Free GIS Software meets zoonotic diseases: From raw data to ecological indicators
Upcoming SlideShare
Loading in...5
×
 

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

on

  • 5,036 views

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

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

Statistics

Views

Total Views
5,036
Views on SlideShare
4,363
Embed Views
673

Actions

Likes
2
Downloads
140
Comments
0

6 Embeds 673

http://gis.cri.fmach.it 622
http://gis.fem-environment.eu 33
http://www.slideshare.net 11
http://www.linkedin.com 3
http://translate.googleusercontent.com 2
http://www.slideee.com 2

Accessibility

Upload Details

Uploaded via as OpenOffice

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

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

  • 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 View slide
      • Why using satellite data?
    Data enhancements in complex Alpine terrain: ... interpolating meteo data? Vallarsa near Rovereto (Northern Italy) Lagorai Trento View slide
      • 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
      • MODIS LST reconstruction 2/5
    Reconstructed MODIS LST map
  • °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 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 5/5
    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
      • 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
      • 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)
  • 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)
      • 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)