Environmental drivers of vector-borne and zoonotic diseases
Leveraging remote sensing for Public Health
Neglected Tropical Diseases (NTD)
You all have seen this, right?
Disease Ecology I
The main objective is to understand the influence of environmental factors and to predict when and where a disease is most likely to occur
decision making, planning of prevention, management or response actions, etc.
Disease Ecology II
Use of RS in Health applications
Most common RS variables used
- LST
- Precipitation
- NDVI
- LULC
- Elevation
- NDWI
Remote sensing basic features
Remote sensing & scale I
Remote sensing & scale II
How to apply RS in disease ecology?
Detecting and mapping species occurrences
- Very high resolution (VHR) imagery
- Hyperspectral data (esp. for plant species)
- Direct and indirect counting (CV, ML, DL)
Detecting and mapping species occurrences
Time series analysis of satellite products
- 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
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
SDM & GIS based approach for HPS risk map
We combined a rescaled probability map of the host with one of the human cases to determine levels of transmission risk
Cutaneous leishmaniasis and LULCC
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.
Urban environmental characterisation for the distribution of ovitraps
- Object-based classification of VHR imagery
- Landscape metrics for polygons
- Clustering to find groups of similar polygons
- Stratified distribution of ovitraps
MSc thesis, Carla Rodriguez.
Predictive system based on population dynamics and weather forecasting
Development of an early warning system (EWS) for dengue. PhD candidate, Tomás San Miguel. 
Online surveillance system
Online surveillance system
Other projects under development
Incidence of asthma as a function of remotely sensed air quality and LULCC. PhD candidate, Abraham Coiman.
Distribution of congenital diseases and access to health. PhD candidate, Carla Rodriguez Gonzalez.
Epidemiological characterisation of intestinal parasite infection in children. PhD candidate, Matias Scavuzzo.
Geospatial modelling of malnutrition in children and adolescents. PhD candidate, Micaela Campero.
Environmental variables associated with non-communicable diseases. Dr. Juan Diego Pinotti and Dr. Ximena Porcasi.
Challenges and gaps - RS
- 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)
Field data will always be needed! :)
Opportunities: low hanging fruits?
- SAR data to avoid clouds, e.g., SAOCOM to estimate soil moisture
- Open LiDAR data, e.g., GEDI onboard of ISS
- Open source solutions for the cloud openEO.cloud, actinia, OpenPlains? ;-)
New missions: hyper-spectral for all
- A number of recent and upcoming missions for hyper-spectral data: PRISMA (recently made open), EnMap, CHIME, TIRS
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
Thanks!
Unhealthy lab
References
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