 I would say let's get started because today we have quite a packed and and yeah thank you very much for everyone who has found a time to connect today for these for these Gavi Porto di Webinar. Today we are going to talk about a topic that has been coming up quite often lately and it's about trying to integrate as many data as possible within the Echinas too. In particular today we're talking about campaign data so any kind of like supplementary immunization activity. So the idea is that of course you have like plenty of activities going out there and plenty of data that normally like remains either on paper or on inspected excels and how can we bring and design the the system to have all the data in one place so we can start triangulating data better so we can start analyzing data better and we can start making decisions with better information. So just like I mean just like as an introduction why is it important to integrate in DHS too. I mean I hope that for some this would be a pretty evident answer but for those who are still exploring the idea of an integrated routine health information system. The main reasons why it should be at least convincing enough it would be because an integrated system where you have like all the kind of data in one single repository where you can just like solve all these informations because you can have a comprehensive assessment. So in this case for example you can have like your routine EPI data that like monitors all your own void efforts of the routine programs. Of course then you can start bringing campaigns or surveys that give like a quick snapshot of one of activities but then of course this also gets regulated also with other other programs and other type of activities that provide also real-time data that can inform your decision making. Of course this also has impact on planning and coordination because of course if you start having more data that are better triangulated and better monitored you can start having stronger and more informed collaborations with other stakeholders. As we know other stakeholders also run similar activities in countries and it's very important that all these results get pulled together so that you can actually have access to a unified dataset where you can triangulate all these information and make sense of all these data as well. This has an impact of course on accountability because once you have your comprehensive data that is all integrated in one place it's more transparent and it's easier also to track progress but also to clean up data that may be like they have outliers or like don't make necessarily a lot of sense and correct these numbers accordingly. And of course all of these when you have like cleaner data when you have more transparent data when you have better collaborations amongst stakeholders you can also have like a better resource allocations so you can start having like better coverage data you can start therefore allocating vaccines probably finding better pockets where of underserved populations or in general like track your population better in order to target your patients and in general the populations in need with better data and of course all of these can contribute to better interventions there are more timely and real-time monitored so you can actually have better overview for your coverage rate so you can have better overview of your responses and you can triangulate better your responses as we know that in the end immunization goes hand in hand also with your surveillance system. One little thing I forgot of course we have like as I said quite a packed hour ahead of us so please if you if you want if you have questions and such of course you can post them in the in the meeting chat but as I'm alone today to present and such so if you see that I'm not able to get to to the questions Alice has kindly put also the link to the community of practice where also the announcement of this webinar was so please post those questions there as well because I will be following up on these questions there directly or send me also an email at vittoria at dhs2.org so continuing on our general overview of why integrating all this information is important we also need to know like what kind of information we are putting there all together of course we can bring in service for example or any kind of information of supplementary immunization activities all this information together can have a role in updating epi data because of course once you have also sia I mean I'm going to start abbreviating supplementary immunization activities with sia and this can create also a reliable blanket baseline that can also update current vaccination coverages I can also help you reconcile any type of discrepancies between epi data and the informations that you just collected during your activities you can also validate this data because of course once you have the discrepancies once you might want to start investigating them whether they are like these substantial discrepancies or just in general like updating your your baselines in order to analyze data properly and accordingly to the most up-to-date numbers of your coverage for example and of course all of these together can trigger action because when you analyze your your information alongside your routine data so your epi data you can start identifying better pockets of for example zero dose children you can start check areas with where low immunization coverage is present but also you can start trying to triangulate better your information with with your surveillance data trying to make sense of of data information that might normally see be seen as odd but that maybe are actually just the red flags of either data quality or of pockets that are undeserved and of course we'll also be talking about rapid convenience monitoring rapid convenience monitoring I mean also type of activities that come in hand in hand when you do sias or in general any kind of like activity regarding immunization and of course these can on top of the type of of roles that we saw for for sias and and the surveys these can also provide information of behavior drivers of vaccination because during rcm's it's also very common to collect data for example on why certain certain children were not being vaccinated during these campaigns which then in turn can impact our for example health promotion policies or in general the way that we target certain type of populations rcm data also provide information on supervision for for sias activities because this can happen either um uh hand in hand or at the very at the same time as as your immunization data campaign data and or right afterwards but in general like it gives you an extra layer of of of check let's say in order to control whether the the information that you gather is is informative enough whether any kind of for example once you start doing your activities or your survey for the coverage whether all the clusters were were addressed and and so in general it can provide a very useful um a very useful supervision tool and then of course as we were saying earlier this goes hand in hand with any other type of information that you can have on on immunization it can highlight any kind of discrepancies it can highlight any kind of pockets it can in general provide an extra layer of information where you can analyze your data either for routine or one-shot activities in the realm of immunization but in general within your routine health information system because we know that in the end programs yes they they sometimes tend to go in a silo where they check their own data but we know that I mean um the the key point of integrating all this information together is of course to be able to check um your your numbers together with your measles coverage nutrition together with malaria so is the idea of really having an integrated platform where you can start checking um all the co-factors that can have an impact in your programs of course we like saying all of these together it has another value for our early learning system it can have a another value for of course our monitoring activities in our immunization immunization activities and of course it can tailor better our interventions because once we start triangulating once we start validating these data we can better tailor these interventions because we are we are more confident about our data and we know that chances are we are better prepared we can have a better micro planning and we are able to actually serve these pockets in a better way because we know where we are targeting how we are targeting and therefore it allows us to identify precisely where what kind of activities and where these activities have to be take place so the idea is of course as we're saying up till now is the the idea is to put everything in one platform so you can have your routine data your epi data but you can bring in also your sia data you can bring in your rcm data you can bring your population your coverage data of course we have like our the component of logistics and cold chain and supply of course and of course you can have other programs as we said we then have nutrition we can have maternal and child health or for example any kind of immunization activities that cause an in hand with like key group of populations or like for example older populations or for example healthcare workers and of course we also know that a lot of other specific immunization programs like for example covid or flu or flu programs have also the tendency of also like being a little bit more isolated so there is really the possibility to bring everything together in one platform so the entire program together with other programs can benefit from this sharing of data and of course it happens very often that countries we collaborate with they see that other organizations run of course other other type of immunization activities so campaigns be coverage surveys and such and they say that therefore it is difficult to integrate this information later on but of course with different kind of approaches we can also bring in information that are coming from third parties there's just like a handful of organizations that we see in the field and you can still bring this information in your data repository because nonetheless you receive reports and you receive at least the outputs of these activities and you can still integrate and put these numbers in your system to have up-to-date kind of information so what are we going to see today in the demo it's going to be about fictional data of course and it's going to be about some fictional campaigns that can happen but in general we're going to like for example of a vaccination campaign that was carried out in March 2023 of course it has like some bits and bobs that have been taken as source of truth from different type of campaigns that we have collaborated with but it's just to give you an idea and just to try to concretize a little bit something that for some people might see but it's still a little bit too abstract to be seen in your routine HMIS then of course we're also going to like have a quick overview on an example of rapid convenience monitoring that was carried out as these MR campaign was being rolled out and we're going to see how this type of data can be collected and how what kind of outputs you can you can obtain from these activities and of course these RCM in particular is going to be it's an event you will see it I will show you and the metadata has been taken from as you see here I mean it will also be available the slides but it's like from the reference manual for vaccination coverage cluster service so the flow the logic and the metadata is very much taken from from these guidelines and it contains like very simple basic info and once you have the information of the household of the children you can start finding out whether this child was was there during the campaign itself and whether if it wasn't during it was present during the campaign whether this child will receive the vaccine or for example you can start tracking any better information with the routine EPI because you can start also checking whether this child has received another vaccine during the routine activities so you there is quite a range of information that can be obtained also from from RCMs and of course there are also different examples of vaccination coverage surveys that can happen post campaign but also as we said before as third party coverage and of course integrating coverage poses a challenge sometimes because of course that means that like you need you are you are using the latest background information when you're analyzing your data and but these kind of can have the added value of resulting in a comprehensive and updated data repository. This particular data set was designed to maintain it's like the most simplistic design ever so either there are like here we have two examples so either it has two basic data elements so you can have like how many children who are depending on your target population who are eligible and how many vaccinated children so you can have you can have your coverage or you can just like directly enter the coverage already calculated because for example your your information is coming either from either type of tools be it from I don't know decay cobo or symbol as an excel or reports from a third parties for example and therefore you might have your your already calculated coverages that doesn't that doesn't prevent you from integrating this type of information for the system so there are different approaches depending on what kind of resources but also what kind of information you have access to. Of course the theory DCT can be tailored to match the different specific needs of the of the context you can set it as daily for example if you are like a cover if you are handling the the coverage survey directly from the system so you want to update as much as possible following the the different organization unit and the different days where the actual survey is is managed or for example it can be simply as we said for example you have your example too and you have only like the outputs from these surveys and you might want to simply like take into consideration the month where the survey happened or the year when this survey happened so it's very much dependent on the type of information and how you want to manage these these information. Of course what's what's very important is the fact that countries need to find a way to make sense of all this information being put in one repository so for example here as you will see also in the system once where I entered the system directly you will see that for example I called this campaign MR2303 because I knew that it was an MR campaign that happened in 2023 and in March but this is just one example of like a hundred different type and ways to to for example name these campaigns but also to recognize the type of campaigns that you are that you are managing. Of course when we are talking about this kind of information we cannot not think about the organization unit because actually the organization unit is one of the first things that has to be considered when managing any kind of program really so for example if you're managing directly the the campaign or the survey probably you want to have a separate instance because this way you might want to if you're entering data directly during during the activities might want to target directly either a community level or in general like being able to access directly the the lowest level where your activities are taking place while for example if you need at a certain point when you are integrating this information with your routine health information system you need to associate this information with the hierarchy that you have in the in the system therefore for example at a certain point you might want to aggregate this data at different levels so you will see this example where I have my campaign at discrete levels simply because for example I don't know the actual rollout of the campaign was handled on on excels and I simply have an aggregation of this data or you can really go down to the bottom level where this data was taken was taken and collected so it's all important considerations that of course are described also in the documentation that we have previously published on the use the implementation and the design of vaccination campaigns but these are to be more like we wanted also to give you the opportunity to see how all of these together may look in the system directly and of course there are also a bunch of of of dashboards that I want to like show you around and so just to give you an idea of some of the information that you can actually extrapolate from all this type of different data so actually going directly here is our our routine at MIS this is coming from our demo environment which is like a fictional I mean you you see that it's it's laws of course we have an agreement with MOH has allowed us to use the country for demo purposes but here you see that it's like dummy data and in general is just to give a better representation of like of an environment that is that is really showing the country not a like a made-up country so you normally would have in your routine at MIS or your different type of of programs that you can see you can start like triangulating with all the different dashboard and such so here for example we are having our data for EPI and the it's the routine dashboard that you've seen many times that you have all the type of information on coverage dropout and AFI and and and also of course LMIS so you you have also your temperature alarm your cold chain function refrigerators so the usual routine type of information that you might obtain from your HMIS and then of course you might have also your I have plenty of tabs already open because I didn't want everything to fail because that happens 100% of the time when you're demoing and then of course you might have also your information that you extrapolating from your your campaigns so for example we were talking earlier that for example your campaign can can have different targets can have a different type of antigens and and can can be aggregated at different levels at this level here in this example we are aggregating at the street side and we are having for example the campaigns target at annual level okay in this particular case there were two different age groups that for example were were were targeted and this particular age groups and population that was our target population but was also is aggregated by the distance through the facility for example so depending on where this facility these people were residing with respect to the facility also different type of approaches for this delivery was put in place so for example this was would have been a fixed place this was an advanced place and this would have been probably a mobile cleaning this is just like an example that we're taking from an actual campaign but just to give you an idea of like how many different possibilities you can have out there of course this is not prescriptive again it's just to give you an idea of what you can do and how this data can be collected at the same level you can aggregate your information and of course at this at this time I put it at daily level because the idea was for example for this particular campaign was to have it at at real time aggregation of all the different sites that were that were being vaccinated under specific districts the type of information is very basic and follows of course the the suggestions of like the most of the of the organization out there in the guidelines just collect what is truly informative and basic so you can actually make actions out of that so how many doses were given if there were any AFI and we have also taken it from our COVID metadata as we also say in our documentation try to recycle as much as possible what you have out there you don't really need to reinvent the wheel you have probably done already a bunch of campaigns and you know what kind of information you're normally collecting so we have also started adding like for example the stuff because it's always handy handy to know what kind of like a volume of stuff you're supposed to have during certain activities and how many stuff actually were present at one note because as we said earlier different rules can also be useful for supervision during certain activities so these are very basic information that you can obtain during the campaign itself and once you are actually monitoring your campaign slowly of course then you can put everything in a dashboard of course you can start having separate dashboard for different campaigns or you can put the same campaigns for the same antigens all together it's very much up to you how you want to manage this type of information but nonetheless you will have updated information on certain areas and for certain for certain antigens for example but you have here for example your target population how many doses were administered your coverage of course you can you can have legends that will allow you to see the different like with different colors the kind of coverage you might have here of course I pushed the data to like the limits just to give you a preview of what type of information you can extrapolate so you see for example that already like the coverage for one age group was higher than the other so you might maybe want to check what is going on if there were like different type of communications if there is any kind of pattern within the community that drives these or simply was like a casualty and there is nothing you can do right now of course the coverage and the math that it was very handy check the age you might want to check also the coverage by age groups to see if there is any kind of like discrepancy and here of course you have your current coverage and the different community values not sorry time values for different age groups for example and you can also extrapolate as we said like very basic information but the type of AFI that you had to have the campaign we're talking about these days in the campaign that in this particular case it was just in the this fictional campaign that was carried out in the north of the country and and and you can see for example where these AFI happened and where the most serious one were happening and of course you can also have informational staff as we're seeing earlier where you can start like here which also like a little note to better interpreting kind of information so you will see for example if there is also any kind of pattern with your staff of not showing up so for example here you see that there is like a negative value and if you calculate the kind of like discrepancy by site you might see that for example in these two sites like people were not showing up as much as they were supposed to and and here it was even worse so like just to see if there is any kind of pattern as well and of course you can start comparing things around here a little purpose like there are like chances or there are like mistakes and such these are like realities that we can see you know in all the environments that that we check but of course these are fundamental to start checking i'm glad that people are laughing um but um okay i'm i'm muting you all sorry uh it's not polite but otherwise we group for this time so um so you can start regulating this information as well then you might have like as we said you start having your destination between our data in the system you start and see maybe like for your real time information what kind of information you can extrapolate for these so you can start checking how your situation is going where your coverage is higher where your coverage is lower if there's any kind of patterns and then we said that like for example further you can also run an rcm so a rapid convenience right and this has been taken from the the manual of of cluster surveys as i said in this particular configuration we're talking about an event so a one-off in uh in uh in uh DHRS turns so you have your report date your coordinates so you can actually like go if you have like better mapping abilities and you have like for example your areas well mapped out even by household you can really start finding the households that you are meant to be targeting directly through the maps of course you have like uh since it's a cluster as well you have your stratum you have your cluster number you can start using it to enter the interview win number and the supervisor number because of course you have your teams you have your supervisors and you can also check who carried out what kind of um interviews you have your household ID you can have like different um number for a child in this case was formed a form but it can be also like i don't know national unique IDs and stuff depending on on what type of information are available in country and then for example you start checking whether the child was was present during the campaign and you can start checking for example what was the primary source of information about the the campaign so there are different options that were the standard options that were available in the in the in the source that were of the manual so let's say i don't know it was school for example and whether the child received the more uh vaccine during the recent campaign so let's pretend it was a he said the parent or the guardian said yes um if there is any perception of um a reaction during the that was followed the vaccination so yes or no depending on on whatever the answer that's pretend that it's a yes and and if the child received a vaccination card um after or during the campaign so let's pretend there was a yes and we can also ask if the before the campaign the child had already received a vaccine so yes it could be that they have a a card and therefore the dates that were available or might not be available or they don't know so depending on that you also can extrapolate this information um or for example we said that the child did not receive the the the vaccination although they were present during the campaign themselves so you can start asking why the child was although it was present uh did not receive the the the vaccination or for example as if well as the child was not present during the campaign at all for a variety of reasons but for example you can still check whether the child had received during the routine activities any other time it's very simple very straightforward of course this is just one example of many different type of forms that I've seen out there for RCMs but this is like probably one of the most basic that delivers the all the key information that might be needed for the purpose of cross-checking the the data collection the outputs and and also for supervision purposes for example undo and to extrapolate also extra information like behavioral drivers any kind of like extra information that we might extrapolate uh to better talk about communities if they are rejecting and the coverage for example we see that it's very low so we can extrapolate all this kind of information and then there is also we were talking about vaccination coverage surveys in this case for example I put it at a lower level why because the data per se uh maybe was collecting uh through uh another tool or in an excel or on directly on paper but for example I wanted to still in the last right the day where this collection happened and I wanted to really have like where like the lowest side in my hierarchy where this data collection happened in this case I cannot do uh I don't have necessarily have to report all the information of the of the vaccine of the coverage survey but let's say I have uh I have only the outputs that are available and therefore I add them directly into my system or for example that we were saying earlier there are other other third party surveys that we have like I don't know reports or any kind of outputs and therefore we don't have the baseline and and we cannot enter all the type of information that we might we might want to enter that's the only less let's say if we have like a uh a report or we have already calculated the outputs of our coverage survey we can still enter our our numbers and let's pretend that in our survey it was a 96 percent for these and a 90 percent for the other age group so you can still report the percentages directly in the system to update the coverage in the area and this can and we saw also in the other examples we might also be that you have access to a larger number of of of of data so you might also want instead of entering directly the coverage you can enter the target population and how many uh and how many people for example with a vaccine you you found so there are different ways of collecting this type of information and you can enter either or depending on yours all the information put together in one place so here again extreme data trying to make like uh to underline the absurdity of like some data that we see out there but that otherwise probably would have been a little bit more difficult to highlight and and find out if the data would have been like scattered in different repositories or if one was in HMIS in the HMIS the other one was in an Excel and the other one was still on paper so you can see really like appreciate it with at last one uh one summary for all the different activities so here for example in the SIA groups here we have like the coverage vaccination survey um and with the different age groups so you see that already there are like uh some differences here and there and of course we have also the coverage that was reported by the RCA again down a completely different different uh uh coverage data could be for a variety of reasons but just to highlight how easy this would be then to uh to appreciate the difference in data and to start investigating if there is any kind of discrepancy you can also compare this data for example here will be your routine data from directly from EPI because we have we are in an integrated environment and next to it we can start putting for example your vaccination coverage survey your actual coverage coming from the SIA and the one coming from the RCA and you see like that you can start comparing what kind of discrepancies if any again this has been put in an upsurge and like a completely different way on purpose because it can be it can be very useful to appreciate um the differences but you can also plot them out in in in maps and you can also start putting for example the different clusters that you have highlighted because you have like of course your summary average um coverage in your area but then you can start seeing that within certain area you might have also clusters that have a better coverage than others for example so you might want for example investigate why there are certain clusters that have low coverage than other and maybe you have to do like any kind of like multiple activity to make sure that your coverage is is in is increased in in those areas for example and here for example you can also compare the AFI that were obtained from the dataset of the of the actual vaccination campaign and the ones that we were seeing from earlier from the RCM have perceived reactions from vaccination so for example this is the one that we were seeing earlier from the SIA dashboard and this is like from what we saw from from the reported ones from the from the RCM you can see that there are differences of course but then it might also be informative to better understand what kind of like a type of adverse event the community is understanding if there are like major discrepancies whether there is like a certain kind of uncertainty and whether for example there is like a big fear of of adverse reaction so they are over reported even though nothing happened so in general it's it can be indicative and can be very useful especially for communication purposes and to organize also informative campaigns in order to target as much possible as not as much people as possible and then of course we know that RCM information can also provide us with behavioral drivers so for example here we have like we can summarize the reasons for people not being vaccinated of course these on purpose I may I put all the possible type of information that you can extrapolate but then here in the map you can also dig in and you can start clicking and see the different type of clusters and communities that you might have whether there are any kind of pattern distributed in the areas just to inform also that the next rounds or in general like your health promotion activities for example so you can start for example digging in and you might know that like in this area most of the people for example didn't know about the campaign so what happened during communication these of course are like very simplistic and and and easy to to manage summaries but you see that like for example when you start having more information and you start digging in also with other programs and such it can be very useful strangulating all this information and for example also like what kind of source information they are using in order to be aware of these campaigns so in these days again I put all the possible information but then you might start like digging in this is like one type of map that we are having available within the system that it's like part of like the poor range of maps that we have you can start digging in and you can start skimming down for example if there are if there is any kind of pattern of of information that certain communities are using more than others so to make sure that for example for the next round for any kind of mock-up information the activities are actually being communicated accordingly to what is being used the most for example if in this area most people are using for example the purple is church for example and no one is reporting that they heard it from radios and such you might want to uh invest more into like getting linkages with with religious communities and such but I mean these are like very obvious thing for people who work in the field but this kind of information can also be present in your in your routine information system and of course these are also like other examples for example here on the left is this the number of children who were present during the actual campaign but they were not reached by activity so you can start seeing where the highest volume was of of children that were not reached and here you can have also the information on the number of children who were already vaccinated by the routine vaccine vaccination activities so by the dpi but independently from whether they were vaccinated or not from the sia so you can start having also this kind of information or for example here you can start comparing your your coverage as extrapolated from of course your routine dpi data but also as reported by the the rcm during during the activity so for example this is the coverage from epi and this is the coverage from from the rcm activity whether there is any kind of discrepancy whether you see that there are like problems in reporting whether it's reliable data and whatnot and maybe like a lot of people were saying yes but you are not able to see the cards so is you can't even certify 100 percent whether these data are reliable not but yet again this is like of course i've done it on purpose to like miss some areas i have like some discrepancy and things like that but it's just like make you understand how like a different layer and a different point of view of your routine data can also try to like trigger some kind of like data cleaning some kind of like question making let's say because if i start seeing that there are like substantial differences between certain kind of reports i might want to investigate further whether there is one side or the other or both side if there is any kind of issue with reporting or whether like these kind of questions are being interpreted and any kind of like reported answers there in the surveys and stuff of course this information needs to be run accordingly to protocols and such this is just the very like last step so the outputs from all the activities of course this information have no validity whatsoever if the the surveys and the and the rcm to per se are now following a substantial and and a well done protocol for implementation and same goes with with the the the campaign itself but you can start appreciating at least that all this information put together can give you an extra layer of information that you can start comparing within the epi and across the different and the different activities as well so um actually i was faster than and and i don't know if i mean i don't see any question in the in the in the chat itself but if there is a if there is any questions that you might have in actually maybe that would be a good time to even just share any kind of like even experiences that you had if you had any experience of bringing um this type of information directly in your routine hms if you faced any difficulty or um whether you'd be willing for example to start considering entering more of this type of of information in your hms for for extra type of data and more updated data of your of your areas and your coverage for example i don't know if like anyone would like to comment or give us an insight um um can you clarify there is a question can you clarify this from the chat for start of course like for vaccination stock as well i mean i don't have much data here because i didn't have time to enter mock data but of course you can triangulate the mock data as well like you have it here as well in our stock in the in the demo you have your stock data and you can start following your um temperature alarms where there is like in this case would have been just like here across the north because that's where our campaign was happening for example you can start checking in kind of like a call chain issue that you were having if there was any kind of west wastage rate and any kind of like stock status if there is any kind of like a um stock outs and such so also for micro planning purposes it's very important to consider your routine data and plan accordingly depending on on your on your comparison also different source of data um could you put aside cobalt and power bi and use dhs to totally it can be done it can be done there are of course a bunch of considerations of implementations to to to keep in mind and these are i think very well outlined in the documentation that we have put out a while ago it's in our documentation page and you find under the immunization program there is a vaccination campaign and there are three chapters one for use one for implementation and one for design where we give you more consideration on how to use the his in order to design implement a vaccination campaign and other type of of of vaccination activities directly in the system of course for example as i said before if you are running directly the campaign you might have like different um or organization units in the in the company that you have to like using the campaign that might not be available in the in the in the routine information system so probably you might want to have a separate instance that then afterwards you that can be used for real-time monitoring of the campaign itself and then you can aggregate the level so that it matches also your routine information system so that you can actually start using this data accordingly together with other other um programs but indeed uh if need be the his could be used from um actual micro planning and monitoring of the of the current status of the of the immunization activities in country or in a specific region in a specific district depending on the level of analysis and and can be used to monitor in real time it can be used also to evaluate or at least report the the outputs which is something that we've seen that happens very often that um especially when it comes to more updated coverages therefore like post surveys and such this information normally gets lost and therefore if there is any kind of updated number that can actually favor um the activities and give also more information it's very important to make sure that this information reaches the the right place so you can actually start using this information um where are some of them uh um i mean we have like plenty uh we have plenty of of implementation that has happened like throughout many countries we have like plenty of implementation that happened already for for vaccination campaigns in uh in uh in west Africa for example Uganda Mozambique and of course these are considerations right now we are talking about specifically campaign but many of these considerations can also be taken into consideration with other type of of mass activities so for example any kind of like distribution of the of the of the supplements of any kind of of the of medicines of course but can also be for campaigns for distribution of the others for example mosquito nets or or indoors plenty of indoors spraying and things like that so these are also considerations that can be useful in general for planning putting into place and monitoring and evaluating the impact of these more um shorter intervention one-shot intervention but that nonetheless can actually um help you having more up-to-date type of the information in in the system um these these indicators can be calculated and displayed on our website of course depending on the type of population that you are using of course that is very much up to the type of uh type of uh uh campaign that you are that you are for example implementing could be a national campaign could be a very targeted campaign for a specific subgroup of key population and such so maybe you have to extrapolate these numbers from another source normally in your routine HMIS system you also have your population data we know we know the the difficulties and and the problems with population data i'm not going to start into these one we have actually quite a lot of resources and other other webinars that have uh have covered these this topic and and deal with the population data denominators and and advanced mapping so if you want you can also go and check in our immunization youtube uh channel about these these type of information and but indeed you can you can calculate them depending on what kind of baseline and the type of population baseline that might be relevant for your activity per se and and of course as Anna is mentioning that these can have of course quite an impact on on logistics but like for example just to give you a an example for example uh Uganda has also um has been using the HIS also for their education information system and therefore for certain type of campaigns they were using as a site vaccination site as schools and therefore their target was also based on schools and attendance and it was based on the attendance sheets that was coming from their education information system so they already had their baseline data for their denominators and with these baseline data they already were able to put in place a micro plan that consider staff that considered logistics and and all the relevant information in order to target the right population at the right time so there are plenty of of of information that you can triangulate and plenty of information that you can extrapolate and use as your as your denominator and baseline depending on your activities so how can I access the data collector from a specific country by DHIS but UIO and any all the other heaps that we do not have access to country data DHIS we provided DHIS as an open platform I mean it's a it's a it's usable downloadable and configurable by the different countries and the different type of implementations but once it's implemented in country it's fully owned and managed by the country itself so I have no data no one has data from countries actually unless you are actually part of the HMIS or MOH or Ministry of Education so there is no way to extrapolate this data belongs to the country and it's only managed by the country if there is any kind of coming out is because the country has any kind of deal and and an agreement with either a university for research or for a paper and things like that but we do not have access to data and I want to reinforce that because it's a common question that comes out I don't know if I missed any kind of yeah this is like actually a nice one if there are any dashboards in the HS2 there are interactive and could be shared to public or only to DHIS to users of course I mean the interest to users depend very much on the type of setup that you have in country so different users have different rights so you can either see your dashboards and there are some users that have admin rights and you can edit the data sets have more administrative roles and be able to like configure and and do it more like a in-detail job directly in the system and of course you also have like data entry rights for example where you only allow to enter data but not edit this type of information and what you are collecting for example so you there is no ability to change the metadata we have examples of for example open public portals that are using data collected through DHIS that's an example actually in Tanzania there is like even if you google it there is like an open portal coming from the MOH that uses fully the dashboard from DHIS and all the information that you see are actually coming from there there are also other type of more customizable dashboards that countries can also implement Uganda often uses for example customizable dashboard that might be a little bit more interactive these are like truly like the core functionalities that you're seeing now and and it's very much depending on the type of implementation different countries have had different solutions and actually public access is something that that's requested very often either because of different uses of the data or because different people have access to so many different type of of platforms that it can be a bit tiring to find every time the user or the password and such but also for transparency and actually accountability so for example the Tanzania MOH has put this data that are available to the public and the public can go and check these numbers and these are full numbers that are fully coming from the national HMIS for example so there are actually examples out there and I would suggest like checking out for example in our community practice or in for example the impact stories specifically for these use cases in Tanzania it's actually coming from our latest webinar that we had so I would suggest checking it out because it has had quite an impact in the reliability and actually the accountability of data in Tanzania because users start flagging if there is any kind of any kind of like possible mistakes say if there is any kind of gaps that they are seeing and like it really triggers the population to be engaged and up to date with the numbers that are collected at national level. Do we have access to the demo side? Yes of course if you if you google if you google the the DHS2 demo and you you check it also in our in our various page and documentation you have access just mind that what you see here it's HMIS depth so this is still in the in our development instance it's simply because we haven't put it into the the public demo quiet but it's a matter honestly of days so yes you will have access to all these dashboards there are already plenty of dashboards of course in the demo but the specific ones of the SIA it's a matter of of days really. I mean we only have like a couple of minutes left just to remind you that if you have any kind of any kind of question doubts that are coming like with retrospective thoughts about what you heard today again inside experiences and such that could be relevant to share also with the wider community at the beginning of the chat we have also posted the community of practice link please go ahead and and post the questions there I will definitely follow up directly in the in the community of practice with with extra kind of information and also relevant links that could be useful also for you who follow up so for example the demo I can put there I can put like the links to the documentation that we had already put together for the implementation they use and a design of the HHS for SIAs but but yeah the main purpose of this of this session today was just to show you what is coming up in the demo itself just to make sure that potential mentors and and and users can really understand a little bit more like pragmatically what they can achieve and what can be obtained directly from HHS and yeah because sometimes it can be a little bit abstract to just think about then I only entered the the coverages how does it work you've seen it it can totally be it can totally be done and you can then obtain all this information like this information here that it was like from the coverage vaccination survey it was entered just with the coverage is coming from outputs for example and with the idea that you can recycle what you already have you can keep it very simple and and and try to really take advantage of your integrated environment truly make use of these numbers because we know that we have like a lot of data out there we know that there is like not enough data use and and yeah these dashboards can really help you to have a glass of extrapolation of data that can really help your informing your programs so with these I thank you very much for for being with us today all the resources will be available including a recording the slides and and relevant relevant links for you to to further investigate the use case itself so thank you very very much again and I hope this session was useful for you and again please feel free to reach out either directly in the community of practice or send me directly an email and happy to to follow up with anyone would be interested in the use case or or who has like a any kind of like a relevant use case that want to share to give also a little bit more sight of the implementations that are really happening thank you very much again and well I have you're having a wonderful remaining of the day morning depending on where you are or even thank you and until next time bye