 And we are now live. Hello, everyone. I would like to welcome you to this DHS2 for Immunization Webinar Series. So my name is Brandon Horst. I'm the LMIS Technical Lead here at the HISP Center in the University of Oslo. And today we will be discussing logistics for immunization programs. So as part of this webinar series, we will then focus on the general approach for stock data and then some different examples from different countries. So just to note that we are live streaming and recording this session, so everybody is aware. First, I will present a general overview on some best practice and the general approach we've taken to stock data and logistics within the DHS2 system, present some very global perspectives and guidance, and then discuss some possibilities and opportunities for implementing that. I will be followed by Barnabas Akumba from our HISP Nigeria team who will then present some examples of stock data in the system and how that's being used. And then finally by Alfredo Muconga from our Soldigious team in Mozambique who will present the same with some integration work there as well. We will have time for questions, so please feel free to share those. And we will take those either in the chat for those of you connected on Zoom or through the community of practice on the live stream. All right, so let me begin. First starting again with the global guidance and the work we've done at the HISP Center in the University of Oslo on DHS2, LMIS for last mile logistics. I'll give you an overview starting with why are we using DHS2 for LMIS? So there are quite a few reasons for that and to say that we're focusing on this last mile facility level where health services are being provided and where stocks are being distributed. So the last medical store. We're looking to maximize the use and the existing implementations of DHS2 as you see and you can check an online map with the information being updated on where it's being implemented at a national scale where you have a country owned DHS2 instance being managed by a DHS2 core team which can be relatively easily configured and implemented to capture the stock data. It's not a new set of software and necessarily users being included but it's making use of the existing DHS2 implementations where a person at a facility may already be capturing health service data. It would be adding a stock data set as part of their monthly reporting as a simple action and as you'll see further in the presentations many countries are already making use of these implementations and of these DHS2 instances to then capture stock data from that level. What we often see in the countries which we work is this very generic representation out of a national supply chain where you have an ERP or an ELMIS operating at a central level at a capital and then maybe region and state level maybe even now to district but the further down you go the less availability of data, digitized data you have a lot of paper-based systems which can be fine and it can be used properly and implemented properly but do not always provide completeness and timely data for decision-making and this is where you see the bubbles there showing now a lack of digital systems you actually have DHS2 being implemented and capturing data in a few of these places and one of the things we'll focus on are opportunities for making use of those existing implementations. Within the ELMIS landscape you have multiple solutions I just have a few here represented but there are actually hundreds of different solutions that are found specific to programs in different countries and also what we're looking to do is provide holistic product management to holistic supply chain management solutions at this last mile and integrating with any one of these different solutions and we have also multiple examples of this in production where we have DHS2 data being shared with a central ELMIS or ERP we're happy and willing to collaborate with any solution that might be existent at a central level. Some of the different guiding documents which we focused on which we've used in developing this global guidance for LMIS and for stock data have been particularly these two documents the target software standard for vaccine supply chain information systems from Gavi and the country guidance on selecting an LMIS and some of the points that we focus on, hello. So one of the points we focus on is end to end visibility within a national supply chain and again here it's digitizing that last mile and connecting it with a central system so that's how we achieve this end to end visibility through this integration approach. Having real time data and for that we're developing tools in the tracker program for the tracker data model to get transaction based data from facilities having this transaction based system but also being able to create and generate reports interoperability with health information which in the sense is the easier challenge being that the health data will already be in the DHS2 system. And lastly some cold chain monitoring related features. So the functionality as we foresee for at the last mile is having your central ELMS or ERP providing your full scale supply chain management within the country from your central medical store and then distribution down to state district and eventually facilities. And only at the facility level do we look to maximize the use of DHS2 for stock management either report based or transaction based and then the other functionalities as I mentioned some cold chain management both equipment and temperature monitoring some quality assurance performance management dashboards and having a simple product catalog for that end user. We also want to stress that these features are available on the web based version which you see an example of with the dashboards on the left side but we like to promote the use of the Android capture app which allows for online offline capability for lower resource locations in more remote areas. And the overall outcome which we're trying to reach and again this is part of the objectives and aspirational aspect that we want to promote here is that you can increase the quality of health program management by bringing together this data bringing together the service level stock data with health service data and comparing here with simple examples the amount of a stock that has been issued and the amount of tests or medicines that have been administered is there a correlation? Is there a huge discrepancy? Should some follow-up action be taken to see the reason for that discrepancy? If you're mapping that data on a map are you able to then focus on areas which require more support where there's a risk of under stock or even stock out and how can you remedy the situation before it becomes more serious? So that's for the improvement of health program management and also improving supply chain management. So this should be useful and helpful for the HMIS team and the health programs but also for supply chain of course and one of the points which we then try to highlight here and one of the targets is reducing the amount of demand distortion within the supply chain by having accurate consumption level data. So this is really providing accurate consumption including potential stock losses into the demand planning and forecasting which again we foresee being done in the central level tool the ELMIS ERP or even an external solution but having this accurate data rather than taking estimates or quantity shipped from a previous period you're actually seeing what's actually being consumed and what you have available for potential redistribution amongst sites. So this is for the pure supply chain management and bringing in these LMIS teams also into the data use aspect. Now this is my last slide and here we just want to then highlight this interoperability approach. We're not developing solutions that are overlapping with other existing tools but if you see again this generic representation of your national health structure with health information on the right pillar DHS2 being used to capture, store and analyze data at multiple levels for the logistics data on the left side we're really looking only at this end user level or service level from community health workers to health centers and hospitals where you're finally issuing stocks and bringing this data through a direct integration or interoperability layer to the ELMIS or ERP providing again the benefits of both the health program management and supply chain management improving health service quality of health services and also reducing demand distortion. And this is the overall approach and what we want to promote as the best practice for using DHS2 within this field. Now what we'll be then showing now and moving on to the presentations from the country cases are opportunities from that are either being implemented or have been implemented some time ago where you have data already available through DHS2 and we want to then show that there's opportunities and possibilities to make use of this data to incorporate it into the more global supply chain management processes and to actually gain insights from this the data is there and available and we can show potential effect and improved services by making good use of this data. So I will stop here for now. I wonder if Alice if there are any questions that we should take immediately or if we should continue on to the next presentation please let me know. Yes, I think we can continue to the next presentation window. All right, great. So again, if you have any questions on this feel free to pose them in the community or in the chat. One thing that I forgot to mention as well as that many times one of the questions we get immediately is how much does this cost? And that's a very important question but not always a straightforward reply because it really depends on the scale of the implementation you're looking at and the context and the existing DHIs to implementation. So there are quite a few factors. So Alice will share with you and we'll share with you afterwards. The link which you can contact us provide some information and we can organize a follow-up call to be able to provide more detailed information based on your needs and also your targets on how this could be implemented. One last point to add also is that we'll be sharing this presentation and other resources that you see throughout the webinar with you after this is over, okay? So now I will move on to Barnabas Akumba from Hisp, Nigeria who will be presenting some routine immunization stock data which is already present in the system and he'll go through data entry and data use cases and please welcome Barnabas. All right, thanks, Bruno. Hello everyone. Can you confirm if you can see my screen? We see it Barnabas, thanks. All right, thanks. So my name is Barnabas Akumba and I work for Hisp, Nigeria and we support the country in the implementation of the DHIs to our national health management information system for the country. We'll just look at some brief statistics about the country, Nigeria. We have a population of over 216 million. The Nigeria is divided into about that 61 state as the federal capital territory and the administrative structure is from federal, state, LGA, world and facility. This structure is reflected on the reporting structure of the country. You will see more details about that as we move on. And my interest you to also know the country has over 38,000 pet facilities and the average life expectancy is about 54.6 million. Martena mortality ratio is around 917 per 100,000 live pets and active GM subscribers as of April, 2022 was around 147.45 million. Reporting of data in the country is done in two weeks. There are some facilities in the country that have some mobile devices issued to them and they are able to report directly to the national platform. In most of the facilities, greater proportion of the facilities, they summarize their data on the monthly summary forms and send to the LGA level and the LGA person there is sent to them is able to submit this data to the national system. We are aware that we are in sub-Saharan Africa and there are infrastructural challenges that have limited the direct reporting from pet facilities to the national system. It comes with its own challenges but we are able to stay true. The various stakeholders at the various levels have their responsibility and the responsibilities are stated as above. So the implementation of the EHIS as data collection for stock and other routine immunization activities came about as a result of the Nigeria routine immunization strategic plan 2013 to 2015 and the integration plan of the National Health Management Information System. This was as a result of the NCHS the National Council of Health declaration where they identified the DHHIS-2 that's the National Health Management Information System that was built on DHHIS-2 as a single source of information for health data in the country. And they went ahead to mandate that all data be collected using that platform. Before then, they were disparate systems where different programs were collecting data on different systems but with this declaration it became a policy and all programs had to see how they can bring in their data into this same system. And the country was able to develop a tool that is called an NHMIS monthly summary form for reporting all data from all health facilities in the country. How was this done? The process was funded by CDC under the ENSTOP project and the implementation of the project was done by AFNET in collaboration with NINJIRRA that provided the technical support for the setup. The guiding document for this implementation was the ARIA accountability framework which was used for this setup. As a result of the non-existence of routine immunization data or it was existing but not complete the logistics component was not there and some additional routine immunization antigens that were introduced were not captured there. So supplementary data sets were set up and one of them was the logistics management and logistics monthly summary form that was set up for reporting logistics data for immunization. After the set up trainings were done starting from the MTOTs down to the state level and LGA levels. You would agree that NINJIRRA is a very large country and this took about three years to do the rollout in the whole country. The data set in question which is that of the logistics it is called the health facility for same utilization summary. You might notice that we call it 2019 version because it's been reviewed from the previous version which was the 2013 version. The country has a policy of reviewing data sets after a particular period to reflect new innovations. So the data set has two major sections VM1A and VM1B. 1A talks about stock situation for antigens and the quantities discarded or losses. VM1B talks about stock situation of syringes and the second section of that section it talks about monthly devices and various status. This is the setup of the tool on the National Health Management Information System. You'll see that this is the traditional design of the GHS2 where you have the hierarchical arguments here and then the selections are reflected and then the tool shows what is achievable there. The screenshots might not be so clever I'm going to do a walkthrough of a demo from the platform itself and you'll be able to see it better. This is the VM1B section of the tool talks about the stocks of the various antigens of the syringes, sorry, and then the device summary status report for each of the analysis. For the visualizations, I would like to switch and go back to the platform so that we can see it. So we, the implementation brought about a dashboard that the country calls into immunization public dashboard that monitors key indicators in the immunization program and you'll see most of them are about consumption and monitoring of activities. We have a chart here at the table that talks about code chain functionality and these charts have been made in a way that they have color codes. You would see where the performance is very high, fine. It's reflected in green and you'll notice here 72.2 is giving you an orange color. This has been explained to the end users and they know the color codes are able to tell them what action they should take when we see those color codes. We have a proportional facilities with stock at all antigens. This is also reflected like a scorecard and the color coding is where we've got, we're used from the return, the ARRA accountability framework that I've mentioned earlier. That was a guidance document that was used for the implementation. We also have a table that looks at stock balances for antigens, those are here for all the states. And the beauty of this is that this can be drilled down and the end users are able to drill down to whatever level they want to go to. Even at the dashboard here, the field house are here and the team, the program level team at the national level is able to go down to any state or LGA that we want to go through. Back to the slides. This is the reporting status of that particular data set that I mentioned earlier. You will see on a monthly basis the expected reports for the vaccine utilization. And yes, the country has not been able to get 200% but this is increasing by the day. You see the actual reports, you see the timeliness and then the reporting rate as well. Going forward, the country intends to integrate open LMIS with UHS-2 in order to be able to obtain full visibility of stock at the last mile because the open LMIS implementation which has just been completed stops at the LGA level and with that you wouldn't be able to see what is happening at the lower level. So it is to dissolve the country that this integration between DHS-2 and the open LMIS would be actualized. We are in the planning stage and we hope that in the nearest future this will come into fruition. Coming back to the usage of the data this data has been useful for the immunization program the various immunization teams. We have the Logistics team, we have the generic at the national level, have CERIC at the state level and even at the LGA level. They have access to these dashboards and they are able to make use of them to take the adequate decisions that they need to take. For example, if you are able to see that there are a lot of code chain equipment that are not functional, they are able to take action that will work on that particular area. Stockout as well is monitored using this and they are able to take adequate decisions and know where to send stock to be able to refill the gaps that exist in those places. I think, yep. There are, at least we have questions concerning this that we can. We do have a question actually in the chat from Raju. So Raju asks Barnabas in VM1B in the used section, does this section include the damage and expired doses or only used doses reflected? How does this data help in calculating the wastage? Okay, so VM1B, let me reflect it here. So you can see doses discarded as due to expiry, breakage, change of VM frozen and never removed. So that data is available. All right, so you have that breakdown? Yeah. Reply to Raju's question that there is the breakdown by type of losses and then if there's a calculation, I didn't see in the visualizations if there's specific calculation on the wasted rate by antigen, but definitely something which could be developed if it's not there. Yeah, those are taking care of indicators. The indicators that are able to calculate that. The data entry is just for the raw data and the indicators are calculated values that can be seen through the various visualizations. All right, and we have a second question from Remy. So hello Remy, do you have a, do you track stockouts for specific antigens? So specific antigens. If there's a visualization showing stockouts, I'm not sure if you had that in one of the dashboards either. Oh, okay, no, what we have here, yes, we have, you would see there's a yes or no, just that the quantities are not there and say yes or no, so they're supposed to indicate if there's a yes or a no. So if you get back to the dashboards where you have stockouts here, here we're looking at, let's see, chart that money source stockouts, though it's not based on the antigens, but that's something that can be done. We have a chart here, this stockouts, sorry. Yeah, you can see, professional health facilities with stockout. This is just, it's not antigen days, but with that variable there, it's possible to calculate the antigen to get a particular for each antigen that passes stockout because the data is collected by antigens, yeah. All right, thank you, Barnabas. There's two more questions which I'll take quickly. One is how do you get the actual stock balance? So that is input as part of the monthly reporting. So once you do your stock counting, you know the quantities that you have, you can input that quantity on hand. And then if you're able to display the previous month's balance as the beginning of the next month, that's the second question that is also possible. So we can use something called a predictor, which can be configured at DHS-2 to take that closing balance from the previous month to start as the opening balance for the next reporting period, which is very useful in saving the amount of manual data entry. If you wanna comment on those two questions as well, Barnabas, go ahead. If not, you can continue. I think you have one or two more slides. Is that right? I was actually at the end of the slide. The last slide was integration with O'Connor and I, so I think I'm right in time. Yep, so if there are no further questions, I'll hand over the button to Alfredo. Yeah, so just- Alfredo, you can take it over for me. Great, thank you, Barnabas. Thank you for the presentation and the examples. And just a word on this integration, as you mentioned with OpenLMIS. So that's one discussion we've had with the different stakeholders there in Nigeria on making use of the existing stop data sets already in DHS-2. And then finding where that, in essence, the red line is where we then connect with the central system, in this case, OpenLMIS. So they're implementing down to the LG level and then the facility level data, which is already available in DHS-2 can be made available to that system for demand planning, forecasting, and other supply chain features. All right, so thank you again, Barnabas. If you have any more questions for him, please continue to share. And we'll now head over to Alfredo Muchanga in Saldigitos Hismos Ambik, who will continue with the presentation. Go ahead, Alfredo. Thanks, Breno. Hi, everyone. My name is Alfredo Muchanga. I am part of Hismos Ambik and we've been supporting Hismos Ambik and other countries on the DHS-2 implementation. And during this presentation, I will be talking about the integrated logistics system in Hismos Ambik. And I have here some topics that I will go through. I will talk about the implementation approach, why and how are we doing the implementation of this integrated system. I also mentioned about data collection tools and also how the data is analyzed and how the information is used within the different challenge and lesson learn and what are we planning to do in the near future. So I will go straight to the motivation for this integrated system. So the fact is, in Hismos Ambik, we do have several platforms that deal with talk management data at the different level, at health facility level, district level and national level. And there are some challenges that we have through the use of all these platforms. And this challenge includes the fact of some facilities are reporting coverage greater than 100%. Even if the vaccine stock, but the facility is not yet exhausted. So another common point is that we have these, we don't have a balance between what exists in all national health systems. So it makes, we have some situation where we have some loss in one facility and we have some stock out in different health facilities because the system was not totally integrated and it was very difficult to have an overview of the real status related to stock data. So because of that, the MOH, together with the different partners, Global Fund, USAID, GAVI and so digital, Hismos Ambik, worked to implement and integrated a solution for supply chain management. So basically the main purpose of this implementation is to use the HS2 as a data warehouse where you can have all stock data integrated and you can easily do a data translation with the different data that exists there and the HS2. So this is a work that was done led by the HMAS department and the medicine and medical arts centers that we call the Artificial Medicines in the coming months. And this integration includes mainly the OpenLMS that is the main system that is used for logistic for data management related to logistic. We have also to include on this process the master facility list that is the HS2 visit, visit on the HS2 and the local implementation of the HS2 is called CSMA. And then finally we have what we call the master product list that is a in-house build solution that aggravates all information for the different systems that I use it to implement logistic data. So basically on that explanation, we have this overview. So this is the sapling chain system overview in Mozambique right now where you have here the Feramental Central tool that is a tool that's used to has a mission before to store the logistic data. So it's going to include LMS and other systems that I use it at different level who have here at this side, the HS2 main instance. So through the implementation of the interoperability layer, it was possible to exchange data within these two systems. So on the HS2, we have the master facility list and we have them coming for the HS2 and for the Feramental Central, we have the master product list and we also have the data, the real data that is coming for the health facilities and for the whole supply chain system in order to go to the HS2. And based on that, it's possible to have dashboards where the end user can do a data analysis in an integrated way. I would like to emphasize about the master facility list because it was one of the key points to the success of this implementation. Because if you want to do a system integration, one of the basic activities is to do a facility alignment where we have to make sure that all facilities that you have in one system, you have an equivalent facility in the different system. So that was one of the big issues that we got during the implementation of this activity but it was also an opportunity to make a master facility list a bit more strong. It was an interesting to the master facility list in Mozambique. And for this project, we managed to align the list between the different systems. And at the end, we also have the master product list. So the master product list contains the list of all medicines, vaccines, and all consumables that are available in the different warehouse. So at the HMA side, we have the list of all health facilities. And at the CMAM side, open LMA side, we have the list of all products that are available and it's possible to do the data exchange using these resources. So with this solution implemented, it was possible to have the data fully integrated in the DHS tool and it was possible to compare the data that we're receiving for the stock side with the data that is at service deliver level. We created data sets in DHS tool and other collection forms in order to guarantee that this data is used properly. And basically what we have there, we have the balance to receive it. They use it, the loss and adjustment, the stock and hand, and the expert date. And the data set was created on DHS tool to receive the data. Here we have the code of the product. So this code is coming for the master product list. And here we have the different type, a data type that is collected for different users. So the screenshot is in Portuguese that is the language that we speak in Mozambique. So here we have initial balance, what we receive, what we use, loss, stock and hand, and expert date. And what was done is that the form was there, but was not, the user only have access to view data. So they cannot edit, they have a read only privilege. So it means that someone that is at health facility data can log in into DHS tool, can view the data related with the stock at his own facelift level, but he cannot edit the values that are there because this data was captured by the Ferramenta Central at tool. In terms of data analysis, we did a very hard work with the different programs at the Minister of Health in order to guarantee that we have a dashboard that they can easily use and go according to what they really want to see. And on the dashboard, we have some visualizations that include a comparison between stock and hand and user, we have comparison between the different programs for each data type. I mean, before data type, I mean for the user, for the loss, we have different comparison for each product along all these options. We also have some analysis of data concepts over the time. So have the same value, the same product and we want to see how this value was, what was the deal of this value along the time. And here we have some example of the dashboard objects and decide you can see over the time, what was the behavior of each product that you have here in this, here. And you can see that we have some issue here and others, something happened. And it was very good because they really identified themselves with the data and they know very well to explain what happened in others, why there is no data here in others. And we also have this chart that show us the comparison between the different firms. In terms of our challenge that we face, phase one is related with the time for that synchronization because at the parameter central level, there is still some review that need to be done before sending data to DSS tool. So it means that in terms of availability, this process can take approximate to the one month. So we have the data in DSS tool in a period of one to two to two months. So we understand that this period must be short in order to make sure that the end users will have data in a more accurate time period and they can be able to take a decision of what they want to do. The second challenge that we have is related with the immunization data because there is a platform call itself that is an open LMS, also open LMS implementation that is not yet fully integrated with parameter central. So has DSS tool is connecting to open LMS through or via parameter central right now there is still a work in progress in order to guarantee that we have all immunization data for the EPA program in the DSS tool. In terms of plans, we expect to improve development of the data as I mentioned the challenge. We also are preparing some workshops to promote the massive use of existing data in DSS tool. So these workshops, the main goals will be to show to the end users what they do have right now because they have no access to this data in a very easy way, but now they can do that. So the first thing is to show to the end users that right now it's possible to have this data and improve the dashboard according to what they want to use. And also has a future plan. Right now we are implementing the electronic immunization registry. We are in initial states of implementation of this module. So what we expect or what we are doing is to integrate the stock data with this immunization registry. So it can make easy this process of data triangulation. And also we can make sure that we can easily have what was used and compare with what we have installed or other metrics that we can use to make sure that we have a data with good quality. We also are evaluating multiple solutions to improve deficit level data capture. And one of the solutions, actually the main one is to implement the DGS2 Android app in order to do a data capture at facility level data. So right now this is an evaluation that is being done and we need to think about how these all ecosystem is going to look like after implementing the DGS2 has a data capture tool at the facility level. How this data is going to be sure with Ferumenda Central and how are we going to make sure that using DGS2 people will be doing data entry and the data will be used at the different levels that we have in the system. So basically that's what I had to present. I can quickly show the system in few seconds. So this is the dashboard that we do have. For example, here we have this dashboard that do a comparison between the different products. And then here we have a dashboard doing a comparison between two specific correlated products. And here we have the different tabs containing all dashboards that we created. In terms of that entry, as I mentioned, we do have the data collection tools that we created at the DGS2 level. So here you can have all fields that I was mentioning during the presentation. So basically here you have the products and then here you have the values. So this value, the values that are filled here, automatically introduce it in the system and the users, they have only access as I was mentioning. So thank you, that's what I prepared for this presentation. Thanks, Brino, I give the word back to you. All right, big thank you to you, Alfredo. That was very interesting, very comprehensive. And I'm actually going to put you on the spot right away because we had a question from Michael asking, so a very technical question, does the integration need additional infrastructure like upgrading server capacity? And I saw that you gave a good overview of the schematic between the integration between different systems. So maybe a more technical question. If I can also add my own follow up question to that one as well Alfredo, if you can mention then, let's say the human side, you're also aligning work processes amongst HMIS teams, LMIS teams, if you can elaborate a bit on that. So first the technical infrastructure server capacity, if you can comment and then any challenges or any learnings between working, aligning work between these different teams. Okay, thank you. So in terms of infrastructure management, in Mozambique, we have a module, actually a system, that we call Interoperability Layer, that is actually a set of service that allow the DHS to be integrated with the different systems that we have in the country that have been implemented in the country. So in terms of the DHS to server capacity, there was no need to make a lot of work, because in terms of data amount was not a very high volume, comparing with what the user do daily. So it was not a problem in terms of that. Of course, we had to do good tests and good checks on this Interoperability Layer to make sure that we have no problems during the data loading process. Now, going to the socio-technical part, definitely we took a lot of time to have these projects implemented. There are several meetings that was led, there are several weekly sessions that we had. And even right now, we still have a channel where we still discuss about how can we improve the process. One of the example is related with the MFL, the master facilitator list. And the point was, how do we do manage the new health facilities that have been created at the HMS site? So when we built the system, we created a set of procedures that we have to take in order to update the values, but we noticed that we need to make something more automatic. And it led us to an implementation of a faction that can automatically notify the other system and they can easily create that health facility at the Ferramenta Central site in order to proceed with data without some synchronization errors. So this is just an example of how are we aligning the technical part with the admin part and the local procedures that have been taken at the health facilities and other levels. Thank you. All right, thank you so much for, again, lots of information with the reply. And I think we can agree that it's not merely about technical solutions, but it's also about the socio-technical, as you mentioned, Alfredo, and described well. I have another question here now from BFY7 asking, please advise if the system is able to absorb a certain surge capacity for outbreak settings, for example, cholera or yellow fever. If so, what considerations are needed to operate with the routine immunization system or others? I wonder if both are afraid or a barnabas. You may have examples here. It also may be relating to the COVID-19 experience. Over to you guys. Okay, I'll go first. In Nigeria, we've been able to make use of the DHS-2 for campaigns, very large campaigns. For example, the COVID vaccination registry that is a case-based data system. And Nigeria is large. The implementation has been able to cover all the LGS and facilities in the country and all these users are able to work on the system at the same time. Of course, you encounter challenges, but the greater part of it is that there are millions of persons that have been vaccinated and the data is available on the system. So the DHS-2 is robust and there are hundreds of such as evidenced by COVID-19 condition. Over. All right, I can only add that there were multiple implementations during the start of the pandemic and that there was configurations were quickly organized for this search needs, but of course it depends on the scope and scale of the implementation you're looking at. So again, something we would need to respond with a bit more contextual information. One other point here to elaborate then on the previous question is that we have in a specific work area around integration and interoperability, I added the link to our website in the chat. So don't hesitate to reach out to look at those resources and reach out to that team as well. If needed, we're really focusing here on stock data and the opportunities with data already available on the systems in country. And there's nearly 30 countries that have this type of stock data in DHS-2. So we really want to make use of those and promote this approach where we're looking at stock management integrated with a central tool to inform health program management and supply chain management. So integration and the implementation, that's yet another layer and another work area. So feel free to reach out also to those teams. Alice has also shared, I think multiple times the form if you want a follow-up discussion and to actually assess any requests or need for implementation and we could work with you on that. I think we've gotten to just about all of the questions. I think maybe, let's see here. We have Emmy from Norwegian Red Cross who asks, have these data been integrated yet within health information systems that look into performance indicators for say vaccination campaigns? So coverage by age or gender, adverse effects, individual vaccinated performance and so on. Again, if any of the presenters have something to add, please go ahead. I know that we do have some indicators showing wastage rates for specific antigens where you can maybe target down to district or facilities which have a wastage rates that are outside a acceptable guidance for the specific antigen. I'm not sure we have that down to the specific vaccination team. And then some of these indicators should be included in our EPI metadata package, but I think we'll have to have a follow-up message to you Emmy on those specifically. Please jump in guys if you have anything to add on this one. And it has to be clear that the implementation, so the stock data in DHS2 for the Nigeria presentation is at the facility level. The open LMIS implementation goes down to the LG level and that's where the integration would be made. Alice, if there's any questions coming in from any other source that I do not see, let us know. Yes, no problem. I haven't spotted any other question. So no question on the COP neither. Hi, Bruno. I don't know if you've responded to this question about from Michael that says, does the integration need additional infrastructure like upgrading server capacity? It depends. There are various factors to it. But of course, depending on the infrastructure you have at the moment, the server capacity, if it's not enough, then as you're working, you might know you need to tweak. And if you can accommodate what is coming in from the older system, then you might need to increase resources. But the idea is you acquire resources as you need them. We don't believe in sustainable systems. So you don't buy what you don't need at a particular time. So the integration, of course, there will be additional work load onto your system that you're trying to integrate into. But you need to monitor. There are various tools that the DHI uses to monitor performance. If you are able to pinpoint to the fact that the resources on the capacity are not enough, then you might need to add what is needed. Over for me. Great, thank you, Barnabas. And we also had quite a lot of information from Alfredo on that. So thanks again for adding even some more context. I think now we have covered just about all of the different questions. I think we just had, sorry, Bruno. We just had one from Pedro. So, okay. So from Pedro is the system in Mozambique fully functional for medicine stock and usage at the facility level as well. Can you comment, Alfredo? Yes, yes, yes, Bruno. Yes, the answer is yes. We, based on the product list that we do have and based on the data that is being, we received from the Ferramenta Central, we would have data about the stock and usage at the facility level. And actually the data is from facility level. When we get the data from Ferramenta Central, we send the data to the facility level. All right, thank you, Alfredo. And if I can refer to one reply that Barnabas gave regarding the need to upgrade the server infrastructure and the reply was it depends. It's also partly the answer we sometimes give when asking how much does this cost, which is another common question. And again, reach out to us and we can have a discussion on specifics related to either simply having stock data and capturing that with DHS2 or a question of integration. And we can look at more specifics to what the use case is with the requirements are and understand the overall context and then provide some more tailored guidance then on eventual costs, time for implementation and so on, so we can support you with that. If there are no other questions, I want to thank everybody for participating for the questions and the engagement. Big thank you to Alfredo and Barnabas for the presentations and for sharing your knowledge and experience. Thank you to Alice for coordinating and organizing and we'll be in touch. Do not hesitate to reach out to us. All the best and bye for now.