 Okay, with that, I think we'll go ahead and start the session. I suppose I can share my video for a moment so that you can see him a real person. Very nice to have you all. This is a continuation of the start of the day where we talked about the WHO packages and that joint effort, University of Oslo working with WHO, Global Fund, Gavi, a number of other partners in creating the individual configuration packages per disease area and health needs. In this session, we'll cover first in the first half a discussion around HIV case surveillance. Dr. Dave Lawrence, who has many years in the field and working from WHO and Geneva at this point is going to talk through the more recent WHO guidance around HIV case surveillance and how that is feeding into the specific package that we've been working on. And then we'll hear some country experiences and see a bit of the configuration of that package. So I won't take up more time with my side or introductions, but I'll turn the time over now to Dave to kick us off. Okay. All right, I've brought up the presentation. Are you able to see it okay? Yes. Yeah, we see it. All right. Well, first of all, hello, everybody. Good morning. Good afternoon. And I wanted to start by thanking the conference organizers. I think this is the third year that I've had the opportunity to present at the DHS to annual meeting the first time virtually great to be here with you. Oh, my dog has just entered the room. Hold on one second. Sorry dog is not leaving the room willingly so I'm going to continue. All right, so I'm going to be presenting today on the WHO HIV case surveillance data use package. Just about addressing implementation gaps using standards based tools and Enzo and I are going to do a kind of a one to on this. This will lead right into the presentation on the end user interface. Very quick background. Some of you have probably seen this before others may be new to it but WHO has two HIV strategic information guidelines one on aggregate data and the other on individual level data. And together these two guidelines cover what we consider to be the four essential data use cases starting at the bottom with patient care and patient monitoring and all the way up to global. In case surveillance just so you're clear is a use case that we consider within program management. These are the guidelines. This is the first time WHO put out case surveillance guidelines believe it or not in 2017 for HIV at least these guidelines together. What they try and speak to is the fact that there's there's something like a core data set, both at the patient level, and all the way upstream to global level. There's a set of priority data and party indicators. And that concept is well kind of captured in this this phrase collect once use many times which is fundamentally about the health information systems and and how effective they are at achieving efficiency for all the various data and users at country level and an upstream. I had to include this definition. I'm not even going to read it, but I wanted it to be in this slide set in case there were any questions about it be happy to entertain the piece in red just to say, it's not something you're going to find in our guidelines but it has been used in the UN AIDS global AIDS monitoring survey, which we develop with them. And it's the basis for monitoring the implementation of HIV case surveillance global. Okay, so I think many of you probably all of you are familiar with the WHO standards based health apps for aggregate data which include the DHS to base dashboards. And this is the, this is a graphic on the HIV package, again with the centerpiece which is the DHS to base dashboards that are metadata which for HIV are partial. And then we have the derivative guidelines and the exercises are as part of that package. So, I wanted to speak to this question of, why are we doing this for for HIV case surveillance. So again, program management, I think from any programs began with thinking about how to make better use of aggregate data which were more broadly available. Even donor partners like Global Fund and PEPFAR had focused, you know, the past few years on aggregate data use. And in our package that we put out in 2018 was intended to help facilitate those efforts. But it but is, I think everyone's aware there's been very broad movement towards individual level data systems. And, and around program management, which is, you know, a big focus for us helping programs and stickled as a country level to make better use of routinely available data to improve programs access quality etc. That that was about case surveillance. So, you know, for the, since we put out the 2017 guidelines, which included this, the situation analysis for case surveillance. And we've been able to track implementation, as we do with other recommendations and we've been made aware of some real limitations and from the uptake of case surveillance. And some of these situation analyses have also given us some insights and I would say in addition to the situation analysis just working with partners like the Global Fund, through their grants, providing technical support to countries. Generally identified two regions with somewhat distinct use cases if you will. In terms of, I would say in this case tracker specifically. So, although that we're talking here about adoption of HIV specific individual level information systems in Afro. On the left side we see Western Central Africa, smaller epidemics, lower investment. We tend to observe less diversity in terms of the software tools that are available in those contexts, fewer EMR specifically and and certainly fewer that are brought to scale, and then we have larger epidemics, higher investment context, where we see greater diversity of software tools and a lot of oftentimes in many countries, EMRs that are brought to national scale. And this has implications for how we've been thinking about tracker specifically. Now before I come back to that, just wanted to quickly run through a selected number of HIV case surveillance recommendations and highlight some key points. Not going to go through all of them. First on the standardization of Sentinel events and indicators. One of the things that's critical to bear in mind in case surveillance, which I hope you'll see reflected in the configuration package is its relative simplicity. Case surveillance is fundamentally a subset of data elements and indicators from what is essentially, you know, the patient monitoring universe of data. I heard some interesting discussions in the TB setting about, you know, you know, the implications for reworking national M&E frameworks and it's important that we understand case surveillance is just a subset if you will. Here's a, here's a snapshot of the sort of describe some of the evolving case around metadata, specifically looking at Sentinel, the Sentinel events. If you look back at our 2017 guidelines in the left, we define six Sentinel events. Now, in our 2020 SI guidelines, which are on, again, on aggregate data, we, we rift on that a little bit and we even though it wasn't necessarily through formal processes, because of the fact that test and treat started just before the 2017 guidelines came up, we really felt compelled to bring in some of the Sentinel events around violet suppression, retention, plus follow up, and it also TV preventive therapy, which I think you'll see when Enzo presents that. And then just to just also say we're actually starting up the process of revising these guidelines already because it's been very dynamic. And in 2021, we're going to, you know, we're essentially going to be reviewing all this and making decisions on on new metadata. Okay, so deduplication of records. This is, you know, I hope this is clear folks, but the ability to duplicate client records and all administrative levels from facility to national is essential and this is about a robust national, but we're calling a unique identification standard. Now, this isn't just a single unique identifier for health or for HIV. This is about identifying all available unique identifiers and or patient identifying information and really characterizing that under a policy, which is then reflected in the digital tools. And in fundamentally, that is about ensuring that the case of illness functionalities reflected in the, in the tools in this case tracker, and that enables us to determine if somebody has been diagnosed in one SNU one SNU two facility, and that person moves and is diagnosed again in a different SNU one different SNU two different facility. We know that. And similarly, if somebody is initiated on treatment, and the same thing happens, cross time and space, we can identify where, where those individuals are. Okay, so this, this last one is just on HIV diagnosis, and I think it's really it's worth highlighting again, you know, because I think there have been misconceptions about this. The new diagnosis is the case reporting is the essence of case surveillance, and it really provides uniquely important epidemiologic information that's something we've tried to bring out in this in this package. Okay, and then I guess this is actually the last one on data systems and I just wanted to emphasize a point which will come up later, which is that case surveillance solutions like all solutions are ideally based on the most generic the most universal data terminology standards. And those include things like a Joe seven fire, I CD, and a number of other terminology standards. And those are the bases for ensuring robust health information exchange within the broader landscape. All right. So, so back to this. So the, the use cases that we had identified for tracker specifically. So certainly in West Central Africa. We were seeing demands for development of tracker for HIV. And, and that gave us the idea that tracker as a primary data capture tool at the, at the point of service or close to it was was a relevant use case or sub use case if you will, in that region then. On the other hand, and this was something that was, was an observation that we made at a satellite session at the Casa 2019 in Kigali, but there are a number of these high investment countries with generalized epidemics, many of several of which have achieved high coverage national coverage of EMR so various platforms, and they're also looking at tracker as a as a solution as the data repository. I'm using interoperability layers and help help data exchange to achieve that. So, again, these two distinct use cases for for tracker for this configuration package that we've developed. And one on the, you know, kind of reflecting this graphic ends is going to go into more detail, but it's really tracker capture data capture, you know, primary, the second. And some folks may may recognize this from a country in East Central Africa, but this is, this is a situation again where your tracker tracker actually in that setting just just to be clear has currently been configured as a intermediate primary data capture tool for case around, but the long term view is to use it more as a data repository which is again something we've seen in other countries in the region. Okay, so, and so how am I doing on time, Mike. You're doing good. Doing okay. I think I'm good. Take as long as you need. So, one thing I wanted to highlight is the fact that later this year, we're going to be putting out what what is called the digital accelerator kit. And this is something, some of the components of this are in the next few weeks going to be used to inform this configuration package. And I'm not going to go into all the detail on that, but fundamentally it's something we've never done before we've never developed a core data dictionary. We've never essentially done terminology mapping so that all of the various terminology standards which are being applied and various health sectors around the, around the world and certainly in the local region, that the business analysts and the programmers and the software development teams, like, like many of you, have that content rendered in formats that are much easier to use. And it won't matter whether it's DHS to tracker or other systems, but it, these are things these are tools that should be available to you and help facilitate the work that you're doing. So just a couple quick snapshots of the core data dictionary this is taken from another kit. You have the core data dictionary with the data elements and then also mapping to our, our indicators. So, basically here with the core data elements you have all of the metadata from our clinical guidelines reflected in our individual level guidelines, which includes case and here, all of our recommended aggregate indicators, you know, in the updated SI guidelines so that's going to be available. Again, ultimately, as you see in this slide, in this broader guidelines update we also are looking to develop fully reputable and machine readable guidelines content, which will include fire resources, fire mapping. So, so that's, that's on our horizon in the short term and we hope and expect that's going to be a great, great resource for all of you. So, quickly, we're working closely with the Oslo team to develop, and this is a really exciting area because it's never been done before even in our primary guidelines to develop some analytic visualizations that really convey the unique utility of case surveillance. And on the top in the red circle, see the five tabs, and this is all draft, but we want to, we want to show that case surveillance data like aggregate data can be very useful, which, you know, providing broad snapshots and, and really get at 959595, but in the second tab you'll see on the demographics, the case reporting, you know, there's unique epidemiologic utility that routine HIV testing data do not provide us aggregate data do not provide us. And then, then you, you take that further into linkage and retention, some of the critical program outcomes and finally, viral load clinical and programmatic outcome. That's, that's the most essential, and then, lastly is TPT or tuberculosis preventive treatment, which is a big, big push right now by by WHO and many partners globally and something we wanted to make sure was was present here. These are just some very, very draft guidelines showing again some of the utility just we're talking about basic epidemiology purse person place time and being able to slice and dice that and really understand more about where new cases are coming from and hopefully using that information to inform HIV prevention programs. Again, just another, another screenshot showing similar data in different ways. Now at the bottom you'll see mode of transmission. This is a key aspect of HIV case reporting. That, you know, allows us to really understand where some of the most at risk populations are with regards to access and quality of services. And also epidemiologically again to understand how well our prevention primary prevention services are doing, in particular for key populations. So just a snapshot. I think this is my last slide. This is actually from the Zimbabwe NACP. This is on track. I believe this is tracker based, and this is a data visualization that they generated, showing some case surveillance data they've been developing up and have a pilot in on case surveillance over the past months. So nice to see that innovation. And I think that's all I had. Thank you for your time and look forward to the ends of complimenting that with a more detailed look at the end user interface over. Great. Thanks a lot Dave. And just to remind you to everybody there's a link in the chat to the community of practice where you can post questions. We'll see how much time we actually have at the end of the session to answer them live but that link will continue anyway in the community of practice where we'll post answers so please add any questions there. Then yes, we'll turn over to Enzo now to give us a look at the configuration for this package. Excellent. Thank you very much Dave. Thank you very much Mike. I shall be sharing my screen now. And I hope that you are able to see it. Yes, like David said we are still working on this. There's some things that are not finalized about this package but we are quite on track. So you can see like the list of different patients that are currently being treated and just move my zoom thing around so I can see what's going on. And then what we're going to do we're just going to register a patient right now. You can register. As you can see already the organizational unit comes from the from the organizational unit tree and the enrollment income as today was what we are doing. We're going to register a patient Juan Lopez. We was born in the 80s. The idea here is that we we need like David was talking about trying to duplicate data we have different ways of doing it we have a code for the health like we have the health facility already in which it's being registered. But then we also have the personal information, and we have the NHS ID which is in this case, automatically added and completely unique and the program ID which is also unique. But of course it's normal that within each facility there will be a code that is assigned to patients maybe if it's been done on paper so that's there too. And if there is a national ID number that's also registered ideas to how as many unique identifier as possible at least for is what we're looking for. Okay, and then we already start with the HIV case report. And I do again move my zoom interface so I can see what's happening. There you go. Okay, so we take up the first thing we do is write down the date of the HIV positive test right. Let's say this happened last week at some point. And as soon as we do that it calculates the age when the person was diagnosed with HIV and we select the probable mode of transmission. So we do that we complete the stage and we continue and as soon as we do it potential duplicates show up. This is a different person so we're going to register it and go ahead. As we can see here the first thing we see is whether the person is registered in any other program and their profile comes up right on top so that the clinician can always see it or whomever is entering the data. I would say the most important of all the stages when it comes to data entering is the visit the visit is a repeatable stage that will happen every time this person gets a visit, whether check his status or get refills on a RT. So his first visit date let's put it to us today. And we market treatment has started and that today is the date of treatment is initiation. If the person is eligible for tuberculosis preventive therapy, we selected and the fields about TPT show up. It was today that they became eligible. And it was today when the treatment initiated, and we select from the list of the different tuberculosis preventive therapy regimes available. And then we go to the next section, which is the treatment section where we first of all select the treatment status. And we get a reminder that viral load fields are only available to patients who have been on a RT for six months or more. And then we select the container RT because it's starting the treatment and we select how many treat days of treatment, we are giving them 30 days, then automatically we can see what the last date with RT is. And then we complete the stage as you see is a very short stage has the bare minimum data elements necessary to to complete all these central events that that David was talking about and make sure that we have the data we need. There is no more that is registered. Of course, the treatment status determines a lot of things. So we have other status that person you have they could be dead. They could have stopped the treatment to a transferred out or they could be lost to follow up. And depending on how they are registered, I would show up on the different indicators and dashboards. We have also included a follow up stage which is not linked to the rest of the program through indicators or program rules, but this is a tool for the people managing this person to be able to record any follow ups have been done. If the person is is has been is is not showing up through their opponents. So we can register whether or not we've sent an SMS phone call home visit etc and whatever notes about it. This is completely optional stage. And it's there to facilitate that kind of follow up. I have here a person a patient who has been on treatment for several months. We select the latest. We can see. We select the latest we can see that the fields for their viral testing show up. And this is only available for people who have been on treatment for six months. And then we select the viral test date, whether or not their viral load value is less than 1000 which would then mark them as as a virus suppressed and the same. And if not, we can enter the test results as a number. We have, of course, the top bar with some some tips and some reminders about what's going on. For example, if the person has not been on our point for certain amount of time will show up here. This person has no does not have has been out of a RT for a certain amount of days, for example, and all of this is used to calculate the indicators and dashboards. As David told you, we are still working on these dashboards and we're working on getting a good set of dummy data. But essentially, we have yes this five different sets, just overall case surveillance demographics, which it takes a bit to load in this demo server. Linkage and protection was mostly focused on the how whether people have been testing doing their testing when they should be viral load and TPT screening. We focus very much on making this as minimum as possible to ensure that if there's anything missing, you could it could always be added. If there's anything the country is doing that is useful to them and their workflow, it can always be added and modified. But we wanted to keep it as simple and as clean and as minimal as possible to match the current guidelines. Of course, as David said, guidelines are constantly evolving and they're still not finished once for 2021 but when that time comes, we will be publishing and we are expecting to publish this package as soon as possible as well. All right, I think that's all I have to show you without going much into detail into this dashboard which are not finished. Is there anything else. Mike, are you ready for the next presenter. I think that was great timing is just about to warn you. So perfect timing. Again, I am starting to see a couple of questions that pop up in the community of practice. So that's great. Please post them there. And then at this point we'll switch over to getting some country perspectives about the use of tracker in the field. We have not yet had a chance to use this particular HIV case surveillance package because we've been working on it this year, but it will be really interesting to hear what the experience is just rolling out tracker for HIV individual level data. So we'll start with the Canema GNU that's working with the SIDERS project in Zambia and I'll turn some time over to you Canema. Thank you Mike. Let me just speak. I hope you guys are able to see my speed. Yes, we can. Okay, yeah, so my name is Carmen Shienu and I'm a strategic information officer at the Center for infectious disease research in Zambia, SIDERS. And my presentation is about how we are using the DHS to tracker capture application for HIV prevention care and treatment in key populations through the Key Population Investment Fund. So just an overview of SIDERS, SIDERS was founded in 2001 as a collaboration between the University of Alabama, the Zambian Ministry of Health, as well as the University of Zambia. It was initially set up to undertake clinical trials research in the PMT city for HIV. Then by 2011, it was incorporated as an independent Zambian organization. Yeah, so much of the bulk of our work is in HIV prevention care and treatment. And as SIDERS we worked in four provinces in the southern part of Zambia that's implementing both direct service as well as technical support to the Zambian Ministry of Health CDC. So, as SIDERS, we have an annual budget of about 45 million dollars and we currently have 19 active funders with 54 active grants. We've completed over 80 research studies and we've got 33 which are ongoing as well as in preparation. And we've done some scientific publication of over 100 between 2015 as well as 2019. SIDERS also supports capacity building programs. We host national and international fellows as well as health corps. Yeah, so the Key Population Investment Fund is CDC funded and CDC has channeled about two million dollars over a period of two years. And we're expected to significantly involve CSOs in the implementation of KPIF. So, currently, we are operating in Lusaka province and we are in three districts, Chilangaka Fui and Chongwe, and these districts are surrounding Lusaka Aband district. So there's a significant amount of movement of people between these three districts. And together with Lusaka Aband district, we bear like 95% of the people living with HIV for the entire Lusaka province. And part of the KPIF strategy to be able to reach out to key populations for HIV testing as well as improve the linkage to care for those found positive as well as offering prevention services for the HIV negative. We've engaged the local CSOs and together with them, we've provided services to, we've provided trainings rather to the healthcare providers and the trainings have included sensitivity training just to ensure that they are aware of the key populations. And that facilitates an independent environment for providing the HIV care and treatment. So, just a brief background on the social network strategy. So, the SNS is just one of the strategies that is used to reaching out to people who are in the same social network, so to be able to provide HIV testing and counseling services. So SNS works on the underlying assumption that people in the same social networks, they share a similar risk phase. So, first you recruit people that will be like initial seeders and they're able to link you to other people in their network. So once you identify them, you engage them and they link you to the people in their network. So once you offer services to the people in their network, you try as well to recruit them so that they can give you more people who can become recruiters as well. So it's a continuation thing as in the recruiters can also become recruiters at some point. Yeah, so currently we've been able to reach out to a number of key populations. So, mainly the challenge with the national EMR system is that it's got legal issues concerning the key populations and it doesn't explicitly identify this type of population. So, you find that when offering HIV care and treatment services, it's very difficult to identify them from the EMR system. So, retention to care and treatment has been a challenge because they are mixed with a general population in the EMR system. So, if you want to get viral with sample collection as well as resulting, it's also a challenge. So, we decided to develop a tracker capture application which will be able to track these clients separately from the isolated hubs where we provide these services. Once we reach out to them. So, once we reach out to them and have their data, once we reach out to them and have their data in DHS2, this data is also entered in the EMR. And using identification information such as ART numbers, we are able to link the client data between the DHS2 tracker application as well as the EMR. And we're able to track these clients. We can get the viral, we can track clients who are due for viral, we can track clients who are supposed to be linked to care and treatment as well as tracking the improvement links between the initial recruiters and the recruiters. So, just a brief diving in how the tracker program looks like and how it works. So, on the initial profile, we are able to generate a unique UIC. This UIC is 19 characters and it's automatically generated from a combination of characters that are quoted from the question. So, you see that we're able to get the two letters from the province as well as the first two letters from the first name and the mother's name. We're able to get the code for the six as well as the code for the faith order and the last two digits of the year when this client was born as well as the month when the client was born. And then, apart from that, we get the unique number from the coupons that are used to track the clients recruiters. Each client who is recruited is given a coupon and then the coupon. There's a number of the of the recruiter and that one is captured as well. And once we have the recruitment links, we're able to import those links into responded reverse apple in software, which helps us to give the recruitment trees. So, just looking at some of the program stages for the HIV cascade. So you see what they keep population screening, which has got a couple of questions about the risk factors that these plans are exposed to. And depending on how they answer those questions. So the trucker program is able to class to automatically classify whether this person is a sex worker or is a period. And then clean call referral if this client is just a positive. They're initiated on art. And then we capture the ART number so this ART number is what enables us to link the clients between the two systems. And then from that we're able to get the viral load data as well as the, the pharmacy refuel data through DHS to trucker program. So just a brief look at the some of the statistics that we currently have from the time the program was implemented in October 2019 up to somewhere around July. So we see that we would have positivity used in the female sex workers and low positivity in the transgender and the linkage seems to be at least above 95% for all the population types. Then on the viral load cascade. We seem to still be having a challenge challenges in in our coverage, because you know, these people are mobile and tracking them sometimes the challenge. But currently in the field was to trying to call them so that everyone was eligible. We're able to reach out to them and be able to to get the views from them. Yeah, but our suppression rate, like for Chongwe, you see that everyone who had the view. Then I had a suppressed viral load, but for, for a few ways to a bit low. Chilanga only started implementation somewhere in April. And as of now we don't have any client who's eligible for viral load. So I just give you a it's a two minute warning here. Thanks. Okay, thank you. I'm almost concluding. So some of the challenges are less on the other. There's no API link between the two systems so the matching of client data between the two systems to a bit money. As we are doing this. The format as well as the as won't be able to match the clients. So the travel cultures is the identification of KPs as well as tracking of client recruitment processes. And it has also improved the retention to care treatment as well as viral load monitoring. And then the initial thanks and acknowledgement to paper CDC and national it's console me show health KPC with societies organization as well as the KP participants. Thank you. Great, thank you so much. Very interesting and it's, it's nice to see that it's working would be interested to see about creating more of an automated link with the other system. For now we'll we'll turn to our last speaker, which is Kwame coming to us from Ghana health services in the PPME unit that's responsible for DHS to. And in Ghana they've been using tracker for HIV now for a couple of years it seems like but we from the University of also also kind of directly been involved in the implementation in recent year, trying to make sure that everything can work with the analytics they produced et cetera so very interested to hear more Kwame are you able to unmute and share your screen. Okay. Hello everyone. Great, we're seeing your screen and we can hear you. So, my name is Kwame Ghana health services. That's going to give you a quick overview on what we are doing in Ghana, as far as HIV track is concerned. So, this is just a brief background we've been doing using DHS from 2011. And then we actively started using the track for HIV in 2019 the process began in 2017 to 2018 but then actively we started using the HIV tracker in 2019. And we have other implementations. So, this is just some background to how we arrived at using the HIV tracker so when you follow through we actually had a standalone system. Which we use we've been using over the years, but then after some assessments, it was recommended that we migrate on to the tracker. So, we started a process and currently we've been able to widely deploy the tracker. So these were these are the objectives. I mean why we decided to migrate on to the tracker. So, first of all to ensure that all our ERT clients level data for generation of indicators are at a critical level and easily accessible by partners. Well, we, the challenge with the standalone system was it's the data or information was not basically enough to key stakeholders. So, it made it difficult for easy access to data for decision making. And also to ensure that size I would collect money analyze store client base records, which is key. And then track lines over time using flexible set of identifies track miss appointments and generate business schedules and then one of the very important things is managers and other users being able to generate or required reports using the various reporting models within the, the teams we have a national audit fee, which still writes on the DHS platform only we have a different thing we call it the teams. Okay. So what's the scope we've successfully implemented in all 16 regions across the country in all ERT sites and the tracker is currently used for client management, and then it's also used to schedule appointment and also track missed appointments. We are also able to generate service reports and other key indicators, example there currently on treatment, which is key. We are also able to generate some reports, which are automated into the teams, which is a national repository to make data available at all levels. And then we are also in the test of rolling out the testing model currently what we work with is for the management of clients who are already positive. So we are now looking at deploying, modifying and deploying a model to capture the testing as well. So we can now follow the client to write from testing all the way to treatment. Now, in, in using in configuring the tracker we look at these broad areas design configuration hosting and security real-time versus secondary, the scale training IT supports, Android versus web and then interoperability with existing APIs. Fortunately, for us, we have over the years built a local team to provide support for all ideas to instances. So the same team was tasked to manage the tracker as well. Our deployment is currently using both the web and then the Android and of course in areas where connectivity is a challenge. We use the Android and then where connectivity is quite okay we use the web. So what are some of the challenges. So the main challenge of course is the acquisition of electronic devices for a full school nationwide deployments. These devices really come at a very huge cost. And most often in our case we did not get the government to provide these also had to fall on partners to support in the procurement of these devices and then also another challenge is funding for training. But of course, this means that you need to take this application to all ART sites across the country train the service providers to be able to use the system for exchange and then of course to register and then manage clients and then also change management in introducing any new system. Of course, you need to go through a lot of trouble trying to get people to buy into the new system. So that's also was a bit of a challenge. But thankfully, over the period we've been able to get everybody on board. Now the stability of the tracker up for offline data capture. We've been trying several versions of the Android app. And initially we're having some challenges with some of the versions but again, once we reported to the Oslo team and they provided us some support to currently we have a fairly stable app for the tracker. Now we also have another challenge which is actually abuse of devices by any users including installation of unauthorized apps. We initially got a mobile device management application but then again it also comes with a cost but then you pay such an amount per device for a month. And we had some support but once they support and that we are struggling to get support for the device management services. So we are still working around to see how we can get support for that. Currently, we are operating without the device management services. So overcoming these challenges we believe in continuous engagement with all stakeholders for support. And then we also believe in progressive deployment instead of a one-time deployment. What this means is that if we want to wait to get enough resources to roll out across the country, we might never have that opportunity. But what we do is we progressively deploy, we start from a point and then as and when we get some more resources, we scale up. And then also one very key thing is we try from the beginning, try to meet the needs of most stakeholders, especially in the areas of generating service reports and indicators of interest. Because once people enter data, the next thing they will ask for is to get their reports, their summary reports. So if their reports are missing, it becomes a bit of a problem for the service providers because then they will have to do entries into the tracker and then also do manual correlation. So to eliminate that problem, we try as much as possible to generate all the required reports so that it will serve as a motivation to get our care providers to actually use the system. And then we are still working with the offshore team to get a very stable version of the Android app to support offline data capture. So these are some of the pictures in pictures. This is all I have for now. Over. Great. Thank you so much, Kwame. And yes, it's you in Ghana where early adopters of the Android app as it was released and the whole platform is benefited from some of the pains that you went through with the stability of that new app. Because we identified a number of things that could be fixed and improved. So it was really great to be able to collaborate on that. So we, we don't have a lot of time left for questions, but there were a couple of them that were put into the community of practice at that link you can see Enzo has already responded to those that have been posted. Maybe one of them that I wanted to address there was a question about when the WHO configuration package would be available. Do you Enzo Dave do you want to take a swing at that about when that would be available. I can. I was just going to say that we are working really hard to get it finalized as soon as possible. And then it's just a matter of doing some testing but yeah I'd rather hear what you have to say there. You know, we have we've had one country virtual consultation or validation and we have another one set up next week. I think we've still got the, you know, finalization of the dummy data in the dashboards, I think that's probably the biggest lift. And, you know, I think we will probably have a solid beta version within the next month. And our timeline is definitely to have this disseminated, certainly from the WHO side and and certainly through Oslo networks. By the end of the year. Hopefully that means more like November instead of later but that's my best best guess at this point. Great, and we've been going through some consultations with a couple of different countries as we've been putting the package together getting experience from Rwanda and Botswana and trying to make sure that what is in that package is useful and available. Hopefully making it easier than the steps that ciders and Ghana had to go through to set up their own. I think one key thing that is a really good principle behind this configuration package is the simplicity of it. I've shown that there's a very strong focus on collecting just the necessary data to produce the indicators for case surveillance. This is one of those challenges that we always see with the individual level data collection systems that as soon as it's possible to collect data. And everybody wants to collect everything, which quickly becomes a burden and a challenge for the poor people at the lowest level trying to do the data collection. So, we hope that this configuration package will really help to drive kind of the push to collect only the essential data for for these programs. So, with that, we'll have to wrap up this session. Very grateful to Dave Enzo Canema Kwame for their for their presentations and for sharing this work with us. We will make the slides from this session all available on the conference website. And again there are answers are ready to questions in the community of practice but feel free to continue to post there and we will be monitoring that thread into the future. Thank you very much everybody for your participation and good luck with the rest of the conference there is the several expert lounge sessions that are starting now at four o'clock. So take a look to see if any of those would be helpful to you. There will be one in particular on tracker that I would promote focused on program rules. So those of you that are struggling with some of the more complicated kinds of tracker program rules please take a look at that session. Thank you again everybody.