Free GIS Software meets zoonotic diseases: From raw data to ecological indicators

FOSS4G 2008 Open Source Geospatial: an option for Developing Nations 29 Sep-3 Oct 2008, Cape Town, South Africa Free GIS Software meets zoonotic diseases: From raw data to ecological indicators M. Neteler Fondazione Mach - Centre for Alpine Ecology 38100 Viote del Monte Bondone (Trento), Italy http://www.cealp.it http://www.grassbook.org neteler * cealp.it

Focus on z oonotic diseases
They are able to be transmitted from animals to humans, usually by a vector (e.g., ticks, mosquitoes)
Both wildlife (e.g., roe and red deer, rodents) and domestic animals are reservoir hosts
Zoonoses involve all types of agents (bacteria, parasites, viruses and others)
Zoonotic diseases cause major health problems
in many countries.
They are driven by environmental and pathogen changes as well as political and cultural changes.
The problem: Emerging infectious diseases in Europe

The problem: Emerging infectious diseases in Europe
Two related research projects at FEM-CEA:
1) EDEN (Emerging Diseases in a changing European eNvironment) i s an FP6 Integrated Project (2004-2009) that aims to identify and catalog those European ecosystems and environmental conditions which can influence the spatial and temporal distribution and dynamics of human pathogenic agents. EDEN consortium: 48 research institutes from 24 countries http://www.eden-fp6project.net
EDEN at FEM-CEA:
Tick-borne diseases: Lyme borreliosis, Tick-b. Encephalitis
Rodent-borne diseases: Hantavirus, Arenavirus
2) RISKTIGER : Risk assessment of the emergence of new arboviruses diseases transmitted by the tiger mosquito Aedes albopictus (Diptera: Culicidae ) in the Autonomous Province of Trento .
Potential disease transmission: Chikungunya, Dengue, ...
J. Lindsey CDC

Why using satellite data?
Data enhancements in complex Alpine terrain: ... interpolating meteo data? Vallarsa near Rovereto (Northern Italy) Lagorai Trento

Why using satellite data?
Data enhancements in complex Alpine terrain

What is the MODIS sensor? Approach: replace climatic station data with satellite data MODIS sensor on Terra and Aqua satellites Typical MODIS overpass: data coverage
Sensor with 36-channels from visible to thermal-infrared
Delivers data at 250m, 500m and 1km pixel resolution
MODIS/Terra (EOS-AM): - launched Dec. 1999 - passes at approx 10:30 + 22:30 local time MODIS/Aqua (EOS-PM): - launched May 2002 - passes at approx 13:30 + 01:30 local time 4 overpasses per 24h Tile h18_v04

MODIS products and processing
MODIS sensor on Terra and Aqua satellites
Data freely available from NASA/USGS
Delivered in HDF format, in SIN projection (2008: product. level V005)
Series of products is made available by NASA:
Land surface temperature ( LST )
Vegetation indices ( NDVI and EVI )
Snow cover maps
LAI/FPAR ... and 40 further products
Data preprocessing
Each map comes with a corresponding Quality Assessment map
It is essential to apply these quality maps pixelwise (bit-pattern encod.)
Reprojection from SIN to common map projections
MODIS processing chain implemented in GRASS GIS ( http://grass.osgeo.org ) Refs: Neteler, 2005. Time series proc. MODIS..., Intl J Geoinformatics Rizzoli et. al., 2007, TBE. Geospatial Health Carpi et al., 2008, TBE. Epidem. & Infect.
Batch processing on PBS and SGE: 1460 LST maps/year Linux cluster

Comparing MODIS LST and meteorological data
Minimum/maximum temperatures [°C] - Meteo: 2 values per day (min/max) - MODIS: 4 values per day (2*day, 2*night) Temperature dynamics: daily min/max temperatures Station/pixel: Temperature [°C] Speccheri (860m; GB 1666033E 5070563N)

Comparing MODIS LST and meteorological data
10 days aggregates: time series processing (GRASS GIS) Comparison of meteo station and MODIS Note: Land surface temperature != air temp. Wilcox.test: W = 679, p-value = 0.9572 minimum/maximum temperatures mean temperatures Station Speccheri (860m; GB 1666033E 5070563N) Station/pixel: Temperature [°C] 10-days period Station/pixel: Temperature [°C] 10-days period

Raw MODIS Land Surface Temperature map °C
MODIS LST reconstruction 1/5

°C
Approach (simpified)
Temperature gradient from MODIS LST image statistics
If too few pixel, use seasonal gradient
Interpolate with Volume Splines in GRASS using elevation as auxiliary variable
Correction for south/north exposed slopes
Reconstructed MODIS LST map

°C
Difference map: filtered MODIS LST – RST3D interpolated MODIS LST n: 448514 minimum: -16.104 maximum: 10.111 range: 26.215 mean: -0.388 mean of abs. values: 1.469 standard deviation: 2.037 variance: 4.149

MODIS sensors night data (22:30, 01:30) Todo: - more fine grain seasonal model - avoid winter outliers Continuous time series

MODIS sensors day data (10:30, 13:30) Continuous time series Todo: - more fine grain seasonal model - avoid winter outliers

Indicators from MODIS sensor 1/5
Base product: Land surface temperature (LST)
LST derived indices relevant for disease monitoring and risk modeling:
(through time series analysis in GIS)
late frost periods : relevant for masting of trees and seed production
growing degree days (GDD) for phenological status
hot/cold summers through mean temperature differences
autumnal temperature decrease, spring warming gradient
annual/monthly temperature minima/maxima
Land Surface Temperature [°C] Trentino LST map 28 June 2006 from Aqua satellite at ~13:30 local time (Deg. Celsius)

Enhanced Vegetation Index (EVI)
EVI tends to perform better than Norm. Differences Veg. Index (NDVI):
less prone to saturation
less sensitive to haze
Derived indices:
seasonal differences by simple pixel-wise map substraction
in a localized way:
spring/autumn detection
length of growing season

Enhanced Vegetation Index (EVI) “Spring/autumn detection”: Trentino 2003 Effect of valley orientation and exposition
3 1 2
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 10 15 20 0.1 0.2 0.3 0.4 0.5 0.6 0.7 10day periods (2003) 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 2 3 Cavedine (570m a.s.l) Val di Non (610m a.s.l) Levico (760m a.s.l) April EVI Vegetation greening 10km

Maximum snow extent map: accumulated over 8 days
Example: Early snow event in October 2004
MODIS sensor based map (satellite, every 8 days) => the “easy way” Situation 24 th Oct. 2004 Pergine, Valsugana (Trentino), Italy Endrizzi, Bertoldi, Neteler, Rigon, 2005. EGU
GEOtop snow-model based map (using climate data)

Situation 17 th Nov. 2004 Pergine, Valsugana (Trentino), Italy Endrizzi, Bertoldi, Neteler, Rigon, 2005. EGU MODIS sensor based map (satellite, every 8 days) => the “easy way”
Maximum snow extent map: accumulated over 8 days
Example: Early snow event in October 2004
GEOtop snow-model based map (using climate data)

Ongoing...
Use of new remote sensing variables in machine learning EVI Snow Use of Machine Learning algorithms (ensemble methods) to create spatio-temporal risk models Rizzoli et al. 2002: Bagging of Trees Furlanello et al. 2003: RandomForest, DSC Benito Garzón et al. 2006: Predicting habitat suitability. Ecol. Mod. Rizzoli et al. 2007, Geospatial Health ABIOTIC BIOTIC GIS data Ticks and host density maps LST

Conclusions
Rich archive of remote sensing data available (thanks to the US legislation)
Data processing is completely based on FOSS4G software
Time series permit for extraction of time series -> seasonality patterns
TBE in goats: synchronous activity of larvae and nymphs driven by climatic condition (autumnal cooling), captured by satellite data derived maps: Ear ly warning system for TBE
New satellite systems provide a wealth of data from which epidemiologically relevant indicators can be derived
Markus Neteler Fondazione Mach - Centre for Alpine Ecology 38100 Viote del Monte Bondone (Trento), Italy http://www.cealp.it/ - neteler AT cealp.it

TBE in Trentino: case study
TBE human cases, autumnal cooling and trapping sites

TBE in Trentino (Italy)
Serological survey in goats: Risk of TBE transmision in raw milk/cheese
Rizzoli et al. 2007, Geospatial Health Significant correlation of TBE SEROPOSITIVE with COOLING RATE of previous year (GLM with binom. error, p<0.05): predictive modelling Mean TBE seroprevalence 7.87  0.93 (PRNT)

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Free GIS Software meets zoonotic diseases: From raw data to ecological indicators

by Markus Neteler on Oct 26, 2008

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Free GIS Software meets zoonotic diseases: From raw data to ecological indicators — Presentation Transcript