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

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

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, ...

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

Why using satellite data?

Why using satellite data? Data enhancements in complex Alpine terrain

Why using satellite data?

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

Sensor with 36-channels from visible to thermal-infrared

Delivers data at 250m, 500m and 1km pixel resolution

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

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.

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

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

Comparing MODIS LST and meteorological data

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

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 MODIS LST reconstruction 2/5 Reconstructed MODIS LST map

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

MODIS LST reconstruction 2/5

°C MODIS LST reconstruction 3/5 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 LST reconstruction 3/5

MODIS LST reconstruction 4/5 MODIS sensors night data (22:30, 01:30) Todo: - more fine grain seasonal model - avoid winter outliers Continuous time series

MODIS LST reconstruction 4/5

MODIS LST reconstruction 5/5 MODIS sensors day data (10:30, 13:30) Continuous time series Todo: - more fine grain seasonal model - avoid winter outliers

MODIS LST reconstruction 5/5

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)

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

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 Indicators from MODIS sensor 2/5

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

Indicators from MODIS sensor 2/5

Enhanced Vegetation Index (EVI) “Spring/autumn detection”: Trentino 2003 Effect of valley orientation and exposition 3 1 2 Indicators from MODIS sensor 3/5 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

Enhanced Vegetation Index (EVI) “Spring/autumn detection”: Trentino 2003 Effect of valley orientation and exposition

Indicators from MODIS sensor 3/5

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 Indicators from MODIS sensor 4/5 GEOtop snow-model based map (using climate data)

Maximum snow extent map: accumulated over 8 days

Example: Early snow event in October 2004

Indicators from MODIS sensor 4/5

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 Indicators from MODIS sensor 5/5 GEOtop snow-model based map (using climate data)

Maximum snow extent map: accumulated over 8 days

Example: Early snow event in October 2004

Indicators from MODIS sensor 5/5

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

Ongoing...

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

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

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

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)

TBE in Trentino (Italy)

Serological survey in goats: Risk of TBE transmision in raw milk/cheese