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, 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 a 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
our mutual commitment to building a more just world through responsible and ethical use of data science
and AI. So the solution that I'm going to demo today has been developed in collaboration with
such partners. So I want to just name them, they're 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 Jakaranda 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 their pregnancy journey and
through motherhood, 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 Cardiata 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 to let you know
a little bit about Cardiata County, it is beautiful, it has valleys and plains and it's sort of a
arid semi-arid kind of climate home to 1.1 million people mostly for the majority of pastoralist
community. You might have heard of the Masei, the Masei, 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 Cardiata 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 is 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
use, 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
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, policymakers, and so on. And also actually front-line
health organizations like Jack Render Health, who partner with governments to assist in strengthening
those health systems. So we spoke to them there from cross 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 and health, yes,
have generally and 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, we've also
noted some promising innovations from around the world. In our report, so 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 a sort of 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 ecosystems level thinking. So enabling more sectors to come together, more sectors to
collaborate, bring their data into an entrepreneurship 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 as localization, 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 standout side 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 develop to those standards with the, 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.
All right, 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 right here, you get to our featured
prototype. And basically, this, this does she because we're talking about Cardiata County,
just wanted to highlight again the, the different data sources. So I'm trying to manage 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
our 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 forecast 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 sort of subject matter classifications of those text messages
aggregated at word levels in 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. That's not showing the rest anyway. It looks a bit different on my computer
than that. So this essentially shows Kajada County's map. You see the different word 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. Identify any hotspots. That kind of thing. It's super helpful.
On the map, you can kind of see, I think, the health facilities that 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 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
Canyon 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 prompts 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,
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 word 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 are just 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 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. For the down, you can actually, I have some use cases where we worked
with the Cardiota 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 moms were raising during those specific
events where climate events. I'll just show you a little bit, speaking of that,
the prompt states are here. Oh, nope, went to the wrong one. That one.
Yeah. So, these 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. We wanted to include that here as part of the perspective to inform the public health
actions. So, in this first set of visuals, 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 pain, 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 percent rise in those types of concerns. We see a 23 percent rise when you look
at questions about labor and in other kinds of, for different health indicators, for instance,
with children and babies, we also saw similar kind of relationships in the frequency of
intents 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
pregnant women. 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.
And 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 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, okay, 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
Harriet Al, I do have the links available as well. We use generalized 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 used it in the Canyon context. To forecast
the relationship between temperature and precipitation from Copernicus and the health indicators
mentioned here. So yeah, 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, so an 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,
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 Cajota 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.
.
Requestions.
Yeah. Do 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, I would be open for that. Thanks.
Hey, hi, thank you for the valuable presentation. This career is dependent on you. What is the decision that thisiders 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? Yes, the development? Yes, and this with the data we have now, what can we do with it completely?
That's a great question. Thanks. So part of why this came about in the first place is because of who we are in 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, you know, before we heard any other issues.
So we initially started working on tooling, which is actually a digital public good open source that's called the data observation toolkit or dot.
And that is available right now for and is in 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, you know, climate was always there.
Some governments are further along in terms of having a climate agenda. Kenya was particularly receptive to have they have a strong policy, but also want to show tooling that 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, 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 don't have to remind me. Just say it all out.
What they can do with it. Thank you.
So our partners in Cajeta 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 that 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 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.
Yes. Hi. On that slide on the way to 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.
So, as you can imagine, it's like, look, sorry.
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 we, when we say red team expertise, we're thinking about some of the skills that are already listed here like data security experts in, 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 in 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, you know, 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.
Is that?
Since we have VP of technology in there, I have a question on the technical side.
My understanding is that this tool is open source.
Could you share a little about the tech stack 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.
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 in layer.
And that's JS, the next GIS 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 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 Tali said, as we progress more on the modeling, is how can we integrate more modeling options in a better way.
So we'll 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.
Can we talk 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?
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.
And more questions, but I think we've still got time for one quick question if anyone.
Okay.
Good.
Yeah.
Thank you very much.