 Hi everyone, good afternoon. I am Jose Garcia, a technical lead for Android Developer with Rebecca's account, Zoom account, long story. But in any case, thank you for joining this session about local innovation and COVID response. Yeah, let's get started then. So as you know, as you have heard from the previous sessions right now, the A2 is being used in more than 30 countries for COVID response and surveillance, which is impressive, I would say. Also, from a global perspective, YoAyo has been implemented and deployed the global packages, metadata packages for COVID, for entry data for COVID, and for analysis as well. That I would say has been widely accepted by the community. However, this pandemic, COVID is bringing us some challenges as well. Some challenges that they have to be addressed in a rapid way. I can think of, for example, we need to use, countries need to use real-time reporting tools that need to be scaled fast. And other challenges like about self-reporting, where the end user is not the health provider anymore, but in these cases, a person that may have a COVID symptoms or even is a positive COVID case as well. Also, we are talking about making complex analysis about contact tracing, that I think is really important in this context. So all these challenges were driving several innovation solutions at a local context. I think that this local innovation just expands the de-access to model, the de-access to functionality, just to meet the country's demand. So what we are going to see today is three use cases, which are really addressing some of these problems and making a real impact in the field. We are going to see how people are using other technologies with de-access to, in order to improve the data collection, in order to improve the data flow, and in order to improve the analysis for decision-making process. So our first guest is Bram Payot and Paikeo, both currently living in Laos. Bram is the Senior Surveillance and Monitoring Advisor for PSI in the Southeast Asia, and Paikeo is the Senior MIA Supervisors. So guys, you have 12 minutes to present your demo and your presentation, and yeah, over to you. Thank you, thank you very much Jose. So yeah, thanks a lot for the opportunity to present our little use case over here. In this presentation, we'd like to highlight the recent work that we've been doing on social media chatbots connected to DHS2 as a new tool for disease surveillance. Our original abstracts that we submitted back in April was actually on the implementation of a Facebook Messenger for a chatbot for malaria case reporting in the private sector in Laos. However, as they did with many things, the COVID-19 pandemic, of course, somewhat changed our plans, and by extension also our presentation today, because it led us to adopting our tools, this malaria chatbot, to strengthen surveillance of COVID-19 and of other diseases. The main reason why we were looking here at PSI Laos for a new approach for malaria surveillance last year was the realization that routine disease surveillance systems typically lack efficient and scalable reporting tools, and also that the private sector is often underreported or it's maybe not even included at all in national HMIS and under this website. Whereas healthcare providers such as doctors and pharmacies, they need a simple and user-friendly mobile tool that doesn't require specific apps or accounts. We wanted to make it really easy for untrained or low people with low digital literacy to report data. In this case, it was malaria cases, but we'll see that this can be extended to other types of data, of course. For us, it was really important that we came up with a solution that doesn't require intensive training or complex software or app installations, and that would come along with very heavy maintenance procedures. I think that we've all seen the vast range of digital solutions for mobile data collection, for routine data reporting that have been developed over the years, all over the world. Many of these are, of course, very powerful and they may be either generic apps or highly specific tools designed for a very specific use case, but for the most part, they're often quite complex and often also costly to deploy because they require significant investments in terms of training of system managers and end-users. They also need quite heavy on the procurement sides oftentimes, where you need to deploy hundreds, if not thousands, of mobile devices, especially the case when you deal with community level surveillance or the lowest level of the health pyramid and health posts, health centers, pharmacies, private clinics. There's obviously a lot of challenges with these different elements, and it can, of course, be overcome, but the challenge is associated with software maintenance and device compatibility, as upgrades are needed, of course, on a regular basis. Remote user management, we heard even yesterday the challenges in Android deployments of managing users, troubleshooting, upgrades, requiring sometimes quite intensive mobile device management solutions. And also, last but not least, of course, HMIS integration. So these mobile tools for routine data collection and disease surveillance, they're often very difficult to deploy at scale, and they may also be hard to maintain so they present sustainability challenges. Now, about a year ago, PSI allows the team here in Barton a journey to try to see if they could come up with a simple yet quite revolutionary solution. And this came down in our case to developing an interactive chatbot for malaria case surveillance using Facebook Messenger. And first thing to know is that here in Laos, internet for many people equals Facebook. And everybody uses it or people who are connected are all familiar with Facebook. That's what they use to communicate with their friends. They send messages, they look up the news, and they're very familiar with the path. So, we're given the possibility to start using a highly familiar communication platform in their own language, on their own mobile device. They didn't really need any separate accounts, no passwords, no applications to install on their phones or tablets, and all of this with very minimal training because the interface was so familiar to them because the chatbot essentially does everything else, right? Once you start the conversation, the bot will guide the user through the various reporting requirements. And that ease of use was especially important because a lot of these users may only need to report data occasionally, because that's typically the case in malaria elimination settings, which is the case over here in Laos, where many areas only see a small number of malaria cases every year. So, unless you use an app on a very regular basis, it becomes hard to remember how to log on or which program to look up and how to report your data. So, in its simplest definition, a chatbot is a service that's powered by rules and sometimes by artificial intelligence, where a user interacts through a chat or messaging interface. Following a set of rules or a workflow, and it's as simple as that, right? You interact with the chatbot to report data in this case, so it can be used for many other things as well. Of course, I mentioned the challenge with integration of others with other systems and existing systems, it's something that has been mentioned a few times in past sessions as well. So, one of our requirements was, of course, that the bot would be fully compatible with DHS2, which is our main MIS platform at PSI, but it's also the platform on which the national HMIS here in Laos is run. We'll see how it works from a technical perspective in one of the next few slides, but essentially it means that any data that's reported through the chatbot gets uploaded automatically in a program or a data set in DHS2. But so, before we do that, let's see how we applied the bots within our regional malaria elimination project in Vietnam, in Laos, in Myanmar, in Cambodia, and starting with Laos. So, I'll hand it over to my colleague Paikeo for the next slide. Thank you very much. Hello, Mosey and Deepika and Halloween, everyone. Actually, PSI Laos has been using Facebook Messenger chatbots last year in 2019. Now, 124 hours or about a third of our network providers are using Facebook Messenger chatbots for reporting malaria data from the field to our DHS2. How to use it? The user just opens up messages on their phone and simply type a large greeting message somebody. And then chatbots keep asking the user questions when the user responds to it until the end. The malaria data includes patient, gender, age, relates of residence, type of malaria-infected treatment provided, and so forth. And the user receives on-site trainings on the use of Facebook Messenger chatbots by medical retailers who often take support with it to the field. On our network providers, particularly those who have own smartphone and use a Facebook account, are encouraged to register for using the chatbot for reporting the malaria cases. Another interesting is that to make consistent with the government reporting system, as in my case, the chatbot recently was updated with the command that applies, or often said, like the name of villages, history, which are used in the government system. In fact, the confirmed malaria cases are automatically transferred to HMIS in malaria cases through the data integration app, which is embedded with our DHS2. Apart from creating convenient and flexibility of reporting data for the government, these increases are the confidence and reliability of data use and sharing within our organization and the government agency. That is from my side now. I give the presentation back to Graeme. Thank you. Thank you. Very quickly in Vietnam, we developed a very similar chatbot and it was very much built on the same model. The only difference or the main difference is that we built it on a different platform, which is called ZALO. You should know that ZALO is the most popular social media platform in the country. The process is very much the same. It just shows that you can build these chatbots on any, let's say, any compatible messaging service. What happened in Vietnam, though, we were deploying this back in March, April. Because of the need to strengthen COVID-19 surveillance, we recently updated the bot to also accommodate for other fever cases. There's a lot of overlap between malaria and COVID-19 symptoms. A lot of the people who come with fever and who we test actually don't have malaria. They have something else. So, in general, we feel that it's important for us to expand the range of symptoms and diseases that we can report from our private sector network. The COVID-19 surveillance or fever case chatbot, as we call it, is being deployed as we speak in a number of hundreds and potentially even thousands of pharmacies and some private clinics. These will be used as an additional layer of surveillance for COVID-19 in Vietnam. It will become possible to identify clusters of suspected cases because they're not confirmed. We don't do rapid testing as we do for malaria. Again, this is where the availability of simple reporting tools in the private healthcare sector becomes really relevant because it's precisely in the private sector that most people will actually seek care for fever or for other reasons. In Myanmar, we're now also piloting a Facebook Messenger bot for malaria reporting. Again, very much the same. This is happening in our extensive social franchise clinic network throughout the country. And we also intend to open this up to other notifiable diseases. And this is actually part of the work that we're doing as part of our emergency operations center strengthening projects in support of the national ministries of health, where we work towards strengthening overall disease surveillance and response systems. Again, the private sector has an important role to play here, but with the potential to expand to other sectors, of course, as well. And actually, Myanmar, the first confirmed COVID case was detected in one of our clinics back in March. So let's see briefly what's under the hood, how the system works. I won't go into too much detail, but essentially, developing these bots is really not very complex and experienced developers can get this done in a matter of weeks. The workflow logic, including the skip patterns and validation rules is all hosted in a web service and uses a library that enables extraction and translation to structured data in DHS2. The data are submitted to DHS2 through the API, which is possible because all the data elements and option sets and user accounts are mapped to the corresponding metadata in DHS2. And again, these are just the basics. We can do a lot more cool stuff such as fuzzy matching based on extensive lookup tables, sending SMS and email notifications. We can send data to multiple programs or even simultaneously to different DHS2 instances, which makes it possible to report both to the HMIS and to our own instance. And so, again, lots of interesting things that we can develop in future iterations of these some early lessons over time. Sorry, so maybe one more minute to wrap up. I have one more slides just very quickly to say that there's a few early lessons that are coming up around the fact that the users really like that they have a familiar interface, local language through very easy app. The bottom line though is that users do want options. There's no one size fits all solution and this will not replace traditional case notification systems. They're still needed for early detection and containment of epidemics. We still need active user management, of course, by central MIS teams. We need to have user enrollment and mapping user IDs and so on. But on the bottom line is that, again, it's a very simple solution that has a potential to be expanded to other notifiable diseases and even for other simple mobile data collection needs. So we are still working on this and hope to present on our progress maybe future opportunities here in the Oslo conference or elsewhere. So thank you everyone. And with this, I will hand it over to back to you, Jose. Thank you. Thanks a lot for a very clear explanation and it's a pleasure to see these workings, to be honest. And then our next guest is Zeferino, who is the head of his Mozambique. So Zeferino over to you whenever you want. Hello, hello, hello Zeferino. I'm not sure if you can see my slides. Yes, we can. Okay, good afternoon, everyone. My name is Zeferino and I'll be presenting or sharing the experience, our experiences of digital on behalf of a team of GHI experts that are sitting in Mozambique in Guinea-Bissau. So then the idea here is to share a kind of experience during the implementation of DHIS to COVID response on the DHIS COVID response and sorry, I need to switch. Okay, so I think now it's okay. So during the COVID response and also to see what we have done so far during this process of implementation. So this is going to be the outline. So as it was mentioned before, we did receive that there was a DHIS COVID package that was developed based on experience from Sri Lanka. I think Pomod is going to talk a bit more when we need this presentation later in today, I think in the following. And we did this, this package has been developed and then we did receive the package from the University of Oslo and adapted that package, adapted the package to be used in the country that you are supporting. So as a digital, we are supporting the use of phone community and especially in Africa, there are the whole five countries and we did get the package and then did the translation and provided that package to the different countries that we are supporting. The package came with mainly the three programs and we did this program as well as possible due to the screening of points of entry and the register and follow-up cases, negative cases on quarantine, suspected improvement cases and also on the case based availability, it was also possible to register information related to lab testing and also the isolation and follow-up with positive cases. So when we took the package presented to our users and then we identified some local needs, so those needs were customized. So for example, we did have this offline information called center that was set up and then the information was all the tools that we are using, that at the time they were using paper-based tools and then some of them they are using Google spreadsheets. So we had to convert those tools into DHS tools so that all information could be stored and find in one place. We did also include inpatient case management the situation where the health workers wanted to track positive cases, so we had to also do those adjustments in the package so that they could be able to follow up the cases. Also the assessments that were done in the health facilities for the health workers, for example, and also to look at the situation of the COVID preparedness in each of these health facilities and all these came from different countries and then we had to adjust them and then make them available to other countries as well. With regard to the use, we did find two possibilities. Someone were using or still using web-based and then they also combined in some situations where they're using Android devices and at the moment almost all the countries except of Santome, all of them they are using Android devices at some point, but also there's a combined, those that are sitting at the national level, some of them, they use web applications to do follow-ups and then to do some statistics on the, based on the information that is collected from every level of the information. These are some examples what was generated. So we do have, for example, the dashboards here showing information, the line, the number of messages, for example, that are recorded in the system and then we have also a scorecard that were developed here to see the situation of the health facilities in a per week to see if they are improving with regard to the preparedness or the situation sanitation in the health facilities. And then on the right side, we also have the some of the dashboards were developed to do the trend to look at the situation of the cases, of the increasing or not, comparing suspected, confirmed and also those that were there or death. So with regard to, we did also do extension of those, some were internal within the system that we did also expand it to be able to display them in some of the selected information to the general public. So these are examples of the web portal that we are linked to DHIS-2 and where the data is extracted from DHIS-2 and then provided to the public so that they can follow up the cases and then they know the situation. As you all know, the protocol or the countries that have been sharing every day the situation of the COVID situation, the surveillance unit specifically. So what we have done after that information, for example, have been presented in conferences the data information is also provided on the web or in this case, taking from DHIS to the web portal. So with regard to the innovation as such, so we did find dealing with some kind of limitation in the tools and then we have, they wanted for example, to know how to track specific cases. So we did develop hubs within DHIS-2 that could enable the surveillance officers to trace these cases. And then for example, based on the, it is based on the referral model that from the DHIS track, we produced this case tracing application. So when you go there, you will be able to see what are the L facilities that are provided services to specific cases. For example, if one specific patient is transferred from one facility to another, so this hub is able to provide you the different places where the different services have been provided. Also, we did have the request to also be able to provide the contacts. So as you know, the package came with the possibility of resisting the contact for one specific positive case. So when then it was not that easy for you to be able to list all the positive contacts that are linked to one specific index case. So what we expanded the app to be able to show, for example, in this case, we will see one specific patient as the female here at 46 contacts, but then we couldn't see on the list of that easily. So we had to develop this app that lists all these positive contacts, the contacts for this positive case. And then based on this positive on the contact, we could also show or display other contact if there is a one specific individual that was linked or connected to these cases. The other innovation or the future that we have to also to add it was at some point, the provinces, all the airports, all the ports of entries were crowded, people coming and then we should take all the number of staffs who are very limited and then the times that were spent, for example, to be at the registration or it was too long. So we had to find ways of reducing that time so in this case, the app of development that anyone put the register of the information, specific of the personal information. And when they arrive at their destination, the earth worker only access the information based on the QR code or specific code. They access that information and then they can validate it and then only the information related to, for example, the temperature and then whether the case is discharged or not. Those are the fields that were added so that this process could make the process a bit more flexible so that since the passenger was a traveler was able also to fill that information or prior to his travel or even when they arrived at the airport, they could though they scan and then do the preliminary answer and when it goes to the earth workers and then they have the information and earth worker was only entering the fields, few fields. So the app, you visit the floor and then you have this app that has these two possibilities where you can also here scan and then enter the related information to the certificate for the traveler. One of the challenge that we faced, it was in some of the countries to special business for Guinea-Bissau where it was the printing of lab results and then, you know, the lab where in the beginning it was only one lab at the national level and then by the results or the lab sample were collected. So the process was all the sample were sent to the lab results to the lab and the national level the lab was doing the test everything. Once the results were out, they had to get the file, get the result and then seal the paper and then send that result all the way to the place or the facility or the community where the case or the sample was sent from. So and then what they approached that is that we wanted something that we could use the system to generate that. So what we have done here is to expand that to the tool. In this case, within the DHIS we could list all the, for example, we could list all the examples that were entered and then we added there the possibility of printing that result. So the end one that has access to the DHIS could go, once the result is sent, there's a notification that is sent, the person that okay your result is added and then that's based on that you could go to the facility and then ask the health worker that have access to the DHIS, they could go there and then print the result and then provide to him. Yes, we are overtime so you need to wrap up in one. Okay, okay, okay, thanks. So that was one and then we did one of the, we added that under that app the possibility of, because that was internal inside the DHIS and we wanted someone to also to be able to access that result from the web. So we added, apart from going directly to the DHIS, anyone could go, the traveler and the customer could go to the web portal and then we're using these codes, the tail and then it could get that. And then once he gets the app, for example here, in this case, it is that result based on the bar code that was provided that is printed. So as a conclusion, we did have some challenges, we do have some challenges, especially with the engaging with several stakeholders, as you know, with the pandemic, there are a lot of people coming, trying to support, a lot of tools coming and then those are one, the challenges, the trainings and we are, as you said, we are located in two different countries, but we are supporting several other countries and then we had to do all most of our training virtual and then where we have issues with connectivity. So those were some of the challenges. The strategies that we adopted, the partnerships were one of the key that the driver has to make sure that this is happening, engaging with the new stakeholders, also having these lot of innovations here that could help, for example, to address some of the needs and also main, the most important, I think, having this community being engaged with the developers and the DHS, the DHS of Devs from Mosul and other countries, the adults a lot, we are having always, every day, let's say, often meetings discuss the challenges and finding ways of addressing some of the issues that we are facing. So this is what I have for today. Thank you. Thank you, Seferino. Thank you, Seferino. Very interesting. Super interesting, I would say. Snopamot is your turn and we can extend a little bit of our time. Okay, so you have around between 12, 40 minutes. You are lost. So whenever you're ready. Right. Thank you so much, Jose. I don't think I will take 12 minutes. I will try to finish as early as possible. So I will explain in my presentation about the local innovations around COVID-19 in Sri Lanka. And towards that part of my presentation, Austin will take it from there and explain how these local innovations became global. So good afternoon, good morning, good afternoon, good evening, everyone. I'm Pamod representing Sri Lanka. So I will take it forward from where I left in the plenary session. So what you're seeing here is the timeline of COVID-19, the disease progression, as well as the information system implementation on COVID-19 in Sri Lanka. So as you can see, it started off in late January and by late April, we were kind of having an ecosystem of COVID-19 surveillance system with more than seven components. And what I would like to highlight in this presentation is one main event that happened, which is the hackathon, which happened around March, the mid-March. And then two main products of that one, the contact tracing application and those ICU bed management module. So let me just start off with ICU bed management module. So now what happened was towards the end of March, we saw a sudden surge in the COVID-19 patients. And of course, some of them were critical. So country was very concerned because we did not have a proper integrated ICU bed tracking mechanism system. So they wanted something very simple, not a complicated one, just to know the status of available COVID-19 ICU beds in the entire country at a given time. So there are a few requirements, which were kind of possible in DHS too, as you can see here, which is of course registration of ICU beds in the system and then remove ICU beds by the hospitals when required. And of course, to show the current status of the bed, the particular bed, and of course, some basic information about the patients. I mean, all of you, the DHS implementers would know that this is a simple tracker implementation of DHS too. But the issue that we had was a bit more complex, which is of course, I mean, there were a few things which are not possible from DHS to out of the box. So they wanted to see a map and the locations also marked and in the same interface, like it's a data entry interface, and then show the distance to the nearest ICU bed available. And most importantly, they wanted a very simple interface, not so they felt our tracker was a bit too complicated. Right. So what happened was now in our system, we had the majority of the modules were customized in the DHS to base surveillance system. But of course, we needed some development, sometimes to support the custom customized content, but also we needed to develop new content, which are not possible with the co application. So this, this was where this hackathon became very important. So it was a simple call. So what happened was like the history Lanka and all the other stakeholders that we're seeing here, the ICT agency of the government development community, everyone got together. So the ICT agency took the initiative and they made a Twitter, I mean, request asking all the volunteer developers history Lanka inside the country outside, please help us build something customized on DHS. So what happened was we got an enormous very, very big response and we declared this weekend over 14th and 15th March this year. And over the weekend, you're just seeing only two people here or two or three people, but there was a big community about 20, 30 developers who joined online and worked on this custom web applications to serve the functionalities, which are not possible with the core DHS to applications. So this was a massive effort, but we never expected that people would really contribute, but this this really happened at a very large scale. And I must also mention the support we got from University of Oslo because what we made what we ensured was like whatever we did in Sri Lanka, we kept in touch with the DHS to community, especially the University of Oslo, so that we can get support based on the agile things that were happening locally inside Sri Lanka. So we informed the University of Oslo and in fact, it was Austin, the core developer who was made available during this weekend to support the local developers to take it forward. I mean, whenever they had had some issue, Austin was available. In fact, the collaboration happened based on the ICT on a Slack platform, a Slack account, which was created by the ICT agency. So all the developers around the globe and the UIO developer as well as his Sri Lanka was available to make things possible. So this is the, what you're seeing is some interfaces from the ICO benchmarking application. So it's a very simple one. I mean, actually it has only made two UIs. So the first one is actually showing where the available ICO bed is. And then we have a separate interface to mark the status of the ICO bed. So that was all that they wanted. And the one issue was this ICO staff had not used DHS to previously. And they found that the tracker interface, the tracker capture interface a bit too confusing. I mean, that was their period. But we were able to do something really fast based on this collaboration that we had. And then the next application, of course, was the contact mapping application. So requirements again, some were possible, just out of the box, like registration of confirmed cases, registration of contacts, and of course building relationship between cases and contacts. So this was just possible with the core DHS to applications. It's basically the tracker was possible to do it. But these things, I mean, to have a visual representation mapping the case to contact was not possible. And then again, maybe to display some attributes, whether it's male or female or some other case number, something like that next to that particular entity in the map. This was also not possible. And then they had some additional requirements such as like obtaining mobile tower data of the suspected suspected persons as well as cases, where they have traveled during last two weeks, also integrated into this contact mapping application. And then of course, I mean, so basically this thing, this had to be integrated. And it was an additional feature. So these were not really possible out of the box. And this is where we had to make a custom web application. So our team, the local team, with of course support from the University of Oslo, they were able to do design something really fast within like one week, something like this. So this is a basic contact mapping visualization. So basically it is showing from which case to which case the disease transmitted based on the information that is obtained from hospitals and epidemiological studies. And also this supported visualization of some basic attribute information like maybe male or female or how many contacts and then again, maybe like the number. So that kind of. But then again, what happened, something interesting happened, that when we posted about this in the community of practice, we noted that this is not not something unique. And there were so many other countries who were wanting this. So then this is where in fact, the University of Oslo took it one step forward and tried to identify the generic requirements and to make the app more generic. So of course, Austin is going to talk more about how this was done. I'm not going to touch on that. But basically, with the help of Austin and his team, we were able to produce something like this, which is a generic application for contact mapping visualization, which is now available in DHS to App Store. So that way, we were able to make some local product, something local, the local innovation. And we were able to cast it to the broad audience by making a generic global product. So what I will do is I will just stop here and Austin will take it forward from here, explaining about more about the web application. But I will just mention this one last slide, which is of course, the tower data location tracker. So basically, what you're seeing here is all the confirmed cases with of course, the consent of the persons, as well as with the authority of the government, they were able to trace the GSM tower locations during a defined period of time. So here, of course, it's two weeks, and we were able to make the visualization on a map so that this will assist the contact tracing process. So this was again, another integration, a custom integration that took place around our COVID-19 effect. So thank you so much. I will just stop here about local innovations, about this contact mapping application, and Austin can take it forward and explain how it became global. Thank you. Thanks a lot, Pomod. It's been really great to work with the Sri Lanka team on, as Pomod mentioned, several innovations and different web applications. As Grant told me just a moment ago, we are going to need to wrap up pretty quickly so that everyone can get to the plenary session in a couple minutes. So I will make this very, very short and introduce you to some of the feedback loop that we're developing and that we're trying to enable with DHS2 as a platform, which is not only supporting local innovations with the global platform that is DHS2, but also feeding back the learnings and the needs of those local innovations to enhance and improve that platform. We did that very well, I think, with this project with Sri Lanka and with several other organizations, and we want more involvement from the community. So definitely don't be afraid to be involved in that discussion, and to talk to us about how we can improve that platform to enable the local innovations as Pomod mentioned. I'm not going to get in too much to the details of this application because we don't have too much time, but as Pomod mentioned, we did make this a generically available application that can be adapted to different metadata models, different types of relationships and tract entity instances that's available on the DHS2 app store, apps.dhs2.org. We took a number of the learnings from that application, the development of it, and fed that back into the generic tools that we make available to web app developers. I'm not going to go into those too much today. They were involved saving and loading different visualizations into the user data store, as well as using reusable layout components, and we're also taking some of the learnings from those applications to enhance the core analytics engine and the tracker capabilities of DHS2 itself. To learn more about this innovation system and how we try to enable that feedback loop between local innovations and the global platform and the generic capabilities of DHS2 as well, we have a number of sessions coming up over the next two days. They're here. They're also on your schedule under the tech track. The first one will be at 2 o'clock Central European time tomorrow, introducing this whole topic. Hopefully, we'll see you there. With that, I will quickly wrap it up so that everyone has time to get over to the plenary session, which is starting just about now. I hope that was okay, Grant. Thank you, everyone, for joining us today for this great discussion about local innovation and the COVID-19 response on DHS2.