Hi everyone, thank you for joining us for this talk today, I'm very excited to demo our
prototype climate health pulse. My name is Tali and I am from Data Kind. I have a few other
colleagues here as well from Data Kind. We are a global nonprofit organization and we have
a reason for existence, if you like, is mainly to close the technology gap that a lot of social
impact organizations face when working with data science and working with AI and essentially
we, the way Data Kind works is we collaborate with amazing experts like yourself and we work
with highly impactful organizations in particular sectors to then design and develop products
that are sector facing. So it's not really focused on just solving bespoke problems for a partner
or social sector organization, but really trying to understand the pain point that impact the
sector as a whole and then work with a few key actors to actually design and develop those products
that are then available as open source products to the sector as a whole and it is it is our
mutual commitment to building a more just world through responsible and ethical use of data science
and AI. So the solution that I am going to demo today has been developed in collaboration with
such partners. So I want to just name them, they are not here by I want to highlight Spectrum
Africa. This is a Canyon-based organization that they work to bring together multiple sectors
to work together on uncovering sort of the hidden connections between climate and health.
And climate health and also generally well-being of people, which as we know has more implications
than just health. I also want to mention Jackarander Health. They partner with governments,
public hospitals, and with pregnant and expect, well, pregnant and new mum directly to design and
develop products to support their mum's on their journey on the pregnancy journey and through
motherhood, the early stages of motherhood. Their flagship platform is prompts and prompts
supports over nearly actually nearly 3 million subscribers at this point, new and expecting mothers
through a two-way SMS messaging platform and they basically send stage specific messages to
those moms and they also respond to queries from those moms. And we wouldn't be able to show you
this demo today without the cooperation, without the support of Cardiota County. This is a
county in Southern Kenya, the Ministry of Health specifically from this county, has partnered with
us to be able to share their data and show you what's actually possible to do now.
Just a little bit about Cardiota County, it is beautiful, it has valleys and plains and it's
sort of a narrowed kind of climate, home to 1.1 million people, mostly for the majority, a
pastoralist community. You might have heard of the Masae, Mara, the Masae, they predominantly
live and work in this area. And they face their share of climate challenges, they really struggle
with heat, stress as well as water scarcity. All right, so climate change in Cardiota County and around
the world is really, it's a current and escalating health crisis. We have recurrent droughts,
we have their ethnic rainfall, heat waves, they directly impact health. And in some cases, climate
changes sort of impacting the, it's increasing the risk of certain climate sensitive diseases. In
other cases, it's changing the way those diseases manifest, it's making it, the seasonality of
those diseases, it's just making it harder to anticipate. When certain diseases will strike and
what to, and to what extent. This is known, we know it, health systems overall around the world,
they know it. And at the most local levels, they're facing health systems are really facing the
strain of these health crises. So it's no longer a question about how we use climate data,
how do we better use publicly of it, you know, just how do we use climate data for health, but
what can you do with that, what kind of action could it support? I just want to mention that
the way we build our products, our data kind, it is based in understanding the needs of the
sector, I mentioned that earlier. So last year, data kind spent about 10 months in dialogue with a
variety of different stakeholders, including data providers, including developers, including
folks who are actually in the decision-making space when it comes to health systems, researchers,
policy makers, and so on. And also actually frontline health organizations like Jaco and
the Health, who partner with governments to assist in strengthening those health systems. So
we spoke to them there from across African Asia and Europe mostly, and some in North America.
And what we found was that health stakeholders, especially across the, you know, thus global
south, definitely recognize the need for an actively seeking climate informed health data
to support the decision-making processes, but despite that demand, there is a critical disconnect.
I think this was actually mentioned in a few presentations, but the data is actually quite segmented
and the formats are such that it makes it quite challenging for folks in the health sector
to use data from let's say the climate sector. Also, the climate health, yes, have generally
traditionally worked sort of in silos. So bringing them together is one part of the problem, but then
even when that data is available, that localized near real-time climate information is rarely actually
integrated into existing workloads. And that's where it could actually have the most impact.
So for what we found was that this really represented a significant missed opportunity to strengthen
health system resilience through better integration of the data.
You can actually, if you're interested, you can read more about that report and the challenges
that limit climate and health data integration in our landscape report, as well as
there, we've also noted some promising innovations from around the world. In our report,
you can use that QR code to get there. And what you'll note in the report is while the ecosystem
is fragmented and siloed, government agencies are absolutely central. They are absolutely central
to enabling that data access and the data utilization. Questions around data availability,
interoperability and capacity, they are persistent, yes, but actually solvable.
And while resources are limited, we can still focus on feasible, cost-effective
open-source approaches to strengthening existing capacity. And this is coming at it from an equity
lens as well. So we wanted to find out then, okay, what if we were able to actually integrate
climate and health data? Rather than developing some kind of a narrow tool, we are working on
an integrated platform that can potentially unlock that more ecosystem's level thinking. So
enabling more sectors to come together, more sectors to collaborate, bring their data onto
onto a shared platform and enable iterative learning. So that is our first step towards that is
climate health pulse. It, in its current form, it brings together climate data and health indicators
at a granular county level. And it actually levels below that. So in Kenya, you have the
national, you have county sub-county and ward level. So this is looking at county and below. So
more, as localised, we can possibly get when it comes to administrative boundaries.
It exists in this iteration again to make climate and health data useful, usable, actionable
for local governments. And really understanding risk is one part of it, yes, but also opening up
those opportunities to act on it. What we really wanted to show is that pulse is not just about
like a data source, so bringing together some, you know, a couple of layers, but it's really
about layering on multiple sources of information from multiple sectors to create that actionable
climate related intelligence on which you can work. Pulse is a solution that is designed to
evolve with new partnerships. This is, this is by design. We're working on it with that. It is
we need new partnerships. We need new data. We need new indicators, new geographies. And I
think we are here for our data kind and part to learn about other tools as well that we should be
aware of that we should be using as well. It's being developed as a free and open source
platform. A data kind we do see open development as the most effective way to maximize transparency
reuse and adaptability. So really ensuring that the tools we build can be sustained, improved,
and deployed long after sort of individual partnerships fade away and, you know, and that
ends. We're also developing Pulse to digital public good standards. And there is, I think,
if, I don't know, if anyone hears from the DPGA, right? The digital public goods alliance,
they have a stand outside as well. It's a really important, particularly when we're working with
government actors to enable them to use open source software. It is an important sort of accreditation.
There is a trust involved in using digital public goods that are open source tools and platforms.
So we use those standards. We devote to those standards with the goal that even if it isn't
certified right now, it's on the path to that certification.
Okay, with that, I'm going to go into the demo. Let's see, I'm going to do this a bit backwards.
Right. So we can just get started. This is the main page. It just contains, you know, the
information that I've already shared with you just now about the project. As we scroll down on
right here, you get to our featured prototype. And basically, this does actually, because we're
talking about Cardiata County, just wanted to highlight, again, the different data sources,
so in front of managed two screens here. Okay, we have health data from the Kenyan Ministry of Health.
This is private data. So it's not right now. If you try to go on there, you can't actually access this.
This tool we are working on a open data set that's going to be available and then we'll be able to open source
project here. We also have not just sort of health, you know, from the health informatic system.
We have the data, but we also have facility list. So really getting down granular to the actual
health facility, whether it's a clinic or hospital in Cardiata County. There are 116 active
government facilities in that county, so we have that level of data as well. All anonymized, we do
not look at the actual patient records right now. We have climate data source from Copernicus, so
for the last group that was here talking about that. It's the error five data set, which is a
really crucial data set for climate research, offering consistent data on temperature,
precipitation, wind, and more at a fairly good resolution of 9 by 9 square kilometers.
And we also have actually four cast data coming in from the Kenyan meteorological department
directly. So we're actually working now closely to build that local relationship with K-met
and make sure that they are the ones who are also providing the climate data.
And then finally we also have the Jack Render Health prompts data that I mentioned earlier.
Mums from across the country are asking thousands of questions about their pregnancy and
about their babies. It's a really valuable data set. They've shared some subject matter
classifications of those text messages aggregated at world levels and different levels.
But this data gets us a community report. It's really the first time that government,
we're working on a tool that not only has a government data and other kind of globally available,
like open data sets, but really trying to bring in community data as well. So how people actually
experience climate. So if you go into the actual tool,
all right. So this is the overview page here. We have essentially four layers of information
that you can see. It's not showing the rest anyway. It looks a bit different on my computer
than there. So this essentially shows Kajada County's map. You see the different world levels.
The color to represent sort of the disease incidence rates and so on is just a fairly straightforward
and easy way to visualize patterns. Identifying hotspots, that kind of thing. It's super helpful.
On the map, you can kind of see, I think, the health facilities we have like the actual,
so you can actually see we've got the health facility locations mapped on as well. Right now,
this just has very basic information about that health facility. But for the actual government user,
the end view that they would have detailed information about that health facility,
including specific for specific indicators, what are the rates and so on.
Okay, and then next down below here is a simple line chart. Again, just showing that health outcome trend
for any given indicator. Over time, this is also extracted from the Kenyan Ministry of Health
Information System. On the here, we have climate information, so total precipitation,
average daily max temperature. This is that Copernicus data set and then below that a simple
line chart again showing those community reports, the prompt data and you can actually like search
those as well and kind of look at the trends there. Some additional features involve, you know,
you can actually select an indicator. So again, you know, you can actually pick your indicator.
I'm going to start looking at malaria in a second, which we know has quite a bit of
relationship with climate. So there, you can look at it for
different time periods.
Really see what's happening. This is now over almost three-year period.
And depending on what you find, it's got a few other key features just to help. You can download
these images in different formats. And yeah, so you can look at it and you can actually drill
down further. You can actually go from that county level to different sub-county level or
word-level even and look at the data more locally. So if you look at malaria, you know, you
set that data in. You can actually have a sense of what's happening over time when you play
it out. And you'll just see that there's a the north part of the country. This is just based on
this is a normalized data set from what we received. You can see the north part of the county
really in general has particularly high confirmed cases. It's a hot spot for malaria.
You can get closer into that and drill down and see what's going on particularly in
this what here you can zoom in and have a look. And the end user would be able to actually see
what's going on at the specific health facilities. The more important thing is though on the right
you actually get to see what's going on and what's been going on with climate, what's been going
with temperature, what's been going with rainfall over the same period of time. And you'll notice
that all the other layers are just, you know, as you sort of drill down all the other layers
also adjust to give you the data about that specific what an area.
Trendline really helps to see and understand like okay what's going on this looks like a
particular, particularly high peak. You kind of see where the where the trendline starts to
ensure an increase and you can actually start to just even that even just trying to look at
what's happening with temperature and rainfall when there is a known where the trendline from
malaria cases increasing. This is something that the local government acts is just weren't able to
do. It was just not something that they could look at on just one simple dashboard.
Further down you can actually have some use cases where we worked with the Cajillo
to county health officials to look at specifically when there was a drought, what happened to
malnutrition cases during that drought, what happened to rainfall and start to actually study
and see what are the types of issues that months were raising during those specific events
where climate events. I'll just show you a little bit speaking of that the prompts data here.
Oh, look went to the wrong one. That one.
So this enables users to also explore the issues cropping up on prompts at the time.
It's an opportunity as an institution to actually learn more about community issues during climate events.
There's an amazing study that Jackaranda Health have done that actually that's published
that shows that prompts data tells us about how those millions of moms are experiencing
extreme heat and we wanted to include that here as part of the perspective to inform the public health
actions. So in this first set of visuals, basically we can investigate the relative distribution
of those intense in sort of different temperature buckets and bins and we can see the changes
in frequency of those specific intents. In general, I would say the questions about
what we found questions about in studying this further.
Pregnancy, newborns, questions about intercourse, questions about pains, swelling, urinary tract
infections. Those kind of questions actually we see there isn't increase at higher temperatures
in those buckets and the effect is modest but it is statistically significant.
A couple more examples again when you search for different intents that we know are related to heat
events or we suspect. There is a relationship here, for example, moms talking about swelling
or a demo. When temperature climbs above 30 degrees, we see nearly 70% rise in those types of concerns.
We see a 23% rise when we look at questions about labor and in other kinds of
different health indicators for instance with children and babies, we also saw similar kind of
relationships in the frequency of intense around things like diarrheal diseases, fever spikes,
inability to feed and so on. What we found basically what we demonstrate to
and this is again think about the institutional user in mind what we are trying to show is heat
isn't just uncomfortable for a pregnant woman, it's changing what they need from the health system.
So this is an critical input that wasn't actually available to
those health system managers until now. I think this is also consistent when you have extreme heat,
when you have a climate event, engagement with that platform increases. It's also part of
the contributing factor as well to the kind of things we're seeing. But beyond just looking at
sort of existing historical data and what's happened, the county health offices
want to use data like this to think about the future and think about what can be done to
adapt to build resilience, what strategies are open to them and this is where we were actually inspired
by the work of Carriette Al, I do have the links available as well. We use generalised additive models
to understand the relationship between temperature and precipitation and maternal and newborn
health indicators. This was actually developed, this methodology was developed in the DRC,
it's open source, it's available and we we use that in the Canyon context. To forecast
the relationship between temperature and precipitation from Copernicus and the health indicators
mentioned here. You can see there's a predictive forecast space that's really right now has
a lot of uncertainty associated with it, it's currently not necessarily actually useful in its
current format, but this is another area where I think we're very keen and open to have partnerships
to bring more relevant data, that more relevant models validate those models and actually make
this, put this in front of decision makers to think about really what can be done, can we think
about more disaggregated ways of supporting health systems? So yeah, like I said we are looking,
this is all about partnerships and cooperation community, we develop pulse and we're starting
to develop those partnerships and community around climate health data integration, modeling,
validation, please consider joining our community, you can select, you know, we have some options
select ways in which you'd like to contribute, or suggest ways in which we haven't even thought of.
I mean, we definitely need a lot of different expertise here to actually build this into a
project that not only serves Cajillo to county and all the counties of Kenya, but actually
make this into a project that can be then adapted to multiple different locations, again,
really localizing the way in which we look at climate data and other sector-based data like health.
So with that, I'll say you can check out the website, but sorry you can't see the tool just yet.
Yeah, and thank you.
Any questions?
Do you provide public data sets for download?
We're working on it, but actually we are for demonstrative purposes, yes, that's what we're
working on, public like open data sets just to kind of see what the tool can do, but I think it would
be quite interesting for us to actually provide public data sets that can be used in specific locations,
or that can be tailored to certain locations. So yeah, we'd be open. Thanks.
Hey, thank you for the valuable presentation.
This career is dependent on you. What is the decision that this either should take in that
particular case? What are the conclusions we can withdraw from this data?
What brought us to build this in the first place? Is that your question? Sorry. Yes, the
Yes, and this with the data we have now, what can we do with it concretely?
That's a great question. So part of why this came about in the first place is because of who
we are as an organization. So we have been working to support frontline health systems with open
source tooling for the last six years, I would say, and one of the major issues actually that came
up early on was around, we have a lot of data, we have digital data, but we don't trust that we don't
want to use it. So data quality was usually what we'd come up against before we heard any other
issues. So we initially started working on tooling, which is actually a digital public good,
it's open source, it's called the data observation toolkit, or dot, and that is available right now
for energy and use by these health systems. But as we got more interactive with the health managers,
with the governments, more explicit use cases started to emerge, and this issue around climate
is always there. Some governments are further along in terms of having a climate agenda.
Kenya was particularly receptive to have a strong policy, but also want to show tooling that
kind of interacts and informs that policy as well. So we found this really favorable environment
for working with government partners on this. Plus, it also provided us, we've been working for
several years and actually open source, well, it's a methodology that is now open source and
available, but how to actually pair climate and health data. So we've been working with the
UK's Office of National Statistics, along with Ghana, Rwanda, and a couple of other places
on that methodology. So this was an opportunity now to start to bring all of those pieces together
on a platform that could work on this. And then I think the second part of your question was around,
sorry, you're going to have to remind me second part to say it all out. What they can do with it,
thank you. So our partners in Kajeta County and from other counties who have so far seen this,
the kind of actions they're keen to take is, for instance, being able to understand a little bit more
of the specific variants of the diseases and the relationship to the climate to think about, okay,
where can you send certain drugs in advance? Where can you impact supply chain, for instance?
What can you do to support the community health workers when you know there is a spike coming up
of certain types of diseases? That's one thing. You can also look at the community reports to
understand the kind of questions that people, the community itself has. And so start to engage
your community health promotion campaign messaging and so on, based specifically on the data
and information getting from people themselves. So there are quite a few operational things that
can be done. It's a matter of now seeing if they have the confidence in the data to be able to take
that forward. Hi, yes. On that slide, on the ways that contribute, you mentioned red team
experience. What does that look like in near use cases? I'm going to ask our VP of technology, Larry,
to speak to that. As you can imagine with a prototype like this, before we publicly start
putting it out for use and having people contribute, we are wanting to think of what are we not
thinking about before we do that. And so when we say red team expertise, we're thinking about
some of the skills that are listed here like data security experts in Kenyan health data and the
rules protecting that. But also saying here are the things that you haven't thought about yet
so that our implementation plan to go ahead and make this not a prototype, but the real tool that
could be used, not just at the county level, but in all of Kenyan first and potentially in the region.
We don't have necessarily all the expertise because of our individual focuses
so what we want is for people who've done implementation, implementations like this to say here
are the things that you aren't thinking about and then help us test it to make sure we've implemented
all those things we haven't thought about and the ones that we've thought about. Does that?
I have a question on the technical side. My understanding is that this tool is open source.
Could you share a little about the text that used to create this tool?
Sure. So currently, as you can see, we, all the way to the left, we are using some of the
public data sources but we are still using some of the private data sources and working on how
to make those available. But the actual tech stack is as you can see we do a bunch of processing
and those are done with both Python and R scripts, shell scripts running the modeling.
And then that is put into the data layer which is a post-gray SQL as well as a GIS extension since
this is a GIS room. And then we're using on that back end layer, Net's JS, the Net's JS API.
The front end is really pretty simple. It's basically react primarily and then for the maps here
we're using leaflet and then all of that we're running right now on a GCP instance which is all
pretty much a straightforward cloud run deployment connecting to a cloud SQL and then for the monitoring
we're using a bunch of both native tools and some third-party tools to do that monitoring
things like data dog which are commercial but to have open source APIs that you can go ahead and
install and then of course the native stuff in GCP. So the primary parts of the stack are
pretty much Python and react. Does that answer? As we go forward we'll be looking for more opportunities
as Tyler said as we progress more on the modeling is how can we integrate more modeling options
in a better way. So let's start to look at some of the options that come with some of the
different modeling packages that can do real-time selection of modeling.
I think we don't time for one more quick question.
Thank you for the for the nice talk. I just have three quick points. One is there was recently
in the news the US bought all the canyon health data from the government. Do you have any
comments on this? How does the fact what you're doing? And two is that I am a member of the
project called HealthSight.io and maybe we can talk afterwards if you would like to collaborate.
Health sites? Health sites? Yes. And three I'm a member of the project working on something
called tomorrow now where they're doing climate forecasting and maybe it's also something we can
chat about. That'd be amazing I would appreciate. So just the one question was about the
US buying all the health data? Yeah I don't think I'm nothing specific to comment right now I
think we are also trying to understand the implications. Any more questions? I think we're still
about time for one quick question if anyone. Okay. Yeah. Thank you very much.