Okay. So it's it's one o'clock. I'm going to go ahead and start the session. So I'm very happy to welcome you all to the second day of our virtual annual conference. I'm really happy that you could be here. So I'm really pleased to first introduce Dr. Samira Asma, who's the Assistant Director General for the WHO Division of Data Analytics and Delivery for Impact, DDI. DDI has been a very strong partner of the University of Oslo and we have been working through a collaborating center agreement for several years now on innovations and health information systems strengthening. So Samira, would you like to say a few words of hello. Yes, absolutely. Thank you so much Rebecca. Hello everyone. Everyone who's joining from all parts of the world. I would like to begin by thanking University of Oslo, Christine and team doing a fantastic job, and WHO is truly fortunate to be partnering with University of Oslo in a very important piece of work that you are doing is to improve the routine health information systems, not with the intention of just collecting data or reporting the data from district to national levels, but in fact, focusing on how the data is used to improve performance of care performance of services and regarding the patient as a VIP. So I think that is going to be extremely important if we are truly serious in our mission to achieve health for all, because primary without primary health care, we will not be able to reach the promise of universal health coverage. The other aspect of the work that is important is how do we integrate the various topic specific areas on which the data is being collected in a number of countries. And yesterday Marcus presented impressive statistics in terms of countries in terms of the people that are benefiting and accessing and tailoring integrating as well as using interoperable mechanisms for DHIS to So it is extremely important how do we bring that integration together, because if we want to get at the country level, improving the performance of essential services, while we have topic specific areas of work that needs to get improved. We also need to make sure how this integrates in terms of giving a full picture of services of care. Another point I want to make is as follows that you and DHIS using the DHIS to platform, we're very quick in standing up to the response for the pandemic. The first ones where that what you did in introducing the COVID mortality surveillance tracker. It was difficult at the early stages of the pandemic to get a good tracking of cases and and debts. It's impressive to see how quickly DHIS to as a platform can stand up to an emergency situation. And now you're doing that for vaccination. I would like to challenge us all to see how we can also introduce other packages that are not yet a part of DHIS to such as NCD package and other risk factors related to reducing the burden of premature mortality due to NCDs. I think that is going to be extremely important as well as infectious diseases in countries that are facing the syndemic as it is called now the burden of infectious diseases, as well as the non communicable diseases from this perspective. We are placing a lot of focus in supporting countries strengthen their data and health information capacities with the launch of the score for health data technical package. Organizing routine health information systems is extremely important for WHO and we stand ready to support you your work and countries and collaborate more closely with the University of Oslo and the partners. I would like to thank the director general who presented yesterday from NORAD and the support that NORAD is giving to this work, as well as to a number of countries in advancing this work to scale up. Finally, leave you with one thought. We have less than nine years away to meet the SDG 2030 agenda. We have already fallen back as a result of the pandemic. We are now to get back on track and to accelerate the progress and we need to know how fast we should go and where we should plug in investments and without data we will not be able to do so nor improve the quality of care. HIS2 and your work becomes increasingly important and truly look forward to the best practices that are emerging from the work that you're doing in the field so that WHO can amplify that and make it available as case studies that can be replicated in other settings. So we look forward to amplify your work, your voice and look forward to being a part of this in person next time when Christine collects everyone together. So count on WHO and our team for any support that you might need and look forward to continue working together. Thank you very much for this opportunity back over to you. Thanks Amira. We just so appreciate the partnership with the WHO and thank you for reminding us that we still have challenges ahead to look ahead to tracking and monitoring and achieving these SDGs in less than 10 years. So good for us to keep an eye on the prize. I'm also really honored that another very busy doctor has joined us today from WHO Dr. Adlin Strand. Thank you so much. She's the unit head for the essential program on immunization within the IVB department at WHO headquarters. And we've had the pleasure of engaging with Anne and the WHO unicef innovations for COVID-19 vaccine delivery working group over the last six months. And it's enabled us to share the his approach for scaling up COVID-19 vaccine delivery systems in countries. But we're also just really grateful to add in the colleagues at IVB and at WHO Afro for providing us technical guidance and inputs to the rapid development of these vaccine delivery packages. They've been operationalized in 28 countries and we've learned so much through this partnership. So Anne Lynn Strand, thank you so much for joining us. The floor is yours. And I believe you're on mute. Can you check your microphone? There we are. And so I'm really delighted to be here because one of my hats right now is working to support countries on the country readiness and delivery within COVAX within WHO. And so on the COVID-19 vaccine rollout. And in this, I've come across Rebecca and her team and the data ties to and in a more close way than I've been before. And I am just amazed at just like some Samira was saying, the reach, the depth, and the rapidity of the reactiveness of the data ties to tools. And we, we hope and really are encouraging to expand on the different modules that are there. And let me just go through a few slides on where we are right now with the COVID-19 vaccine rollout. And so narrowing down on all the modules that is actually available for India ties to into what can be a ties to do for the COVID-19 vaccine rollout. And how are we thinking in the workgroup that I'm contributing to which is called the innovation part of country readiness and delivery. But just a step back. So our director general at the World Health Assembly very recently actually called on our member states to do a massive push to roll out even more the COVID-19 vaccines and reach 10% by the end of September and 30% by the end of December. In some high income countries, these goals have already been passed and way, way passed to other countries and particularly low income countries. This is almost an unattainable goal, if we continue with the rollout, as is today. And so a sprint is needed and doses are needed. And just in the last couple of weeks, there has been a lot of commitments and engagement on the global scene for donating doses, reaching and supporting the rollout of these essential tools both vaccine and diagnostics and therapeutics MPP and which is very encouraging. And because in all these four, it's reiterated that to be able to stop this pandemic, we need to roll out these tools equitably across the planet for us to be able to hinder and get us back into some kind of normality and the economy back on track. And so IMF, for example, did the calculation of 50 billion US invested now, will actually in the end save us up to $9 trillion instead in the future. So a case for equity and equitable rollout. And just to give us a hint on where we are at the moment on the rollout 2.6 billion doses have already been rolled out and 88 million of those through the covax facility and 231 participants, and this is increasing very soon. And so this is the map of an equity where you have the lighter green is where the doses administered per hundred population are still not at the level as in high income countries, which we really hope to change. And it needs to be a global commitment to how to do that. In the digital or the innovative patient group. We started, I think, six months ago or something. And where we try to look at what are the tools that can support countries to do the most efficient way of distributing the COVID-19 vaccines. And what are the digital solutions that needed to support the health services during the COVID-19 pandemic. So the challenges is that you, we all know this now that particularly in low and middle income countries but actually all countries. Health systems are under extreme pressure, both to respond to the pandemic, but also to to monitor and to roll out the COVID-19 vaccine at a never before seen space speed and scale of delivery. So 2.6 billion already administered. And that is a year and a half after the genetic code of the virus was actually cracked, just an extraordinary accomplishment. But to really use the potential of these different tools that have the vaccines that have shown a very great success factor when it comes to efficacy and safety. And the scale up is is absolutely essential and to do that as smart as effectively as possible. I don't need to convince this audience that digital tool is really one way of being most efficient and be able to tailor actions quickly. And when when it comes to the delivery of these of these systems. So what we did was come together a group called the COVAX innovation working group. And it's a cross-partner organization is WHO, it's UNICEF, and it's their Gavi, BMGF, World Bank, Global Fund has joined. So we have, it's a coordination called you can say of groups and the first thing we did was what are the tools that really we think are the most needed. And how can we work on these to improve them and advance them. And here are the tools that we then the seven tools that we said would be quite essential. And we have working groups, I mean, ongoing work in these different organizations towards these tools has been going on before and now during the time that we have the innovation working group. So first of all, on the GIS and micro planning, super important important to be able to map out the target groups and be able to reach them, do the micro planning of the best possible delivery. The second one is counterfeit and detection and we've already seen how some counterfeit vaccines have really damaged trust and demand for the vaccines with the solution being being able to have a barcode and track and trace and then also have a global trust repository for authenticity verification. It will start in UNICEF, it will probably lean over into WHO after that. The vaccine status be able to track and have a proof of vaccination. That's the global, the smart card certificate, and which is in Samira's work, I think, and then the vaccination monitoring. So have digital monitoring of doses given with rare details to is one of the key key key and most used solution so far on the safety monitoring details to is also and the module that is and is used by many countries but not enough countries. We are really calling on our countries to use the safety monitoring tool that was developed in collaboration with the safety team at WHO. And because tracking and tracing these new vaccines is just so crucial. We need to get all the safety signals in hand to be able to advise countries about the safety signals coming up and what to do about them. There are also infodemic and then healthcare worker training that can use the e-learning platforms in a more efficient way. So all of these are separate working groups. We come together and organize between ourselves and some of the outputs from all of these working groups are on this slide. So first of all, congratulations on the fantastic deployment of the DTIs to through regional webinars to and the DTIs to centrally to be able to roll out in, I don't know, even beyond 30 countries, I think now these modules for vaccine monitoring and safety are out. And there is a set up of global adverse event digital monitoring. So the streamlined standards and requirements for this and many apart from DTIs to tools have been able to use these standards. There has been a stood up UNICEF Secretary co-hosting a digital health center excellence and I'll come back to that later. And we are working to help the supporting global fund and gov COVID-19 innovation funding through country awareness building and guidance on use of funds so we're having global seminars to be able to push out the aligned funding that comes out. That is now in focus from these big donors on innovation and digital tools and the global trust repository smart cards certificate and then GS GIS enabled micro planning are some of the other accomplishments that are have been in the making and are ongoing by the different teams and contributing into the innovation group. So one of the first through fruits and I wanted to bring this to you is the digital health center of excellence. So this is a UNICEF WHO co-hosted secretariat. UNICEF is running day by day, but in the funding discussions, they have had a lot of funding coming in lately to be able to coordinate standardized the support to governments to respond. And to particularly now looking at what are the mature digital technologies that can support health services delivery now in the context of the pandemic. And then particularly the aligned donors and support governments to identify and apply for funding for deployment using costed investment cases. So these this digital health center of excellence have many partners, not just WHO and UNICEF, but there is in kind support from many of the donors on the technical side as well as on the funding side. And here you see the link to the DICE support. And so it would be fantastic if you also in all your different network can make known this way for countries because we realize there are many tools out there. And we wouldn't want to promote one over the other, because countries can make their own choices but sometimes it's good to have a vetted list with mature and tested existing digital global goods that have evidence of being effective to like just identify DHIs too. And then the support to the decision making can be done through this DICE, the digital center of excellence. So that's what I wanted to say today. I'm delighted to stay and listen in today and tomorrow and I'm deeply impressed by the work of DHIs too. And I can see that it's really contributing to the most efficient delivery of COVID-19 vaccine in its different modules that are assisting. Thank you very much. Thank you, Anne, so much for taking the time to join us and to really be able to frame the work of the community here within these just unprecedented goals, how fast we've moved from a new emerging disease to a vaccine preventable disease and how are we going to get these preventive tools out there. So our sessions today, they celebrate not only the innovations in the scale up for COVID-19 surveillance, which was a big focus of our conference last year, but also the huge improvements that have been made to the routine systems. So the routine surveillance systems, the mixing and matching of different data models for emergency surveillance routine integrated BPD case based surveillance, and then also on the flip side now we do have preventive tools we have vaccines to prevent COVID-19. So now we've seen unprecedented scale of these systems as well moving towards COVID-19 vaccine delivery. And of course this sits on the shoulders of years and years of work with the ministries of health through the DHIs to community to really be able to strengthen these national immunization platforms. So I'm very pleased for our plenary session today. We have two representatives from countries that have made really impressive moves in this space. So first I would like to introduce Bridget Magoba, who will present for Sierra Leone. She is a technical advisor, public health informatics specialist with Athena Sierra Leone. And Sierra Leone has strengthened the integrated case based surveillance system, and also integrated aspects of COVID-19 vaccine delivery, the vaccine tracker as well as AFI. So I'm very pleased to hand over to you Bridget to present on Sierra Leone's work. Thank you. Okay, thank you Rebecca. Let me share my screen. Okay, good morning, good afternoon, good evening everyone. I'm presenting on behalf of Sierra Leone and the implementation of customizing the DHIs to WHO package for COVID-19 vaccine data collection. Originally Sierra Leone already had the integrated case based disease surveillance system. And this is what we expanded on to, to have COVID-19 incorporated into the system, which is on DHIs to platform. And since we already had this from the start of the pandemic. We had, it collects a pot of entry data of travelers coming in through air and through land. Then it also collects case based surveillance data for other diseases, including COVID-19. It also collects contacts line listing data and follow up data on quarantine and also case management outcome data. So we built on that and, and we're able to add the vaccine data collection tools as well. So we use it for data collection. At the same time we have a visualization tools. You can see an example of the charts and graphs that we have in the system from the different data sources that we have, including transmission chains and so on and so forth. So with the vaccine package we started in January 2021 when there was a strategic planning session and we needed to demo how DHIs to can be used to collect the COVID-19 vaccine data and the technical working group was amazed with how DHIs to can be used to collect this data. So they were able to approve DHIs to for this, for this purpose. So we went ahead and configured the system using the University of Oslo package with specific customization in relation to what Sierra Leone wants. So then later on in March we were able to conduct a simulation exercise and also launch the vaccine. Then up to date data is being collected and analyzed and used to make informed decisions. So in the next slide where we shall have a look at the data flow. The system has a collection of the video registry data. It has the vaccine stock, the AFI, emergency drug stock management as well. It sends reminders to clients who are due for second dose and also congratulations messages for those who have completed their doses. So on the side of analysis data is used to, data is analyzed to determine the vaccine uptake throughout the country. This is across all the districts and also data is used to engage the social mobilization team to ensure that the community is destabilized on the importance of taking the vaccine as well as completing their doses. We're able to also monitor defaulted filters and also share situation reports on those. So then this is the flow of the data, the vaccine data. So the initial design was to have the pre-registration of all the priority groups. So as the previous slide we looked at the patient level data, which is a vaccine registry under the aggregate data, which looks at the target population, the vaccine stock. So data is collected offline or using data bundles but most of the sites do not have internet so they collect data using tablets offline and then later on synchronize the data when they get connectivity. So as the previous slide we have to send reminders to the vaccinees who have not yet returned for their second dose and also those that are due for their second dose. Then AFI registration was incorporated into the vaccine registry program. Unlike the original design of the package was to have AFI separate from the vaccine registry but Sierra Leone preferred to have it as a stage integrated into the vaccine registry program so that we can keep monitoring the flow of one vaccinee. Then data that is collected is analyzed, monitored for trends, we use it for reporting to Africa CDC and also WHO in country and AFRO and even for planning within the country by the National COVID-19 Task Force. So we use the package to design the data collection tools. So we're able to come up with the data collection tools, the manual data collection tools as in the picture. And then these are the images of how the data is collected using the mobile application in the field and the different sites. So the achievements we have so far is that we've rolled out this system to over 36 static vaccination sites and 18 mobile sites through a regional training that we conducted. And these sites cover across the 16 districts in the whole country with more sites in Freetown which is the capital city. The vaccination process has been going well. However, it has taken longer than expected. The initial plan was that it would sort of be like a shorter time but it has taken longer than expected. So the process is still ongoing since March and each vaccination site has a dedicated data entry clerk who has a tablet. So it's easy to transfer the paper-based data into the tablet. The country prefers to have both paper-based as well as the electronic. So we keep the paper-based as a data quality for data quality checks. So, yeah, we're able to integrate the AFI stage into the vaccine registry program. However, we had the first challenges. We had little bugs and we had to drop it and then later on had to incorporate it again. And so the AFI cases kept accumulating and there's a lot of backlog that is being captured right now. So data is also analyzed regularly to monitor the vaccine uptake and the technical working group sits every Monday and Thursday to review the data so that they can make decisions on following up the defaults, assessing the uptake per region and also uptake by the priority groups and the different groups that have received the vaccine. So also situation reports are developed from the data and then they are disseminated to different stakeholders across the country and we also managed to incorporate the vaccine variables into the case investigation form so that we are able to keep track of any infections from vaccinated people. Then we have SMS reminders that are sent to clients to remind them for the second dose. This is an example of an SMS reminder which clearly states the vaccination sites where you go to your first dose but also it is possible for the vaccine is to receive their job from any other vaccination site across the country and these sites are always communicated through the situation report that is disseminated across. So we also have the congratulation message, which is very important and has been motivating people to go for the vaccination so we always send congratulations messages to those who completed their two doses and we have the manual vaccination card that has the unique ID which is used to track the vaccine when they go back for the second dose through the electronic tool. So we also had a smart card vaccination card however we had we first hardware challenges and we're not able to implement it so we maintain the manual vaccination card which is in the image below. Then for capacity building we were able to conduct training through the regional districts then after that due to less funding we're not able to go on ground and keep supporting and mentoring the different vaccination teams on electronic data capture. So we had to come up with user manuals and also topic specific videos to explain how to go about capturing the second dose, explain how to go about capturing NAFI using the tablet. And also troubleshooting videos were shared with the users to ensure that they're able to follow through any challenges that they face while using the system. Then we've been having real time user support we have WhatsApp groups and users normally make calls so we are able to follow through any challenges that they face while using the system. We also had a hitch in the SMS functionality, but eventually it was resolved. So for data use we configured indicators, we used part of the indicators that come with a package but also came up with other indicators that were requested by the EPI program and the vaccine technical working group. So these are the indicators that are routinely monitored and used to also they are put part of that situation report that is disseminated regularly. So we have also an interactive dashboard for visual representation at the EOC and also the EPI program office so that they keep monitoring the trend and also the data, the vaccinations that are going on across the country from different regions. We use smart display application for the visualization and also we do routine data cleanup at the district level so that the data is of quality. So this is the vaccine interactive dashboard that is used to monitor the vaccinations across the country. However, we had a few challenges. One is we could not get the pre-register, key target groups due to unavailability of the key information that we needed so we did not do pre-registration from the start. So registration happens as of when the vaccine uptake by the priority groups and which prompted the technical working group to lessen the age group to 30 and other people who are interested in taking the vaccine. Then data being captured on both paper and electronic makes work slower so there's accumulation of the paper based forms which reduces on the real time reporting. Then we also faced a few hitches on the package where we're getting bugs especially on the AFI program metadata so but we eventually resolved them and were able to catch up with the data capture. Stock data is also not often captured as expected in the system because there is more focus that is put on the vaccine registry and so for stock you can hardly find any stock data in the system. However much the infrastructure is there, the system is set up and configured but there are very few districts that report on the stock which makes it difficult for national to keep track of the stock. Then the spiral manual reporting, sometimes the districts have to call in manually and report on the numbers that have been vaccinated per day while they are still capturing data because of the paper based backlog accumulation so which makes it confusing for national in terms of relating DHIs to and the manual reporting. However, DHIs to reports are what is used for the situation report because it is more reliable. So there are some districts that keep reporting the power manually, then also the dependency on data bundles, the fact that they need to synchronize their data sent to the server, and the government has been loading data bundles on these districts and at times that it is not loaded and so work keeps accumulating and so on and also operational issues like some stuff abstract because of nonpayment and so on which makes work difficult to go on. Then the next steps we are planning to incorporate Ebola vaccine data into the vaccine registry as a stage so that we keep track of the health workers that have been vaccinated for both Ebola and COVID-19. Sierra Leone is looking into this because there have been an outbreak in Guinea which is the neighboring country, so they was as care of Ebola and the border districts had to get their health workers vaccinated for Ebola. So that is one thing that we are looking into and it is work in progress and also US CDC is planning to conduct an evaluation on the use of DHIs for COVID-19 AFI data management. So it will be good to go through this evaluation and assess the findings and also this plan to integrate the AFI data into the Sierra Leone Regulatory Authority so that we are able to communicate and have the same information into one place. And we are also looking at ensuring that the relevant AFI data is uploaded to the WHO VG base which is the global database. Then directly or indirectly have the vaccine registry data for the international travels. So we need to ensure that Sierra Leone vaccine passports are also recognized. Finally, as I started, this is incorporated in the case-based disease surveillance system which is the national database for COVID-19 data and we are integrating so many other applications into it like the travel portal data that is used for international travelers to register for their COVID tests and so on and so forth. So we also have the quarantine site monitoring data application and so many other sites. So we are looking at integrating most of these applications into the case-based disease surveillance system to have the data into a central database. Thank you very much. Thank you so much Bridget for taking the time to share the amazing accomplishments from Sierra Leone. I just think we have so much to learn about how to use DHIS-2 as an integrated platform and particularly your stories around being able to keep the data users in mind. And I just find it quite inspiring that the same data can be used by the EOC, by the EPI programs and these types of things are what we're really aiming for. So thank you Bridget and now we are going to shift to our colleagues from Hisp, Ethiopia, who have done impressive work on strengthening the national surveillance system for COVID-19 with a number of local innovations and custom apps and real results to share around improving key bottlenecks such as laboratory results turn around time. So with this, I'm very happy to introduce Sayid Hussein and Malika Sirwit from Hisp, Ethiopia to please share the learnings from the system. The floor is yours. Thank you Rebekah. Thank you everyone. Malika will be sharing the screen and start. So our presentation is around the agility of DHIS to how it is responded to our changing requirements. Next slide please. Where people, Malika is myself and Adiotz also will be in this presentation. Let me give you some background. DHIS was selected for COVID-19 surveillance and vaccination. Currently we are customizing the vaccination package and on the piloting stage it will be rolled out in the coming weeks. Last year, the advent of COVID emergency operations center and EOC was established at the Ethiopian Public Health Institute for Integrated COVID Response. In that center, a digitalization section was established at the same time. DHIS 2 was handling phase registration, some collection, result computation, admission and discharge through the tracker capture and daily and weekly aggregate data were captured in infrastructure supplies. So registration was usually being done in the field using Android but entering results in the tracker especially on the web was proving cumbersome and it was not suitable for us during the emergency. This was very evident, highly evident during the Mandelung campaign last summer whereby thousands in 20,000 to 30,000 specimens were being collected in a specific day. And they used to come mainly to the main at the south of the Ethiopian Public Health Institute labs where four of the labs were situated. So this lab, the Ethiopian Public Health Institute used to dispatch some of these specimens to other labs in the city. So this is when we get some issues. Let me give you some background, some figures and we'll go to the specific data. So until now we have collected almost 2 million specimens for the tests and 950,000 people have been communicated their test results. For SMS we have developed about 4 local apps to cater to the local requirements and we have trained more than 1000 people through the decision section team at the EKH. So this is the first app we developed was the custom imported. As it usually happens Excel templates are being created and used to facilitate capture of data by management and other people. So they are familiar with this because they are familiar with Excel templates. The users in the different sections of the USC is to work with Excel templates. So in the beginning results were being filled into Excel and then imported to the HHS either one by one or using by us the development customization team involved creating some custom scripts to import into the HHS. So what we did first was to align with these sections, align with the way they are working. We created this custom export tab. What it does is it generates the test captured specimen list in the predefined Excel format. So we used to enter the test data there and we will import it and it's still being used by these sections because of familiarity sometimes. Next slide please. So next, once we did that was people will be started using the HHS too, but we have some problems. For instance, staffs and clubs at the decision section wanted to plan TEIs easily and like to see if the result is ready. And because of issue with identifiers because we don't have national ID, we were using the specimen IDs that are collected during specimen collection as identifiers. So to search for a specific TI using a specimen ID, which is captured in an event, not in a TI environment, it was difficult for us to do so. So result status cases were needed, but we don't want people to see the results. And the data specimen ID of a specific person was needed in the custom list. But these were not possible because pushing data from event to AI is difficult, because it's not for us. And what does a middle layer is pushes relevant data from latest case event to the TES, the corresponding TES, which will be visible from the customers. So what happened after this was the clerks will come to a specific organization with it and a custom list will be displayed where they can see the test result of a person, a specimen ID of a person, the latest specimen ID of a person because people will get tested more than one time. So this helped data managers identify if there are errors, for instance, if there is an error capturing the latest specimen ID, it will not be visible in the custom list. So it will be handled. After this, we, because of a surge of specimens being collected every day, we had another issue. So as I told you earlier, there was a long campaign for COVID test. So thousands of specimens were coming to ETH. So we developed an app to handle the surge of specimens coming to the ETH. We found that the tracker capture was cumbersome and it was not efficient for capturing data, especially if you are regarding a specific data, not more than one data. If you are capturing one data or two data, opening the tracker capture app when navigating to specific event or creating a new event and editing data was being cumbersome. So we created this app to act as a one-stop place to approve specimen for test, to distribute the specimen for specific laboratories and record test results. It's currently being used across all laboratories and reception networks all over the country and result is also being captured by this app. So what happens is each specimen, the team collection team brings to this part of specimens during like this time when I'm presenting, teams are bringing their casework to the ETH site. And during that time, during the campaign, what used to be there were 20 teams waiting for the term to get for each specimen to be approved and sent to a specific laboratory. So it was taking a long time, sometimes hours to approve each specimen. At the laboratory, we created this app and what now they can do is at the laboratories, these clerks will select a specific organization where this specimen came from, the lab request form and the laboratory to which they are going to send. And after that, they can scan each specimen one by one using the barcode and just approve. Since there is a copy of the barcode and a paper form, it was easier for us to scan and approve each specimen. So what this app specifically does is it will search for the lab request event, and if this place the event, the specific event, and if there is any problem, it will show an error. So what the clerks at the reception will do is, if it's a small error, they will rectify it themselves. If it's a big error, what they do is they will ask the teams who rectify the error. And they will approve each specimen one by one. The approval process will take only seconds. Before this app, it will take minutes to identify a specific TI, open the event and approve. Now it will take only seconds. This app also was used to capture result data. So the relevant data from the sample collection event are being collected and they are being pushed to the result event as well because they are needed for indicator calculation. So it will take some data from the test event pushes to the result event. And after that, what the clerk need to do is just enter the test result after scanning the barcode. I can demonstrate this in the next slides. This is what happens during the approval process. As you can see, the clerk at the reception will select the organization you need. And after that, it will select the program, the surveillance program. After that, it will select the stage where handling two stages here, one is the request form, which is the sample collection form. And the other one is the result form, which I will show later. After that, what they do is select the laboratory, this space batch of specimens will be sent. After this, what the app does is it will, after selecting the number of days you want to fetch, it will list all the specimens collected on a specific organization for a number of days. After this, what the clerk will do is just scan the specimen ID and if it's ready, it will just upload. So what we can do is here, as you can see the first four steps, including the days, it will take maybe a minute to load all the specimens collected for three or four days. And after that, approving will be a matter of two seconds. Scan, approve, scan, approve, scan approved. And these reduce the number of the time required to approve and dispatch each specimen. And the people who used to wait for hours for the specimen to be approved and sent to a specific laboratory, the teams now they used to it only for minutes. Within 10 minutes, they can get all their specimens approved and they can go their way. Next slide please. So we use the same app to capture results. So yeah, to capture results as well. The first four types start the same. After that, what the clerk will do is again scan the specimen ID and after that just enter the results. This specially used to take Guinness for us because using the tracker capture app, opening a specific TI and after that opening the creating a new event result event, entering the result dates, other relevant data like the collection time submission time. And after that, the result time and at last the actual result to take minutes to capture this. But using this app, it took only seconds to capture the result. I'll show you some other impact what we have the impact of this app is tremendous. It has decreased the time to approve a test as well as capture the results to second instead of minutes. Sometimes it will take five minutes if there is a connection problem. It has stopped the needs for bulk reporting from the developer team. What we used to as I mentioned earlier is when the results used to come in Excel templates, we have to clean the data, map the data with the TI and create an event using Excel and importing which will take hours. But now using this app, people at the laboratories can capture the results themselves. It has helped us improve data quality as well. They are caught during that approval process. If there are any errors, I will catch them and identify immediately. It copies relevant data from the sample collection event to the result event as well. That will help us calculate better indicators as you can see the next slide, like the results, better run out of time. So this is a sample dashboard. It shows result to run out of time. That means the time between sample is collected and result is issued. As you can see, we have visualizations that show the result to run out of time for going months, going back months. And if there are any problems, like the spike you are seeing here, it will be investigated and dealt with by the digitalization section. Next slide please. This is a two minute warning. Okay. Thank you. I'll try to ask. The other app we created is a certification app. It's a public portal where relevant bodies can verify the value of any given case. It's a standard case, which includes a QR code from that. It was initially being used for discharge, but now we have extended it to professional travelers from anywhere in the world. People can go maybe to London and when they are required by the immigration agents there to show if they are negative, they can look up in this public portal. We are extending the same certification app for vaccination app. The other thing we do is SMS notifications, positive results are communicated via phone, a phone call, but each negative result is notified by two of the person. So leadership wanted to see how many SMS notifications have been sent to each individual. So we have a summary that sent to leadership, including the minister that includes number of SMS sent, delivered, ejected and invalid for. The other thing we are doing is integration with third party systems. We have a third party app developed by other others that there was a third party tool developed to assist self-reported by individuals whenever there was a surge of COVID cases. People were started being treated at home. So there is an app developed to self-assessment. Data is pulled from this app in the HHS tool and we have created a digital engine. The other integration we are currently working on is PCR machine integration. TI and event data will be sent to the PCR machine and the PCR machine will register the test result and that data is being pulled from the PCR machine to the HHS. So in general terms, what we have seen is changing requirements and new challenges have led us to create innovative solutions. The HHS is working on the development, customization and support. We couldn't have done more, but we are only doing it. But one good thing is there is a vibrant young community and a strong leadership at the HHS which relieved us from most of the routine and handling the reception app, data management, the training and other things that has helped us focus on other bigger tasks. We have also seen strengthening local teams and creating communities around them is very important for local departments. We appreciate the leadership of the ministry and collaboration with other partners which has been just proven important. In the name of all the people at the digital team, all the young, vibrant, intelligent, independent speech-op-ya, we thank you. Thank you, Said, and to all of our presenters at the plenary this morning. So we will quickly have to transition to our next set of sessions and we have really amazing country stories. Again, I can only say that I'm so humbled by the speed and scale at which the community has operationalized the HHS too for these unprecedented demands and challenges with COVID-19 surveillance response, vaccine delivery, and strengthening the routine system at the same time. So we will transition to the next set of sessions and we look forward to seeing you all throughout the day and on the community of practice. So thank you very much.


In video 4, we develop Fermi's golden rule. If a quantum system with Hamiltonian H hat 0 has stationary states, phi n, e to the minus i omega nt, then adding an interaction term, H hat i, to the Hamiltonian will cause an initial state, i, to transition to a final state, f, at a rate proportional to the squared magnitude of the matrix element, mfi. Where to first order, mfi is the projection onto the final state of H hat i operating on the initial state. To second order, we add the sum over all intermediate states of the product of first order matrix elements between the initial and intermediate states and between the intermediate and final states, divided by the energy difference between initial and intermediate states. By the conservation of energy, the energy of the final state must equal the energy of the initial state, but the intermediate states are occupied only briefly, so the uncertainty principle allows their energies to differ from the initial energy. We have worked out the details of the interaction Hamiltonian for a hydrogen atom in an electromagnetic field, and expressed it as a sum of two terms, H hat i prime and H hat i double prime. We further separated H hat i prime into absorption and emission terms. The absorption term is the sum over all pairs of electron states m and n and over all photon states k alpha of a coefficient mA prime times a creation operator for electron state m, a destruction operator for electron state n, and a destruction operator for photon state k alpha. The emission term is similar with a coefficient mE prime and a photon creation operator instead of a destruction operator. The two coefficient functions differ only by a single factor. For absorption, this factor is E to the plus i k dot x. For emission, it is E to the minus i k dot x. k dot x is zero at the origin and increases to 2 pi at a distance of lambda, one wavelength along the propagation direction E k. The size of the atom is determined by the spatial extent of the electron orbital. For the hydrogen wave functions, this is typically less than about 10 to the minus 9 meters. While the wavelengths of hydrogen transitions are larger than 10 to the minus 7 meters, much larger than the size of the atom. Therefore, k dot x is very small everywhere within the atom. And E to the i k dot x varies only slightly from one plus i zero. So to a reasonable approximation, E to the i k dot x is equal to one. This is called the dipole approximation. With this approximation, the absorption and emission coefficients are identical. Now let's analyze the absorption of a photon by a hydrogen atom using quantum field theory. We will focus on three of the lowest energy orbitals. These are the 1s, 2s, and 2pz orbitals with the spatial wave functions shown. Here A is the Bohr radius, about 53 picometers. The s orbitals depend only on the radial distance r, so they are spherically symmetric. The 2pz orbital has a factor of z, which is responsible for its two lobes structure. The wave function is positive for positive z and negative for negative z. The absorption and emission coefficients contain a factor with terms having the form of an integral over all space of the conjugate of the nth atomic wave function times the derivative with respect to x, y, or z of the nth wave function. Without computing the derivative and doing the integration over all space, we can make the following general statement. The integral over all values of x of a function f of x will be 0 if f of x is an odd function, by which we mean f of minus x equals minus f of x. The s orbitals are functions of r alone. r is an even function of the x, y, and z coordinates, meaning if we replace any of those coordinates by its negative, r is unchanged. Therefore, the s orbitals are even functions of x, y, and z. The pz orbital is an even function of x and y, since its dependence on those coordinates is only through r. However, due to its factor of z, it is an odd function of z. The 1s orbital is an even function of all three coordinates, x, y, and z. However, the derivative of an even function is an odd function, likewise for the 2s orbital and indeed for all s orbitals. The pz orbital is even in the x and y coordinates, and the corresponding derivatives are odd. But the orbital is odd in the z coordinate and the derivative of an odd function is even. For these three wave functions, our integrals will be the product of one of the blue curves times one of the red curves. For a transition between the 1s and 2s orbitals, this product will be an even times an odd function. This is an odd function, and its integral will vanish. Therefore, there can be no transitions by this mechanism between s orbitals. For a transition between the 1s and 2pz orbitals, for the x and y coordinates, we have the product of the top left and bottom left functions, even times odd, which is odd. So those terms vanish. But for the z coordinate, we have the product of the top left and bottom right functions, even times even, which is even. So that integral can be nonzero and produce a nonzero transition rate. This consideration of the even and odd symmetries of the wave functions and their derivatives are summarized in so-called selection rules. For the wave functions we have examined, the integrals for transitions between 1s and 2s orbitals are all zero. And in general, no first-order dipole transitions are possible between s orbitals. By first order, we mean the first-order form of Fermi's golden rule. For transitions between the 1s and 2pz orbitals, the x and y integrals are zero. But the z integral is not. And in general, first-order dipole transitions are possible between s and p orbitals. These selection rules have an interesting physical interpretation in terms of angular momentum. In videos 7 and 8 of the quantum mechanics series, we show that an electron in the hydrogen orbital is characterized by four quantum numbers, n, l, m sub l, and m sub s. n fixes energy, and l fixes orbital angular momentum magnitude, but not direction and space. m sub l fixes the z component of orbital angular momentum, and m sub s fixes the z component of electron spin angular momentum. s orbitals have l equal to zero, and therefore no orbital angular momentum. p orbitals have l equal to 1, an orbital angular momentum of square root 2h bar in standard units. Photons carry angular momentum of square root 2h bar in the form of spin. Therefore, a photon plus an s orbital electron have total angular momentum of square root 2h bar. If these are destroyed and a p orbital electron is created, the total angular momentum of square root 2h bar is conserved. Likewise, angular momentum is conserved if a p orbital electron is destroyed and a photon and an s orbital electron are created. On the other hand, if both electrons are in s orbitals, angular momentum will not be conserved. In the first case, angular momentum decreases, and in the second, it increases. Fermi's golden rule ensures conservation of energy, and we have seen that the selection rules ensure conservation of angular momentum. What about linear momentum? Suppose our atom is at rest, and it absorbs a photon, causing it to transition to an excited state. In video two of the quantum mechanics series, we showed that a photon has linear momentum equal to its energy divided by the speed of light. Since the atom initially has no linear momentum, after the photon is absorbed, its linear momentum must equal the linear momentum of the photon. Since momentum is mass times velocity, and the mass of a hydrogen atom is nearly the mass of a proton, the atom should end up moving with a velocity of nearly e over mpc. For a 1s to 2p transition, this is about 3 meters per second. If the proton mass was infinite, then this velocity would be zero. Essentially, the nucleus absorbs the photon's linear momentum. However, there is nothing in our quantum mechanical model of the hydrogen atom that accounts for motion of the nucleus. So it should not be surprising that nothing in our quantum field calculation enforces conservation of linear momentum. Now, let's go through the steps to calculate the transition rate for a photon in the state kα to be absorbed by an electron in the 1s hydrogen orbital and transition to the 2pz orbital. The initial quantum state of the system is simply a list of the occupation numbers of the electron and photon states. We've labeled the 1s orbital as the first electron state. So the initial state of the atom is one electron in the first electron state and zero in all the others. For the radiation field, we assume there are no photons present, except in the kα state, which contains nkα photons. As a shorthand, we write this as a 1 ket for the atom and a nkα ket for the radiation. In the final state, the electron is in the 2pz orbital, which is the third electron state in our bookkeeping, and there is one less kα photon. The shorthand notation for this is a 3 ket for the atom and a nkα-1 ket for the radiation. The term from the interaction Hamiltonian that connects these states is ma' of 3,1kα times bhatt3 plus bhatt1 minus ahattkα minus. We need the squared magnitude of hhatti on the initial state projected onto the final state. We substitute the expressions for the initial and final states and the operative part of the interaction Hamiltonian. The bhatt1 minus operator destroys the electron in the first electron state. Then the bhatt3 plus operator creates an electron in the third electron state. This converts the 1 ket into a 3 ket, which is projected onto the 3 bra of the final state. The ahattkα minus operator converts the nkα ket into square root of nkα times the nkα-1 ket, as we showed in video 1 of this series. This is projected onto the nkα-1 bra of the final state. The two projection factors are both 1, so we obtain nkα times the squared magnitude of the ma' coefficient. We see that the transition rate is proportional to the number of photons present in the radiation field, that is, to the intensity of radiation. Plugging in our expression for the ma' coefficient, we obtain the transition rate predicted by Fermi's golden rule. In addition to the integral factor, which produces the selection rules, there is a delta function. This is 0 unless its argument is 0. And this ensures that the photon frequency equals the difference of the atomic orbital frequencies. Since energy is proportional to frequency in quantum theory, this simply enforces the conservation of energy. There is also a factor of the electrons charged to mass ratio squared, and a geometric factor, eKα dot ez squared. The dot product of two unit vectors equals the cosine of the angle between them. This is 1 for an angle of 0 and decreases to 0 for an angle of 90 degrees. This factor tells us that the less the polarization of the photon is aligned with the z-axis, the smaller will be the transition rate. An analogous factor is present in the expression for the power received by a wire antenna illuminated by a plane wave. In video 6 of the quantum mechanics series, we saw how the superposition of 1s and 1pz orbitals produces a time-varying probability distribution for the electron that oscillates at the difference of the orbital frequencies, which equals the radiation frequency. This geometric factor tells us that the absorption process is most efficient when the photon polarization is aligned with the direction of this oscillation, which we have taken to be the z-axis. Now, let's look at the emission process. We start with an electron in the 2pz orbital. A photon in the k-alpha state is emitted, and the electron transitions to the 1s orbital. The initial state is an electron in the third electron state and nk-alpha photons in the k-alpha photon state. The final state is an electron in the first electron state and nk-alpha plus 1 photons in the k-alpha photon state. The relevant part of the interaction Hamiltonian is the emission term Me' of 1,3k-alpha times B-hat-1 plus B-hat-3 minus A-hat-k-alpha plus. And we need the squared magnitude of h-hat-i acting on the initial state projected onto the final state. Substituting the appropriate terms, we have an expression in which B-hat-1 plus B-hat-3 minus operate on the third electron state, and A-hat-k-alpha plus operates on the nk-alpha radiation state. The first operation destroys the third electron state and creates the first electron state. This leads to the projection of the first electron state onto a cell, which is 1. The second operation converts the nk-alpha photon state into square root of nk-alpha plus 1 times the nk-alpha plus 1 photon state. This leads to the projection of the nk-alpha plus 1 state onto itself, which is 1, times the factor square root nk-alpha plus 1. After squaring, we end up with nk-alpha plus 1 times the magnitude squared of Me' of 1,3k-alpha Plugging in our expression for the Me' coefficient, we obtain the transition rate predicted by Fermi's golden rule. As it did for absorption, the delta function enforces conservation of energy, and we have the same charge to mass ratio and geometric factors. The transition rate is proportional to nk-alpha plus 1. The plus 1 term tells us that even if nk-alpha equals 0, that is, there are initially no photons present, there is a non-zero probability of emission. This is spontaneous emission. The nk-alpha term tells us that the more photons are initially present, the larger is the transition rate. This is stimulated emission. A general result from integration theory tells us that our integral factor is unchanged if we swap the orbital functions. This means that except for the nk-alpha and nk-alpha plus 1 factors, the absorption and emission rates are identical.