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---
title: Environmental drivers of vector-borne and zoonotic diseases
subtitle: Leveraging remote sensing for Public Health
author: Verónica Andreo
title-slide-attributes:
data-background-color: "#1A428A"
format:
revealjs:
hash-type: number
slide-number: true
chalkboard:
buttons: true
preview-links: auto
logo: "https://ig.conae.unc.edu.ar/wp-content/uploads/sites/68/2022/04/G-UNC-CONAE-C.png"
theme: [default, assets/css/IG_style.scss]
---
## About me { background-color="#1A428A" }
<br>
:::: {.columns}
::: {.column width="60%"}
- Researcher and lecturer at Instituto Gulich
- Background: Dr. in Biology, MSc. in Spatial Information Applications
- Remote sensing and geospatial applications in disease ecology
- Member of the GRASS GIS Dev Team & project chair; OSGeo Charter member & FOSS4G enthusiast
:::
::: {.column width="40%"}
```{r}
#| echo: false
#| fig-height: 4
#| fig-width: 4
library(leaflet)
leaflet() %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng=-64.4653258, lat=-31.5204194, popup="IG")
```
:::
::::
{{< fa globe >}} <https://veroandreo.gitlab.io/>
## Overview { background-color="#1A428A" }
- Motivation
- Health Geography
- Disease Ecology
- Leveraging remote sensing for Disease Ecology
- Resolution vs scale
- How can we use RS?
- Examples
- Gaps, challenges and opportunities
- Conclusion
---
:::{.r-stack}
{.fragment fragment-index=2 .fade-in-then-out width="90%"}
{.fragment .fade-in-then-out fragment-index=1 width="55%" fig-align="center"}
{.fragment fragment-index=3 .fade-in width="100%" fig-align="center"}
:::
::: footer
<https://sdgs.un.org/goals/goal3>
:::
::: {.notes}
Vector-borne and zoonotic diseases are responsible for one-sixth of disease and disability worldwide. This is one of the reasons why good health and well being is among the 17 goals from UN for a sustainable development. More specifically, the target is to end epidemics of NTD among other communicable diseases.
:::
---
### Neglected Tropical Diseases (NTD)
<br>
::: {.columns}
::: {.column width="50%"}
{fig-align="center"}
:::
::: {.column width="50%"}
{.fragment .fade-in width="80%" fig-align="center"}
:::
:::
{.fragment .absolute bottom=40 right=380 width=150}
{.fragment .absolute top=5 right=-30 width=170}
{.fragment .absolute bottom=130 right=-30 width=120}
::: {.notes}
Now, which are these NTD and why is it that remote sensing might be relevant for their study? Well, as you can see most of them diseases are transmitted by animals, they need a vector to carry the pathogen from host to humans or among humans. Animals, specially hectothermic ones, are highly affected by the environmental conditions of a certain area... think of Aedes aegypti mosquitoes for example... and what information can be most easily extracted from RS? Yes, environmental
:::
---
::: {.center}
You all have seen this, right?
:::
::: {.columns}
::: {.column width="60%"}
:::
::: {.column width="7%"}
:::
::: {.column width="30%"}
<br><br>
:::
:::
::: {.notes}
Before we reach the core of remote sensing uses and applications there are a couple of things that happened first and some definitions I'd like to share... You all have seen this right? This is the first class of a GIS course, but also, the first time that the association between health/disease and place/space was clearly mapped.
:::
## Health Geography
<br>
::: {.columns}
::: {.column width="33%"}
::: {.color-box-light-blue .fragment fragment-index=1}
**Environmental health**: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.
:::
:::
::: {.column width="33%"}
::: {.color-box-light-blue .fragment fragment-index=3}
**Disease ecology**: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.
:::
:::
::: {.column width="33%"}
::: {.color-box-light-blue .fragment fragment-index=2}
**Health care delivery and access**: spatial patterns of health care
provision and patient behavior.
:::
:::
:::
::: {.notes}
This was the beginning of what was the called health geography; the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Within this discipline we might identify 3 fields of study: Environmental health, Health access and disease ecology
:::
## Health Geography
<br>
::: {.columns}
::: {.column width="33%"}
::: {.color-box-light-blue}
**Environmental health**: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.
:::
:::
::: {.column width="33%"}
::: {.color-box-light-magenta}
**Disease ecology**: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.
:::
:::
::: {.column width="33%"}
::: {.color-box-light-blue}
**Health care delivery and access**: spatial patterns of health care
provision and patient behavior.
:::
:::
:::
::: {.notes}
While RS has applications in all fields, I'll focus on those related to disease ecology as it is where I have worked the most
:::
## Disease Ecology I
::: {.columns}
::: {.column width="57%"}
<br><br>
{width="700"}
:::
::: {.column width="43%"}
The main objective is to understand the influence of environmental factors and to predict when and where a disease is most likely to occur
::: {style="display: flex; align-items: center; justify-content: center;"}
{{< fa solid angles-down size=2x >}}
:::
decision making, planning of prevention, management or response actions, etc.
:::
:::
## Disease Ecology II
::: {.r-stack}
{.fragment width="150%" fig-align="center"}
{.fragment width="85%" fig-align="center"}
::: {data-id="box" .fragment style="background: rgba(232, 31, 118, 0.2); border: 5px solid; border-color: #e81f76; width: 700px; height: 370px; transform: translate(0%, -26%);"}
:::
:::
::: footer
@lambin_pathogenic_2010
:::
::: {.notes}
This relationship with environment is usually represented by a triad, the
epidemiologic triad. For disease to occur (in humans) we need proper environmental
conditions, a pathogen and competent hosts and vectors.
These relationships might be a bit drawn in a more complex way, but the basis is
the same, i.e., environment encompasses climate, LULCC, habitat fragmentation,
human behaviour, and all the relationships among them. This is what we usually try
to observe with RS.
:::
## Use of RS in Health applications
{width="90%"}
::: footer
@viana_remote_2017
:::
:::{.notes}
Indeed, the use of remote sensing for public health applications has been increasing
since 2007 according to this review, but I'd say it started even earlier. I particularly remember a set of publications from the 4 corners area when sin nombre virus appeared.
:::
## Most common RS variables used
:::: {.columns}
:::{.column width="70%"}
{fig-align="center"}
:::
::: {.column width="30%"}
<br>
:::{.color-box-light-blue}
- LST
- Precipitation
- NDVI
- LULC
- Elevation
- NDWI
:::
:::
::::
::: footer
@parselia_satellite_2019
:::
:::{.notes}
The most common variables used in general are: LST, Precipitation, NDVI,
Elevation... This is for mosquitoes, if we think about ticks or mice, for example,
I beat vegetation variables are more frequently used
:::
## Remote sensing basic features
{fig-align="center"}
::: footer
@dif_resolutions_2018
:::
::: {.notes}
However, we should take into account some basic features of remote sensing before selecting which data to use.
:::
## Remote sensing & scale I
<br>
{fig-align="center"}
::: footer
@pearson_predicting_2003
:::
:::{.notes}
To decide which RS dataset most suits our problem we also need to think about the
scale on which variables operate. This shows for example the relationship of env var and distribution of species according to scale.
:::
## Remote sensing & scale II
::: {.panel-tabset}
## Taxonomy
{fig-align="center"}
## Plants
{fig-align="center"}
## Animals
{width="65%" fig-align="center"}
:::
::: footer
@leitao_improving_2019, @lechner_applications_2020, @rumiano_movement_2020
:::
:::{.notes}
Also if we study other biological or ecological phenomena we should consider scale and think which type of RS product would be the most appropriate.
:::
## How to apply RS in disease ecology?
<br>
::: {.r-stack}
::: {data-id="box1" .fragment style="background: rgba(232, 31, 118, 0.2); border: 5px solid; border-color: #e81f76; width: 300px; height: 140px; transform: translate(-115%, -77%);"}
:::
::: {data-id="box2" .fragment style="background: rgba(232, 31, 118, 0.2); border: 5px solid; border-color: #e81f76; width: 300px; height: 160px; transform: translate(-115%, 30%);"}
:::
::: {data-id="box3" .fragment style="background: rgba(232, 31, 118, 0.2); border: 5px solid; border-color: #e81f76; width: 330px; height: 300px; transform: translate(100%, -10%);"}
:::
:::
::: {.notes}
Going back to disease ecology then, what do we use RS for?
- To map the response variables, i.e., species occurrence or abundance, infections, disease cases
- To map the predictor variables
- To validate predictions
:::
## { background-image="assets/img/lecture/satellite_and_earth.jpg" }
::: { style="color: #ffffff; font-size: 1.5em;" }
Let's have a look at some real cases...
:::
---
### Detecting and mapping species occurrences
<br>
- Very high resolution (VHR) imagery
- Hyperspectral data (esp. for plant species)
- Direct and indirect counting (CV, ML, DL)
::: {.columns}
::: {.column width="53%"}
:::
::: {.column width="47%"}
:::
:::
::: footer
@wang_surveying_2019
:::
---
### Detecting and mapping species occurrences
<br>
{width="70%"}
::: {style="float: right; transform: translate(0%, -100%);"}
{width="550px"}
:::
::: footer
@meng_landsat_based_2022
:::
---
### Time series analysis of satellite products {.smaller}
- MODIS LST temporal and spatial reconstruction (LWR & splines)
- Estimation of relevant indices (GRASS GIS temporal framework!)
- Detection of spatial and temporal clusters of favorable conditions for the occurrence of West Nile Fever cases in Greece
::: {.columns}
::: {.column width="33%"}
{.absolute top=350 left=-100 width="85"}
:::
::: {.column width="67%"}
<br>
{.fragment .grow}
:::
:::
::: footer
@metz_new_2017, @andreo_identifying_2018
:::
---
### Environmental risk of Dengue
- MODIS LST is used to estimate number of extrinsic incubation periods (EIP) that virus might complete; the higher this number, the higher the environmental risk
](assets/img/lecture/riesgo_dengue_por_localidad.png)
::: footer
@porcasi_operative_2012, <https://github.com/InstitutoGulich/RiesgoAmbiental>
:::
---
### SDM & GIS based approach for HPS risk map
::: {.columns}
::: {.column width="50%"}
<br>
{width="30%" fig-align="center"}
:::
::: {.column width="50%" .smaller}
We combined a rescaled probability map of the host with one of the human cases to determine levels of transmission risk
{.absolute top=230 right=50 width="210"}
<br>
:::
:::
::: footer
@andreo_modeling_2011, @andreo_estimating_2014
:::
---
### Cutaneous leishmaniasis and LULCC
{.absolute top=5 right=-30 width=120}
<br>
::: {.columns}
::: {.column width="50%"}
{.fragment .absolute top=400 left=25 width="90"}
:::
::: {.column width="50%"}
:::
:::
::: footer
@andreo_ecological_2022, [*i.cva*](https://grass.osgeo.org/grass-stable/manuals/addons/i.cva.html)
:::
<!-- --- -->
<!-- ### Mosquitoes: towards operational high res maps -->
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<!-- ::: footer -->
<!-- @andreo_towards_2021 -->
<!-- ::: -->
---
### Spatial distribution of temporal patterns
- Temporal and spatial patterns in *Aedes aegypty* in Córdoba
- Association with variables derived from Sentinel 2 imagery analysis to predict temporal patterns over the whole city.
{.r-stretch}
{.absolute top=220 left=200 width=120}
{.fragment .absolute bottom=30 right=420 width=130}
{.fragment .absolute bottom=30 right=30 width=100}
::: footer
@andreo_spatial_2021
:::
---
### Urban environmental characterisation for the distribution of ovitraps
:::: {.columns}
::: {.column width="40%"}
- Object-based classification of VHR imagery
- Landscape metrics for polygons
- Clustering to find groups of similar polygons
- Stratified distribution of ovitraps
::: {style="font-size: 0.7em;"}
{width="60" style="float: right;"}
MSc thesis, **Carla Rodriguez**.
:::
:::
::: {.column width="60%"}
:::
::::
::: footer
@grippa_open_source_2017, @georganos_scale_2018, Rodriguez Gonzalez et al.
:::
---
### Predictive system based on population dynamics and weather forecasting {.smaller}
:::: {.columns}
::: {.column width="60%"}
:::
::: {.column width="40%"}
{width="75%" fig-align="center"}
<br>
::: {style="font-size: 0.8em;"}
Development of an early warning system (EWS) for dengue. PhD candidate, **Tomás San Miguel**.
{width="65" style="float: right;"}
:::
:::
::::
::: footer
@aguirre_implementation_2021
:::
## {{< fa person-digging >}} Online surveillance system {{< fa person-digging >}}
<br>
{.fragment fig-align="center"}
<br>
{width="500" fig-align="center"}
## {{< fa person-digging >}} Online surveillance system {{< fa person-digging >}}
::: {.r-stack}
{fig-align="center" width="67%"}
{.fragment fig-align="center" width="85%"}
{.fragment fig-align="center" width="82%"}
:::
## Other projects under development {.smaller}
::: {layout="[1, 20]"}
{width="51"}
Incidence of asthma as a function of remotely sensed air quality and LULCC. PhD candidate, **Abraham Coiman**.
:::
::: {layout="[1, 20]"}
{width="51"}
Distribution of congenital diseases and access to health. PhD candidate, **Carla Rodriguez Gonzalez**.
:::
::: {layout="[1, 20]"}
{width="51"}
Epidemiological characterisation of intestinal parasite infection in children. PhD candidate, **Matias Scavuzzo**.
:::
::: {layout="[1, 20]"}
{width="51"}
Geospatial modelling of malnutrition in children and adolescents. PhD candidate, **Micaela Campero**.
:::
::: {layout="[3, 18]"}
{width="51"}
{width="51"}
Environmental variables associated with non-communicable diseases. **Dr. Juan Diego Pinotti** and **Dr. Ximena Porcasi**.
:::
## Is everything studied then?? { background-color="#1A428A" }
---
## Challenges and gaps - RS
{.absolute top=5 right=-15 width=120}
- **Trade-off** between different RS resolutions, the problem under study, the data and methods available
- **Gaps in optical RS**: clouds, shadows in optical RS (spatial and temporal interpolations)
- Need for **corrections** if high level data is not suitable
- **Limited access** to VHR, LiDAR, Hyper-spectral (US$, tricky to scale yet)
- **Investment and capacity building**: huge volumes of data vs. limited bandwidth, storage and computational capacity (cloud computing, parallelisation | resources)
::: {.color-box-light-blue}
[**Field data will always be needed! :)**]{style="color: #1a428a;"}
:::
<!-- ## Challenges and gaps - Ecology and Health -->
<!-- <br> -->
<!-- - Missing **baseline** distribution information of hosts, vectors, infection -->
<!-- - Updating and digitisation of disease cases and intervention data, data still missing in large parts of the world -->
<!-- - **Harmonisation of records** at different administrative levels -->
<!-- - Facilitating access to (aggregated) health data -->
<!-- - __Political decision and resource allocation__ -->
## Opportunities: low hanging fruits?
- SAR data to avoid clouds, e.g., [**SAOCOM**](https://saocom.invap.com.ar/) to estimate soil moisture
- Open LiDAR data, e.g., [GEDI](https://gedi.umd.edu/) onboard of ISS
- Open source solutions for the cloud [openEO.cloud](https://openeo.cloud/), [actinia](https://actinia.mundialis.de/), OpenPlains? ;-)
::: {.columns}
::: {.column width="34%"}
{width="180px" style="float: right;"}
{width="200px"}
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{width="70%"}
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{width="80%"}
{width="80%"}
:::
:::
::: footer
@torresani_lidar_2023
:::
## New missions: hyper-spectral for all
- A number of recent and upcoming missions for hyper-spectral data: [PRISMA](https://prismauserregistration.asi.it/) (recently made open), [EnMap](https://www.enmap.org/), [CHIME](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Going_hyperspectral_for_CHIME), TIRS
:::: {.columns}
:::{.column width="65%"}
{width="85%" fig-align="center"}
:::
::: {.column width="35%"}
{fig-align="center"}
:::
::::
::: footer
<http://database.eohandbook.com/database/instrumenttable.aspx>,
:::
## Specialized cameras onboard drones
- Cheaper UAVs with different types of cameras, e.g. thermal multi- or hyper-spectral sensors to detect and count animals in inaccessible places
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@carrasco_escobar_use_2022
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So, it's clearly not all done already, and RS can contribute a big deal to reach SDGs in general and SDG 3 in particular, especially if we consider that for human good health and well being, environmental health and animal health are also necessary conditions.
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Thanks!
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<br><br>
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[{{< fa envelope >}} [email protected]]([email protected])
[{{< fa brands twitter >}} VeronicaAndreo](https://twitter.com/VeronicaAndreo)
[{{< fa presentation-screen >}} Slides](https://veroandreo.github.io/grass_ncsu_2023/introduction.html)
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Unhealthy lab
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## References {.tiny}
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