Tracking emerging diseases from space: Geoinformatics for human health

©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?"

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

Did
You
Ever
See
This
Mosquito?
Photo (and host): M Neteler

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/

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

Infections causing also “hidden” problems ...

JC Semenza and D Domanović, 2013

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)

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.

Medlock et al. 2013, Parasites & Vectors, 6:1
Distribution of the tick Ixodes
ricinus: Remote Sensing and
Geoinformatics

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

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

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)

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

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)

MODIS LST daily time series
Example of aggregated data
2003: Deviation from baseline average temperature
(baseline: 2002-2012)
Aggregated by FEM PGIS

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/

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.

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):

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

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
● 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

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/

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

PostGISomics

Moisture/Humidity in time and space
Moisture / humidity
proxies
Long-term average
Saturation deficit
Water stress
Tasseled Cap
DWSI (Disease Water Stress
Index)
● 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)

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

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

WNV in Europe: complicated pattern
Marcantonio et al. (in prep.)

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.)

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!

Tracking emerging diseases from space: Geoinformatics for human health

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