richard kiang nasa goddard space flight center greenbelt, md 20771 malaria modeling for thailand...
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Richard KiangNASA Goddard Space Flight CenterGreenbelt, MD 20771
Malaria Modeling for Thailand & KoreaMalaria Modeling for Thailand & Korea— NASA Techniques and Call for Validation Partners
AcknowledgementAcknowledgementAFRIMS Dr. Jame Jones WRAIR Dr. Russell Coleman
Dr. R. SithiprasasnaDr. Gabriella Zollner
USU Dr. Donald Roberts NDVECC Dr. David ClabornDr. Richard AndreDr. Leon RobertMs. Penny Masuoka
NGA Mr. John Doty DOS Mr. Andrew Herrup
UC Davis Dr. John Edman Cornell Univ. Dr. Laura Harrington
Mahidol Univ. Dr. S. Looareesuwan Thai MOPH Dr. J. SirichaisinthopDr. P. Singhasivanon Mr. S. NutsathapanaDr. S. LeemingsawatDr. C. Apiwathnasorn
RTSD Gen. Ronnachai Thai Army Lt. P. SamipagdiDr. Kanok
Mekong Malaria & FilariasisMekong Malaria & Filariasis
Malaria Cases
Tak
Kanchanaburi
Ratchaburi
Narathiwat
Ban KongMong Tha
Test SitesTest Sites
Ikonos
Filariasis poster Field work / Mahidol Field work / AFRIMS
Kanchanaburi
Ban Kong Mong Tha
Source: SEATMJ
DECISION SUPPORT
Vector Habitat Identification:• Determine when and where to apply larvicide and insecticide
Identification of Key Factors that Sustain or Intensify Transmission: • Determine how to curtail ongoing transmission cost effectively
Risk Prediction:• Predict when and where transmission may occur and how intense it may be
VALUE & BENEFITS
• Increased warning time
• Optimized utilization of pesticide and chemoprophylaxis
• Reduced likelihood of pesticide and drug resistance
• Reduced damage to environment
• Reduced morbidity and mortality for US overseas forces and local population
Mekong Malaria and Filariasis
Data
- temperature- precipitation- humidity- surface water- wind speed & direction- land cover- vegetation type- transportation network- population density
MEASUREMENTS
• Ikonos• ASTER• Landsat• MODIS• etc.
MODELS
• Vector Habitat Model• Malaria Transmission Model• Risk Prediction Model
HABITAT IDENTIFICATION
V & V
TRANSMISSIONPREDICTION
V & V
RISKPREDICTION
V & V
RISKASSESSMENT
SURVEILLANCE
MONITORING
CONTROL • Vector Control• Personnel Protection
PROJECT OBJECTIVESPROJECT OBJECTIVESINTEGRATED PEST
MANAGEMENT FOR DODINTEGRATED PEST
MANAGEMENT FOR DOD
Objectives, Approaches & Preliminary Results
Habitat identification Identifying key factors that sustainor intensify transmission Risk prediction
Primary schizogony
Asexual erythrocytic
cycle
Hypnozoites relapses
Gametocytes
HUMANVECTOR
PARASITE
Fertilization
Oocysts
Sporozoites
blood meal oviposition eggs larvae pupae adults destroyed
pre-patent incubation delay treatment infectious relapse immunity
Textural-contextual classifications significantly increase landcover mapping accuracy using highresolution data such as Ikonos.
Discrete Wavelet Transform is used to differentiate confusion vegetation types.
Local environment
Landcover
Satellite & meteor.data
Population database
Dwelling
Vector control
Microepidemiologydata
Medical careVector ecology
Host behaviors
Evaluated Thail military airborne data and establishedneural network rectification capability.
Nonparametric model computes the likelihood of disease outbreak using meteorological and epidemiological time series as input.
Wavelet Transform and Hilbert-Huang Transform Empirical Mode Decomposition identify the driving variables that lead to disease outbreaks and provide more accurate predictions.
Spatio-temporal distribution of disease cases
4000
3500
3000
2500
2000
1500
1000
500
0
Nu
mb
er
of
Pf
& P
v C
as
es
1251007550250Month Number
Pf casesTemperature (deg C) x 100Rainfall (mm) x 5 + 1000
Tak
10
0
-10
1992.51990.01987.51985.0
Mode 1
-10
0
10
1992.51990.01987.51985.0
Mode 2
-5
0
5
1992.51990.01987.51985.0
Mode 3
Mode 1
Mode 2
Mode 3
Bamboo CupsKanchanaburi
Washington, D.C.Washington, D.C.
Space Imaging’s Ikonos imagery
Steps in Performing Discrete Wavelet Transform
Steps in Performing Discrete Wavelet Transform
image
low passon rows
high passon rows
down
sample
cols
low passon cols
high passon cols
down
sample
rows
approx
vertical edges
horizontal edges
diagonal edges
Textural Feature Extraction using Discrete Wavelet Transform
Textural Feature Extraction using Discrete Wavelet Transform
HorizontalEdges
VerticalEdges
DiagonalEdges
H
V DA square
neighborhoodin the imagery
data
A square neighborhoodin the imagery
data
n-D entropyvector
n-D entropyvector
Approx
Class Separability with Textural Features extracted by Discrete Wavelet TransformClass Separability with Textural Features extracted by Discrete Wavelet Transform
Entropy Derived from DWT as Textural Measure to Aid Classification
Entropy Derived from DWT as Textural Measure to Aid Classification
Last 8x8 neighborhood Its WC from DWT
Largest entropy 2nd largest entropyCombined withpanchromatic
Ikonos
1m resolution
North Korea – Malaria TransmissionNorth Korea – Malaria Transmission
Camp Greaves and Surrounding AreaKyunggi, South Korea
Camp Greaves and Surrounding AreaKyunggi, South Korea
Space Imaging’s Ikonos imagery
kr4_truecolor_brightened.jpg
Pseudo Ground TruthPseudo Ground Truth
Kr34_pseudogt.jpg
(R+G+B)/3 (N+R+B)/3Panchromatic
Intensity
Space Imaging’s Ikonos imagery
From Cook et al. “Ikonos Technical Performance Assessment” 2001 SPIE Proceedings, Algorithms for Multispectral, Hyperspectral, ..., p.94.
1.00
0.95
0.90
0.85
0.80
0.75
0.70
0.65
0.60
Ove
rall
Cla
ssif
ica
tio
n A
ccu
racy
38363432302826242220181614121086420Case Number
originalwith & withoutpanchromatic
sharpenedper pixel
only
sharpenedplus
original
sharpenedplus neighborhood mean
sharpenedplus
neighborhoodvariance
sharpenedplus
neighborhoodmean &variance
sharpenedplus
medianfilter
sharpenedplus low pass filter
sharpenedplus
dilation
sharpenedplus mean,
variance& median
filter
Classification Accuracy using Pan-SharpenedIkonos Data ( 1 meter resolution)
Classification Accuracy using Pan-SharpenedIkonos Data ( 1 meter resolution)
Detection of Ditches using 1-meter Data(Larval Habitats of An. sinensis)
Detection of Ditches using 1-meter Data(Larval Habitats of An. sinensis)
NDVI from AVHRR MeasurementsNDVI from AVHRR Measurements
NDVI = Normalized Difference Vegetation Index
AVHRR = Advanced Very High Resolution Radiometer
Compiled by NOAA/NESDISfor Feb. 13, 2001
NDVI = (near infrared – red) ÷ (near infrared + red)
Can be used to infer ground cover and rainfall.
Can be derived from other sensors as well.
Post-Processing with Class Frequency FiltersPost-Processing with Class Frequency Filters
From a Beechcraft B200 Super King Air Effective surface resolution approx. 1.5m
Sample Image of Royal Thai Survey Department’s Airborne Instrument
Figure 2. (a) Raw, and (b) rectified images for Flight 1. Dark pixels are training samples. Open squares are target locations.
Figure 3. (a) Raw, and (b) rectified images for Flight 2.
(b) (a)
(a)
(b)
Simulated MeasurementsGenerated by Scanner Model
Rectified
open squares = real positionsshaded squares = fitted positions
Using Neural Networkto Rectify AircraftMeasurements
Using Neural Networkto Rectify AircraftMeasurements
Ban Kong Mong ThaSanghlaburi, Kanchanaburi, Thailand
Ban Kong Mong ThaSanghlaburi, Kanchanaburi, Thailand
Anopheles dirus forest; shaded pools; hoofprints in or at the edge of forests; with increasing deforestation, adapting to orchards, tea, rubber and other plantations.
An. minimus forest fringe; flowing waters (foothill streams, springs, irrigation ditches, seepages, borrow pits, rice fields); shaded areas; grassy and shaded banks of stable, clear, slow moving streams.
An. maculatus seepage waters; streams pools; pond edges; ditches and swamps with minimal vegetation; sunlit areas.
An. dirus
An. minimus
TRANSMISSION MODEL
Local EnvironmentLandcoverSatellite & Meteor.
Data
Population Database
Dwelling
Vector Control
MicroepidemiologyData
Medical Care
Vector Ecology
Host Behaviors
Primary Schizogony
Asexual Erythro.
Cycle
Hypnozoites Relapses
Gametocytes
HUMANVECTOR
PARASITE
Fertilization
Oocysts
Sporozoites
blood meal oviposition eggs larvae pupae adults destroyed
pre-patent incubation delay treatment infectious relapse immunity
Spatio-Temporal Distribution of Disease Cases
hx, hy, hproof
rsex, rage, rimmune, revout, rgamet
bx, by tegg, tlarva, tpupa, tmate, tovi, tspor
wbtoh, whtoh, whtob
mage, mspor
tincub, twait, tgamet, theal, tpost, trelapse
100
80
60
40
20
0
500400300200100
NRWELL
NRINF
30
20
10
0
500400300200100
NINFBITE
NBITE
12
10
8
6
4
2
500400300200100
RATESPOR
RATEGAM
200
150
100
50
0
500400300200100
NMWELL
NMINF
2-Year Prediction of Malaria CasesBased on Environmental Parameters
(temperature, precipitation, humidity, vegetation index)
Tak, Thailand
5000
4000
3000
2000
1000
0
20001999199819971996199519941993199219911990
CASES
FITTED
CASES
PREDICTED
Landsat TM Image over Mae LaLandsat TM Image over Mae La
Mae La CampMae La Camp
Sources: CDC DVBID Rutgers Univ. Entomology Dept./NJMCA
Airborne Remote Sensing
Airborne Remote Sensing
ER-2 Fleet
In late 19th Century …
Proteus HeliosAltair
Neural Network Classification of GER 63-channel Scanner DataNeural Network Classification of GER 63-channel Scanner Data
Architecture Training Acc.Rel. Classif.
Acc.
1 hidden layer
with 1 node 88.41 85.52
1 hidden layer
with 3 nodes 99.07 97.93
1 hidden layer
with 5 nodes 98.86 97.52
2 hidden layers
each with 3 nodes 99.07 97.62
2 hidden layers
each with 5 nodes 99.38 97.83
1985-1999 SIESIP ½°×½° temp, precip
1985-2003 NCEP2½°×2½° rel. humidity
1985-2000 AVHRR PF8×8 km² NDVI
1999-2003 MODIS 8×8 km² NDVI
1999-2003 MODIS 5×5 km² surface temp, lifted index, moist., etc.
2000-2003 SIESIP ½°×½° temp, precip
1998-2003 TRMM ½°×½° precip
Time-Frequency Decompositions Dengue Cases – Kuala Lumpur Fourier Transform
Hilbert-Huang Transform Wavelet Transform
RISK PREDICTION MODEL
Nonparametric model computes the likelihood of disease outbreak using meteorological and epidemiological time series as input.
Wavelet Transform and Hilbert-Huang Transform Empirical Mode Decomposition identify the driving variables that lead to disease outbreaks and provide more accurate predictions.
4000
3500
3000
2500
2000
1500
1000
500
0
Nu
mb
er
of
Pf
& P
v C
as
es
1251007550250Month Number
Pf casesTemperature (deg C) x 100Rainfall (mm) x 5 + 1000
Tak
10
0
-10
1992.51990.01987.51985.0
Mode 1
-10
0
10
1992.51990.01987.51985.0
Mode 2
-5
0
5
1992.51990.01987.51985.0
Mode 3
NASA Goddard Space Flight CenterNASA Goddard Space Flight Center
Landsat-1 MSSLandsat-1 MSS Space Imaging’s Ikonos imagery
NASA/GSFC – Close-UpNASA/GSFC – Close-Up
Pan: 1m MS: 4m Space Imaging’s Ikonos imagery
2-Year Prediction of Malaria CasesBased on Environmental Parameters
(temperature, precipitation, humidity, vegetation index)
Ratchaburi, Thailand
800
600
400
200
0
20001999199819971996199519941993199219911990
CASES
FITTEDPREDICTED
CASES
Ban Kong Mong ThaSanghlaburi, Kanchanaburi, Thailand