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

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

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

    1. 1. 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
    2. 2. <ul><li>Focus on z oonotic diseases </li></ul><ul><ul><li>They are able to be transmitted from animals to humans, usually by a vector (e.g., ticks, mosquitoes) </li></ul></ul><ul><ul><li>Both wildlife (e.g., roe and red deer, rodents) and domestic animals are reservoir hosts </li></ul></ul><ul><ul><li>Zoonoses involve all types of agents (bacteria, parasites, viruses and others) </li></ul></ul><ul><li>Zoonotic diseases cause major health problems </li></ul><ul><li>in many countries. </li></ul><ul><li>They are driven by environmental and pathogen changes as well as political and cultural changes. </li></ul><ul><ul><li>The problem: Emerging infectious diseases in Europe </li></ul></ul>
    3. 3. <ul><ul><li>The problem: Emerging infectious diseases in Europe </li></ul></ul><ul><li>Two related research projects at FEM-CEA: </li></ul><ul><li>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 </li></ul><ul><li>EDEN at FEM-CEA: </li></ul><ul><ul><li>Tick-borne diseases: Lyme borreliosis, Tick-b. Encephalitis </li></ul></ul><ul><ul><li>Rodent-borne diseases: Hantavirus, Arenavirus </li></ul></ul><ul><li>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 . </li></ul><ul><ul><li>Potential disease transmission: Chikungunya, Dengue, ... </li></ul></ul>J. Lindsey CDC
    4. 4. <ul><ul><li>Why using satellite data? </li></ul></ul>Data enhancements in complex Alpine terrain: ... interpolating meteo data? Vallarsa near Rovereto (Northern Italy) Lagorai Trento
    5. 5. <ul><ul><li>Why using satellite data? </li></ul></ul>Data enhancements in complex Alpine terrain
    6. 6. 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 <ul><li>Sensor with 36-channels from visible to thermal-infrared </li></ul><ul><li>Delivers data at 250m, 500m and 1km pixel resolution </li></ul>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
    7. 7. MODIS products and processing <ul><li>MODIS sensor on Terra and Aqua satellites </li></ul><ul><li>Data freely available from NASA/USGS </li></ul><ul><li>Delivered in HDF format, in SIN projection (2008: product. level V005) </li></ul><ul><li>Series of products is made available by NASA: </li></ul><ul><ul><ul><li>Land surface temperature ( LST ) </li></ul></ul></ul><ul><ul><ul><li>Vegetation indices ( NDVI and EVI ) </li></ul></ul></ul><ul><ul><ul><li>Snow cover maps </li></ul></ul></ul><ul><ul><ul><li>LAI/FPAR ... and 40 further products </li></ul></ul></ul><ul><li>Data preprocessing </li></ul><ul><li>Each map comes with a corresponding Quality Assessment map </li></ul><ul><li>It is essential to apply these quality maps pixelwise (bit-pattern encod.) </li></ul><ul><li>Reprojection from SIN to common map projections </li></ul><ul><li>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. </li></ul>Batch processing on PBS and SGE: 1460 LST maps/year Linux cluster
    8. 8. <ul><ul><li>Comparing MODIS LST and meteorological data </li></ul></ul>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)
    9. 9. <ul><ul><li>Comparing MODIS LST and meteorological data </li></ul></ul>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
    10. 10. Raw MODIS Land Surface Temperature map °C <ul><ul><li>MODIS LST reconstruction 1/5 </li></ul></ul>
    11. 11. °C <ul><li>Approach (simpified) </li></ul><ul><ul><li>Temperature gradient from MODIS LST image statistics </li></ul></ul><ul><ul><li>If too few pixel, use seasonal gradient </li></ul></ul><ul><ul><li>Interpolate with Volume Splines in GRASS using elevation as auxiliary variable </li></ul></ul><ul><ul><li>Correction for south/north exposed slopes </li></ul></ul><ul><ul><li>MODIS LST reconstruction 2/5 </li></ul></ul>Reconstructed MODIS LST map
    12. 12. °C <ul><ul><li>MODIS LST reconstruction 3/5 </li></ul></ul>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
    13. 13. <ul><ul><li>MODIS LST reconstruction 4/5 </li></ul></ul>MODIS sensors night data (22:30, 01:30) Todo: - more fine grain seasonal model - avoid winter outliers Continuous time series
    14. 14. <ul><ul><li>MODIS LST reconstruction 5/5 </li></ul></ul>MODIS sensors day data (10:30, 13:30) Continuous time series Todo: - more fine grain seasonal model - avoid winter outliers
    15. 15. <ul><ul><li>Indicators from MODIS sensor 1/5 </li></ul></ul><ul><ul><li>Base product: Land surface temperature (LST) </li></ul></ul><ul><ul><li>LST derived indices relevant for disease monitoring and risk modeling: </li></ul></ul><ul><ul><li>(through time series analysis in GIS) </li></ul></ul><ul><ul><ul><li>late frost periods : relevant for masting of trees and seed production </li></ul></ul></ul><ul><ul><ul><li>growing degree days (GDD) for phenological status </li></ul></ul></ul><ul><ul><ul><li>hot/cold summers through mean temperature differences </li></ul></ul></ul><ul><ul><ul><li>autumnal temperature decrease, spring warming gradient </li></ul></ul></ul><ul><ul><ul><li>annual/monthly temperature minima/maxima </li></ul></ul></ul>Land Surface Temperature [°C] Trentino LST map 28 June 2006 from Aqua satellite at ~13:30 local time (Deg. Celsius)
    16. 16. <ul><ul><li>Enhanced Vegetation Index (EVI) </li></ul></ul><ul><ul><li>EVI tends to perform better than Norm. Differences Veg. Index (NDVI): </li></ul></ul><ul><ul><ul><li>less prone to saturation </li></ul></ul></ul><ul><ul><ul><li>less sensitive to haze </li></ul></ul></ul><ul><ul><li>Derived indices: </li></ul></ul><ul><ul><ul><li>seasonal differences by simple pixel-wise map substraction </li></ul></ul></ul><ul><ul><ul><li>in a localized way: </li></ul></ul></ul><ul><ul><ul><ul><li>spring/autumn detection </li></ul></ul></ul></ul><ul><ul><ul><ul><li>length of growing season </li></ul></ul></ul></ul><ul><ul><li>Indicators from MODIS sensor 2/5 </li></ul></ul>
    17. 17. <ul><ul><li>Enhanced Vegetation Index (EVI) “Spring/autumn detection”: Trentino 2003 Effect of valley orientation and exposition </li></ul></ul>3 1 2 <ul><ul><li>Indicators from MODIS sensor 3/5 </li></ul></ul>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
    18. 18. <ul><ul><li>Maximum snow extent map: accumulated over 8 days </li></ul></ul><ul><ul><li>Example: Early snow event in October 2004 </li></ul></ul>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 <ul><ul><li>Indicators from MODIS sensor 4/5 </li></ul></ul>GEOtop snow-model based map (using climate data)
    19. 19. 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” <ul><ul><li>Maximum snow extent map: accumulated over 8 days </li></ul></ul><ul><ul><li>Example: Early snow event in October 2004 </li></ul></ul><ul><ul><li>Indicators from MODIS sensor 5/5 </li></ul></ul>GEOtop snow-model based map (using climate data)
    20. 20. <ul><ul><li>Ongoing... </li></ul></ul>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
    21. 21. <ul><ul><li>Conclusions </li></ul></ul><ul><ul><ul><li>Rich archive of remote sensing data available (thanks to the US legislation) </li></ul></ul></ul><ul><ul><ul><li>Data processing is completely based on FOSS4G software </li></ul></ul></ul><ul><ul><ul><li>Time series permit for extraction of time series -> seasonality patterns </li></ul></ul></ul><ul><ul><ul><li>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 </li></ul></ul></ul><ul><ul><ul><li>New satellite systems provide a wealth of data from which epidemiologically relevant indicators can be derived </li></ul></ul></ul>Markus Neteler Fondazione Mach - Centre for Alpine Ecology 38100 Viote del Monte Bondone (Trento), Italy http://www.cealp.it/ - neteler AT cealp.it
    22. 22. <ul><ul><li>TBE in Trentino: case study </li></ul></ul><ul><ul><li>TBE human cases, autumnal cooling and trapping sites </li></ul></ul>
    23. 23. <ul><ul><li>TBE in Trentino (Italy) </li></ul></ul><ul><ul><li>Serological survey in goats: Risk of TBE transmision in raw milk/cheese </li></ul></ul>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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