REMOTELY SENSED INDICATORS OF MICRO-CLIMATE IN PREDICTING NEW AREAS OF HUMAN RISK OF LYME DISEASE USING SPATIAL STATISTICS AND ARTIFICIAL NEURAL NETWORKS

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REMOTELY SENSED INDICATORS OF MICRO-CLIMATE IN
PREDICTING NEW AREAS OF HUMAN RISK OF LYME
DISEASE USING SPATIAL STATISTICS AND ARTIFICIAL
NEURAL NETWORKS

• A PRESENTATION TO THE SUMMER COLLOQUIUM ON
  CLIMATE AND HEALTH
• JULY 26, 2004, NCAR, BOULDER COLORADO
• RUSSELL BARBOUR PH.D.
• VECTOR ECOLOGY LABORATORY
• YALE SCHOOL OF MEDICINE
• NEW HAVEN CT.

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PROBLEM STATEMENT

• HUMAN CASE DATA HAS BEEN PROVEN AN UNRELIABLE
  INDICATOR OF LYME DISEASE RISK
• UNDER REPORTING, MIS-DIAGNOSES, AND OVER
  REPORTING DISTORT HUMAN CASE DISTRIBUTION
• COLLECTION AND TESTING OF INFECTED NYMPHS COSTLY

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PROBLEMS CONTINUED

• Ixodes scapularis TICKS HAVE NOT EXPANDED INTO
  ALL AREAS OF SUITABLE HABITAT
• INVADING TICKS ARE NOT NECESSARILY INFECTED WITH
  Borrelia burgdorferei ( BACTERIAL AGENT OF LYME
  DISEASE)
• ONLY INFECTED NYMPHAL TICKS POSE A THREAT TO
  HUMANS

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NEW APPROACH TO RISK ESTIMATION AND PREDICTION

• INTEGRATE HUMAN CASE DATA WITH LANDSCAPE
  INDICATORS OF THE NIDALITY (FOCI) OF INFECTION OF
  Borrelia burgdorferi
• BUILD DATA LAYERS FROM REMOTELY SENSED
  MICRO-CLIMATE INDICATORS, PUBLISHED CANINE
  SEROPREVALENCE AND PREVIOUS HUMAN CASE DATA
• DERIVE PROBABILITY OF INCREASING RISK THROUGH
  MARKOV-BAYES MONTE CARLO SIMULATIONS

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KRIGING VERSUS MARKOV-BAYES MONTE CARLO CHAIN
(MMCC) SIMULATION

• KRIGING GIVES THE MOST LIKELY EVENT AT ALL
  LOCATIONS.. THE TOP OF A PROBABILITY DENSITY
  CURVE
• KRIGING IS BASED ON JUST ONE ITERATION OF
  POSSIBLE REALITY
• KRIGING DISHONORS THE ORIGINAL DATA EVENTS
  MORE PROBABLE THAN REALITY
• MMCC GIVES OTHER PROBABILITIES AT EACH LOCATION
• MMCC HONORS THE ORIGINAL DATA
• MMCC IS BASED ON METROPOLIS-HASTINGS RANDOM WALK
  (ALGORITHM USED TO DEVELOP H-BOMB). THE NEXT
  STATE IS ONLY DERIVED FROM THE CURRENT STATE
• RANDOM WALK CREATES A NUMBER OF ITERATIONS
  ALTHOUGH EVENTUALLY THEY WILL CONVERGE TO KRIGED
  VALUES

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EVI AS A FACTOR IN ESTIMATING LYME DISEASE RISK

• MORE SENSITIVE TO PERIODS OF LIGHT VEGETATION ,
  SPRING AND FALL WHEN NYMPHAL AND ADULT TICKS ARE
  ACTIVE
• DISTINGUISHES BETWEEN WOODED SUBURBS AND TRUE
  FORESTS DURING THIS TIME PERIOD
• IDENTIFIES DISCONTINUITY IN LANDSCAPES BETTER
  THAN NDVI

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MODIS Products
MODIS
Ocean
Atmosphere
Land
Products MOD04 Aerosols MOD05 Water Vapor MOD06
Cloud MOD35 Cloud Mas
Products MOD36 Ocean Color MOD28 SST
Products MOD09 Reflectance MOD12 Snow
Cover MOD13 Vegetation MOD14 Thermal Anomaly
SOURCE http//modis.gsfc.nasa.gov/
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MODIS Data products come in different
Spatial Resolution
But most products do NOT come with all these
resolutions and versions
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RELATIONSHIP BY DATE BETWEEN HUMAN CASES AND EVI
BY MOVING WINDOW ANALYSIS
HUMAN CASES MODIS EVI DATES CORRELATION
1992 MAY25 2001 JULY 28 2001 .12 .01
1993 MAY 25 2001 JULY 28 2001 .11 .02
1994 MAY 25 2001 JULY 28 2001 .18 .06
1995 MAY 25 2001 JULY 28 2001 .14 .06
1996 MAY 25 2001 JULY 28 2001 .16 .06
1997 MAY 25 2001 JULY 28 2001 .14 .06
1999 MAY 25 2001 JULY 28 2001 .17 .06
2000 MAY 25 2001 JULY 28 2001 .20 .06
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MATHEMATICAL DATA INTEGRATION
Most Abundant Data
NEW REMOTELY SENSED VEGETATION INDEX (EVI)
PREVIOUS HABITAT SUITABILITY MODEL
CANINE SEROPREVALANCE DATA POINTS
COMBINED BY ANN
Sparse Data
1992- 2000 HUMAN CASE DATA BY COUNTY
SPATIAL STATISTICS MMCC SIMULATIONS
ESTIMATED HUMAN CASES BY LOCATION
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PREDICTIVE VALUE OF 1995 MID-WESTERN CASE DATA
WHEN INTEGRATED WITH PREVIOUS YEARS AND LANDSCAPE
INDICATORS OF INFECTION BY MULTILAYER ARTIFICIAL
NEURAL NETWORKS
PREDICTIVE VALUE
YEAR
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PREDICTIVE VALUE OF 1998 MID-WESTERN CASE DATA
WHEN INTEGRATED WITH PREVIOUS YEARS AND LANDSCAPE
INDICATORS OF INFECTION BY MULTILAYER ARTIFICIAL
NEURAL NETWORKS
YEAR PREDICATIVE VALUE
1998 .99
1999 .99
2000 .91
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PROBABILITY OF HUMAN PREVALENCE HIGHER THAN
25/100,000 FROM 1992 HUMAN CASE DATA AND
LANDSCAPE INFECTION INDICATORS
URBAN AREAS
PROBABILITY
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PROBABILITY OF HUMAN PREVALENCE HIGHER THAN
25/100,000 FROM 2000 HUMAN CASE DATA AND
LANDSCAPE INFECTION INDICATORS
URBAN AREAS
PROBABILITY
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URBAN AREAS
1992 PROBABILITY OF HIGH PREVALENCE
URBAN AREAS
2003 PROBABILITY OF HIGH PREVALENCE
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PROBABILITY MAP
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MODEL AGREEMENT WITH CASE DATA

• PREDICTED SPATIAL HUMAN LD PREVALENCE BY FROM
  LANDSCAPE AND PREVIOUS HUMAN CASE DATA AGREED
  WITH ACTUAL CASES BY 81

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WEAKNESS

• VEGETATION DATE TOO SPECIFIC
• LARGE AREAS OF UNCERTAINTY
• NO QUALITY CRITERIA FOR ORIGINAL CASE DATA
• NOISE STILL PRESENT

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STRENGTHS

• HUMAN CASE DATA LINKED TO NIDALITY OF INFECTION
• REASONABLE PREDICTIONS OF HUMAN RISK POSSIBLE
• THREE YEAR ADVANCE OF INFECTION WALL APPEARS
  VISIBLE

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WILDLIFE URBAN INTERFACE DATA
LOW DENSITY INTERFACE AREAS WITH HOUSING
DENSITY BETWEEN 6.2 AND 49.4 HOUSING UNITS PER
KM 2 AND 50 VEGETATION COVER WITHIN ALL 2 KM
AREAS WITH 75 COVER Source SILVIS Lab
Spatial Analysis For Conservation And
SustainabilityForest Ecology Management
University Of Wisconsin - Madison
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ASSOCIATED WITH HUMAN LD CASES In WI
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SPATIAL STRUCTURE OF 2000 HUMAN CASE DATA IN
WISCONSIN
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CROSS VARIOGRAM2000 HUMAN CASES AND LOW DENSITY
WUI
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CO-KRIGE OF 2000 HUMAN CASES AND LOW WUI LAND
COVER
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SPREAD OF INFECTED NYMPHAL Ixodes scapularis
TICKS AS ESTIMATED FROM HUMAN CASES
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FUTURE RESEARCH

• CUBIC SPLINE REGRESSION OF ALL HUMAN CASE DATA TO
  REMOVE NOISE
• ADDITION OF MODIS ATMOSPHERIC DATA TO CAPTURE
  HUMIDITY
• FILTER OF UNSUITABLE LANDSCAPES FARMLAND
• CALCULATION OF THE RATE OF INFECTION SPREAD,
  CURRENTLY ABOUT 6 KILOMETERS A YEAR, BASED ON
  MMCC PROBABILITY MODELS, NOT CASES

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MATHEMATICAL DATA INTEGRATION
REMOTELY SENSED MODIS DATA
VEGETATION INDEX (EVI) AND NEAR GROUND HUMIDITY
PREVIOUS HABITAT SUITABILITY MODEL
FIRE MODEL, AND TICK ESTABLISHMENT DATA
COMBINED BY ANN
CANINE SEROPREVALANCE DATA POINTS
Sparse Data ARCHIVAL CANINE SEROLOGY
SPATIAL STATISTICS
HUMAN CASE DATA BY COUNTY
ESTIMATED HUMAN CASES BY LOCATION
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