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©2014,Neteleretal.-http://gis.cri.fmach.it/
Tracking emerging diseases from
space: Geoinformatics for human
health
Markus ...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Fondazione Edmund Mach, Trento, Italy
● Founded 1874 as IASMA -
Istituto Agrar...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Did
You
Ever
See
This
Mosquito?
Photo (and host): M Neteler
©2014,Neteleretal.-http://gis.cri.fmach.it/
Focus on zoonotic diseases
transmitted from animals to humans, usually by a ve...
©2014,Neteleretal.-http://gis.cri.fmach.it/
http://www.thelancet.com/journals/laninf/article/PIIS1473-3099%2814%2970781-9/...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Infections causing also “hidden” problems ...
©2014,Neteleretal.-http://gis.cri.fmach.it/
JC Semenza and D Domanović, 2013
©2014,Neteleretal.-http://gis.cri.fmach.it/
Yellow fever Dengue
West Nile
Saint Louis encephalitis
Chikungunya
Spread of t...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Medlock et al. 2013, Parasites & Vectors, 6:1
Distribution of the vector:
Ixod...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Medlock et al. 2013, Parasites & Vectors, 6:1
Distribution of the tick Ixodes
...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Environmental factors derived from
Remote Sensing
Pathogens
Vectors
Hosts
Temp...
©2014,Neteleretal.-http://gis.cri.fmach.it/
What does remote sensing offer?
Requirements: operational systems, regular obs...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Temperature in space and time
Temperature
time series
Average
Minimum
Maximum
...
©2014,Neteleretal.-http://gis.cri.fmach.it/
EuroLST: MODIS LST daily time series
Example: Land surface temperature for Sep...
©2014,Neteleretal.-http://gis.cri.fmach.it/
New EuroLST dataset:
Comparison to other datasets
(and advantages of using rem...
©2014,Neteleretal.-http://gis.cri.fmach.it/
MODIS LST daily time series
Example of aggregated data
2003: Deviation from ba...
©2014,Neteleretal.-http://gis.cri.fmach.it/
BIOCLIM from reconstructed MODIS LST at 250m pixel resolution
BIO1 BIO2 BIO3 B...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Examples:
“Hot” year 2003
and effects
MODIS Land Surface Temperature
January 2...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Ae. albopictus winter survival from MODIS LST
Neteler et al., 2011: Int J Heal...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Growing Degree Days from gap-filled MODIS LST
2003
2006
Grey: threshold
not re...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Precipitation in space and time
Precipitation Long-term average
Annual sum
Sea...
©2014,Neteleretal.-http://gis.cri.fmach.it/
ECA&D: gridded meteo data
Average annual precipitation
mm
ECA&D 25 km (gridded...
©2014,Neteleretal.-http://gis.cri.fmach.it/
GPCP V1.2 data: remote sensing based
Average annual precipitation
mm
NASA GPCP...
©2014,Neteleretal.-http://gis.cri.fmach.it/
PostGISomics
©2014,Neteleretal.-http://gis.cri.fmach.it/
Moisture/Humidity in time and space
Moisture / humidity
proxies
Long-term aver...
©2014,Neteleretal.-http://gis.cri.fmach.it/
NDWI in time and space
NDWI (Normalized Differences Water Index): June 2011
Ve...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Emerging concern: West Nile
Virus spread in Europe
To better address the ongoi...
©2014,Neteleretal.-http://gis.cri.fmach.it/
WNV in Europe: complicated pattern
Marcantonio et al. (in prep.)
©2014,Neteleretal.-http://gis.cri.fmach.it/
Methodology
We are testing the association between West Nile Fever
incidence a...
©2014,Neteleretal.-http://gis.cri.fmach.it/
Markus Neteler
Fondazione E. Mach (FEM)
Centro Ricerca e Innovazione
GIS and R...
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Tracking emerging diseases from space: Geoinformatics for human health

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European and other countries are at increasing risk for new or re-emerging vector-borne diseases. Among the top ten vector-borne diseases with greatest potential to affect European citizens are Dengue fever, Chikungunya, Hantavirus, and Crimean-Congo hemorrhagic fever. Despite the risk of disease transmission, many vectors like the Asian tiger mosquito or ticks are also a nuisance in daily life. The examination of disease vector spread and a better understanding of spatio-temporal patterns in disease transmission and diffusion is greatly facilitated by Geoinformatics. New methods including the use of high resolution time series from space in spatial models enable us to predict species invasion and survival, and to assess potential health risks. Geoinformatics is able to address the increasing challenge for human and veterinary public health not only in Europe, but across the globe, assisting decision makers and public health authorities to develop surveillance plans and vector control.

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Tracking emerging diseases from space: Geoinformatics for human health

  1. 1. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Tracking emerging diseases from space: Geoinformatics for human health Markus Neteler Joint work with M. Metz, D. Rocchini, M. Marcantonio, A Rizzoli IFGI symposium - 11 June 2014 "Geoinformatics: Solving global challenges?"
  2. 2. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Fondazione Edmund Mach, Trento, Italy ● Founded 1874 as IASMA - Istituto Agrario San Michele all'Adige (north of Trento, Italy) ● Research Centre + Tech. Transfer Center + highschool, ~ 800 staff ● … of those 300 staff in research (Environmental research, Agro- Genetic research, Food safety) http://cri.fmach.eu/ S. Michele all'Adige
  3. 3. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Did You Ever See This Mosquito? Photo (and host): M Neteler
  4. 4. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Focus on zoonotic diseases transmitted from animals to humans, usually by a vector (e.g., ticks, mosquitoes) reservoir hosts: wildlife and domestic animals 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 http://healthmap.org/en/
  5. 5. ©2014,Neteleretal.-http://gis.cri.fmach.it/ http://www.thelancet.com/journals/laninf/article/PIIS1473-3099%2814%2970781-9/abstract http://www.ft.com/cms/s/2/dd4ac6c2-eb4b-11e3-bab6-00144feabdc0.html#axzz34GSWNYQU
  6. 6. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Infections causing also “hidden” problems ...
  7. 7. ©2014,Neteleretal.-http://gis.cri.fmach.it/ JC Semenza and D Domanović, 2013
  8. 8. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Yellow fever Dengue West Nile Saint Louis encephalitis Chikungunya Spread of the tiger mosquito (Aedes albopictus): infectious disease vectors and globalization De Llamballerie et al., 2008: Chikungunya ● Tiger mosquito: Disease vector ● Spreads in Europe and elsewhere ● Small containers, used tires and lucky bamboo plants are relevant breeding sites ● >250 cases of Chikungunya in northern Italy in 2007 (CHIKv imported by India traveler and was then spread by Ae. albopictus)
  9. 9. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Medlock et al. 2013, Parasites & Vectors, 6:1 Distribution of the vector: Ixodes ricinus Current known distribution of the tick species at ‘regional’ administrative level (NUTS3); based on published historical data and confirmed data provided by experts from the respective countries as part of the  VBORNET project.
  10. 10. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Medlock et al. 2013, Parasites & Vectors, 6:1 Distribution of the tick Ixodes ricinus: Remote Sensing and Geoinformatics
  11. 11. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Environmental factors derived from Remote Sensing Pathogens Vectors Hosts Temperature Precipitation Topography Land use Vegetation cover Moisture / Humidity proxies (incomplete view) e.g. SRTM, ASTER GDEM e.g. MODIS based e.g. MODIS based … not covered here
  12. 12. ©2014,Neteleretal.-http://gis.cri.fmach.it/ What does remote sensing offer? Requirements: operational systems, regular observations, data access Example: NASA Terra and Aqua satellites (MODIS sensor, 4 maps per day) ● Land surface temperature (LST) ● late frost periods ● hot summer temperatures ● autumnal temperature decrease ● annual/monthly minima/maxima ● Urban heat islands, … ● Normalized/Enhanced Difference Vegetation Index (NDVI/EVI) ● seasonal differences ● spring/autumn detection ● length of growing season ● Normalized Difference Water Index (NDWI) ● as humidity proxy (?) ● Maximum snow extent (SNOW) Time series are essential
  13. 13. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Temperature in space and time Temperature time series Average Minimum Maximum Seasonal temperature: Winter, spring, summer, autumn Spring warming, Autumnal cooling Anomalies, Cool Night Index Growing Degree Days (GDD) Land Surface Temperature from satellite Late frost periods Selected references: ● Kilpatrick et al 2011 (WNV transmission) ● ECDC 2009 (Aedes albopictus risk maps) ● Roiz et al 2011(Aedes albopictus distribution map) ● Randolph 2004 (tick seasonality) ● Tersago et al 2009 (Hantavirus) ● Rios et al 2000 (Tubercolosis) ● Kalluri et al 2007 (mosquito abundance) ● Epstein et al 2002 (infectious diseases) ● Morand et al 2013 (infectious diseases) ● Peréz-Rodriguez et al 2013 (VB parasites)
  14. 14. ©2014,Neteleretal.-http://gis.cri.fmach.it/ EuroLST: MODIS LST daily time series Example: Land surface temperature for Sep 26 2012, 1:30 pm Metz, Rocchini, Neteler, 2014: Rem Sens EuroLST: http://gis.cri.fmach.it/eurolst/ Belluno Reconstructed, i.e. gap-filled data
  15. 15. ©2014,Neteleretal.-http://gis.cri.fmach.it/ New EuroLST dataset: Comparison to other datasets (and advantages of using remote sensing time series) Degree Celsius (reconstructed) Metz, M.; Rocchini, D.; Neteler, M. 2014: Surface temperatures at the continental scale: Tracking changes with remote sensing at unprecedented detail. Remote Sensing. 2014, 6(5): 3822-3840 (DOI | HTML | PDF)
  16. 16. ©2014,Neteleretal.-http://gis.cri.fmach.it/ MODIS LST daily time series Example of aggregated data 2003: Deviation from baseline average temperature (baseline: 2002-2012) Aggregated by FEM PGIS
  17. 17. ©2014,Neteleretal.-http://gis.cri.fmach.it/ BIOCLIM from reconstructed MODIS LST at 250m pixel resolution BIO1 BIO2 BIO3 BIO4 BIO5 BIO6 BIO7 BIO10 BIO11BIO1: Annual mean temperature (°C*10) BIO2: Mean diurnal range (Mean monthly (max - min tem)) BIO3: Isothermality ((bio2/bio7)*100) BIO4: Temperature seasonality (standard deviation * 100) BIO5: Maximum temperature of the warmest month (°C*10) BIO6: Minimum temperature of the coldest month (°C*10) BIO7: Temperature annual range (bio5 - bio6) (°C*10) BIO10: Mean temperature of the warmest quarter (°C*10) BIO11: Mean temperature of the coldest quarter (°C*10) Metz, Rocchini, Neteler, 2014: Rem Sens EuroLST: http://gis.cri.fmach.it/eurolst/
  18. 18. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Examples: “Hot” year 2003 and effects MODIS Land Surface Temperature January 2004: Lake Garda still “warm” after hot 2003 summer --> local heating effect = insect overwintering facilitated Metz, Rocchini, Neteler 2014.
  19. 19. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Ae. albopictus winter survival from MODIS LST Neteler et al., 2011: Int J Health Geogr, 10:49 2009: Positive trap Negative trap Lakes as “heating” (Jan 2004):
  20. 20. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Growing Degree Days from gap-filled MODIS LST 2003 2006 Grey: threshold not reached Grey: threshold not reached Number of Day-Of-Year (DOY) to reach 440 accumulated growing degree days (GDD) in the years 2003 and 2006: ● proxy for life-stage survival analysis of insect ● satellite-derived GDD are delivered as map, each pixel is “measured” Data: EuroLST 440 GDD threshold
  21. 21. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Precipitation in space and time Precipitation Long-term average Annual sum Seasonal precipitation: Winter, spring, summer, autumn Length of dry season / drought Length of raining season Anomalies Selected references: ● Semenza & Menne 2009 (precipitation) ● Kilpatrick et al 2011 (precipitation – WNV transmission) ● Estrada-Peña et al 2008 (seasonal precip.) ● Epstein et al 2001 (seasonal precip.) ● Reusken & Heyman 2013 (snow - hantavirus) ● Morand et al 2013 (precipitation – Typhoid fever) ● Greer et al. 2008 (precipitation – Trichinosis) ● Tourre et al. 2008 (precipitation – Epidemics) ● Srivatsava et al. 2001 (precipitation – Malaria vectors) ● Reisen et al 2008 (precipitation – mosquito abundance) Presence of snow
  22. 22. ©2014,Neteleretal.-http://gis.cri.fmach.it/ ECA&D: gridded meteo data Average annual precipitation mm ECA&D 25 km (gridded from meteo stations, daily) average annual precipitation (1981-2010) http://www.ecad.eu/
  23. 23. ©2014,Neteleretal.-http://gis.cri.fmach.it/ GPCP V1.2 data: remote sensing based Average annual precipitation mm NASA GPCP 1 degree (satellite based, daily) average annual precipitation (1997-2012) http://precip.gsfc.nasa.gov/ Globally available
  24. 24. ©2014,Neteleretal.-http://gis.cri.fmach.it/ PostGISomics
  25. 25. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Moisture/Humidity in time and space Moisture / humidity proxies Long-term average Saturation deficit Water stress Tasseled Cap DWSI (Disease Water Stress Index) Selected references: ● DWSI: Brown et al. 2008 ● NDWI: Estallo et al. 2012 ● Saturation deficit: Perret et al. 2000 ● Tasseled cap: Rodgers & Mather 2006 ● Hashizume et al. 2008 (low humidity – Gastroenteritis) ● Baylis et al. 1998 (soil moisture – mosquito vectors) ● Kalluri et al 2007 (relative humidity – VB diseases) NDWI (Normalized Differences Water Index)
  26. 26. ©2014,Neteleretal.-http://gis.cri.fmach.it/ NDWI in time and space NDWI (Normalized Differences Water Index): June 2011 Venice Israel Madrid NDWI time series Derived from MODIS VIS Humidity proxy: Usability yet to be verified! Aggregated by FEM PGIS
  27. 27. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Emerging concern: West Nile Virus spread in Europe To better address the ongoing spread of WNV in Europe, there is ● a need for an early warning system of potential outbreaks which is crucial in order to timely raise the awareness of the clinicians and speed up diagnosis to implement the blood safety regulation Specifically, we need ● to identify predictors of WNV circulation and outbreaks ● Modelling: to consider the continental scale in order to apply these predictors across Europe and neighbouring countries
  28. 28. ©2014,Neteleretal.-http://gis.cri.fmach.it/ WNV in Europe: complicated pattern Marcantonio et al. (in prep.)
  29. 29. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Methodology We are testing the association between West Nile Fever incidence and a wide range of potential predictors: ● including temperature data, land use, human presence, urbanization, water body density, landscape fragmentation and heterogeneity, protected areas ● avoiding weak interpolation methods from sparse point data by use of spatially continuous input data ● Use of multi-model inference to gain a consensus from multiple linear mixed models predicting WNV incidence at a scale of NUTS3/GAUL1 administrative units Anomalies from LST PGIS HPCPrecipitation NDWI Biomes: Anthromes Globcover: land useNDVI Marcantonio et al. (in prep.)
  30. 30. ©2014,Neteleretal.-http://gis.cri.fmach.it/ Markus Neteler Fondazione E. Mach (FEM) Centro Ricerca e Innovazione GIS and Remote Sensing Unit 38010 S. Michele all'Adige (Trento), Italy http://gis.cri.fmach.it http://www.osgeo.org Conclusions ● Emerging diseases need to be considered among the “emerging themes” to be covered by integrated research strategies because of their dramatic impact on well being and economy ● Current and potential distribution of disease vectors (like Ae. albopictus) can be modelled at high resolution, relevant to many health projects ● New reconstructed high temporal resolution datasets allow for real modelling ● ... bring it all together in Geoinformatics!

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