We're going to start there for our first global livestream from Oxford,
and we are very lucky to have Nick Tallentire from the University of Southampton and the world for Project Nick.
He's an epidemiologist particularly interested in the spread of infectious disease,
and it has been doing a really groundbreaking work on the use of big data for well-being and especially using
computational methods and the digital trace data such as Google location history or mobile phone data to track human,
though. And this is meant to track human mobility and the spread of diseases.
So this would be our first okay.
And it's great that we have a talk about computational social science for the public good and to change the world for for the better.
And Nick is going to talk to us about mapping human population and mobility in low and middle income countries for malaria elimination.
In the second part of this talk, we're going to have an hands on kind of exercise and the link is on the Oxford locker and as well on the
general sadako of all locations for anybody would be or might be interested into doing this with us in remote.
All right, cool. Thank you, Neal. Yeah. Thanks for the introduction and thanks for having me, everybody.
Yeah, I'm coming from the University of Southampton within the World Pop Project,
which is a project that has lots of different projects working towards broadly sort of mapping human populations and their dynamics through time,
including another non-profit group called Flow Minder, which works with various mobile operators to get mobile phone data,
to scale research to be able to support things like disaster response and efforts like that.
So there's a lot of us. I'm going to touch a little bit on the broader work that goes on at World Pop, but then I'll focus on my own work,
which largely has to do with malaria, and we have lots of partners around the world to do all that work.
So just to give you a really quick outline of what I'll be talking about today before I jump into it.
Within World Pop, one of the main things that we work on is trying to create accurate maps of populations around the world,
particularly in low and middle income countries. I'll talk about why that's important.
What that means and what data we use that to start off with.
And then I'll move into sort of how these different concepts apply to malaria, particularly the idea that populations are dynamic through time.
So they move, they change. People change residents, they change where they go, you know, over the course of a year.
And that affects how they get exposed to things like malaria.
And then I'll end sort of my talk with what I think the future directions are for this field,
the way things should move forward, where the big questions are into the future.
And I think this might tie really well into what you've talked about this morning where I think you had ethics discussions and things like that.
And so I'll talk about various big data that we've used.
And I think, keep in mind, sort of in the back of your mind what the questions are in terms of how do we make sure we're getting informed consent?
What does the ethics process looks like for various data? And what ethics questions do you get when you use data that I'll be talking about today?
That'll be sort of the first hour, and then after that, we will move into sort of an interactive exercise.
So what I wanted you all to be able to do was to look at your own location history data.
This is true if you use Google Maps, if you, especially if you use an Android phone,
so basically are instructions I have at the end for you to download your Google location history data and
then you'll make some plots in order to plot it on top of a map of London with a map of green spaces there.
So there's the barcode there as well, and a couple of shaped files in a zip file on your slack.
So to go back to why having population maps that are accurate, why that's so important we can can start off with the UN Sustainable Development Goals.
There are 17 goals here,
from twenty fifteen to twenty thirty one hundred sixty nine sort of quantitative targets that they want to achieve things like no poverty,
zero hunger, good health and well-being, quality education, gender equality, things like that, and ultimately to try to get to each of these goals.
Underneath this really nice picture of things we'd like to get to is a population map.
We need to know where people are that actually aren't achieving these sorts of goals so that we can implement new strategies,
put out new infrastructure to move towards them. And, you know, that's the idea, so they used the slogan sort of leave no one behind.
And implicit behind that is that you have to be able to know where those people actually are everywhere to be able to create your policy accordingly.
And essentially, the idea is that the key data behind the Sustainable Development Goals is that you want to
have people able to access specific services or resources or have a certain level of social,
economic or physical health and actually understanding how these various aspects of health vary subnational.
It's critical to actually addressing these goals.
So to be able to address these issues, you have to know what the subnational geographic variation in health status,
wealth and accessibility, where those occur and where those inequities lie.
To do this, you have to have a consistent, comparable and accurate population map of where those people are and where they're going.
So you don't just need where they actually live, but you actually need to know where they go,
where they spend time and their demographics and their socioeconomic status and things like that in order to get at these stages.
The reason I think this is so important is because if you look at different health risks,
we actually get pretty different pictures of where risk actually is and really large heterogeneity is in terms of accessibility and things like that.
So here's how the picture differs across the world in terms of different types of pathogens on the top left,
we have a map of risk to zoonotic pathogens from wildlife.
On the top right is zoonotic pathogens from nine wildlife and the bottom left drug-resistant pathogens and bottom right.
Vector borne pathogens. And so you need to have a clear picture of of where populations are in terms of interacting with where this risk is,
but also where this risk is itself in order to effectively plan into the future.
For a couple more examples,
here's what it looks like on the left in Africa for lymphatic filariasis and on the right you have malaria and these are, you know,
while there are similarities between the two maps,
they are broadly different and you want to know what you're dealing with before you actually implement any interventions.
So in the case of malaria, the reason why having these accurate population maps is important to be very sort of specific,
we want to know questions like how much chemical, how much insecticide do we need in order to spray all the households in a given province?
That's going to depend on how many people live there, how many people actually in the province in the month that we do the spraying campaign.
Alongside that, we need to know how many people do we need in the spraying team?
How long do they need to be out there? And thirdly, sort of how do we measure how well we've done so if we know how many houses we've sprayed,
how many people we've covered with our insecticide campaign? We want to compare that against how many people we actually expect to have been there,
and that helps us make sure that we've actually covered everywhere throughout each country.
So partly to do that, one idea you might use is to get maps of all the settlements in residential buildings,
which is occurring in some high in some malaria elimination settings,
but also discuss other ways that we get similar questions using different data where these are often really difficult to get.
It's also important for responding to outbreaks, so in this case, we have some specific event that we want to deal with.
We don't necessarily know when it's going to happen or where it's going to happen.
And then using population maps will want to figure out things like how significant of an outbreak it is.
You know, how much, how many people are at risk. Where did it originate?
Where am I to go next? And how can we control it? And questions that and things we might use to address those questions would be
sort of maps of the actual population during the time that the outbreak occurs,
as well as the travel patterns of the populations that are actually at risk of outbreak spread of the disease.
So in terms of high income countries, the basic data that we would use answer these sorts of questions are reasonably straightforward.
We have in high income countries regular reliable national censuses with really strong geospatial components.
So we know exactly how many people are in each census area.
Census areas are really small, so we can have a good, really effective, really highly resolved population estimate.
Oftentimes, we also have really comprehensive civil registration and vital statistics to kind
of get an idea of the demography of those people in each of the census areas.
And then other statistics like air traffic data,
sort of population movement between cities to get an idea of how different parts of these countries are actually connected.
It's not exactly the same situation in low and middle income settings, so in low and middle income settings,
we often do get sensitive censuses and ideally, you know, if we use those census,
this will get within census aerial units,
the number of people and each of them the age and sex distribution amongst age and gender distribution amongst those populations.
We can get a prediction of what that population is going to look like through time.
We can compare against past censuses to see how it looked in the past,
but then also project into the future and we could also get some vague sense of movement.
So oftentimes they'll ask people where they lived in the past.
We can see how provinces different areas in different countries are linked by looking at data like that from censuses.
There are problems with these data, however, particularly in low and middle income settings.
So, for example, in some countries, you know, maybe the most recent census would have been 2011.
In other countries it a little bit earlier. Twenty three.
And this is something that greatly depends upon the stability of the country, the ability of the government to pay for a census and things like that.
So oftentimes the most recent census in these low and middle income countries is a really long time ago.
So here's the plot of that around the world, where in some countries say in Africa, you know, 11 to 15 years ago or more than 15 years ago.
So we don't really have an accurate picture of the population throughout the country based on that really old census.
Just to give you a few examples of the most recent census across different countries around the globe in the DRC, the most recent one was in 1984.
So over 30 years ago. And that's true for a lot of different countries around the world.
And even in some, we've never actually had a census to use. So we have to be able to get to reach these SDGs.
We need to have population information. But in these cases, and in many cases around the world, the censuses are not going to be reliable.
Not only that, the ones that do occur, we may be missing populations. There may be inaccuracies in the data.
The mobility data might be incomplete. So often times they'll ask people, What country did you come from five years ago if you moved to this country,
but they don't ask where within the country they came from. So we don't really have a clear picture of how people are moving.
And again, you know, because there's potentially long periods between censuses, we don't have a great picture of what changes occur between censuses.
So in terms of meeting those SDGs and providing data sets for the SDGs,
since national census data will always sort of be one of the most important data sources, it is comprehensive.
You are getting a picture of everybody, you are getting their demographic information with it,
and it does give you subnational detail, but oftentimes it's not enough.
So we want to add supplementary information on top of that, especially even in good.
Even in the best case scenario, they often only occur once a decade. So we don't know what's happening in between.
They often lack population characteristics. So like really fine scale mobility information typically doesn't exist.
And it's also a static picture. So, for example, we know that some populations move seasonally.
Censuses won't capture that, and those seasonal population movements can matter for diseases that tend to be seasonal, such as malaria.
So nowadays, there's a lot of new types of data that we can use to understand these population changes and those dynamics.
So on the left are the data sets that we would have typically used traditionally wear on the x axis.
We have sort of the spatial scale of how those dynamics occur from neighbourhood scale.
So do you move to the next neighbourhood over? How often do you do it?
Which ones do you go to all the way, to the right, to international scale?
And that on the y axis you have the temporal scale.
So on the bottom, it's sort of a very fine scale picture. Where are people spending their time based on the hour versus daily all the way up
to long term migration events that you might only do a few times in your life?
So in the last, we have traditionally the types of data sets that we would use that. So census migration data are typically available.
I mean, you can use cross-border surveys or traffic surveys.
So in this case, if people cross the border and there's a border post that records that that movement happened,
then we can look at that information for sort of long term or seasonal movement, or we can use travel history surveys.
So in that case, we have people going out just asking, where were you two weeks ago?
Where where are you eight weeks ago and where have you travelled? How long did you spend there?
It's a really exciting time to be thinking about these sorts of questions because there are lots of new data sets to address them,
and I'll talk about some of them later on. But here on the right, we have sort of more contemporary data sets that we can use,
including things like personally carry GPS trackers, social media data.
So data from Facebook, Twitter to kind of get an idea of where people are spending time and when they're moving mobile phone records,
satellite nightlight data and then Google Location history data as well, which is the one that you'll be playing around with a bit later today.
So just to show what some of these newer data sets look like, if we think about the satellite derived data,
you know we might get things like used satellite imagery and then use crowdsourced,
crowdsourced effort to sort of map households and buildings throughout that landscape.
So that's what this missing maps project does, which is a really effective way.
If you have an estimate of how many people are going to be in each household of getting out,
what the actual population size in that community and seeing how it changes through
time because you can see how the actual buildings and their layout changes their time,
you can look at road networks here on the bottom and then you can also look at the social media data so they're on the right, shown his Twitter data.
That's basically densities of Twitter users in a city.
So you can look at how that changes, where that actually is and see if that tells you something about what the population actually looks like.
More satellite imagery data that we might use,
so we might get land cover estimates that would tell us a little bit about what that land is being used for.
If that implies certain things about disease risk or where people will spend time on that landscape.
And yeah, and the top right there, you can see why mapping the buildings is so important.
So here on two different sides of a community,
we have two very different household sizes that might imply different things about how many people are living there,
the population density of those areas and so on. So this is a really nice sort of emerging data set that a lot of people are using into the future,
particularly at World Pop, to get better pictures of population densities on a subnational scale.
This is a nightlight data set, which is pretty neat as well.
So basically, satellites look at pictures of where lights are in different communities, and you can look at that sort of across months,
across days, across years to see where, how the extent of that city changes through time.
Use that to infer maybe how that population is changing through time, how many new people are in it.
And that's that's one new dataset that we're exploring to use to look at seasonal population maps in various countries.
So. Another one hour mobile phone data.
So this is one that's really become, I think, especially exciting in the past 10 or 20 years,
particularly as mobile phones have become really pervasive and oftentimes actually the first device that people
in low and middle income countries get to access the internet in a lot of low and middle income countries.
Now what you find is that rather than getting a computer, people buy a smartphone and that's how they actually contact people.
That's how they actually access the internet. So it's a really huge, increasingly viable for these settings kind of data set that you can use.
And essentially what that looks like a go into a little bit more detail about it later.
But you have cell towers across the landscape. And by looking at when people, when and where people are making calls and texts,
you can track them through time and see how they spend time across that landscape.
And so that's what this is, what that looks like across Namibia, where you could see clear sort of patterns of connectivity across the entire country.
Here in the middle, we have Ventec, which is like a big attractor. It's the main capital city in the main city in the country.
It's a huge detractor of people from throughout the entire country.
But you can also see local population centres up here in the north and over here in Zambezi, in the Panhandle as well.
GPS based data are really exciting as well, so personally carry GPS trackers can be a really useful field tool.
So if you provide GPS trackers to people, they'll wear it, maybe, you know,
for a week or two based on the battery life based on their capability to sort of charge those GPS trackers.
And you can see where people in a really fine scale way, where they spend their time.
So this is from a study in Kenya from somebody in our group where she looked at how people are moving with
their livestock through time and how that what that means about the potential risks of zoonotic disease.
So we can combine this really fine scale picture of where time is spent with land use data with survey data about why they're
making these trips to get a better picture of where disease risk occurs and where they're actually spending their time.
Alongside that is Google Location History data and similarly collect passively collected smartphone data.
So this is a really fun one because if you have an Android, well, if you have a smartphone.
Most smartphones nowadays have GPS devices within them, and unless you opt out, they're actually actively collecting GPS points through time.
And that's what I'll be looking at later. And we actually did a pilot study to see if we collected these from people at Southampton.
What we would get and what we found was that we actually got on average a year of fine scale GPS tracker level information from participants.
And because this was a Southampton sample, you can see here on the bottom right.
This is actually what those data looked like, aggregated over about 20 20 students at the university.
So you can really see really clearly sort of travel patterns around the country.
You can see where people went from Southampton. A lot of people travel to London.
You can see the three. I think it is the ring road around London that they travel on things like that.
And yeah, so this it's an exciting data set as well, particularly because we did a survey of people in the UK, the US, Brazil, Japan and the last one.
I think Mexico was the last one. We did surveys in those countries and we found that a majority of people of
Android smartphone owners in those countries actually had this data set enabled.
We didn't look at download their data or anything. We were just asking if they had it.
But we did found that find that a majority of them actually still have it enabled.
So. So that's a little bit of a picture of why population densities matter,
why having accurate sort of seasonal pictures of population densities is really important for meeting a SDGs.
Now I'm going to move over and talk a little about my own work,
where I apply these concepts and data to think about malaria and specifically malaria elimination in countries that are really close.
So this is southern Africa, Southeast Asia and Mesoamerica.
So just to give you a quick primer on malaria itself, it's a vector borne disease,
so it's a disease that infects humans, it gets into your liver and then into your bloodstream.
It's spread by a mosquito vector and obligate mosquito vector. Part of the malaria life cycle occurs in the mosquito.
So that means that part of the life style style occurs in the human.
A mosquito has to bite a human and then pick up the parasite and then transmit it to somebody else for transmission to occur.
You don't really see human to human transmission transmission in natural settings, and because of that,
you only really get transmission in areas where mosquitoes live and mosquitoes live near water bodies.
That's where you tend to find mosquitoes in forested areas.
So you end up getting a really heterogeneous picture of malaria risk based on where those mosquito populations are.
So if you look in any realistic landscape of transmission,
what you get is that you get hot spots of malaria transmission near wetlands, near areas with ponds.
And then what will happen is when during the wet seasons, when transmission ramps up,
you'll get really, really high transmission in the centre of those hot spots.
And then mosquitoes and people diffusing out will make that disease spread out
from those hot spots from those transmission centres out into other areas.
So it's a really interesting place to think about human mobility and population dynamics because your exposure and risk to
malaria depends entirely on whether or not you spend time in these areas that have mosquitoes that will transmit the disease.
So that's where the exposure comes into play. People will spend time in areas with mosquitoes.
Oftentimes these might be areas like, you know, farmlands, areas where you get water, things like that.
So there are different reasons why people will spend time in these locations and they'll spend different amount of amounts of time there.
So you can see here on the right is a picture of Zanzibar, and this is a apt picture of malaria risk because it's off the island.
One of the islands of Zanzibar, where it's generally found in the south and it's really strongly correlated with geospatial geospatial
covariates like land use and land cover in particular here in the south of Zanzibar on this island.
And I think it is, you have lots of sort of forested areas, lots more ponds,
and therefore that's where the mosquitoes are and that's where the transmission occurs.
This is really specifically important for these population dynamics of how people are actually
moving in and out of these areas with mosquitoes is really important for malaria elimination.
So Zanzibar are a set of islands off the coast of of Tanzania and on mainland Tanzania.
You have really high malaria transmission still.
So essentially, what's happened in Zanzibar is you've had a really effective malaria control programme that began in the mid 2000s.
And you can see here on the left is sort of how the malaria burden across the island has dropped over time.
So it's low, but not very well. It's 30 percent of people infected in the early 2000s.
Then they had a really effective malaria campaign that dropped things down really far from sort of twenty three to twenty seven.
So that only one or two percent of malaria people actually had malaria. But ending those last few cases has been really difficult.
So they've been able to stop transmission in those areas in the south of the island,
for example, but they haven't been able to stop the final few cases.
And that's because these final few cases have to do with how those people on the island are spending time.
People in Zanzibar are travelling by ferry to mainland Tanzania, getting infected, bringing back cases.
Mosquitoes are hitching a ride on those ferries carrying malaria with them.
And then you also have residents from mainland Tanzania travelling over to Zanzibar and infecting mosquitoes that remain on that island.
So this is a really practical and important question is, you know, how do we figure out who's actually spending time in these areas with transmission?
How do we target them? And how do we actually stop this final bit of transmission so that Zanzibar can fully eliminate malaria and
then hopefully sort of ramp down these really expensive control measures that they have to do each year?
So that's a lot about that's a lot of what I do. This is broadly important across southern Africa as well.
So these are predictions say, if we have a picture of malaria from the malaria outlets project here at Oxford
on the left that we might do something like try to figure out how people
infected with malaria is in those areas are actually going to move throughout the
entire region and plan surveillance and intervention programmes accordingly.
It matters on a local scale as well, so this isn't just about specific islands or specific countries and how mobility moves across that landscape,
it matters in terms of what our very fine scale picture of risk looks like.
So here's Namibia. And essentially, when you usually map malaria,
what you get is what you use are case counts from health facilities and you try to
predict incidents based on where those people are actually showing up with malaria.
And I'll talk a little bit about how we deal with it later. But the the assumption there,
the implicit assumption that you make is that transmission occurred in the place where the person reported as a malaria case.
In reality, they're spending time in other areas.
So a population covered by this health facility here might actually spend a lot of time in this other community B,
which has a lot of mosquitoes, and that's where actually the infection is occurring.
So we'd like to be able to figure out not just where the cases are appearing in health facilities,
but we actually want to plot where the transmission is occurring intrinsically.
So I'll talk a little bit about that as well.
But essentially then we have different scales so we can think about population dynamics on national scales,
on regional scales, or we can think about local scales.
And these population dynamics do vary with space and time.
They vary with individuals and it affects what you do across these different spatial scales.
So here on the right is a picture of what that mobility looks like and those dynamics look
like and what might cause them at different spatial and temporal scales on the bottom left.
We have really frequent sort of local scale movement on the top rates, really rare international movement where on the bottom left,
you know, you might that might be school or work related movement that has to do with your exposure within a community.
Whereas on the top right, you've got sort of diaspora related movement that spreads diseases across countries rather than really local scale places.
So. So I'll talk a little bit about how I've addressed we've addressed those issues in world pop towards malaria,
using a few different data sets and crossing those different spatial scales.
So the three sort of examples I want to give you is that at a local scale is how we use various data sets like mobile phone data,
GPS tracker data to measure a risk account for mobility when we actually plot risk on a long on sort of regional scales.
I'll talk about how we plot connectivity between countries and subnational at administrative units
within countries and what predicts what that means for a reintroduction across that landscape.
And then in the middle sort of how we account for seasonal population dynamics to get better,
better estimates of disease incidence throughout throughout a country.
So. So starting from the bottom in terms of really local scale dynamics,
essentially what this question has to do with is if we have two different populations around the city,
we want to know where if we know where disease risk is or some kind of health risk,
we want to know how much time people are spending at risk and which populations are actually visiting them.
So let's say we have this map of air pollution around London.
Let's say we have two different populations on either side of the city they're equidistant.
These two populations might differ pretty significantly in terms of actual realised risk of air pollution related
events where the one on the left might actually go into central London more often than the one on the right.
And we want to actually account for that when we say which populations should we think about in
terms of mitigating air pollution risk in terms of placing interventions and things like that?
So in this case, what we're thinking about in terms of dynamics and in terms of mobility are repeated frequent movements.
This is about places where people spend lots of time.
So your workplace, your university, our two key sort of locations that where you might personally go there,
there are really strong individual differences in terms of these movements based on sociodemographic.
So, you know, people who work at in one type of in one type of industry might spend their time in the city centre,
whereas farm workers might spend their time more out in rural areas and based on
the health risk or thinking about this can yield really different risk patterns.
So we need data to inform this that can give us really high resolution mobility.
So you know, what we want to do is do things like combine that with map of disease risk or land use.
So in this case, the types of data sets that I think of our GPS based data sets, the Google location history data and then CD-RW data as well.
So the call detail records for mobile phones. Yeah.
And the example that I'll give has to do with thinking about how a local population dynamics affect malaria reporting.
The idea here is again to go back to that picture of health facility based disease mapping.
We have cases appearing at a health facility.
Those numbers get reported to statistics agency based on the health facility, location or the household location.
And typically what we do is we take that map of where cases are and predict prevalence across the landscape using those data.
So that's essentially what's occurred here where we have sort of prevalence of disease in different populations
based on where those cases showed up and their associated health households or their associated health facilities.
So again, this doesn't necessarily account for where the infection actually occurs.
So the central idea is that observed burden where people are actually showing up with malaria is a combination of where
people are actually getting infected and how people move there and where they are and how much time they spend there.
So the question we want to ask is, can we predict where infections occurred?
So the data that I used for this was mobile phone data from Namibia from October 2011 2010 to September 2011,
with one point nine million sims and nine billion communications in general.
So essentially, the idea is we have here at the top right is a picture of where infected people show up.
We want to actually combine that with a picture of mobility to say, Where did they go?
Where did they might? Where have they gotten infected originally? So to give you a bit more detail on what those call detail records look like.
Essentially, whenever somebody makes a call or a text events whatever they call or SMS somebody,
that data gets routed through a mobile phone tower and it goes to the network operator and gets routed to the person that it needs to go.
From there, the network operator records the time of that event and the tower location.
So let's say we have a hypothetical person who makes six different SMSes.
They text somebody and it gets around it through Tower A initially.
Then they make three texts. It gets routed through Tower B, then one gets routed through Tower C and another gets routed through Tower B.
And if we look at where those towers are and how those calls occur through time, then we can sort of plot how they move.
So here we infer that this person started at Tower A somewhere near it, then moved to Tower B,
spent some time there that had a quick trip to see and then back to be.
So based on that, we can plot sort of how people are exposed and how they move around the given landscape.
In terms of how we practically work with these data, these coders get recorded by the operator.
We always keep the individual data behind the network operators firewall.
We'll run our models sort of aggregate data beyond the individual level.
And so the only thing that we work with within Southhampton are aggregated data sets sort of at the population level.
So that's that's designed to sort of help keep the confidentiality and privacy of these data secure.
So here in Namibia, here's the picture that we have we have on the right the observed burden from the Malaria Atlas project.
We know the mobility patterns across the entire country.
And we want to sort of back calculate and figure out where people actually got infected based on that map.
To do this, we use the mathematical transmission model, essentially a of population mathematical model where we had a bunch of different patches.
Each patch was a cell tower. People spent time in other patches based on those mobile phone data.
And on this landscape, we could have we took the proportion of people that were infected in each cell tower
area and essentially said that that depends on the transmission in each area,
how quickly people recover in each area and the time spent across that landscape to try to back calculate the hour.
So in this picture, we know three things we know the proportion of people infected.
We can assume the recovery rate and we know the time spent from the mobile phone data.
Based on this, we try to infer the actual transmission rates that occur in each patch.
Based on that entire landscape. So if we do that and we don't actually account for movements here, we have sort of an estimate or not,
which is whether or not malaria is self-sustaining, has self-sustaining transmission in that given area.
So an area in red means that there's enough mosquitoes that you have self-sustaining transmission.
Any areas in blue mean that there's actually cases there, but it's it's mostly getting imported.
There's actually not enough self-sustaining transmission to keep the parasite going within that population.
So if we don't account for movement, then we get a picture that makes sense.
Any area throughout the Namibia that has any cases has to have self-sustaining transmission because there's no importation going on.
Once we plug in this mobile phone data and account for movement, we get something really different.
So we get a much more heterogeneous picture of transmission where the red areas actually have lots of
mosquitoes and are parasites sources and are exporting cases to blue areas which are parasites sinks.
And if we isolate them, then they would actually not have enough mosquitoes to sustain transmission.
So this is a really practical picture for malaria control programmes, where the parasite sources are areas where you'd want to do vector control.
Those are areas where you want to damp down the mosquito populations and reduce transmission,
whereas in the blue, we already have pretty low mosquito populations.
All we want to do is stop disease spread and importation.
So that might be places where control programmes would do things like surveillance,
track people at health facilities, tests go out and test people actively things like that.
So on local scales, you know, we can use fine scale mobility data to figure out how populations interact with their environment.
See what that means for the implicit risk, the implicit transmission landscape in the particular case of Namibia.
I think that there's a way to move forward there because we did base this on mobile phone data.
It is limited by the resolution of the cell tower. And there's also no sociodemographic information.
So these CDER's, we only have sort of anonymous location data.
We don't know the gender of those people underlying it. We don't know their age.
We don't know anything else about anything that would tell us about their malaria risk.
And that would be a really interesting way forward as other data sets that could inform that.
So. So, you know, there are other data sets to actually get towards those specific questions to include sort of that sociodemographic information,
GPS tracker data are a really useful way to do that.
So in this case, you actually give people GPS trackers get they're really fine scale mobility patterns,
but also get their sociodemographic information. See how that correlates with the mobility.
See where people spend time based on things like occupation, gender and age.
So this is a study from a Ph.D. student in our group where she looked at exposure in Kenya to different land cover.
How that varied with seasons and things like that. And with with or without livestock, so for example,
you see pretty different patterns in terms of how people move when they have livestock in blue versus without livestock.
People with livestock move further and then end up more likely to end up in
farmland areas and grassland areas because that's where the ruminants feed.
Google location history data are a really interesting way to do that, too.
So this is what you'll be looking at where we can say, for example, if we want to look at a population and figure out who's using green spaces,
how often they go there in which green spaces are actually used, we can overlay those data with green space maps.
So this is our pilot study in Southampton, where we look to see which green spaces are moved the most used the most often by a university population,
which might be useful to say, OK, these are the ones that need to be maintained.
They need to be invested in, whereas other ones we need to figure out why people aren't using them, whether or not there's way to get them to be used.
More things like that. So I'll move on then to go to the mesoscale, to go sort of a step up.
Right now, we've been talking about really local scale, you know,
how does your risk and your exposure to a disease depend upon your really fine scale movement dynamics?
Then we'll now move to sort of the subnational picture to say, how does it affect metrics of, say,
incidents and things like that when you measure them across the entire country at the health facility level?
So the general idea behind this is that you don't just get sort of day to day dynamics, you get also seasonal dynamics.
Farm workers, for example, will move between different parts of the country seasonally.
People aren't always at home. You want to target resources based on the timing based on the months that you're actually doing that efforts.
We might want to think about whether or not those mobility patterns correlate with transmission.
So like, you know where in terms of malaria? Malaria is a disease that occurs during the wettest months of the year.
Where are people spending their time in the wettest months of the year? And is that in the high risk areas?
So to look at this, we look at Namibia again.
This is a slightly larger data set of mobile phone data where it spanned from 2010 to 2014 to get a better picture
of the seasonal population dynamics throughout the entire country and how it varies across different months.
So if we just plot this, we see some really interesting patterns. So this is Namibia again.
This is population change from month to month. So in the centre, we have then took in the north.
We have a more rural area, but some populations again in the north centre and then in the northeast in Zambezi.
If we look at sort of. To December into November.
Or rather, November into December, we see that people leave in Turkey, they go up north into the north part of Namibia.
This is because Christmas is a pretty big holiday. People are visiting family, things like that.
And then December to January, we actually see the opposite pattern where people come back from the north,
go back to work in Ventec and these and it's just so happens that this pattern correlates really strongly with the malaria transmission season.
The peak of the malaria transmission season occurs in sort of March,
but it starts to ramp up around January so you can envision the people get infected up north and then bring it back into then took with them.
But then if we look at later months in the year, we see more people coming back into work in February and then it sort of evens out after that.
So that's the main pattern that we see in that country.
But we can take these data and we can say, OK, we know the relative population sizes in each of these cell phone catchments.
We want to use that to scale the denominator that we put when we measure incidence of malaria across the entire country.
So if we look at various health facilities throughout the country, here's one in Ventec, we see that exact pattern.
So in December, we actually have fewer people around that health facility than in other months.
And then sort of as the month moves on into February and March, we get more and more people and we see the opposite pattern up north.
Again, people go up north for holiday and then they come back.
So for example, here in this health facility up north,
we have a much larger population than we expect and that affects our incidence estimates and we want to be
able to account for that when we calculate how many people are actually getting infected per thousand.
The other thing that happens in this data set is that it does increase over time,
so there are more and more subscribers, so we have to scale those populations based on that.
But then essentially what we can do is using a seasonal population,
estimates with case counts at each health facility is get a new map of incidents throughout the entire country that are specific to each month.
So we see here the ramping up of malaria in January, February and March.
We get really high numbers of cases and then that sort of drops off into April, May and the rest of the year, it's really low.
So by mapping where those populations are in each month and how many people are in each
cell phone catchment tower in each cell phone tower catchment during those months,
we can get a better picture of these actual estimates of cases per thousand across the entire year across the entire landscape.
So we're trying to also operationalise this, so the seasonal population maps can improve targeting surveillance,
so if we know what months people actually at home,
we can target those months and those homes so that we can actually spray everything over the course of the year.
So this is work building sort of web dashboards, particularly with the Clinton Health Access Initiative and partners in southern Africa.
So, so the last topic I'll talk about is going larger scale than that.
So we've talked about the really local scale movement, how that affects exposure and risk.
We've talked about the mesoscale, how that affects seasonal population estimates and our measurements of things like incidence.
So now I'll talk about long distance mobility,
sort of that rare migration based movement and what that means for reintroduction and migration of parasites in regional and regional scales.
So this is sort of migration movement across international movements, borders,
it's relatively rare it has to do with reintroduction of malaria into countries.
So here are the top right we're looking at Panama. Panama is really, really close to elimination,
so they're trying to think about where our new case is going to come in from other countries after we eliminate.
And here's a picture of where importation is occurring from sort of other countries
throughout South America and how may actually happen in each province in the country.
This is going to become more important of an issue as we have increased global connectivity around the world.
So on the bottom right is a picture of sort of air travel. Each of these trips by plane might carry malaria parasites or other diseases with them.
And so we're only going to think about this sort of issue more and more with time.
So to do that, one of the most interesting things for policymakers is trying to figure out the countries in areas that are most closely connected.
So, you know, for example, for Panama, what countries are most likely to import malaria into that country?
If they can know that they can know which national malaria control programmes they need to coordinate with,
which countries they need to be able to best understand the migration in the context of.
And so one sort of analysis that's been really useful. There are community detection analyses where you take different countries, different units,
see how they're connected and then partition them in a way that you get more and more movement within a community, then between communities.
So here across Africa, what we have countries that are in the same colour are more likely to have connexions
between them based on migration than countries that are in different in different colours.
So, you know, initially this occurred using census migration data.
We want to be able to update this information. It used it was at the national level.
We want to include subnational patterns and update this sort of information.
So to do that, we've combined various types of data.
So we combine census data with mobile phone data across various settings to get sort of subnational predictions of movements,
draw community structure based on that and then include international components. And when we do that, that's what this looks like.
So this is on a regional scale sort of connectivity of populations using mobile phone data and census data in conjunction.
And we can do things like predict sort of sink dynamics across that entire region.
So areas in red or exporter's and malaria areas and blues are important areas that get lots of imported cases,
and we can draw a similar community detection map across that entire region.
But in a finer scale way to say what countries should what countries,
but also what subnational units should coordinate their malaria elimination efforts.
Another neat data set to do that, this is fairly new to do so, but we can use road network data so we can infer that imply that,
you know, if areas are connected really well by population movement, then they're likely to have roads that connect them.
We can draw communities based on that. Those shared road networks where, you know, here's a picture of roads across the region.
We can say if if intersections are nodes and the roads themselves are the connexions between the nodes,
then we can draw a community structure around that to be able to map a plot
of sort of communities across the region based on that road network as well.
And so ideally, what we'll do is we'll take different types of data, like the Community Structure Map based on this network road network data.
Compare it against community structure maps from census data, from mobile, from mobile phone data,
see if they correlate and see if there are similarities, and use that to be able to say,
OK, we're pretty certain that these communities are real and that they're ones that
malaria control programmes or other ministries of health should take into account.
So that's a broad picture of sort of how various types of new data are being used to inform human mobility,
population dynamics and issues like risk mapping incidents and mapping regions of communities across continental scales.
You know there are lots of new data sets to do so, and I'd say we've only scratched the surface of doing so.
GPS the type of data satellite derived data have a really long way to go to being implemented in these contexts.
So the last thing I want end up with is talking about what I think the next steps are for this kind of research to push it forward,
particularly in the context of social sciences and sociology.
So that's I think, the future of doing this kind of work. Part of it has to do with generalising models.
So in the places where we can actually get that fine scale picture of population dynamics,
the gold standard data like mobile phone data or GPS data are really difficult to obtain.
We want to be able to predict these patterns using more commonly found data like census data
to be able to say to predict these sorts of patterns in areas where we can't collect them.
And I think the other really important picture that we need to move towards is including demographic and socio economic information.
So like I was saying earlier, mobility datasets like mobile phone data like Twitter data like Facebook data, they're anonymized.
So they don't give us any information deliberately on the demographics and socio economic context of the people that are moving around.
This is obviously really critical. Certain subpopulations are going to be really high risk of, say, malaria transmission.
Farmworkers might spend more time outside. For example, children under five are especially at risk of malaria transmission because it means
they're the most likely to get really adverse malaria related health effects.
So where are we? Can I think getting sociodemographic and economic information alongside these mobility data will be really,
really critical, but there are going to be ethical concerns along there.
So I think that that hopefully links to what you've talked about earlier today.
Within World Pop, I'd say we've started moving in that direction in terms of just mapping static populations specifically.
So for example, there are people within world popular mapping women of childbearing age in 2010 across Africa,
but using satellite derived data and other types of data.
So this is a picture on the left of women of childbearing age, but it's the same sort of issue.
This is the first step. This is a picture of where these populations are at a really fine scale, a really fine spatial scale.
But we don't actually know how they're changing through time, how they're changing in terms of seasons and where they're spending time.
So that's a really important, I think, missing picture. So in the context of migration mapping, we haven't done that in terms of migration mapping.
All we really have are, you know, here's an aerial unit.
Here's where people might be moving, but we don't know anything about those people.
And that's really important missing link, I think, into the future.
So, you know, in general, I'd say I focussed on sort of malaria for most of my talk, but this is important for a lot of things.
Beyond that, getting the population denominators,
getting human population densities right is a really important thing to do because it complements national data sources like censuses
that are only occur once in a while that don't include things like seasonal movement that don't include exposure based mobility.
It influences things like infrastructure, planning, measurement of health metrics and targeting of effort.
Things like Where are you going to put your vector control efforts? And those denominators change over time.
And they actually vary in terms of associated demographics.
So we need new data to inform this and create those links that are currently missing between things like mobility and sociodemographic.
So that's that's all I wanted to talk about now, I wanted to move into sort of a more interactive exercise,
so what I wanted you all to be able to do is look at your own Google location, history data, the basics.
So what I wanted to do.
So using the code that I provided, the things you'll do will be looking at the relative amount of time spent in London across various days,
across various hours, the locations that view the locations that are visited, the green spaces that you potentially visit.
And in terms of hopefully being useful in terms of take home stuff,
this exercise will involve interacting with your own location data, downloading it, seeing what that process looks like,
just seeing actually what those data look like for you and what that would mean in terms of what you're collecting from other people and
how we load those data into our working with those spatial data within AH and then plotting these data within the plot to our package.
So, you know, hopefully that's useful for some people.
I'm not sure the span of experience in terms of working with these sorts of things, but essentially the code that you'll be working with will be.
So these are plots from my data. You'll be plotting the number of points across different days of the week that you spend in London.
So you can see for me, I tended to spend my days in London,
Fridays and Saturdays when I was going for fun Mondays and one day Wednesdays when I was going for not fun.
But then I also tended to doing during the day. So day trips to London and you'll see these patterns in your data.
Hopefully if you have them and then this is what it looks like for me in terms of where I spent time across the entire city of London.
So in red, that's where I tended to spend time and you know, I'm a tourist.
Being an American spent a lot of time visiting all the tourist areas, so central London was pretty popular,
but then also the specific sub setting the points that are actually within green spaces across the city here on the right.
So see it on the left one here. I can't see it quite so on the right, but essentially, you know, the popular parks within the central London area.
That's that's where I went because I was being a tourist. So.
So I'll leave this up for you. Basically, what you've got is our code to process the data.
These are the steps to actually access and download your own data.
You'll go to Google, take out and log in as needed and then select, you know, these are the basic steps,
or go to select the data to include all you want to download are your location history data.
Download that archive. It'll take a few minutes to download. It'll send you an email when it's done, and then you'll it saves it as JSON file,
which will load into our using the from JSON package out of the JSON light package and from JSON function.
If you don't have data or if you don't feel like accessing your own, I've actually provided you a subset of my own data.
So if you do that, you'll see in the code you can plot that and that plots the creates the plots that I just created.
So I figured for this last 30 minutes, I'll be around to answer questions that you have.
But also, you guys can sort of hopefully have a little interactive demonstration of these location data.
So thanks. Yeah, sure, yeah.
You guys have any questions. The microphone.
And is there a reason why you only focus on the low and middle income countries because I guess there are a lot of applications?
Yeah, yeah. No, I think that that just tends to be our focus on low and middle income countries is purely based on
the premise that I initially got interested in malaria because I was interested in disease ecology.
Malaria is a nice one for that. And then malaria tends to be a low and middle income, country based disease and across the world,
pop that has to do with sort of our partnerships with the UN W.H.O.
But you're right, there's a lot of really fascinating potential applications of these data.
So I was talking with somebody up to Liverpool, for example, who's studying the health effects of owning dogs.
And we talked a little bit about from dog owners getting Google Location history data,
seeing if they walked more before and after actually getting those dogs.
You know, air pollution is another really exciting, I think, potential application, seeing how it's a big issue throughout the UK,
seeing where that air pollution exposure happens, who it happens to and when that happens, those are really exciting applications, too.
I mean, I haven't say I can't say I've done too much along those lines, but it is a really exciting avenue as well.
Wait, wait. OK.
My question is more to do with the majority of the talk you spend on straight line distance measurements.
Yeah. And they're actually not that good. Yeah, there's a mountain range in between then.
Yeah, a straight line is missing everything. Yep. So a straight line is nice.
Yeah, it's very coarse. Yeah, right.
And given that there's no much information on specific mobility patterns in developing countries using things like ruggedness or slope,
but you can only compute with edges. Yeah, you could get a much better idea of specific connectivity that's not measured in straight line.
Yeah. Patterns. And I thought in the end, I assume, is that an awful map?
The road map is that one from the North? Yeah, yeah. Yeah. So that might be nice to compute this.
Yeah, with to get some time of travel distance felt and travel time to get a better measure of connectivity.
Yeah, that's one. And then the other question is, this is more exploratory.
Can you actually check mobility of mosquitoes rather than you go on the other side?
Yeah, yeah. Yeah.
So to the first one, I think that is really, really important and some of the work in terms of creating good models of how people are moving around.
We are starting trying to incorporate that sort of information.
So like, you know, taking accessibility surfaces, looking at the shortest distance between areas and like how long that takes things like that.
But actually beyond that, I think those sorts of satellite imagery is where if you can get really fine scale data,
you can get an even better understanding.
So for example, what I've done so far with those road networks is assumed that the speed limit tells you a little bit,
that the speed limit basically tells you how fast you can move on that road, which is really not realistic.
I mean, there's rush hour a lot of time to go slower. Sometimes there are roads that people go a lot faster on.
So I think if you can, you know, one thing that I'm really interested in is if we can get those really fine scale data from
sources or from smartphones overlay the implied speed from those points on to the road network.
We can even improve on those satellite derived data and get an even better picture of like accessibility and things like that.
You know, another thing there is like with those studies, usually people assume that you take the easiest route.
That's not always the case. And so if you have, say, the mobility data that tells you, Oh, well, actually,
they tended to go to this other city on the way because it has some, you know, gas stations or fun things to do, things like that.
The second one, yeah, I'm not. I haven't tried to do that much with tracking mosquitoes.
There is, you know, the issue with mosquitoes. Oftentimes what people will do is try to do mark recapture studies.
So like you put fluorescent dust on mosquitoes, see if you can catch them two or three days later, see how far they've gone.
But then you can imagine you get like point zero one percent of the mosquitoes that you released.
Yeah, there are people doing that. I haven't personally worked with that. It seems like it's a challenging setting, but obviously it would be.
It's it's the other side where I've worked with sort of the human movement dynamics parts,
the other side of it to think about the mosquito mosquito dynamics as well. So.
Not able. Yeah.
Yeah. He would tell you something about the.
Yeah, yeah, yeah, that's true.
So there are people who are modelling, yeah, if you if you release not infertile mosquitoes, how far do you actually expect?
That's a fact to be expected to be dispersed because they're going to move so
far that it'll just be diffused across a really large mosquito population? Yeah, yeah.
I haven't done that work. But there are people who are using, say, dispersal models, mathematical models to do that kind of thing as well.
Yeah. Thanks a lot.
That was fascinating.
It struck me that with some of your data, for example, the satellite imagery and mapping data, so OpenStreetMap and Google Maps and so on,
these are publicly available and kind of accessible to a large degree, but with cool data records in particular and mobile phone coverage.
Yeah, these are often very protected.
So if you could speak a bit more about future initiatives to make these more available if these exist, yeah, yeah.
No, I think that that's why the where I was talking about generalising the data, creating models that actually fit to these mobile phone data,
for example,
I think that's why that's so important because even for us with the even when we actually have an agreement in place with network operators,
sometimes it can take months, years to have the legal part of it finalised to be happy on both sides and actually get the effort because you know,
when it actually is, that sort of one on one effort with the mobile operator to get those data.
They they are the ones who ultimately have to do it, and oftentimes you don't have the personnel support to do it.
So there are cases where I've waited. Well, there are data. There are data sets right now that I've waited over two years to get.
Just because it's difficult to get those links going and to make sure that they move on.
So, you know, that is something that yeah, I mean,
it's something that I'd like to do in terms of if we can choose a bunch of layers of satellite imagery of other types of mobility data,
get a reasonably strong prediction of these sorts of patterns in mobile phone data,
then that that really opens it up to other groups being able to work with it for us, being able to apply to other settings and that sort of thing.
Yeah, but it is. It's a challenge. Even if you have those agreements in place, actually being able to do it still remains a challenge.
Go. Yeah, so like I said, I'll be hovering around, hopefully, if you're playing around with that arcade, it'll work and then be useful.
Let me know if you have any questions as you work on it.
Nick, I have a question, too, and I'm wondering if you have if you've seen kind of prototype research that have used.
I mean, you mentioned you've been using Google location history in some of in some of your work.
But do you think they're kind of good examples of prototypes of work where people have, for example,
done a survey where they have asked an embedded location, history or other kinds of sensor direct measurement?
Yeah. And they're so in a sense combining through this hybrid design self-reported survey based measures along with with these digital traces.
Yeah, there's there's a really interesting study in Zambia where they got they had people give GPS, they gave people GPS trackers,
they looked at and they gave them one week each month across the course of the entire
year to look at those so that they can actually get those seasonal patterns of movement
and then actually correlated it with things like the weather that occurred during that
time to look at how much impedance there was introduced by rainfall in a given week.
And then also, you know, the occupation of people and like the sociodemographic information collected by
the survey through GPS as well at the same time as they did the GPS sort of study.
And so how those how those data correlated, you know, that's where I think a lot of that work has been is like sort of personally collected,
personally carried GPS trackers that occur in a survey based design.
I think the power of passively collected data is that it is a lot less research intensive, effort intensive.
So for example, with these data, this is essentially the process that we have participants go through.
They just go through and download it and then give it to us. They don't actually have to say, Kerry there.
We don't talk to them initially say, carry your phone for a week and then give it back to us at the end of that week.
Whereas with personally collected GPS trackers, it's this thing of you give them the GPS tracker, you have to follow up with them, get it back.
So I think, you know, that's where if we can apply those sorts of methods in the past that have been
done with GPS trackers apply it to new data sets that are passively collected,
then we can get a much larger sample sizes in a much better sort of population level picture of these correlations between sociodemographic,
economic status and mobility.