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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=1 .fade-in-then-out width="90%"}
{.fragment .fade-in-then-out fragment-index=2 width="55%" fig-align="center"}
{.fragment fragment-index=3 .fade-in width="100%" fig-align="center"}
:::
::: footer
<https://sdgs.un.org/goals/goal3>
:::
---
### 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}
---
::: {.center}
You all have seen this, right?
:::
::: {.columns}
::: {.column width="60%"}
:::
::: {.column width="7%"}
:::
::: {.column width="30%"}
<br><br>
:::
:::
## 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}
Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care.
Mencionar potenciales usos y aplicaciones del SR en los 3 campos
:::
## 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}
1. Landscape attributes may influence the level of transmission of an infection
2. Spatial variations in disease risk depend not only on the presence and area of critical habitats but also on their spatial configuration
3. Disease risk depends on the connectivity of habitats for vectors and hosts
4. The landscape is a proxy for specific associations of reservoir hosts and vectors linked with the emergence of multi-host diseases
5. To understand ecological factors influencing spatial variations of disease risk, one needs to take into account the pathways of pathogen transmission between vectors, hosts, and the physical environment
6. The emergence and distribution of infection through time and space is controlled by different factors acting at multiple scales
7. Landscape and meteorological factors control not just the emergence but also the spatial concentration and spatial diffusion of infection risk
8. Spatial variation in disease risk depends not only on land cover but also on land use, via the probability of contact between, on one hand, human hosts and, on the other hand, infectious vectors, animal hosts or their infected habitats
9. The relationship between land use and the probability of contact between vectors and animal hosts and human hosts is influenced by land ownership
10. Human behaviour is a crucial controlling factor of vector-human contacts, and of infection
:::
## Use of RS in Health applications
{width="90%"}
::: footer
@viana_remote_2017
:::
## 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
:::
## Remote sensing basic features
{fig-align="center"}
::: {.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
:::
## 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
:::
## 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}
- 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
::: {.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
- 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
:::
---
### Mosquitoes: towards operational high res maps
::: {.columns}
::: {.column width="60%"}
{.fragment .absolute top=200 left=2 width=80}
{.fragment .absolute top=300 left=530 width=85}
:::
::: {.column width="40%"}
{width="32%" style="float: right;"}
:::
:::
::: 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**.
:::
---
## 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 (ARD)
- **Limited access** to VHR, LiDAR, Hyper-spectral (US$, not easy to scale yet)
- Investment and capacity building: huge volumes of data vs. limited bandwidth, storage and computational capacity (cloud computing, parallelisation | learning time and US$)
::: {.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 levels, i.e., municipal, provincial, national
- 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
- GEE vs open source solutions [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%"}
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:::
::: 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
{.absolute top=100 right=-20 width=120}
{fig-align="center" width="90%"}
::: footer
@carrasco_escobar_use_2022
:::
---
{fig-align="center" width="95%"}
# { background-image="assets/img/lecture/CONAE_aereo.png" }
::: {style="color: #ffffff; font-size: 5em;" .fragment}
Thanks!
:::
<br><br>
:::{.fragment style="float: right; font-size: 0.7em;"}
{.bio-img}
[{{< fa envelope >}} [email protected]]([email protected])
[{{< fa brands twitter >}} VeronicaAndreo](https://twitter.com/VeronicaAndreo)
:::
::: {.fragment style="float: left;" .color-box-white}
Unhealthy lab
:::
## References {.tiny}
::: {#refs .tiny}
:::
# Extra slides { background-color="#1A428A" }
---
### App to count mosquito eggs in ovitraps pics
<br>
[https://ovitrap-monitor.netlify.app/](https://ovitrap-monitor.netlify.app/)
::: {.columns}
::: {.column width="45%"}
:::
::: {.column width="55%"}
:::
:::
::: footer
Hamesse et al.
:::