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Optimizing Last mile Vaccine Supply Chain in Northern Nigeria using FOSS4G Solutions

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so all last presentation was going to be about a new application in the in the field of medicine or health and that is going talk about the last mile WorksIn supply chain in Nigeria so the floor
is yours good afternoon everyone my
and my name is Danny I need the geographic information system on data for eHealth Africa and the other Africa is an NGO with a profit company based in Nigeria without headquarters in northern Nigeria and we work across from West African countries Nigeria Guinea Liberia serially on and we support public health interventions using data-driven technologies in different um African countries and Mobley talking to us today date about what hell Africa has done using open source GIS tools to optimize vaccine and delivery and of supply chain management in northern Nigeria some our focus is going to be in kind of states and which is a state with about over a million people in the population and so there is there is a national challenge and there is a national Boolean Nigeria to have about 87 % of vaccine coverage from across the country and currently and that is about of all 41 % and and if we're going to meet that challenge and it needs to be done in the supply chain system in the country and so and that's because the new challenge here is this is mainly in in the supply chain system the availability of commodities before or to support scrutinization activities within the country them and to do this and a lot needs to be done in terms of building capacity at training training
staff and bomb why importantly in optimizing the transportation network and the and the country and so over the
here's an eHealth Africa and we've been working with the kind of state governments and to to supply vaccines to health facilities within within the states and currently and this is the structure and this is the distribution fearful fluxons in kind of state and as you can see the candidate pool and and vaccines go directly to the States cold start and from this state calls against distributed to and so now stores the above 6 on our hopes and then you start having and different and more detailed distribution of state stalls going directly to allergies and then you know someone going straight to hell facilities and um so with vaccines you need to store them in which include coating equipment and 1 or the other challenges is not all health facilities have coating equipment so that gives you some you know it pushed plus system whereby the boxes get delivered to health facilities which cold-chain equipment and then held and those that are classified as parent held facilities and those without coating equipment then go on fluxes and from the there and help facilities that have and coaching and equipment and this happens sometimes you know on a monthly basis from the coastal and then and the
end of rule system from child facilities and you know to facilities it is sometimes happens on a weekly basis and the this all plots and that we worked on with the kind of state government lasted this the difference on some in in kind of state we deliver these numbers of deliveries last year and using optimized and supply chain and transportation consistent
so this does explains the current challenge is that that I talked about in recently but you know the the the the system I just described as a lot of challenges in the 1st is you know the the complex and system whereby yet delivering to multiple the true multiple distribution and and the and the way you do this you know the disorder System is not optimized rights and continue doing this leads to high operational costs and the transportation when is not optimized if you spend more 1 of the few and you having multiple tests and they're not needed generate the similar over administrative and the challenges you know stuff that are not needed at different levels so well we've been working on is to see how we can work with the government to optimize on this system to reduce some of these challenges and make vaccine model label at state level and you know by reducing stock-outs and at these facilities
that is what did we do come with open source GIS and 1 of the 1st thing we did was and the roots of some optimization and delivery scheduling and we also need to you know some network optimization using GIS and and supported Visualisation and Analytics and using the open-source GIS tools
is that it's all of these are some of the application and tools that we employed and to do this so we we use the DGU GIS to come to the routing Analyses and and data management and working with that on on posters and that 1 that actually was used to call information and you know and we could we leverage lot on OpenStreetMap platform and and some of these and you will see how we use them when I start to talk about the methodologies that we that we used now in
doing these we also made some assumptions and goes of because of the unavailability of data and in northern Nigeria we had to build in a lot of assumptions for example and I doing routing and that in the there is no standard and some information from about some of the things about the and speed limits for example well on different routes so we and we use the term OpenStreetMap on others are into buildings assumptions on on speed limits for different road classifications and 1 of the other here challenges in northern Nigeria is the issue of administrative boundaries and so what we did was to leverage on some of the work we've been doing With the polio eradication program and we we've built operational world boundaries based on the data we've collected from different so we also use this as options to get our administrative boundaries for clustering and the difference of yeah so also
we also did so and with this idea of some of the terms that we use so for example from a cluster a cluster is the total number of deliveries and then there will be made within the working hours and during the day and I know that this is the like and so on 1 of the things that we do it in the and the the sound and delivery system work is because of no cold-chain equipment and and in some facilities and some brick walls on some facilities of underlying gets so polite and uncoating equipment which means you have different possibilities added to your delivery schedules by someone and the large band comes into play because you can't have if more than 2 days in 7 and b to your previous delivery and your next delivery so if I deliver on Monday last month and I can deliver liter that undermines the the following month so it's it's in minds so when you're doing your scheduling and you need to factor that come into the analysis and we also assumed approximately priority in the based on the fire this paradigm facility and I'm in a particular class that takes higher priority than the 1 there is closest to you and and this
is the methodology that we use the 1st thing was to get some data to build a
business and there's is a huge debt of on Bayes maps staying in Nigeria and so 1 of the things we've been working on is to leverage on the OpenStreetMap to do this all took about them and then we do the analysis of you know the routing this and the transportation optimization and then when there was no we builds dashboards to share the data and and that then gets carried and the that gets used by the drivers to take the truck seems to follow the scared drops and to deliver the vaccines through different facility now including the Bismarck we'd like a so leverage on these OpenStreetMap left and this is an example of what happened in Canada so when we started and this is what kind of looked like and we had to basically among all the roads and buildings and In all over 200 thousand kilometers of roads and mapped to support this project and they also want to digitize only in the and thousands of of buildings to and to get the project started and once the roads were digitized and index was to unmarked the health facilities in the challenging Nigeria is there's multiple leased out there you know short of coordinates of the health facilities to be delivered so we needed to ensure this and so we leverage on existing lists and harmonized and get I use the open directory and to send them so it if you would like to confirm from the coordinates of health facilities we also adopted the satellite image analysis techniques to understand and value of health facilities and doing these we reduce the amount
of data collection we needed to do on the field so changing the different characteristics of both types of what they facility should look like and the size of a health facility based on the classification of the help facility with able to streamline I didn't a collection and validate dates we're held facilities are among the bond issue
in understanding the parent-child facility relationship we had to do some cascade mapping and to understand the relationship between it by the end of hostility that has a in the equipment and the different child health facilities that should be collecting vaccines and from their health facilities and this is the result of of the cascade mapping that we did so this is classic and GIS and benefit analysis to understand relationships between the different points on the map and as you can see we have a mean and then leveraging on the boundaries and what we notice was a lot of some health facilities for example we're teaching from there and facilities is that we not we didn't understand what's on from them over 80 % of them these are all of them administrative challenges that there needs to be looked into and these
are samples of the Dutch quotes that we built and so did the software team that had the organization building logistic and management information system and to share the data from all the results of the analysis done and Mandarin drivers can take this to diffuse and can use them to to to to to to plan their roles and at the same time to collect data about vaccine availability and to plan for on this time next under the phrase
and once the that that is done and the next thing is the drivers and then take the information and go to the field and go altitude to each facility to to deliver developed and now in doing these again and 1 of the assumptions that we made is that the drivers would be of good behavior and Bo and the drivers that company drivers and that we we know that will be of that behavior and for the exact role that we've generated for them to deliver the and these are the
outputs of OK I think that I missed a slide here and so
this is the result of this scheduling and that we do and so when the when the latter incorporated we cluster each of the facilities to be delivered and that for that for each of the driver to deliver to on day to day business value you see a result of 2 different clustering here so this is what it looks like I'm at the end of the day when we and create the results for the drivers to to deliver the flux and and
using this approach and we were able to um improves the boxing delivery rate and it's a little about and 98 % delivery of some success rates and at the end of today then the floor tiles on deliveries from all but all but the you know different rounds of deliveries we went and we've delivered to the left of a thousand deliveries to different facilities cross and 35 around and you know as a result of these we've we've delivered over 4 million doses of vaccines from within canister and in doing
this and gained there challenges and some of the challenges 1 of the main challenges like I said is data and for example traffic data when building an effective from we should building on traffic data but because of the unavailability of that data we have to just be within our assumptions we know traffic information and so you know in the future 1 of the things that we will be doing is to actually find effective ways to collect traffic information and built and into the current system and 1 of the challenges aging but where at the Lorillard infrastructure and due to seasonal allergies so when you create around for a driver for example to to leave of absence and if it because of the rural areas and the performance in sometimes a driver against it and the rod has been washed away and they can't move across the the baby need to find other out and sometimes some of these cause delays and you know indeed scared and that's it
thank you very much thank you very
much for this great presentation of each questions from the audience comments but in the end I would have 1 you said you were working with the hot always M
community the is it is it easy to get people interested in in an initiative that is more like a long lasting only compared to like single disasters that hit that get a lot of media attention or is easy to to get people working on on your basement up through the heart was and platform that's a good question so but most of the base maps that we build on the OpenStreetMap platform will be used by our G. Iisten so this is 1 of the challenges that we had some of that especially in Nigeria and we're trying to what we have some hot and that's what it is because there is no way so if it's not an emergency for example you hardly gets the higher traffic of people and you know working on on on your test on the OpenStreetMap and and this this is why if you look at Nigeria you find out that you help Africa is 1 of the largest and contributors to the OpenStreetMap blood from an for example over 60 % of the roots in Nigeria with digitized by us most of the settlements and that you see on OpenStreetMaps us that means that we have contributed to the OpenStreetMap platform as part of the data we generated and from the from the you're education program so yes it is as been a real challenge and we're hoping that you know in the future his for sustainability of the mapping in Nigeria we can get more traction and get more and you know comminuted based mapping in these areas have many people working with you and and IGI esteem and not detecting is about 21 thousand in total and so we have a fairly large and efficient and that works on the spot if I can't comment on that mapping the universe their work also for encoding and that's uh so the fence and then there's 1 leaving 1 afternoon 1 evening and then a lot of students for mapping and I shall the pictures were about 50 but that's 1 evening and 250 thousand kilometers I don't think they're doing 1 the things that you can get some people that only if you promise and also speech and stuff I chiefly yeah and and then to answer that yes I I agree with the university mapping and so 1 of the things we did is to and do which universities in and northern Nigeria and we really bring their students together on integer program and get them to do so mapping ball believed that and you are limited to people that leave wood in a particular area to to do that mapping right so you know that works for a few months and then and again there's a knowledge gap which is 1 of the things we're working on building the capacity of the students and to be able to do efficient mapping and generate quality geospatial information so how about them lowering the bar for the people actually get into mapping and then coming crowdsourcing is realized that good and you will like go to the government and announced from ground to give a monitor incentive for people to map interstellar matter you know and then and you have people mapping however that if well known in dealing with the government is is not very easy and so 1 of the challenges in Nigeria is it's not sometimes I said skew but not just a skew problem there is also a coordination problem right the different agencies in Nigeria where if you you know you you it's not easy to go to anyone and and get some information you know out of them and some of them don't even have that information about for example what we've done and with the help facilities for example is to work with the and Primary Health Care Development Agency and they have been really cooperative and they've done a lot of mapping and themselves up and 1 of the products that we've been able to build em out of that is the possibility registry and for Nigeria that house all other facilities you know within Nigeria and we get different classifications different services rendered I will be with Application Programming Interfaces are around on these and for easy and integration into other applications and we have in mind interferes around is that the government can use for and decision support on on the interplay services integrating all the different settlements they found that pollution data that we've collected and generating means and all other sorts of just Bayesian from analysis for for decision support around the health sector so yes there is a perfect example of how to work with the government however full information like roads buildings and these are things that are not generally readily available and the ability to adapt most and we tried to work with the government sometimes I what they want to do is to sell that information to you and to do you know I'm interventions which is not an effective way to to produce have the bite one's right yes well how do you keep it up-to-date so and and some sort of crowdsourcing using like cell phones just installing a with the people and you know have and then running in just so you know and then they get paid for that something like that some some some sort of crap they crowdsourcing basic is not something that now we should really look into you know giving incentives to so people to to generate as much and then you need to also be you know we need to their work we talked to you know to get good validation mechanism because then is not just a question of you know generating um and the
geometry it's also confirming that the the information and the actually is generated with these data is correct this does not just been marked for for monetary for about thank you thank you only as a problem was just justifying 1 so you can put a bomb on OpenStreetMap if you don't have the proper license so some of its it might help but then you need to have the proper license terms as well the more questions get basically just a general question about the Nigerian government under the southern African governments for any of you mentioned working with open source software they become incredibly paranoid and ask how securities and that might just think you've down just because you mentioned the word open source as want to know what is the Nigerian government's stance toward open source in general well them the method of questions and and it stalled because I'm I'm not very widely experience working with the government a ball with the set of with the health sector and and you know we've got with the government more within the health sector and I think this is not the case and the the governments and in that sector there open to some open-source tools and their writing from the the embracing state they did do get paranoid about this and especially when it comes to hosting the data but in terms of leveraging on open source tools to collect data I think everyone is quite informed about these strategies so what would you know if you need to know and that that that also you know addresses the question when it's over source or something you know you start talking about the security and I think the key is understanding that there is some data out there that will be open and there is definitely some data that should not be open and it's you know making them understand I still generality information for example and the outline of the building there's no reason why it should be open but by the time you start talking about the owner of the building which goes into more of you know about management of land administration issues and those are things that you probably should not be made open book I'm not sure I'm not sure what the governments and perceptions of this is we don't work around but generally you know for generality data that should be open and I think it's it's quite it's quite OK it's just that you know for people don't have the time I think is not just the government now for even for although vendors that have data some of these developing being kept and outdated but people still want to keep in mind so that when they should be made a more so they should get an object this area but all what what is your plan about expanding this are you you just started with Nigeria and so are you and going to do this in other countries in every color which J. R. adjusts the collective experience in 1 country and then we could explain around this is the and you talking about the data all at the transportation optimization I mean the the entire project all their hydrogen so so we just make the is a case study for all kind of state ball actually we've been doing this in all the states in Nigeria for example we do the same and in biology state we are planning to expand to other states in Nigeria we are working with different partners on building and supply chain models to actually optimise and the whole network supply chain network system for for these different states and we have offices also in in other countries for example we are working on serial on with the government and to optimize the laboratory and you know transportation system and we will we will we the plan is to expand this into other countries it but as they need be you know as you see the need in other countries we're we're working with the government to expand this and you know and again this the full mapping for example we've just finished marking a pleasing thinking it is small town again you know they can all the points of interest generating all the time bayes marginal cost response to to support the different projects that are going on so it's an ongoing process so yes so he's taking the lessons we learned from different places and improving them and then deploying these and other countries where they are needed I have a final question on when you when you do the the digitizing of the basement you probably use of satellite imagery rate on how do you get a good quality images you have partnerships with Stella providers or so so we've been working with B the gates foundation for example on only eradication in Nigeria and to do these we've we've got supports with satellite imagery and to do some automated feature extraction and also to to actually be within you know and tracing and wrote tracing on on the OpenStreetMap so yes we do get some support and work with partners to get satellite imagery and sometimes we use imagery alters 0 . 5 1 centimeters and 0 . 5 meters rather that's 5 centimeters and sometimes walk with 5 meters high resolution imagery depending on what sort a label on the application and it's needed for so the answer is yes we've been working with the OK I think your thank you very much
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Metadaten

Formale Metadaten

Titel Optimizing Last mile Vaccine Supply Chain in Northern Nigeria using FOSS4G Solutions
Serientitel FOSS4G Bonn 2016
Teil 118
Anzahl der Teile 193
Autor Sonoiki, Dami (eHealth Africa)
Lizenz CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/20372
Herausgeber FOSS4G
Open Source Geospatial Foundation (OSGeo)
Erscheinungsjahr 2016
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract In 2012, the Federal Government of Nigeria launched the Saving One Million Lives Initiative aimed at expanding routine immunization to 87% coverage that will protect over 6 million children against vaccine preventable diseases such as measles, meningitis, polio, tetanus, hepatitis, yellow fever and tuberculosis . In 2013, the GAVI Alliance approved US 21 million to help improve vaccine supply chains . Despite several innovations and initiatives to optimize the supply chain and storage of health commodities in Nigeria, there are still known gaps in the supply chain in the health sector in Nigeria. Achieving a good coverage of routine immunization is almost impossible without the optimization of last mile delivery – especially in the hardest-to-reach places of Nigeria. Geographic Information science (GIS) techniques play a big role in the design and optimization of last mile delivery of vaccines down to Local Government Areas and health facilities in remote places. This presentation discusses how eHealth Africa has implemented FOSS4G solutions to enhance an effective vaccine delivery system in Kano State, Nigeria; the second most populated state in Nigeria using open source tools like OpenDataKit, QGIS, JOSM, OSRM. We also discuss the importance of OpenStreetMap and open data to replicating this in other countries.

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