 expertise. Also, our regional college colleague, Christian, who was not here today. So to you. Thanks, Rebecca. So as Rebecca alluded to, I am neither an expert in climate nor in health, but I have been talking to a number of people who are experts in both. I've been trying to try to summarize what I've learned from them and what I've learned from reading a bit about the topic as I've worked on it in the past year or so. So this is just a quick introduction as we get started today. So first off, just a question. We're mostly here working on health programs. So why should we be concerned with climate change in a health context? This is a quote that I took from the WHO website, which is pretty long. You can read it on the slides of the video. The key point here is that climate change affects everyone. It affects the health of everyone, but it particularly affects people in low resource countries for a number of reasons that are both related to the climate in those countries, but also the resources, infrastructure and other issues that are also present. So as the WHO points out, climate change can set back the entire development agenda for 50 years and really cause a lot of harm and negative progress in the kind of programs that we've been working on in the DHSU communities and working on for public health. So just that fact alone means there's something to be concerned about because it's going to affect all the progress that's been made and all any number of disease programs. So I did take a slide from my colleague Ernst Cristiano Rodlund from the Norwegian Institute of Public Health that tries to diagram a bit of how climate change and biodiversity loss have effects on health and how to lead to certain health threats. So here you can see a sort of diagram that shows exposure pathways leading to existing vulnerabilities or how they relate to existing vulnerabilities, including a number of health system capacity and capability issues and the potential outcomes from theirs. So you can see that they range from very cute risks like physical injury, which you can imagine coming from things like a flood event or an extreme heat wave or extreme wind, all which can be caused by climate change variation and down through more longer term or second-degree sort of effects such as influence in vector-borne diseases, influence in malnutrition, influence in mental health. So a number of ways in which climate change is interacting with human health to cause a variety of health outcomes that also are either immediate or long-term or both. And you also see this number of physical impacts here in infrastructure, impacts on the health system itself, such as the ability to deliver health care, for example, in a flood situation, it might be harder to carry out routine services. So this is a giant interconnected web, which he also, this is another slide from, or it's Christian, you can see here a few different kinds of climatic or environmental events and a few outcomes. And the reality is that these are not sort of one-to-one relationships. A lot of yous do interact with each other in fairly complex ways. So you might have an issue with a flood that leads to both food and water security, the stress that can also contribute to mental health, it can also contribute to migration, which migration can also contribute to things like spread of diseases. So a lot of this is kind of interconnected, which makes it a very complex problem to deal with. And I think nobody has ever said that climate change is a simple problem to deal with. And so here in the DHS2 context, what we're primarily talking about is the health part of climate health, not how do we fix climate change, but how do we deal with the impacts of climate change on health? And to do so, we have to kind of quantify it a little bit and have to quantize it a little bit, because given the network effects that exist and how complex they are, I think what we're looking at in this presentation is how do we isolate some that we can actually work on productively in the shorter term as a starting point. I think that's where we're at right now with the DHS2 ecosystem is just starting to move in this direction of given the climate change as a pressing problem, what concretely can we do in the short term to start addressing it and addressing the effects that it has on human health. So that's why we're starting here with health. So I put this as the good news, which I put that there because really when we're talking about climate change and health, we're not really envisioning new health risks per se. What we're seeing is an intensification of existing health risks, or potentially existing health risks that are now present in areas where they weren't present before due to things like increasing heat or changing weather patterns that might lead to vector-borne diseases being present in areas where they haven't been previously, such as island areas of Africa, which may now be more exposed to mosquito-borne illnesses than they previously were. So having sort of an idea of how we can quantify those changes and then use that information to proactively respond to them or to predict them. And so as I put here, the other part of that good news is a lot of countries already have DHS2 systems in place that are collecting health data on these health risks. And here, these icons are taken from our website, and they sort of represent metadata toolkits or health toolkits, which exist in which a lot of countries have used to strengthen these health programs through digitization. This is kind of a different diagram that approaches the material from a few slides ago in a slightly different way. I like this one, which is taken from a WHO website because it really clearly shows the health outcomes in a sort of context that we're used to dealing with at BAN, which is this kind of health program context. And so as I put here, a lot of these health outcomes are actually health programs that DHS2 is used for to collect data to manage health programs, to make decisions about prioritization, about resource allocation. And then on the more general side, health systems of SOAS outcomes, this is the kind of service data and the kind of health system operational data that's also collected in a lot of HMIS systems. So we already have a fairly good data foundation to use to construct climate health systems because DHS2 is already being used in so many countries to work on these health areas, which we see as potential health risks related to climate change. In thinking through this with the eye towards making a concrete project, we have started to further group it into what we see as some potential areas of DHS2 use case development. This is not intended to limit the use of DHS2 in this area. It's really just a starting point that we see as being high potential based on the existence of current DHS2 systems. So here we're talking about three different broad categories, infectious diseases, nutrition and food security, and hydro-mediological events. This is taken from a book that I recommend you read if you're interested in the topic, which is Climate Information for Public Health Action. And the first two columns here I think are ones that we're going to hear about later today in this presentation. We have a couple of systems that have been piloted at various stages that deal with climate sensitive infectious diseases. The Mozambique team will be here today presenting about one of those and they'll work on it so far. There's another example from Laos that has been developed and is in the sort of proof of concept stage. Here you can see that we can use in this context climate data to try to predict outbreaks in different time scales and also to, as I said before, to monitor the spread of diseases into different areas where it hasn't been present before. For nutrition and food security, these are a little bit different topics, but they're linked obviously by food. And the Malawi team will present today on an agricultural management system that uses climate data to try to proactively help farmers react to changing climate and extreme weather events. The last column here, this is I think more exploratory for us because we don't typically have a lot of health systems using DHS2 to monitor the kinds of health outcomes that are related to extreme heat. However, some of those outcomes relate to things like NCDs, if it's hypertension, for example, or mental health. And there are some few countries that have started exploring those use cases for DHS2 and that could be potentially linked to a monitoring system here. But also the use of DHS2 is a surveillance platform. I think also provides a foundation for constructive work in this area. If you know extreme heat event is coming and you know you have a vulnerable population in the area where it's expected to occur, you can do things like proactively send messages to health providers or even to the vulnerable populations if you're using a tracker system that could alert them and help them to take adaptive action in advance of that event. So these are the kinds of things we're thinking of in the heat context. And flooding, we have seen DHS2 used to assist to flood response in Pakistan last year. And flood response isn't just the immediate physical damage from the flood, but also affects things like service delivery of health care. It also affects the spread of infectious diseases, such as waterborne diseases. And so here's a lot of linkages between that kind of event and health that we could potentially explore with the DHS2 system. Now we need to the hard part, which is climate. This is an area where I think the DHS community does not have a lot of expertise. And it's also an area which is incredibly complicated. So I don't want to underestimate how difficult it will be to build climate-formed DHS2 systems. The good news is we've been talking to people who are experts in this field, and I think they're interested in working with the HIST community, with the DHS2 community, on systems like this. And there are a number of ways to approach the challenges that are listed here. But these are challenges that we have to really take seriously when we start thinking about designing these systems. The data needs to match, needs to be the same geographical scale, the same temporal scale in order to make it be useful for analysis. There are severe challenges in a lot of low and middle income countries with actually getting granular climate data. And so part of this project that we hope to embark on might be liaising with meteorological agencies in different countries to look for ways to assist in digitization of climate data, especially through our partners who work in the climate demand. We also have challenges of how to actually link DHS2 as a platform with climate data, given the potential for immensely large volume of data for climate analysis, and the complex and advanced statistical analysis you have to do to make these projections on predicted events. So these are all technical challenges that we'll have to be looked into and resolved in order to resolve that part. And I think some of the projects we'll hear about today have already taken some approaches to incorporating this climate data. So we're hoping to learn from them as we look forward to trying to make more generic approaches or share generic approaches that other countries can take advantage of. And finally, these organizational challenges, this is something we especially heard very clearly from our conversations with WHO. Looking into systems that use climate data to create predictive indicators also requires a sort of organizational change. It requires a mindset change and requires a buy-in and trust in the data that's coming out of the system. Otherwise, you're asking people to contribute a lot of resources potentially to something that they think will be a risk or they think will be a problem, but hasn't actually happened yet. And so having that kind of health system that reacts proactively to a climate informed prediction is something that would require a certain level of institutional capacity building and funding and organizational change. So there's a bit of good news there, which is I think, especially due to the comment at the beginning about climate change affecting low and middle income countries very severely, despite those countries not being the primary drivers of climate change, there are a lot of potential funding sources out there for countries to get climate adaptation assistance. So if countries are able to build systems that can use climate data to help population health, there are a number of funds that exist to support countries in their climate change strategies and climate change and health strategies. So here's some examples of the systems I mentioned. You can just see some starting work on incorporating climate, weather, rainfall data, trying to map it against health data to see what the useful patterns are for analysis. Here's a screenshot from the Lively Agriculture System. So we'll hear about a couple of these today in more detail. And I'd just like to close by saying that we are actually trying to take this on as a project and to learn from countries that are leading the way on climate and health and try to develop them into a generic approach and generic tools can be shared and adopted in the open source model. So if anyone is working on a project like this or has expertise that would be relevant to us, we encourage you to get in touch and we will be sharing information on our work as it continues. And it's a very exciting opportunity, obviously a very big challenge. We hope to work with you on it. Thank you. Thank you so much, Max. I think we're all really looking forward to seeing where this work takes us. So it's a pleasure to introduce a pair of colleagues. So blessings in Tezemo Kamanga and Matthew Mavola from the Ministry of Health Malawi and Public Health Institute Malawi, who are going to share about building a resilient one health surveillance platform and responding to public health emergencies. And just a little teaser that following these colleagues, we have a representative from another ministry in Malawi. So we can start to see how these cross sectoral data platforms are going to come together. So welcome. Good afternoon everyone. Yeah, blessings in Tezemo Kamanga from Malawi. I work for the Ministry of Health. So together with my colleague, Matthew Mavola from Malawi as well, working at Luku International. Thank you. We'll be presenting on building resilient one health surveillance systems to respond to public health emergencies as well as responding to the pandemics that might be there. So through the presentation, we'll look at the background of EIDESA implementation in Malawi. We'll look at how the one health surveillance platform was developed and then we'll look at success installers, challenges and then we'll look at what we aim to achieve in the wrong run. So the adoption of EIDESA in Malawi dates back to 2002. And then from that time, there have been several initiatives just to ensure that there is some streamlined processes in the IDESA. So for example, in 2015, there was a project funded by World Bank, which was looking at how IDESA can be digitalized. And then later on in 2017, we also had another project funded by UNICEF where we were looking at implementing EIDESA with the use of a short message service. And then considering the relationship that is there between the human beings as well as the environment, there was a need to take a one health approach whereby we're looking at several sectors coming together, looking at different ways how optimal health can be achieved. So in 2019, that's when the one health surveillance platform was built on the DHS2 platform. And then during COVID-19 pandemic, that's now when its usefulness came to light. As during that time, there are several things that happened. So for example, we used the same platform to ensure that we do the port of entry screening, case-based surveillance, contact tracing. And then it also went further even to involve the education sector, where we're also looking at COVID-19 surveillance in schools. And then when we started administering the COVID-19 vaccine to the population, the OHSP platform also came in handy as all the people who have vaccinated, all the details were captured on the system. So by and by, things started changing due to the imaging factors. So for example, we had the several cyclones hitting Malawi, cyclone Gombe, cyclone Anna, and then recently cyclone Freddie. And then we also have some outbreaks like cholera. So we said, are we going to have a system for each and every pandemic that comes? I said, no, let's look at what we have and then let's enhance whatever we have to ensure that it is resilient to the point that it should be able to supply the decision makers with the data that they need in order to respond to such images. So I'll call upon my colleague, Matthew, just to highlight on what sort of enhancements did we make to the OHSP to ensure that it is resilient. So thank you so much. Blessings. So in the early days of cholera, the cholera cases before the sage were not that much. And in the southern region, that's the southern part of Malawi. That's where it started and it started in the dry season. And we had two cases. So it was easier. In Sanjay, they started reporting using OHSP. But during the sage of the cases, it was overwhelming in terms of the workload. So they were not able to capture data using the OHSP. Instead, they started using Excel, Microsoft Excel, and they were generating some languages from the districts and sending them. So the challenge is that they kept on having poor quality data. So the cholera eastern management team decided that we should revamp reporting cholera cases through one health surveillance platform. So there are many issues that it was too long for them to report during the cholera situation. So the solution was to enhance it so that we can shorten the data capturing process. This was based on the input that we got from FEMA specialist. So the team spent three days to fulfill the request. So that is confirmation of the requirements, the configuration, communication as well as the development of the training materials and even conducting the training of trainers. So this was done during that period. So the results that we got is that the DHS2 based one health surveillance platform has demonstrated high flexibility as well as it is yes. That is it's adding the Minister of Health to improve reporting in terms of timeliness as well as accuracy. And at the same time we have also devolved dashboards within the DHS2 one health surveillance platform as well as in Tableau so that various stakeholders can have access and make well informed decisions. So their data tracker has also been deployed after conducting hybrid training both physical as well as virtually and the various surveillance officers are using across Malawi. Now the 29 districts that we have in Malawi and as we are talking they are even doing some bug data entry so that we can reduce the workload that we have because we had a large form of data. So this is just an interface of one of the examples of the stage in patient registration as well as in lab results. So we have also faced some challenges in terms of the implementation as well as long-term challenges like for the implementation the challenges in terms of connectivity in some areas as well as lack of gadgets when capturing data as well as in adequate training plus supervision. And for the long-term one data security is also an issue as we have already said this is where we are keeping even individually for data not just aggregate data so we are looking in that as well so that we can enhance our data security as well as the sustainability in terms of financial infrastructure as well as human resource. So these are some of the challenges that we have and on the way forward we continue to integrate the OHSP with other health information systems like the HMIS as well as the development of other components within the OHSP. As we said this is the one health surveillance system but we have just implemented the human component so the other components that the animal as well as the environment that we are looking forward to implement of course discussions are still underway with the animal as well as the environment. So looking into integrating having climate data as well as the NAMIS system that we have. So we have to appreciate these organizations that have helped us along the way even for us to come here the development and all other stages like looking international, UNICEF, the BHO, GIZ and all these other organizations. Thank you so much. Thank you so much Blessings and Martin particularly like a part about enhancing the case-based reporting by making it easier making it feasible to do so that's not an easy thing to do. Our next presenter is Jennifer Nicosi from she's an economist and also the the system coordinator for the Integrated National Agricultural Management Information System and what I really liked about these two presentations going side by side was that from each of these sectors it is a focus on integration of data and also resilience. So the floor is to you. Thank you. I hope you can hear me. Am I audible? Okay thank you very much. Good afternoon everyone. My name is Jennifer Nicosi. I was really indicated by Rebecca and I work from in the Ministry of Agriculture in Malawi. So in the Ministry of Agriculture we have developed our own system using the DHS2 platform and we are calling it the National Agricultural Management Information System and this system we are using it for different use and one of it is crime intrusions and agrarian extension. So my presentation are focusing on how we want to use the system to strengthen crime intrusions and also enhance agrarian extension to the farmers that we work with in Malawi. So my structure in terms of the interview are focused on the NAMIS overview, what's the vision and the current status where we are and also the climate smart drive, what we want to achieve in terms of climate and also in terms of agrarian extension, the challenges that we are currently facing and the way forward and the next steps that we want to take as the Ministry. So the NAMIS is a comprehensive integrated system that we have developed to collect and arise and disseminate agrarian information from the point of collection to the national level. The NAMIS system was developed with a view of enhancing agrarian information in the Ministry and the system was developed based on the existing structures that the Ministry had and from that we came up with 18 modules that will be embedded in the NAMIS system and the system we started implementing it in 2018. That was when we started developing the conceptual framework, the implementation plan, but the actual rolling out we started last year and also in the system we have also customized applications that we have developed on top of the system to facilitate the data collection and the reporting. So our point of data collection for most datasets in the system, it's at the formal level the ones we work with and the data from the NAMIS system is being used by different partners that we work with. We have the Ministry of Finance, the Ministry of Trade, we also have development partners, the SCSOs as the civil societies, they're able to get information that we generate from the system. So what has been currently, what have we achieved? So out of the 18 modules that we have developed and customized in the system, we have managed to roll out 12 modules and one of the modules that is gaining momentum in the Ministry like the donors are interested is the farm organization where we are registering households that we are working with at household level and also we have the lead farmer, we're also registering them in the system and this system we are being implementing in 12 out of 28 districts. This is where we had to source funds from the project that have supported us in implementing this system in 28 districts. So we have currently trained almost 1622 extension workers who are collecting data on different modules. We have the farm organization, we have the weather information that we are collecting and the market prices is one of them and also the food situation where we are able to assess the current food status of the households and from there we are able to generate visualization and reports that are being used by our seniors for informed decision making. So my focus on today's presentation is on two modules that we have in the system. We have the material module and also the farmer organization. So on the material module we are focusing on the rainfall data because this is what we have been doing in the past 10 or more years collecting rainfall data by the extension workers and also here we also want to enhance agash extension whereby we are registering the lead farmers that we work with as a module of disseminating extension messages. In our ministry we have a challenge there whereby we have high vacancy rates in terms of the number of station workers. So we use lead farmers who are volunteers within the community that they are trained on different technologies and we work with them to disseminate agash extension messages and technologies to the farmers. So on climate change Malawi as already also indicated by the previous presenter from minister of race we have been hit by a series of cyclones for two or three concerted years. The recent one was the friday cyclone friday which had a huge impact devastating impact on the farmers in terms of their livelihood including agriculture aspect and nutrition. So for our system we are looking at how can we strengthen these farmers in climate resilience and also linking the climate data that we have with the extension delivery system. So for the system we are looking at having advisory services based on our climate focus that's our drive that will also guide in terms of crop selection and planning for the farmers and also if the farmers should be able to access financial and insurance services. At the end of the day we want to build the resilience of the farmers against climate change and our vision for the system is that we need to have a better weather data our calendar we are just collecting rainfall data but we want that we also collect other parameters of weather data including temperature and humidity so that we can have a comprehensive information that we can analyze and generate extension services and send it to the farmers. Planning guidelines based on data and time based on the data that we want to be generating the system that also we have started already where we want the farmers to be able to come up with based farm practices that they can use to mitigate against the effects of climate change. Disaster planning and management that's at national level even at community level and also we want to be able to have a combined analytics in terms of combining how climate and weather is affecting our crop production and more production and nutrition. I think we also had some where we are we are working with the Ministry of Health on one health system so we want the system to be as comprehensive as possible. So what are the tools that we are working and that we have done in the system? So currently we have 370 annual weather stations that have been configured as the part of the reporting hierarchy so these weather stations are stationed at community level and these are managed by the extension workers and once they collect that information they're able to send it into the system and also this information also is used by the Department of Meteorological Services. They also gather some of the information that we have that we collect at the community level to do with IRN4 and as I said it's a community level reporting so it's on the IRN4 data. In terms of our efforts through extension services in the system we have registered 66,003 lead farmers and 85 farm field schools that have been registered in the system so these are the modules that we models that we use in dissemination of acacia technologies and messages so we want that once we generate this climate data the climate information and products in the system we are able to send this information to the lead farmers and the farmer field schools that we have registered in the system and they disseminate the extension messages based on the products that have been produced in the system so we are also able to monitor and map the interventions that are being implemented by these lead farmers and the farmer field schools. So this is just a snapshot of the visualizations some of the data that we have started collecting that was in we started last year as indicated we started last year in 2022 and this has been the progress in terms of the lead farmer registration in 12 districts so what other efforts that other projects and partners are implementing outside the NAMI system and also we are looking at the efforts of having collaborating with them so we can have more like an integrated system we are not able to duplicate efforts so we have the share of various transformation projects they are generating their partnership with the Department of Metrical Services where they get the climate information products with a focus and from there they are able to generate a great extension messages and they are sent to the farmers that they are working with under the project and also we have the Department of Met themselves they do produce weekly weather forecasts and do share with partners and also Minister of Agriculture is one of them and also we have a project called participatory integrated climate services for agriculture so this one it's being managed by the Department of Extension within the ministry together with other partners so they are implementing this the same approach whereby they do get weather forecasts from the Department of Climate Change and from there they are they generate extension messages guiding them on what the farmer should do based on the the weather information that has been generated by the department so what has been the challenge is we limited a variety of data beyond rainfall as I indicated earlier we're just concentrating on the temperature we're concentrating on the rainfall but we are not collecting the temperature and humidity data and that's our focus we want to devote more tools to collect other parameters of rainfall of weather information insufficient estimation for collecting weather data so you find that most of the weather stations that we have at community level they only have a linkage but they don't have other weather data collection instruments challenges in importation of historical data so that has been a challenge not only to do with weather but also other modules since we have started migrating from paper based to electronic so some of the information the historical information that we have some of them are personalized others in paper form others they are missing so it has been a challenge to gather all that historical data and importing into the system and also that that challenge is limited capacity development for the staff we are dealing with extension workers that have different capacity in IT skills so that has been a challenge as well for us but we are trying as much as possible building them their capacity on how they can use the dynamic system and yeah utilize it then we have our technological infrastructure limitations we're having a challenge of gadgets an unfunctional gadgets that has also been one of a main challenge that has been drawing us back in terms of data collection in the system and also limited financial resources for implementation and maintenance talent we are only receiving support from one project and that project we're calling ASWAP XP2 is being implemented only in 12 districts so the data that we are collecting right now we are focusing on the 12 districts but our vision is that the information that we are collecting under the system should cover countrywide so that's one of the challenges that we have so whether the current efforts and directions that we want to take as the ministry on NAMIS we have tried to gather a 10 year rainfall data from all the districts the 28 districts so we wanted to start importing rainfall data we have been working with Dr. Duongamanda and his team from the rest of Malawi they're the ones who developed the NAMIS system on the DHIS2 so we want to have a partnership with the University of Malawi in capacity building and development of instruments for additional weather parameters a collaboration with the rest of Oslo for climate change here we also want to have in the partnership a collaborating with the rest of Oslo so whereby we can have more research on how we can use the DHIS2 system in the agriculture sector to do with climate change to do with agriculture station and the other modules that we have developed under the NAMIS system collaboration with the Department of Meteorological Services in our ministry in our sorry in our country in the dissemination of our climate products so we want that we need to link with this the system that they are using and our system on the best approach on how we can be disseminating these climate products to the farmers and to the public in general and also farmer advisory services we want our system not to be a system where we just collect information but also should be able to benefit the farmers themselves how can they utilize the system how can the NAMIS system be able to benefit them so we want it at the end of the day this system should be able to provide advisory services to the farmers that we work with and another one that we want to do is integration of remote sessing and GIS in yield estimation so as a ministry we are mandated to collect agricultural production estimates also that guides us in terms of the food security of our country and currently we are using traditional methods or methodology in a collection of production figures but our aim is that we want to use the remote sessing and the GIS in yield estimation so we want to use a how we can use the DHS2 platform in estimation of these production estimation so lastly I would like to acknowledge the assistance that have been provided by the Matidona Trust Fund it's a group of donors that they pull their resources into one basket and finance different projects and they had to finance the ASOP ASP2 which has a hugely supported NAMIS system thank you very much thank you so much Jenner that was it's really impressive how many different types of users and different types of data have been brought into into DHS2 and how that data looking backwards actually helps to plan forwards so our last presentation we have Ophelia Mimele from the Ministry of Health Mozambique HMIS units to be delivered with Zafirino Saojeni of South Digitus refresh my name is Ophelia I'm working Minister of Health Mozambique in Information Systems Managing so we are here we share we share our experience in integrating climatic dietists in DHS2 but now my colleague we continue our presentation thank you I'm Zafirino Saojen so I'm working with Ophelia supporting the Ministry of Health in the process of integrating climate data in the DHS2 oh it's okay okay okay so I hope that now it's okay so yes as I was saying that we are we are we are going to share the experience on sharing or integrating climate data in the with the DHS2 and then this is work from Mozambique as a contest contestualization with the outbreaks have been overwhelming in Mozambique I think you had even the Malaw experience as well so this is a situation that we are we are facing in most of our countries so so in in in order to to deal with that we need data on time and there is not only health data but we need also data coming from other source like crime and other other other type of data so means of health is committed in the development of a platform I think in the morning we shared in the in the plan that the effort that the Ministry of Health has been doing in the last couple of years in the in the development of this integrated disease surveillance module with him to be used for several areas in managing special the disease surveillance data to empower the earth workers with the real-time data and also be used to allow the different stakeholders to have a platform where they can use health data for to as a support to response to respond for the different crisis so this is a example of a screenshot with the different modules that have been developed as part of the disease surveillance and also this is the he means also take other data not only the one that is here but we also shared the means of health and several other modules so the disease surveillance or surveillance data that is produced through those systems will be somehow shared to this into this this surveillance module so that it can be used for to respond to the different emergencies so there is also another effort on having an integrated earlier warning system that can aggregate data coming from different different sources to this process it comes from a couple of years back where we had projects in some provinces to for example to alert the farmers with a notification when for example there is certain emergencies so we start building this repository with some information and then then this repository is based on DHS2 is the same that we are going to extend or to link that with HMIS in order for to empower means of health with a platform that will be used to manage prevention or mitigate some of the problem that we will always are when whenever there are issues like the cyclone and also other recent threats that affect the country so this as part of that process we started there was a project also that we started using climate data in for health that project for that for the implementation of that project we were using global data climate data but when we started doing some analysis of the data and discussion on those data there was advice that it will be good to have the local produced data so in that in order to to to deal with that we approach the national the meteorological institutes in order to understand our very collecting the data and being able to generate or collect or collect all the local data and then we start visiting these weather stations there are some pictures here from that we are taking some of these are manual weather stations the other the automatic weather stations they generate data frequently routinely the manual they do it every hour but they are the automatic adjuncts for frequently so the idea is to get this data into a platform and then make sure this data can be shared or used to without the modules and then to link it with the DHS to so that information could can be used to predict some specific outbreaks or like for example what what we have we we did the the the first exercise in adopting the the the climate data in DHS to we did with malaria program where it's possible to have some prediction on possible causes of malaria outbreak which we are sharing this is an example where we are taking climate data and then using some statistical model to predict the probability of having any incident or other aspects and then this is is where we are generating it on weekly basis and then this information as I mentioned it is it was taken from global weather services and the idea is to get this data being collected from our weather stations and here are the examples of dashboard that were produced and then where we have weekly data and then there are also prediction or possibilities of getting data for example if there is an outbreak you will see in the map there more dark colors there showing that there is a possibility of having outbreaks in those those specific districts get using the specific data from previous previous weeks yeah so this is where we hand our presentation thank you very much and to Zeferino and I particularly like that you really showed us the way to early warning which is not just waiting until you have enough data to see what happened before but actually using and bringing those predictive capabilities based on the weather data into your system so I believe we might have time for one or two questions before the transition so are there any burning questions from our audience and examples for I think it's a new buzzword one health intelligence but some some tangible things behind it and I think what we also have seen that I was thinking a bit about the use cases in the second presentation from Malawi and we have seen a perfect example in the latest presentation in Mozambique that is pretty much in the area of modeling and a little bit in the area of forecasting and now casting so these aspects of early warning and I'm wondering it's very interesting to see after some time in evaluation of this how how much this really is feasible for early warning aspects because it's pretty much it's it's very new that we we all in this climate data for forecasting public health events vector impact and so on and I think especially in over the next couple of years the results will be really really important to draw the lessons learned from this thank you I don't okay thank you thank you very much I'm Doreen Ali I'm from Malawi but I'm in the health sector I know that NAMISI is equal to I think HMIS but I think we are going to the community level how is your work going to be aligned to the integrated community health information system because we are using the same community we are using the same people at the community level how do you see these two aligning because we are already there my department is already in the community your department is in the community how do we align these two systems to each other secondly on the issues of collaborate I was thinking are you collaborating with the aggregate and natural resources or also the University of Rwanda are you also tapping some of the information from there why you are doing your work thank you okay then I think unless Jennifer wants to take a moment to respond or just consider this the comments I'll let you respond Jennifer and then we can close the session okay thank you very much Madame Ali for the two questions that you have raised with with my colleague so maybe they're going to just just add up some key points on that one yeah so on the alignment of the two systems I think we have to draw an advertisement when we go to the when we go back to the country and discuss on the base approach on how we are going to align the two systems looking that the ministry has already has its own administrative structure and also the minister of agriculture as well so it's already our own administrative structure where we we collect our data and the ones that are collecting information in the healthy and the culture sector there are two different staff so I think that's an area whereby the minister of race and the minister of agriculture have to sit down and look at the best approach to align these two systems on yeah on the yeah on the correlations yeah we work with the natural resources the minister of our natural resources through the department of major culture services and also we work with Rwanda when we are doing our research and also when we are coming up with different reports within the ministry thank you thank you so much to our presenters and you have our platform product manager David just behind you and you can talk to him about aligning org units so ready to move on to the next session thank you everyone