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Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data Analysis Center University of New Mexico Work performed under NASA NNSO4AA19A Erice International Seminars on Planetary Emergencies, 40 th Session August 19-24, 2008
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Page 1: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Improving Public Health Services through Space Technology and

Spatial Information SystemsStan Morain

Earth Data Analysis CenterUniversity of New Mexico

Work performed under NASA NNSO4AA19A

Erice International Seminars on Planetary Emergencies, 40th Session

August 19-24, 2008

Page 2: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Classification of Diseases• Infectious and zoonotic

– e.g., AIDS, TB, Influenza, plague, hantavirus,

• Degenerative– e.g. Arteriosclerosis

• Environmental– e.g. Asthma, cholera, meningitis, malaria, yellow fever

• Neoplastic– e.g. Cancer

• Metabolic– e.g. diabetes

Page 3: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Components and Sizes ofParticulate Matter

Molecules Viruses Bacteria RBCs Cells Pollen Pin Hair

PM0.1 Ultra-fineparticles

PM10-2.5 Coarse fraction

PM2.5 Fineparticles

PM10 Thoracic particles

0.01μm 0.05 0.1 0.5 1.0 5.0 10.0 50.0 100.0 limit ofvision

Source: Science 307 (25 March, 2005), p.1859

Page 4: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

SYRIS = Earlier Detection and Immediate Response to Outbreaks

0

10000

20000

30000Exposure and

Infection

5 10 15 20 25Days

0 2 4 6 8 10 12Hours

Num

ber A

ffect

ed

Incubation

Epidemic(Disease)

Outcomes(Casualties/

Fatalities)

SYRIS Identification &

ResponseClassical Identification

Classical Response

Exposure: People / Animals are exposed to infectious agentsEpidemic: People / Animals begin to show signs of infectionOutcome: People / Animals begin to die or get very sickSYRIS Outcome: 80% fewer People / Animals get sick or die

Outcomes with SYRIS

Adapted from ARES, 2007

Page 5: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

122-126

HPS CasesHPS Controls

110-114 116-118 121-122

% Frequency of HPS and Control Sites w/i NDVI Intervals

0

0

0

0

0

0

0

0

0

0

0

HPS CasesHPS Controls

HPS Cases & Controls as a Function of Elevation

AVHRR NDVI- 1991-1997Distribution of the deer mouse

Peromyscus Maniculatus

Reservoir forSin Nombre Virus

Hantavirus Pulmonary Syndrome

5000 5500 6000 6500 7000 7500 8000

Page 6: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Reported Predictors &TriggersOf Asthma

Respiratory Predictors1. Temperature2. Precipitation3. Humidity4. Gender5. Age6. Urbanicity7. Traffic density

Respiratory TriggersA. Outdoor Environment

1. Dust2. Pollen

B. Indoor Environment1. Wall-to-wall carpet2. Cockroaches3. Stuffed toys

Page 7: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Sources and Dust TransportStorm of December 15-16 2003

New Mexico Texas

Mexico

Page 8: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

DREAM Equation

( ) ( )SINK

k

SOURCE

kkZkH

kgk

kkkt

Ct

Cz

CKz

CKz

Cvwy

Cvx

Cut

C⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

−⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

∂∂

−∇∇−∂∂

−−∂∂

−∂∂

−=∂∂

ISDS, Baltimore, 2006

Page 9: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

MODIS Aerosol Optical DepthDec. 15, 2003 (2055 UTC)

Clouds

Clouds

Page 10: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Changing Ozone Patterns—Southwest USA

Page 11: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Average Dust Deposition (g/m2/year)

Source: Science 308 (1 April, 2005) p.70

0.00 0.20 0.50 1.00 2.00 5.00 10 20 50

Page 12: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

AOD Column Concentration and Type, Atlantic, Jun-Aug 2002, MOD04-L2

Optical thickness is represented by the brightness of the image; type by color.Red = sub-µm particles (smoke, NOX, SOX, and other pollutants);

Green = dust or sea salt.

Page 13: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data
Page 14: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Aims and Goals

• Focus on SW, dust storms, respiratory diseases, and syndromic surveillance

• 3 thrusts– Assimilate EO data into DREAM as part of

NCEP/eta forecasting system (DREAM/eta)– Verify and validate incremental improvements

to DREAM/eta outputs as inputs to SYRIS– Collaborate with public health authorities to

assess relationships between dust episodes and respiratory conditions

Page 15: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

DREAM/eta Model Domain

• Domain center at (109°W, 35°N)

• Horizontal semi-staggered Arakawa E grid

• Horizontal grid spacing 1/3 degree

Page 16: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Baseline DREAM/eta Simulationred isolines = temperature

blue isolines = geopotential height

Observed Geopotential Height

Observed Temperature

DREAM/eta vs Observed Synoptic Patterns,12Z 16 Dec 03

Page 17: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Observed Visibility vs. DREAM/eta DustConcentrations Dec. 15-16, 2003

TexasContinuous Air Monitoring Stations Baseline DREAM/eta

Page 18: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

AMSR-ERes.=2min.; categories reduced to texture categories

Soil Moisture: simulated land surface model

Look-up table based on MOD12 land cover

Estimate dust entrainment potential

Aerodynamic roughness length: based on 12 SSiBland cover types

SRTM30 Res.=1kmRes.=1kmUSGS terrain data

MOD12 Res.=1kmLand cover; Res.=10min.Olsen World Ecosystems

NCEP/eta global forecast model

Initial & boundary conditions; Res.=1°

ECWMF medium-range weather forecast model

Enhanced DREAM/eta Parameters

Function/PurposeBaseline DREAM/etaParameters

Baseline and Replacement Parameters

Page 19: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Barren Ground(Most Likely Dust Sources)

Olson World Ecosystemsbarren ground class

MOD12Q1 Land coverreduced to binary format

Page 20: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Geographic gridGeopotential height

Wind directionWind speed

Surface roughness length

Humidity

Soil texture

Soil moisture content

Digital elevationSlope

Aspect

Land coverLeaf area index

24, 48, 72 Hour precipitation

Soil temperature

Air temperature at ground

FPAR

Surface conditions

Terrain

Atmospherics

Geospatial base

Assimilation StrategyAims are to:(1) replace selecteddata sets in themodel with higherspatial and spectralresolution data thatcharacterize surfaceconditions, andatmosphericparameters thatdrive DREAM/eta;(2) improve modeloutput withoutaltering the validityof the model’s originalfunction;(3) convert the model toa dynamic forecast.

Page 21: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

MODIS MOD11A1 Land SurfaceTemperature/Emissivity, Daily @ 1-km

Page 22: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Sample Model Runs of DREAM/eta and Assimilated Parameters

YYYYRun 10aYYRun 15a

YYRun 6aYYYRun 5bYYYRun 5a

YYRun 4aYRun 2c

Run 1a

AMSR-E FPAR Surface

roughness length

SRTM MOD12Run #

Page 23: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

0.710.95

0.740.76

0.740.75

Agreement index

4.092.67

51.7647.85

1.972.03

Meanerror

-1.200.72

-4.80-1.02

-0.88-1.16

Meanbias

275.56277.48

226.60230.38

4.654.37

Mean modeled

276.74231.405.53Mean observed

Definition(M = modeled; O = observed)

Temp.(K)

WindDirection (°)

WindSpeed (m/s)Metrics

∑=

N

iiO

N 1

1

∑=

N

iiM

N 1

1

∑=

−N

iii OM

N 1)(1

∑=

−N

iii OM

N 1

1

=

=

−+−

−− N

iii

N

iii

OOOM

OM

1

1

2

)(

)(1

Blue = DREAM/eta (model run 1a)Red = Enhanced DREAM/eta (model run 10a)

DREAM/eta vs. Enhanced DREAM/eta(model run 1a vs model run 10a)

Page 24: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Storm of December 15-17, 2003PM10, Lubbock

Page 25: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Incremental Improvements to Model Performance

Baseline DREAM/eta Enhanced DREAM/eta Enhanced DREAM/NMM

Page 26: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Storm of January 7, 2008PM10, Wilcox / Silver City

Page 27: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Storm of March 14 2008PM10, Yuma

Page 28: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Storm of March 14 2008PM2.5, Yuma

Page 29: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Dust Storm of January 4-6, 2007

Burbank

Riverside

Palm SpringsIndio

El Paso

Selma

Mission

1500

1200

900

600

300

0

µg/m

3

Black = PM10 observed (AIRNow data); Red=PM10 (ED/eta Run 15a); Green=PM10 (ED/eta Run 20a)

Page 30: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

y = 3.52xR2 = 0.57

n = 512(8 sites)

0

500

1000

1500

2000

2500

3000

0 200 400 600 800 1000

Observed PM_10 (ug/m3)

DR

EAM

pm

_10

(ug/

m3)

perfect correlation line

Magnitude Correlation - Jan 4-6, 2007

Page 31: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

y = 0.99xR2 = 0.95

n = 24(8 sites)

0

12

24

36

48

60

72

0 12 24 36 48 60 72

Observed Max Hour

DR

EAM

Max

Hou

rTiming Correlation - Jan 4-6, 2007

Page 32: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Enhancing SyndromeSurveillance Tools

Page 33: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Data Access & Statistics

Page 34: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Premature Mortality RiskAttributable to PM2.5

Source: Science 307 (25 March, 2005), p.1860

< 2526-5051-7576-100101-125>125

Deaths per100,000adults

Page 35: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Source: Emerging Infectious Diseases, 2005 Centers for Disease Control and Prevention

Leading Causes of Death, Worldwide:for 2002 (Estimated)

Total estimated deaths= 57,029,000

Nearly 15,000,000(> 25%) are attributed to

Infectious diseases(UN/WHO)

Cardiovascular Infectious & parasitic

Malignant neoplasmsViolence/injuries/

Accidents/suicidesChronic lung

Pregnancy-related

Other

Digestive disorders

Neuropsychiatic dis.

Diabetes mellitus

16,733,000 (29%)

14,867,000 (26%)

7,121,000 (12%)

5,168,000 (9%)

3,702,000 (6%)

2,972,000 (5%)

2,398,000 (4%)

1,968,000 (3%)

1,112,000 (2%)

988,000 (2%)

Page 36: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

Net Change in Componentsof Human Well-being

Scenarios: Reactive on left; Proactive on rightSource: Millennium Ecosystem Assessment

Page 37: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

PHAiRS Research Team• PI & Co-PI

–S. Morain (UNM)–W. Sprigg (UA)

• Project Scientists–A. Budge (UNM)–K. Benedict (UNM)–W. Hudspeth (UNM)–T. Budge (UNM)–D. Yin (UA)–B. Barbaris (UA)–S. Caskey (SNL)–D. Holland (NASA-SSC)–J. Speer (TTUHSC)

• Research Assistants– Gary Sanchez (UNM)– Beena Chandy (UA)– Chris Cattrall (UA)– Patrick Shaw (UA)

• Public Health Partners– City of Lubbock Dept of Health– Pima County Dept of

Environmental Quality– Arizona Dept of Health

Services– NM Dept of Health– UNM Health Science Center– ARES Corporation– ABQ Air Quality Office

Page 38: Improving Public Health Services through Space Technology ... Year 5...Improving Public Health Services through Space Technology and Spatial Information Systems Stan Morain Earth Data

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