it's making the link between our data infrastructure and the future information system we're going to use. Making our data management by standardizing dashboards ready for the next steps toward information management. So not providing standard tools will incentivize using alternative and uncoordinated solutions leading to a slower and less effective operational and medical response. So we need to prepare for the next steps now. So the data is being collected anyways. It's just not processed to its full potential on time. We don't change anything, we just provide the glasses to see it through. The dashboards are built for the user according to their needs. And we have great feedback from Sierra Leone and DRC so far. So what are we looking at? We're looking for other use cases, collaboration with more missions in the field, more Ministry of Health operation centers to develop it further and to secure its development and sustainability. Plus obviously integration with the health information system or the data sets, Ministry of Health logistics and so on. So finally we need to move towards information which answers the who, the what, the when and the where. Don't forget the mapping component. So I would like to thank Sierra Leone Mission, Sierra Leone Ministry of Health at the district level, the British Red Cross and Missing Maps for me that possible. And thank you all for staying. Thank you very much.