Climate Change and Health Seminar: Tools for environmentally-informed malaria control in the Western Amazon

October 21, 2021

Information

Dr. Benjamin Zaitchik, Professor, Department of Earth & Planetary Sciences, Johns Hopkins University

Dr. Zaitchik joined YCCCH for this monthly seminar to discuss his work on malaria control in the Western Amazon.

September 13, 2021

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00:00<v ->Which is hosted by the Yale Center on Continent Health.</v>
00:04So today we have a hybrid seminar due to the COVID pandemic,
00:11so we have the students joining us in person,
00:16but also for the students who could not join us,
00:19they can also join us online (indistinct).
00:26But before we move on,
00:28I just want to have two quick kind of housekeeping rules.
00:33So, you guys have submitted questions to our speakers.
00:36So at the end, we will have a Q&A session,
00:39so you guys feel free to ask your question,
00:41raise your hand so the speaker
00:43can actually hear you quite clearly.
00:46And for the folks online, if you have any questions,
00:50also please don't hesitate put them in the chat box.
00:55And we will also go through those questions
00:57on behalf of the attendants.
01:00So, it's my great pleasure today to introduce
01:05our first speaker of the seminar series,
01:08Dr. Benjamin Zaitchik.
01:11Dr. Zaitchik is a Professor in the Department of Earth
01:14and Planetary Sciences at the Johns Hopkins University.
01:18His research addresses hydro-climatic variety,
01:22including fundamental work on atmospheric science
01:25and hydrological processes,
01:27and application to program on water resources,
01:30agriculture and human health.
01:34Dr. Zaitchik is actually also the President
01:38of the Two House Session
01:40of the American Geophysical Union, in short AGU.
01:45So another thing he want to mention
01:48is Ben actually got his PhD from here in 2006,
01:53from the Department of Geology and Geophysics.
01:58So we are very pleased to welcome back Ben
02:03at Yale, although virtually.
02:05So without further ado, let's welcome Dr. Benjamin Zaitchik.
02:12<v Benjamin>Great. Thanks so much Kai.</v>
02:13And thank you for the opportunity to speak.
02:16You know, I have to admit,
02:18I've somewhat enjoyed this remote world
02:20and our ability to talk and interact at a distance,
02:22but I was a little disappointed when I'm not able
02:25to be up there in Newhaven right now,
02:28because it would've been fun to come back.
02:29As Kai mentioned, I did do my PhD there,
02:32but not in public health, kind of cross 34 on Science Hill
02:35in geology and geophysics.
02:37But while I was there,
02:39I was not yet working in the geo health area,
02:41but I got to see a lot of collaboration going on,
02:44particularly between Durland Fish
02:46and some of his students in public health,
02:48and my geology department,
02:50which really was my first exposure to this idea
02:52that you could really make use of some
02:54of our environmental information analyses
02:57to inform infectious disease analysis.
03:02So the talk today,
03:03I'm going to be focusing on malaria in the Western Amazon.
03:07I've got a long list of names here,
03:09that's only a partial list.
03:11I want to particularly acknowledge Bill Pan
03:13at Duke University,
03:14who has led most of the work I'm going to present on today
03:17from the epidemiological side, as well as Mark Janko,
03:21Cristina Recalde, and Francisco Pizzitutti,
03:23whose results I will be showing.
03:28So, might start with some deep background
03:32and perhaps an apology in that the idea
03:35that malaria somehow in an environmentally mediated disease
03:41is not particularly new, right?
03:42It shouldn't come as a surprise to anybody.
03:46In ancient times,
03:47malaria was associated with the rise of Sirius,
03:51the dog star,
03:53which would come in the days of mid to late summer,
03:56around the Mediterranean, where the Greeks and others
03:59were studying this and aware of its impact.
04:02That is why we call them the dog days of summer,
04:04because that's when Sirius became visible.
04:06And you can see writings about this across Mediterranean
04:11at the time.
04:12Hippocrates, who was famously very interested
04:14in the relationship between environment and meteorology
04:18and health wrote specifically about how
04:20these cyclical fevers that we now understand
04:23to be malaria were associated with the season,
04:26and clearly understood quite clearly
04:28that this was not an astrological phenomenon,
04:30but that this was a phenomenon tied to the seasonality
04:33and to the oppressive heat built at that time.
04:37Now, this is millennia before the mosquito-mediated
04:40pathway of malaria transmission was confirmed,
04:45as well as before the plasmodium was identified,
04:48certainly as the parasite.
04:50And yet this understanding that malaria
04:52was sensitive to these changes was clear.
04:56I mean, the very fact we call it malaria, right? Bad air.
04:59It's the disease that is most associated inherently
05:02in our naming system with this idea
05:04of environmental sensitivity.
05:07And so, you might think that we had this
05:08kind of figured out, right?
05:09So why in the year 2021,
05:11am I here to talk to you about our attempts
05:14and our struggles to continue to understand
05:16in a predictive fashion,
05:18the way in which malaria responds
05:20to environmental variability?
05:22And I think the answer is that it's a bit complicated.
05:24And so what I'm going to talk about here
05:26is something where we really need to understand
05:29the environmental influence,
05:31and the climatic influence as well as
05:32other environmental influences,
05:34in the full context of a coupled natural human system
05:37that evolves with time.
05:39And so, simply understand that malaria has the potential
05:41to be sensitive to environmental factors
05:44is not in and of itself a useful or actionable
05:47predictive system.
05:49So the talk today, I'm going to start off
05:51with some background on malaria in the Western Amazon,
05:54and apology in advance if doing so is insulting
05:58to folks in public health who have a deep understanding
06:00of malaria in this region,
06:01but I'm not sure of everyone's background.
06:03So we'll go through a little bit of that history
06:05and current dynamics.
06:07Then, I'm going to spend a little bit more time
06:08than you probably want me to on physical geography
06:11and hydrometeorology because that's really
06:13what I bring to these set of analyses.
06:18Then I'll move on and just give three of the cases
06:21in which you've tried to integrate
06:23these kinds of environmental information systems
06:25to our understanding and forecast of malaria in this region.
06:30And I want to emphasize something that Kai said,
06:32was certainly type into the chat
06:34if you would like to say anything.
06:35Also feel free just to unmute and interrupt
06:38if I say something that is unclear.
06:44So again, based back on the malaria in the Amazon,
06:46this is from the malaria Atlas.
06:49And what we see here is that the dominant type of malaria
06:52will be vivax that is present
06:55throughout the Amazon basin,
06:57but you also see falciparum in some concentration,
07:01and the Western Amazon part of Peru and Western Brazil,
07:05and then we focusing on, you will see both
07:09in significant amounts.
07:13I should note that I'm zoomed in here on the Amazon basin.
07:17The Amazon is home to over 90% of malaria
07:20in the Western hemisphere.
07:21And so it's really in terms of studying the Americas,
07:26it's the place that one would want to be focusing
07:29a lot of effort on malaria reduction.
07:33And in this region,
07:36malaria is classically associated with deforestation,
07:40encroachment into the natural forest.
07:42So, it's just a satellite time-lapse,
07:44showing over about 30 years
07:47what we all know to be true, this massive deforestation.
07:49This particular lapse rate is from Brazil.
07:51You see similar things throughout the Amazon basin.
07:54Classic pattern here is a road gets built,
07:56you surmise from that flash across the screen
07:58at the beginning of the time series.
07:59Once the road is built, you get this herringbone pattern
08:02of deforestation as land is cleared for logging,
08:05but then also for agriculture and ranching.
08:08And this dynamic was associated
08:11with a massive burst of malaria in the Amazon region,
08:15particularly in the '80s and 1990s.
08:18And so that was really the time
08:19of the most rapid deforestation going on
08:21over much of the Amazon.
08:23Continues to be a major issue today,
08:25but that's when the rate was the highest.
08:28And what you had there was a situation
08:30where epidemiologically naive populations
08:33were entering into a region where the anopheles mosquitoes,
08:38the dominant vector of malaria,
08:42were present in large numbers,
08:44and the kinds of livelihoods we were seeing in particular,
08:46this kind of entering the wilderness for logging and such,
08:50and then a lot of mobility going on
08:52all led to this really strong epidemic peak.
08:56And from observing the dynamics,
09:00this what now we would consider
09:02to be a classic hypothesis emerged
09:04called the malaria frontier.
09:06And so you have frontier malaria in situations
09:09where you have populations that do not have immunity,
09:13and who do not have behavioral patterns
09:15associated with trying to avoid malaria,
09:16because they're new to the area,
09:18enter into the wilderness frontier,
09:21and you get this burst of epidemic peaks,
09:25followed by a gradual adjustment
09:28as you get some resistance building up,
09:30as you get populations' behavior changing,
09:32and as you get livelihood changes
09:35that maybe are a little less mobile
09:36and include less interface with wildlands,
09:40and you settle into an endemic pattern,
09:42this endemic malaria.
09:44And so, you know, this has flashed through enough times
09:46that maybe you've noticed by now
09:47that you can kind of see the timing, right?
09:49While things change throughout the time series I'm showing,
09:52after about the year 2000 or so,
09:55the change isn't as rampant.
09:57You don't see as much clear cutting, right?
09:58That mostly happened in the '80s and '90s.
10:01Again, this is a time series from Brazil.
10:03You'd see similar things in the parts of Peru and Ecuador
10:06that we're focusing on.
10:08So, when I talk about malaria today,
10:11I'm going to be focusing on the last 20 years,
10:12which is really coast frontier malaria.
10:15Okay, so this is the time where we say, okay,
10:17we've kind of been through that initial burst of malaria
10:21that happens when you enter the frontier.
10:24And now, we're in the situation where we are looking
10:26at transmission patterns in populations
10:28that I wouldn't say that it's a stable population.
10:32There's always movement going on.
10:33But you're no longer talking about this encroachment.
10:35You're talking about interfaces within
10:38what is more or less a settled area.
10:44Okay, and so what does that look like
10:46if you just look at case numbers in the Amazon?
10:48So here, I'm showing a time series from 2000 on.
10:51And so what you're listing over to the left here
10:53are there really high numbers that preceded this?
10:55So the numbers on this curve, you can kind of see Brazil,
10:58that red curve coming down, right,
11:00from what was a really big peak in the 1990s.
11:03And if you ignore Venezuela,
11:05which as we all know has had its own challenges,
11:08you would generally say,
11:09"Oh, this is kind of a story of cases falling, okay,
11:12from that frontier malaria peak."
11:16But if you look a little more closely,
11:19over the last 20 years, you'll see that progress
11:21has stalled and even reversed.
11:23And so expanding the Y axes a little bit here
11:26to look at Columbia, Ecuador and Peru,
11:28just over the past 15 years or so,
11:32what you see is a rather significant peak in Ecuador.
11:35It came down a little bit after that
11:36but it's come back up.
11:38Peru, quite a significant percent wise increase,
11:40because the case has got so low in the the early 2010s.
11:48Sorry, that was Ecuador.
11:49Big, significant increase in Ecuador.
11:51I missed my labels here.
11:53Then bottom one is Peru showing
11:55the significant increase, again.
11:56And so you see these large percent wise increase
11:58in these Western Amazonian countries.
12:02Focusing on Peru specifically for a moment,
12:05because that's what a bunch of our data
12:07are going to come from, that I'm going to show
12:08in the next section.
12:10What you see here is a phenomenon where, again,
12:13cases were quite high in the 1990s,
12:15but there seemed to be a period where you were at
12:17a kind of a stable level in the 2000s,
12:20and then a rapid decline to the point where
12:22it was really getting close to elimination around 2010,
12:25before it burst back up.
12:26And so now what's been happening?
12:28So that period, as I'll get to it towards
12:31the end of the talk,
12:33was a period of a significant intervention
12:35and attempt to eliminate malaria from this region.
12:39So the PAMAFRO program,
12:40which ran for about five years involved
12:43a number of malaria control activities.
12:46Again, details come later, and it really did seem to work.
12:50Then in 2011, you had this historical flood.
12:53There was a flood of record over much of the Amazon,
12:55the biggest one in the observed record.
12:58And it had tremendous impacts across the region.
13:02But one thing that happened was what we saw
13:04an increase in malaria cases, this reversal, okay?
13:09Now this flood coincided with the end
13:10of the PAMAFRO program.
13:12And so we have some disentangling to do,
13:14about what's going on when it increased.
13:16And when this first happened,
13:17there was a sense of like,
13:18"Okay, a flood happened,
13:19there's going to be a bunch of malaria,
13:20and it'll come back down,"
13:21But didn't. Just kept going up and up and up.
13:23In the time since that flood,
13:25you've had several other destabilizing events.
13:282015, as you might be aware, was this mega El Nino,
13:32with global effects.
13:34You also had dengue and Zika,
13:36and particularly with the Zika scare
13:37coming through this region at that time,
13:40which really stressed health systems.
13:42And so, one thing that we're trying to do now is say,
13:45"Okay, in this context of intermingled climatic effects,
13:49social effects, epidemiological effects,
13:52what exactly is going on here?"
13:54And this is critical, because, you know, 10 years ago,
13:57if I were giving this talk 10 years ago,
13:58we'd be talking about elimination of malaria in the Amazon.
14:01We are not talking about that right now.
14:02We're talking about trying to control
14:03what seems to be an increase...
14:05Though you don't see it on this graph,
14:07because Peru seems to settle down a bit,
14:09not just an increase, but really,
14:11maybe a significant continuing increase of malaria
14:15in the region.
14:18Okay, so let me jump into the physical geography
14:22and hydrometeorology of the problem.
14:27So, let me start off with a little bit about the vectors.
14:29So as I will attempt to stress throughout this talk,
14:33when we talk about the influence
14:35of environment and hydrometeorology,
14:36we're not just talking about mosquitoes, okay?
14:40Mosquitoes are a big part of it.
14:42So, that's why I start off with them,
14:43but we always want to be thinking about mosquitoes.
14:45You want to talk about the pathogen,
14:47and we also want to talk about human behavior.
14:50Nevertheless, the influence of land cover
14:53in hydrometeorology in particular
14:55on an anopheles mosquitoes is going to be
14:57a big part of our story,
14:59so I want to make sure you're familiar
15:00with what's going on in the Amazon.
15:02So, the red here is showing anopheles darlingi.
15:05That is the dominant malaria
15:09competent vector in the Amazon.
15:14There are a whole bunch of others,
15:15a great diversity of anopheles mosquitoes here,
15:18but the darlingi is going to be the number one.
15:23And if we zoom in a little bit,
15:24so just a little box there,
15:25around this portion of the Western Amazon,
15:28centered on the Laredo district of Peru,
15:31which is kind of the Northern Amazonian district in Peru,
15:35you can go and study this there,
15:38because a lot of really good work has been done
15:41by some of the members of the team
15:42that were on my title slide,
15:43and people who preceded them or partnered with them
15:46in this area doing really strong work on mosquito surveys,
15:51or collecting or doing species typing.
15:54And this happened along various areas in the region.
15:58And I don't know how well this is showing up on your screen,
15:59but that red inset there is a Landsat satellite snapshot
16:04of the area.
16:05And you might see red dots, yellow dots, green dots.
16:09Those are all showing collection sites
16:11where breeding habitats and mosquito species types
16:13were collected at larval and adult stages.
16:16And they were organized along transportation corridors,
16:19these surveys.
16:20And so the red dots are along a highway
16:22that connects Iquitos to Nauta, a town to the south.
16:26The yellow dots connect Iquitos to Mozan up in the north.
16:30And then the green dots are going along various rivers
16:33that are used as transportation corridors.
16:36Let me just zoom in on that a little bit,
16:39so you get a sense of this region.
16:41So here, this is just kind of a true color satellite image
16:45of what I showed in the previous slides.
16:47You see the Amazon river flowing south to north here
16:50through the region.
16:51That urbanized area that you see
16:53along the banks of this meander is Iquitos.
16:58Iquitos is famously the largest city in the world
17:01that you can not reach by road.
17:03You either have to come in on the river
17:04or you have to fly in.
17:05The rivers are the dominant transportation networks,
17:07but we have these roads I showed before.
17:10There's one to the north that kind of cuts off
17:11here, going to Mozan,
17:13but this highway here, the Iquitos to Nauta highway
17:17is kind of the biggest road in the area.
17:18And you see that herringbone deforestation
17:21coming along that road.
17:24And so, what we have here are mosquito collections
17:27in an area of land use contrasts,
17:31including the pristine forest
17:33and breeding into areas of significant agricultural activity
17:36and urban activity.
17:39And so, we can then use our satellite images
17:41to classify the different types of cover we see here,
17:45and these range from different water types.
17:46We always want distinguish between clear water
17:48and silky water in the Amazon.
17:49They're very different ecologies.
17:52And then different kinds of Amazon basin land cover type,
17:57including the anthropic types,
17:59such as disturbed vegetation and bare ground,
18:02and roads and buildings,
18:03and the natural vegetation types,
18:04including different types of forest.
18:07Okay.
18:07And so when we analyze these together,
18:10the land cover information with the mosquito information,
18:14you find some interesting patterns.
18:17And what I have here are all anopheles species.
18:20Okay, I didn't bother spelling out all
18:22of the species names, because they're long
18:23and it doesn't matter too much.
18:25But what this box plot is intended to demonstrate
18:28is that, as your forest area decreases, okay,
18:32as you go down on the Y axis into the negative area here,
18:36you will see decrease.
18:38You will see different relationships with different species.
18:42Okay, and when you have a...
18:46Sorry, I apologize. Let me step back.
18:48The Y axis here is the association. Okay?
18:51And so you see negative associations
18:53between forest area and some species,
18:55and positive associations between forest area
18:59and other species.
19:01Okay.
19:02And so, what's interesting about this is that you say,
19:04"Okay, there's going to be changing species assemblages,
19:06as land cover shifts from natural forest
19:11to more cleared area."
19:13But it's somewhat systematic,
19:14in that the species here over to the left
19:18are the malaria competent species.
19:20You'll see anopheles darlingi here on the far left.
19:23And so, that's a dominant vector and all of these others
19:25are vectors, also.
19:27These are not, okay?
19:29So it so happens that as you clear forest,
19:32you might not actually see an increase
19:33in the total number of anopheles mosquitoes.
19:35You often will see a decrease in the total number
19:37of mosquitoes of all species,
19:39but you'll see an increase in the prevalence
19:42and absolute number of darlingi, of your vector species.
19:45And in fact, it's even quantified.
19:47Here's some data we had.
19:48We found that for every 1% increase in clear land area,
19:51you have close to a 4% increase in the odds
19:53of finding anopheles darlingi at a collection site.
19:57So we have here is human wildlife interface
20:01causing more mosquito human interactions.
20:05And also, the anthropic disturbances of the landscape
20:08increasing the proportion of your competent vectors.
20:13So this is a recipe for increased malaria transmission.
20:16So this is a fairly detailed study
20:17that we could only do in places where we had
20:19really detailed entomological collections.
20:23We don't have that everywhere,
20:25but at least from the satellite perspective,
20:26we can take this kind of last
20:28and done at high resolution and zoom out of it.
20:31And so as we try to look across all of the Laredo states,
20:36this shows Laredo state of Peru,
20:37and this analysis has now been extended to include
20:40the Amazonian portions of Ecuador,
20:42as well as parts of Colombia and Brazil.
20:46We can make use of satellite data.
20:49And here I'm showing the MODIS satellite data.
20:51If you're not familiar with MODIS,
20:52it's a NASA-supported mission has been up
20:54for about 20 years now.
20:56And unlike the previous images that I showed,
20:58which is a Landsat higher resolution, 30 meter resolution,
21:01but you only get snapshots every once in awhile,
21:04MODIS is giving you 250 to 500 meter resolution,
21:08but it's giving you daily images.
21:09And these really cloudy areas that's important, right?
21:11So you need to catch when you can
21:13a view through the clouds.
21:15And that allows us to use phenology.
21:16That is the seasonality of the vegetation
21:19to do a more detailed classification of land cover types.
21:22And it says on the left, just a classification using MODIS.
21:25We can then, because the satellite's been up for 20 years,
21:28look at change in these forest types over time.
21:31All of that can go into our malaria risk analyses.
21:35And on the right, what I'm showing you is a card
21:37that I did not develop,
21:38that NatureServe developed,
21:40which used a combination of satellite data
21:42and measurements on the ground to come up
21:44with ecological systems,
21:46that we view as potentially relevant to malaria.
21:49In particular, the red areas on this map
21:52are areas that are forested,
21:54that are flooded by what they called black water.
21:56So those tannic waters of the Amazon.
21:58And then in the light green,
22:00you'll see other areas that are flooded
22:01by what they're calling white or clear water.
22:03Might have sediment in it, but it's not tannic, okay?
22:05So again, different water quality, different ecology.
22:11And so, what I've taken here is land use,
22:13look at really high resolution land use,
22:15to understand the scale of distribution.
22:17Used a different satellite assets in order to zoom out
22:19and say, "What can we say at scale about land use
22:23and vegetation types?"
22:25And also, thanks to the NatureServe analysis,
22:28link that somehow to hydrology, right?
22:32Because now we're talking about ecological zones
22:34that are defined, in part, by their flooding regime,
22:37which is a key consideration in the Amazon, right?
22:40There's a lot of forest
22:41that's different from other forests,
22:43and much of that has to do with these flooding regimes.
22:46So this brings hydrometeorology into the picture, right?
22:48And so, how does hydrometeorology matter?
22:51As I mentioned, it's going to affect the vector, right?
22:53We're concerned about breeding sites,
22:54survivability of different life stages,
22:56the life cycle, speed of the life cycle of the mosquito,
23:00dispersion of mosquitoes,
23:01influenced by winds and temperature.
23:04And so, wind, temperature and certainly precipitation
23:08and moisture conditions in the soil and surface puddles
23:10are going to be a big deal.
23:11We also know the plasmodium has temperature sensitivities,
23:15and that the vector's competence transmit the plasmodium
23:18is a function of temperature.
23:21On top of that, you've got human behavior.
23:23And so migratory labor in particular,
23:25logging in this area is very sensitive to the river height,
23:29because you need the rivers to be a certain height
23:31in order to float the logs downstream.
23:33And so that will have an influence.
23:34And then of course, agricultural activities
23:36will be sensitive to the seasonality of hydrometeorology,
23:42as well as the inter-annual variability.
23:45When you get interventions,
23:45you also have an issue that anyone
23:48who's worked in malaria knows, which is,
23:49"Will people use bed nets?"
23:51And when it gets really hot, very often,
23:53it gets harder to comfortably use a bed net.
23:58So, how are we going to do hydrometeorology?
24:00So there are a lot of different ways you can do this.
24:03The system that my group uses,
24:05and kind of one of our major contributions
24:07to this malaria problem is called
24:09the land data assimilation system.
24:11So that probably doesn't get discussed too much
24:13at schools of public health, which is appropriate.
24:15So let me give you a little background,
24:17because this is an area where any of you
24:19potentially working on various climate environment
24:23influence on disease,
24:25but really any host of public health issues
24:28might be able to make use of such a system,
24:31collaboratively or on your own,
24:34to really bring environmental data in, in a powerful way.
24:37So what an LDAS does is it merges observations
24:39with numerical models,
24:41in order to get your best possible estimates
24:42of what's going on with the land surface
24:44and the lower atmosphere than your surface meteorology.
24:47Why do you do this?
24:48You do this because satellite observations
24:51are amazingly powerful tools, but they're snapshots
24:54of single variables.
24:56And so, if you want a comprehensive view
24:57of what's happening with all the potential
24:59variables of interest, you kind of want a model, right?
25:02You want something to give you spatially
25:03and temporally complete and consistent representation.
25:10But those models don't necessarily represent reality,
25:12particularly in data limited environments, like the Amazon.
25:16And so what you do with an LDAS is you basically
25:19pick at the best of both worlds to the extent possible.
25:22You have an advanced, physically based model
25:24that is trying to simulate what's going on
25:26with your weather and with your hydrology.
25:28And then you've got satellite observations
25:30that inform that model and kind of keep it realistic.
25:34And so, in schematic form,
25:36what you have is a bunch of landscape information,
25:38such as the land cover analyses I've just shown you,
25:41often satellite-derived.
25:43You have meteorological data,
25:44which is also often from satellites,
25:46or from other weather analysis systems.
25:50Those all drive a numerical model,
25:53which is then going to produce estimates
25:55of energy balance and hydrology, okay?
25:57So that'll get you, you know,
25:58the temperature, radiation, wind, moisture conditions
26:02you care about.
26:03As you run this model forward, you assimilate observations.
26:07And so you can update observations.
26:08So for example, information about soil moisture variability.
26:12Graded estimates come from satellite
26:14can be brought into the numerical model
26:16to update the model's estimate of soil moisture.
26:19And so, you end up with a system.
26:21This should be obvious,
26:22because we're using updated observations.
26:24This isn't like a future projection model, right?
26:27The model itself might be able to,
26:28but the LDAS system is retrospective,
26:31up through real-time monitoring,
26:33where you're bringing in these update observations,
26:34because the observations you can only have
26:37after we've taken the observation.
26:39Okay?
26:40And so these LDS systems are in a lot of places, you know?
26:45It's related, first of all, to weather forecast.
26:47Weather forecasts use LDAS, as well as assimilation
26:50of atmospheric variables.
26:51So those are used all the time.
26:53We also use these LDAS in a lot of the work we do,
26:56for example, on agricultural monitoring
26:58in the United States,
27:00climate assessment reports are very often include LDAS,
27:04like the National Climate Assessment of the United States.
27:06Work we do with the Famine Early Warning System in Africa.
27:09These LDAS are known to be pretty useful ways
27:10to get information.
27:12And so some of them have outputs that are available,
27:16that you can just get,
27:17because there's already someone running it.
27:18If you're interested in that,
27:20please contact me and I'll try to put you in touch.
27:22And then sometimes we run them ourselves
27:24to optimize them for a region we have here.
27:27There's a couple more minutes on this,
27:30just so you understand the basic principles here.
27:34One of the most important starting points
27:36is satellite-derived rainfall.
27:37We're using a couple of products here.
27:39I'm not going to bother with the acronyms.
27:40They don't matter.
27:41They are, in case anyone attending today
27:42is from the satellite world and is interested
27:44in what we're using, okay?
27:45So CHIRPS and GPM-IMERG.
27:49We then use that MODIS satellite that I already described,
27:52get our land cover and vegetation characteristics.
27:54And this cartoon here is showing you our model.
27:57It's called the Noah MultiParameterization
27:59Land Surface Model.
28:01And what it's doing is it's simulating
28:02multiple layers of the soil,
28:04different vegetation types, shallow groundwater.
28:08We also work into it a downscaling routine
28:10to get better surface meteorological estimates.
28:13It doesn't simulate the atmosphere,
28:14but it can help to downscale atmospheric conditions.
28:19And it also does snow, which actually does matter to us
28:23because we want to get the runoff coming out of the Andes,
28:24but it doesn't matter locally in the Amazon, obviously.
28:29So, that's all kind of for the local energy
28:32and water balance solution.
28:33We use Noah MP.
28:33We then couple it to a river routing model called HyMAP.
28:38And HyMAP, the hydrological modeling and analysis program
28:42that's what that stands for,
28:43allows us to model things like the flood plain,
28:45and that's, as you can imagine,
28:46really critical when you're talking
28:47about mosquito habitats.
28:49So we get the river heights.
28:50We also get the river width,
28:51and the area of flooded river boundary at any given time.
29:00We run this at five kilometer, gritty resolution.
29:02Five kilometers by five kilometers, or 25 square kilometer.
29:05And then around Iquitos,
29:06that city that has the largest population center.
29:09We nest into one kilometer
29:11for some higher resolution analysis.
29:15As we run the model forward,
29:16we can take advantage of these assimilation capabilities,
29:18and we run multiple simulations for different purposes.
29:21Sometimes we might be assimilating satellite-derived
29:24estimates of soil moisture, or leaf area index,
29:26or water storage, terrestrial water sources,
29:28meaning all the water stored in the soil column
29:30and groundwater.
29:31These are all observables at different resolutions
29:34from space using different civilian space missions.
29:39And those will all help to improve the performance
29:41of our model.
29:42And then you can get an output like what I'm showing
29:43on the right-hand side of the screen here,
29:44which is just a standardized anomaly in soil moisture,
29:47showing a period where, in our area of interest,
29:49for example, there were some drought going on
29:52in the Northwestern Amazon,
29:53as shown by a negative standardized anomaly
29:55in soil moisture, as captured by our system.
30:00I'll come back to this in a moment,
30:01but this particular snapshot is an interesting example,
30:05and that's showing what might be considered
30:08the classic El Nino pattern, okay?
30:10So it's an old snapshot. This one's from 1998.
30:13I've accidentally cut the date off of it.
30:15There's the monthly anomaly from a month in 1998.
30:19And what you're seeing here is the 1997, '98 El Nino
30:22bringing catastrophic flooding to the coast
30:25of Peru and Ecuador, and drought to the Amazon basin.
30:28Okay, I'll return to that in a moment,
30:31but that's kind of a classic El Nino pattern in the region.
30:36And so, here's just a quick animation
30:37to show what you're getting through time.
30:39I'm showing monthly up what's here.
30:40In fact, we get, you know,
30:42hourly outputs from the system that we can then extract
30:47for different geographies to perform our malaria analysis.
30:51Information on things like your air temperature anomaly,
30:53your rainfall, your soil moisture anomaly,
30:55your runoff, your river height, et cetera, et cetera.
30:57Okay, and so this is all the information
30:59that we're going to be bringing in,
31:01combining with the land cover and ecological information,
31:04to try to get this environmentally informed malaria analysis
31:09and early warning systems set up.
31:12So, one thing that you might be wondering is,
31:15"Okay, I just mentioned this was a data scarce area, right?"
31:19And these are outputs of some system
31:21that's combining satellite data with its uncertainties,
31:23and a model with its own uncertainties.
31:26How good is it, right? And can you trust it?
31:29And the answer is that in any study you do,
31:32where you want to make use of this
31:33kind of environmental data,
31:35you want to make sure that either you or someone else
31:37has done a good, clean analysis of how well
31:40that system performs in your region
31:43and season of interest, okay?
31:45You don't want to just take this off the shelf and say,
31:47"Oh, I know this going, going to be fine where I am."
31:49And so we've done some analysis.
31:53I'm not going to make you sit through
31:55our whole analysis kind of thing that we spend our days,
31:58nights and weekends doing, right?
31:59Make sure the systems work well
32:00and trying to fine tune them.
32:03But we have some data here that Cristina Recalde,
32:05a PhD student working with me has from Ecuador,
32:09and some data from Peru, looking at things like,
32:11"Okay, how well do we do in observations in blue,
32:15versus our model simulation on rainfall?"
32:18And there are good and bad things
32:19if you stare long enough at this chart,
32:21like, yeah, we're in about the magnitude
32:23of rainfall is not bad.
32:24The seasonality is pretty good most places,
32:26but then you'll find there's some wet and dry bias
32:28in different places that we're investigating.
32:31Similarly, you can then look at the soil moisture.
32:33Soil moisture is harder, because rainfall,
32:35there actually are rainfall observations.
32:37Not many, but there are some, right?
32:40Soil moisture, there's like basically
32:42no in-situ observations in a consistent way
32:44in the study area,
32:46and so we have to use satellite data to compare it to.
32:48So here, we're comparing this observation in gray,
32:50which is really a satellite observation,
32:52with our model simulation.
32:54And again, seeing some good, some bad.
32:57Here, we really do have to question the fidelity
32:59of both the observation and the model,
33:01since the observation is satellite-derived.
33:03At least it gives us a sense.
33:04Do we have a consensus across our different estimates,
33:07as to what's going on here?
33:10And this is tricky, right?
33:11Because getting soil moisture right in a complex hydrology
33:13like the Amazon is no trivial task.
33:16So this is a scenario where we spend a lot of our effort.
33:21Last point I want to make on this physical hydrology
33:24hydrometeorology before finally getting
33:26just the natural malaria results:
33:29it's really important,
33:31whenever you're doing a study like this, right,
33:34to distinguish between,
33:36when I say that there's hydrometeorological variability,
33:39am I talking about geographic variability?
33:41You know, wet versus dry places.
33:43Am I talking about seasonal variability, right?
33:46A wet season versus the dry season, for example.
33:48Or am I talking about something
33:50like inter-annual variability?
33:52Like, "Oh, we had a drought year,
33:53or we had a year with more flooding."
33:56And that's really important, you know,
33:58first and foremost, to understand process, right?
34:01You want to know that you get a statistical result
34:03that comes out of throwing some environmental variables
34:06into your model.
34:08They're significant. What is it that you're seeing?
34:10Right?
34:12And also, is what you're seeing a proxy for something else?
34:16Right?
34:17If you classically see like,
34:19"Oh, there's a wet versus dry season response,"
34:20or a warm versus cold season response,
34:23and when I look at my cases of malaria,
34:26is that because temperature's affecting malaria,
34:28or is it because there's a seasonal cycle in temperature,
34:31and seasonality for some other reason
34:33is affecting the malaria, and I'm calling it temperature?
34:36Okay.
34:37And so, you want to be clear on whether you're looking
34:41at the geography, the season,
34:42or the inter-annual variability.
34:44And this is on my mind a lot these days,
34:48both because I do a lot of this work.
34:51And as I know Kai appreciates and probably others
34:52in the audience as well,
34:54there's a lot of conflation of these things
34:56in the COVID-19 literature,
34:58with different claims or attempts to claim
35:00environmental sensitivities.
35:02Some really good work, right?
35:04But also a lot of these kind of naive, I would say,
35:06studies that came out showing correlations
35:08or associations that were simply showing a seasonality,
35:11or, you know, a coincidence of two patterns.
35:13The whole correlation versus causation problem,
35:16that I think part of the problem there
35:19was a misunderstanding or there's a mis-framing
35:23of what kind of climatic variability we're talking about.
35:28Okay, got off that soap box.
35:31And simply say for that third thing,
35:32all I've shown you here is seasonality
35:35and spatial variability.
35:37I haven't shown you inter-annual variability.
35:38I want to comment a little bit on that in this region,
35:41because anyone who's worked on malaria in the Amazon
35:44or other malaria zones probably are aware
35:46of a lot of studies, good studies, right?
35:50That have associated malaria
35:52with various large scale climate modes.
35:55Certainly these drivers of variability, okay?
35:59And so the big one is El Nino.
36:01The El Nino Southern oscillation, okay?
36:04But there are many others.
36:05It's an alphabet soup that I won't get into.
36:08El Nino, in this part of the region.
36:11One might well expect an El Nino effect here, right?
36:13It's called El Nino because of the effects it had,
36:16you know, was first characterized in the coast of Peru,
36:18and what it does to the sardine fisheries
36:20off the coast of Peru.
36:21And so, this is kind of like the home of El Nino, right?
36:24And so, we certainly expect an El Nino effect.
36:26And as I showed a few slides ago,
36:28a classic pattern would be high rainfall on the coast,
36:31drought in the Amazon,
36:32for dynamical reasons that I won't get into.
36:37It's not that simple or that predictive
36:41as a simple univariate association
36:44in this part of the Amazon, at least.
36:47There's some other parts of the Amazon
36:48that respond a little bit more reliably,
36:50but I'll tell you, it's always a little complicated.
36:53But here, just taking it again from Cristina's work here,
36:57looking at El Ninos and La Ninas the past 20 years.
37:01And if it's red, it means you've got drought,
37:02or drier conditions.
37:04If it's blue, it means you have wet anomalies.
37:06And again, during El Nino, we should be seeing red
37:08in the Amazon.
37:09And here, you know, we got our Laredo state.
37:12Sorry, it was just Ecuador and Peru I'm showing you.
37:15So we've got this kind of, here's your Northern Amazon
37:18portion of our study region.
37:21And what you're seeing is that, yeah, during some El Ninos,
37:24you do see that drought pattern, okay?
37:26But you also see it in this La Nina,
37:29and then there are some El Ninos
37:31where you don't see it at all,
37:32and in fact, that big monster El Nino that hit in 2015
37:36and had effects globally, it was wet
37:39in our part of the world,
37:41when you might've thought it was supposed to be dry.
37:44And so, there are some complications here, okay?
37:49All I can say that one could use, and so El Nino,
37:54La Nina oscillations effectively, statistically,
37:57in a forecast in here,
37:59if you accounted for enough other variables.
38:01I'm highlighting the fact that it's not enough
38:04of a predictor of rainfall in its own right, okay?
38:06But combined with other factors,
38:08you can probably get some scale.
38:10But we decided to take a different approach,
38:12which is, rather than using these kinds of teleconnections,
38:15these like remote connections to El Nino directly
38:17in our model,
38:18we run a dynamically based forecast.
38:21And so what we're doing there is, again,
38:24this one's a little detail for those who might be
38:26working at this interface of climate and health.
38:29We run what we call subseasonal to seasonal forecast.
38:32You know, a few weeks out to...
38:34Well, you can go to nine months.
38:35We're really only going up to three months right now,
38:36for this application.
38:39And what you do is you take what I already showed you
38:41in the LDAS, the satellite landscape analysis,
38:43run it through a land data simulation system.
38:46That provides initial conditions,
38:49from which you generate an ensemble.
38:50So your seasonal forecasts are
38:52these probabilistic ensembles, just like weather forecasts.
38:55And these are these global atmospheric models
38:57that we run forward.
38:59We run them forward using initial conditions
39:01of the hydrology locally, and the ecology locally.
39:05We downscale the meteorology
39:07from those global forecast systems
39:09using some algorithms that, again, I won't get into,
39:12but happy to follow up with anyone doing this kind of work.
39:15And then, we put that into hydrologic work.
39:17As we run it through the same modeling system,
39:19it's no longer data simulation
39:21because we don't have observations.
39:22We run that system forward.
39:24Okay. So why do all of this?
39:27Because it pushes your forecast time horizon out.
39:34If I, as the climate guy in the team,
39:36give Bill and Mark, the epidemiology guys on the team,
39:39a monitoring system that is operationally saying
39:41what the moisture is right now,
39:43they can forecast malaria because it's a time lag, right?
39:46So they'll get a pretty good forecast,
39:48because it takes time for the signal I'm sending them
39:50to propagate through the ecology, and the human systems.
39:54But if I can give them a forecast of what it's going to
39:56be like in two months, that gives them, you know,
39:58eight weeks more lead time,
40:00and you can make a different set of decisions,
40:01given an extra two months, right?
40:03So it's all about this uncertainty time horizon
40:06trade-off year.
40:07The more we push out for a greater time horizon,
40:09the greater our certainty,
40:10but also potentially the greater power
40:13of the decision-making
40:14that that kind of system can empower.
40:18So, how did these forecasts look?
40:22I'm not going to make you sit through
40:23a whole forecast scale analysis,
40:24but just want to make one point here.
40:26If you just focus, let's say, on correlation here,
40:29for the sake of time,
40:31if there's hashing,
40:32it means a statistically significant scale.
40:34And what you see here is that looking at something
40:36like soil moisture, we get really good forecasts
40:40for one month, and then it begins to degrade,
40:43particularly degrading these wet areas.
40:46You've maintained some forecast scale out in the dry areas,
40:48because there's so much memory, right?
40:49If it's not raining much,
40:50most of the initial conditions that matter.
40:53But as you go out,
40:54the result here we might say is that
40:56we can really do a nice job of getting you
40:58an extra four weeks, right, on the system.
41:00If you want eight weeks or 12 weeks,
41:02and you know, we're not going to be contributing
41:04that much stuff in the forecast.
41:05And so it's important both to have the capability,
41:07and to understand the limitations of the capability.
41:10All right.
41:11So we do all those analyses.
41:14And then, this is not my work.
41:16This is work that Bill led.
41:17He took all of this ecological and hydrological analysis,
41:23and did an objective regionalization,
41:25did principal components analysis on the variability.
41:28End up with these three different factors
41:29that are loaded by different properties of the system,
41:33and counting for about, you know, human systems,
41:35as well as land use and hydrometeorological conditions.
41:40And from that, derived these seven
41:42socioenvironmental regions.
41:45And the principle here is that these regions
41:48are reasonably homogeneous and regionally distinct
41:51from each other,
41:51with respect to human and environmental factors.
41:55And also, as it happens,
41:57this was not necessarily integrated to that,
41:58but because you've included the human systems
42:00in the analysis, most of the travel stays within the region.
42:05And you typically have similar vector species
42:08within a region.
42:09Okay, and similar livelihoods.
42:11So, what we then say we're not going to develop
42:13one malaria risk model.
42:16And again, this is now, we're seeing Laredo regions,
42:18so this part of Peru.
42:19We're going to develop a system that has customized models,
42:23based on socioenvironmental regions.
42:27So, in the remaining time that I have, which isn't much,
42:29I know, so I'll touch on these lightly,
42:32but these are just examples of how we can
42:34pull this all together, all right?
42:36And so the first thing,
42:38kind of the motivation for this whole presentation,
42:40this whole project is forecast, right?
42:44And so, using these socioenvironmental regions,
42:47then aggregate malaria data,
42:49which we have about 300 health posts contributing data,
42:52passive surveillance.
42:54They get aggregated to a socioenvironmental region.
42:56And then we try to predict whether there's an outbreak,
42:58based on the Ministry of Health's definition
43:01of what an outbreak is, which is, you know,
43:04exceeding a certain threshold,
43:05in terms of case number per population.
43:09Again, this work led out of Duke by Bill,
43:11and he uses observed components model
43:13as a statistical method,
43:15and was seeking to get a time horizon of four to 12 weeks.
43:20And again, because it's customized by region,
43:22what you'll find is that the model
43:24has different variable importance
43:27and is structured differently for the different models.
43:28So region one, which includes Iquitos,
43:31so it's kind of like our most urban area,
43:34we can describe that in terms of the characteristics
43:36of the socioecological region.
43:38And then we can say, "Okay, what explanatory variables
43:41from our environmental suite end up being significant?"
43:44It turns out to be soil moisture.
43:46We can then look at a region like region three,
43:48kind of really out in the forest,
43:49very low population density.
43:51It has a different description.
43:52It's going to have different statistical characteristics
43:54to this unobserved components model.
43:56And in this case, minimum temperature
43:58came out of the more significant variable.
44:00Both of these variables, of course,
44:01if you look at the literature, are using malaria prediction.
44:04So they're both plausible, they're possible pathways,
44:06but different ones came out as more predictive
44:08in these different regions.
44:11Okay? So then we run the system.
44:15We have to run the system starting four weeks
44:17before the present.
44:18Why?
44:19Because it takes about four weeks
44:22for surveillance to come in.
44:23Here's the percent of health post reporting
44:26of malaria data.
44:27As you can see, this is time, this is the present.
44:31At the present, you have fewer than 20% reporting.
44:33If you go back four weeks,
44:34you have close to 100% reporting,
44:36which means that you have a good...
44:37You know, previous cases are important predictor
44:39of future cases.
44:41So the forecast includes a four week forecast of the past.
44:45And then, we want to go out to eight or 12 weeks
44:47in the future.
44:49We have this moving outbreak threshold,
44:51because it varies seasonally and by location,
44:53what MINSA, the health ministry decides
44:55is the right threshold to declare an outbreak.
44:57And then we might have an observation,
44:59and a competence interval around that observation.
45:03Just to give you an example of performance,
45:042016 was the first year we really tried this.
45:07So this isn't just a systematic analysis,
45:09just showing you the kinds of things you look at.
45:11True positives, false negatives, false positives,
45:13true negative.
45:15For an outbreak in any of these eco regions,
45:18looking at eco region one and three here,
45:20over the different forecast time horizons,
45:22our sensitivity and our specificity.
45:25In a nutshell, we do really well in eco region one.
45:28Fades a little in specificity as we get out
45:30to 12 week time horizon, still pretty good.
45:33eco region three, we do not do that well, okay?
45:37And so again, small sample one year,
45:39but just our first test was showing us
45:41that we're going to get different performance
45:42in different eco regions.
45:46Okay.
45:47And so, that's all at the eco region level.
45:51I'm not going to get to too many more results
45:52at that level right now,
45:54but rather say that to be decision relevant,
45:56we have to go down to the district level.
45:58So, the lines here on this map are separating the districts.
46:02Okay.
46:03And so the colors of the eco regions
46:04aligns with the district.
46:05We really want to be at a district level.
46:07And so for this, again, won't get to the details right now,
46:10but Mark Janko implemented this hierarchical
46:13Bayesian spatio-temporal logistic model,
46:16where you basically have your district outbreak probability
46:20being a function of the probability of an outbreak
46:23in the eco region that contains the district,
46:25and some district-specific properties.
46:29When Mark downscaled and looked at some of these analyses
46:31and then did an evaluation over a retrospective period,
46:36these are the kinds of sensitivities and specificity
46:38we're getting for different districts
46:40within each eco region.
46:41Again, just showing you eco region one and three here
46:43as examples.
46:44And you'll see that again, pretty high variability.
46:47So we were doing well in eco region one at eco region level,
46:50but you'll see that, for example,
46:51in the district of Fernando Loris,
46:53there were some pretty significant errors
46:55in this retrospective period,
46:58and different kinds of errors in different places.
47:00So also for us to look at,
47:02in eco region three, kind of uniformly doing worse
47:05in general, than eco region one.
47:08So why is that? Why are we doing poorly in region three?
47:10Multiple reasons.
47:11One thing I want to emphasize is that eco region three
47:16was very much located kind of up in this area.
47:18So first of all, malaria cases are generally low there
47:20in total, because it's such a sparsely populated area.
47:23But it's also a border area.
47:24It's a border area that is transected
47:27by trans boundary rivers.
47:28The trans boundary rivers are the transportation
47:31in the region.
47:33And so what we find is that our model fits most poorly here
47:36in eco region three and another eco region
47:39dominated by trans boundary river.
47:41Doesn't do well in places along the rivers. Okay?
47:45And so that's one big weakness in the model
47:48that we're working on.
47:51And oops, the slides got reversed.
47:54And I just want to point out that we are looking at,
47:57and we had a paper recently, led by students.
47:59And so this is students from Duke, Johns Hopkins,
48:02Ecuador and Peru, who took the initiative
48:04to really lead an analysis of this cross-border spillover.
48:08And that's something we're looking at now.
48:11Okay.
48:13So, that's where the forecast system is.
48:15We brought it in 2019.
48:17We did some operational forecasts for the Health Ministry.
48:20Was all looking good.
48:21Then there's political change and COVID,
48:23so we're a little bit on hold right now,
48:24but we've got a system that we've proved
48:26we can use operationally.
48:27We continue to try to improve the performance.
48:30Policy evaluation. Okay.
48:33So I'm going to give one example
48:35of policy analysis we've done.
48:38That was PAMAFRO, which was this project for malaria control
48:41on the Andean border areas, active 2006 to 2010 or 11,
48:45depending on how you counted.
48:47They did four kinds of things.
48:48Long-lasting insecticidal nets,
48:51better rapid diagnostic tests, and other monitoring tools,
48:56case management, with antimalarial drugs and training,
49:00and environmental management for vector control.
49:02So doing these four kinds of things.
49:04And it kind of worked, right?
49:06So this is by vivax and falciparum in Laredo.
49:09And it sure looks like over the PAMAFRO period,
49:11the case counts were going down, down, down,
49:13approaching eradication, which was the goal of the program.
49:17Then stops suddenly in 2011, cases start coming back up.
49:21And what we can do is we can leverage that district model
49:24that Mark Janko developed, right?
49:27Not only using it for forecasts, but then saying,
49:29"Well, let's include in that model structure
49:31the different interventions, especially with PAMAFRO."
49:33Because we know at district level and with monthly timing,
49:37what kind of interventions were done where.
49:39Let's integrate that to a model and then do
49:41an interrupted time series analysis,
49:44and see what those interventions actually accomplished
49:47on the background of climate variability,
49:50and all the other variables in our model.
49:52So kind of an environmentally controlled analysis
49:55of the effectiveness of the intervention.
49:58Mark's found is that, well, you can kind of quantify this.
50:02So the blue line here in the top left,
50:04top is vivax, bottom is falciparum.
50:07Blue lines are the model, dots are the observation.
50:11On the left, we have the PAMAFRO period.
50:13And we see that our model,
50:15if you don't tell it about the intervention,
50:16systematically overestimates the cases in this period,
50:19for both vivax and falciparum.
50:21In the post PAMAFRO period, starting in 2011,
50:25quite the opposite.
50:26Our model has cases down here.
50:28The observed cases were much higher.
50:32And so, take those together and come up with estimates
50:36that about 150,000 cases were averted by PAMAFRO.
50:38That was the amount of malaria averted thanks to PAMAFRO,
50:42and had you continued it for another five years,
50:45you would've averted another 150,000,
50:47not to mention the long-lasting impact
50:49of driving cases that low, right?
50:52And so here we have an analysis of both the effectiveness
50:55and the cost of removing a program
50:58without a good continuity plan.
51:01And then you can zoom in, because again,
51:03we have this district level information
51:04on each kind of intervention.
51:06I see I'm running out of time,
51:07so I won't spend too much time walking through these maps,
51:09but green shows incidence ratio less than one.
51:13And so we can look district by district
51:14and say, "Okay, for falciparum and vivax,
51:20for each of the four intervention types,
51:22environmental management, bed nets, et cetera,
51:25in which districts do we see the most effect
51:27when we add or remove this from our interpretive
51:29time series analysis?"
51:31And there's some interesting patterns that appear
51:32that we're in conversation with some of our partners about
51:36to figure out what might be effective in the future.
51:40One of the cool thing just mentioned
51:41that you can do with this
51:42is try to figure out how much malaria and dengue there is
51:46right now in this area, because we have no idea.
51:49If you look at what happened in 2020 with surveillance,
51:52I mean the health system basically shut down.
51:54And so, it looks like it was a great year
51:55for malaria control, but of course it wasn't.
51:59So we can then use this same modeling approach
52:02to try to estimate how many cases there really were
52:04in the year, 2020 and 2021.
52:06And as you can see, we estimate that there were
52:08at least three times as many cases.
52:13Okay.
52:14Last point I want to make here is that
52:17I've showed you some malaria modeling cases
52:18that are process-informed,
52:20but at their heart, statistical, right?
52:22These are empirical analyses.
52:24And looking at intervention scenarios,
52:26we are also looking at explicit simulation of behavior,
52:31okay, to get these coupled natural human systems right.
52:34And the way that we are doing that,
52:36led by Francisco Pizzitutti,
52:38is with agent-based modeling.
52:40And this is a kind of Coolidge based model Francisco built,
52:42in that it has agents that are mosquitoes, humans,
52:46and plasmodium, okay?
52:47So. you have all of these are agents interacting.
52:50And here is just an example of one of the villages
52:52where he's applied this,
52:53where you can have different households,
52:56and all these agents are interacting
52:58and influenced by the environment.
53:00In that here, we see different kinds of breeding habitats
53:03influenced by seasonal flooding,
53:05with information from our environmental analysis system,
53:08changing the hydrology.
53:09And then you've got the cases happening in this household,
53:11each of which is also experiencing
53:13its own environmental conditions, okay?
53:16You can then run scenarios of control.
53:18For example, vector control strategies,
53:21one thing we like to look at.
53:22And so we're looking at here
53:23at one of these environmental control applications,
53:25and saying, "Well, what if you do larval habitat control
53:28around a certain buffer radius,
53:30around each household, right?"
53:32How well do you do at 50 meters, 100 meters,
53:34150 meters, 200 meters,
53:36when you talk about malaria incidents?
53:37Total vivax falciparum.
53:39And the idea here is that,
53:40by understanding this agent based model
53:43movement patterns, right?
53:45And the sensitivities of the different agent types,
53:49we can get a sense, say,
53:50"Well, really you want to probably get out
53:52while you take your pick,
53:53but I would say at least 150 meters
53:55might be considered very effective.
53:56Anything beyond 200 is unnecessary."
53:59All right.
54:00And so this is parametrized for one set of villages.
54:03It's very data intensive, but nevertheless,
54:05I think it indicates a powerful way to,
54:07you know, use your environmental information
54:09in a different manner, not as an empirical predictor,
54:12but as a variable within a model
54:16in which different agents are responding
54:17according to decision rules to this variability.
54:24You can also use the same tool, and Francisco has,
54:26to look at the importance of mobility, right?
54:28So that's something people talk a lot about
54:29in the past couple of years, right?
54:30How much mobility influences disease transmission.
54:33It's an old story from malaria.
54:34What you'll see here is if you look
54:35at your observed black line here
54:37of the average monthly malaria incidents
54:39along the Napo river,
54:42first thing you know, is that,
54:43"Well, okay, if I run this model with no asymptomatic cases
54:46considered in travel,"
54:47you assume that no asymptomatic people are traveling,
54:50you way underestimate the incidence rate.
54:52So we know there's a lot of asymptomatic activity going on.
54:56And then we can say,
54:57"Okay, as the percent of traveling workers increase,
55:00we would expect the incidence rate to increase."
55:03And we're right about the right order of magnitude.
55:04And it looks like some of this movement
55:06really does need to be accounted for,
55:07to understand the incidence rates
55:10with significant implications, again,
55:12or how you would do monitoring and control in the region.
55:16So, ran a little longer than I wanted to. Sorry.
55:19That's what happens when you let professors talk.
55:22But just a few of the next steps here.
55:24I break them into four categories.
55:26We're really working on the application here.
55:28As I noted, there's been a lot of political turnover
55:31in Peru for those who know the region,
55:32which has hampered our ability to operationalize a forecast.
55:35So now, we're starting to train and transfer
55:37to some universities and research institutions
55:40in the region, rather than straight to the government,
55:42to be able to spare stability.
55:45We're just having our first meeting this week
55:47on an effort to expand to include Columbia and Brazil.
55:50So it's a big up-scaling of the effort.
55:53And we're also seeing,
55:54can we transfer this to an area in central America,
55:57working with the Clinton Health Access Initiative, sorry.
56:01Flipped the letters.
56:04On Central America, where the case counts are low
56:06and therefore the ecology and the environmental sensitivity
56:09of the system shifts.
56:11It seems to cross a threshold.
56:12So we want to see how the approach works there.
56:15And last, but certainly not least,
56:17through these combined methods, but again,
56:19all trying to leverage the power of the different fields
56:21to understand malaria sensitivities.
56:24How can we continue to explain these coupled
56:25natural human mechanisms, which,
56:28despite the fact that we've known about these relationships
56:31since ancient times,
56:32we continue to struggle to understand
56:35in a predictive manner today.
56:37So, thank you again for the opportunity to talk.
56:39I realize I didn't leave too much time for questions,
56:41but maybe we have time for a couple.
56:51<v Kai>Thank you, Ben, for the great talk.</v>
56:52So, we actually have a class right after this seminar,
56:55so I think we only have time for one question,
56:59and the students have already read the papers
57:02that you mentioned published in your page.
57:06So, any of you want to ask a question directly?
57:11(indistinct)
57:13Okay, so let me ask you this question.
57:15So Ben, you gave wonderful talk on the importance
57:21of value, time and migrating,
57:24the importance of having the data,
57:26and then from the very state of the art
57:29subseasonal to seasonal forecast.
57:32The students when they read the paper, they have question
57:35regarding (indistinct) also COVID-19 related.
57:38So, did you see how to apply this malaria focus system?
57:45The application to COVID-19 control focus system?
57:52<v ->Yeah. Interesting point.</v>
57:54So, I'm going to answer in a very general way.
57:58They're obviously very different diseases, right?
58:00We're talking about a vector-based tropical disease
58:02versus a pandemic virus with a lot of airborne transmission.
58:07But I would say that the general challenge
58:10of bringing these different data sets together
58:12is really critical.
58:13And we can do cross-learning across diseases,
58:16because one thing we've really struggled with in COVID
58:19is to bring all the information together
58:20in systematic databases for responsible analysis.
58:24And we were able to leverage some of the things
58:26we've done with malaria and other tropical diseases,
58:29to build COVID information databases, to support research.
58:32And I know that Kai did his own work
58:33to pull his own database together.
58:35So moving forward,
58:36how can we use all of these diseases
58:37to inform those kinds of data structures,
58:39I think would be...
58:41And cross-learning approaches will be the way to go.
58:43I wouldn't necessarily endorse any single thing
58:45that I did here on malaria as the answer for COVID-19 model.
58:48They're too different.
58:49But if you can really focus on that kind of
58:53informed integration, I think there's a lot to be learned.
58:57<v Kai>Thank you so much, Ben.</v>
58:58And thank you, guys, for coming today,
59:00and thank you for our online audience.
59:03And just kind of reminder that today's lecture
59:07is recorded and will be available online,
59:10on our (indistinct) websites, so you can check that.
59:15Want to sincerely thank you, Ben,
59:17for giving this incredible talk.
59:21<v Benjamin>Great, thank you.</v>