Transcript Mekong Malaria & Filariasis

Malaria Modeling for Thailand & Korea
— NASA Techniques and Call for Validation Partners
Richard Kiang
NASA Goddard Space Flight Center
Greenbelt, MD 20771
Acknowledgement
AFRIMS
Dr. Jame Jones
Dr. R. Sithiprasasna
Dr. Gabriella Zollner
WRAIR
Dr. Russell Coleman
USU
Dr. Donald Roberts
Dr. Richard Andre
Dr. Leon Robert
Ms. Penny Masuoka
NDVECC
Dr. David Claborn
NGA
Mr. John Doty
DOS
Mr. Andrew Herrup
UC Davis
Dr. John Edman
Cornell Univ.
Dr. Laura Harrington
Mahidol Univ.
Dr. S. Looareesuwan
Dr. P. Singhasivanon
Dr. S. Leemingsawat
Dr. C. Apiwathnasorn
Thai MOPH
Dr. J. Sirichaisinthop
Mr. S. Nutsathapana
RTSD
Gen. Ronnachai
Dr. Kanok
Thai Army
Lt. P. Samipagdi
Mekong Malaria & Filariasis
Kanchanaburi
Malaria Cases
Test Sites
Test
Sites
Tak
Ban Kong
Mong Tha
Kanchanaburi
Ikonos
Ratchaburi
Narathiwat
Source: SEATMJ
Ban Kong Mong Tha
Filariasis poster
Field work / Mahidol
Field work / AFRIMS
[email protected]
Mekong Malaria and Filariasis
DECISION SUPPORT
MODELS
VALUE & BENEFITS
• Vector Habitat Model
• Malaria Transmission Model
• Risk Prediction Model
Dat
a
MEASUREMENTS
•
•
•
•
•
Ikonos
ASTER
Landsat
MODIS
etc.
-
temperature
precipitation
humidity
surface water
wind speed & direction
land cover
vegetation type
transportation network
population density
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
• 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
[email protected]
PROJECT OBJECTIVES
HABITAT
IDENTIFICATION
INTEGRATED PEST
MANAGEMENT FOR DOD
RISK
ASSESSMENT
V&V
TRANSMISSION
PREDICTION
SURVEILLANCE
V&V
CONTROL
RISK
PREDICTION
V&V
MONITORING
• Vector Control
• Personnel Protection
Objectives, Approaches & Preliminary Results
Textural-contextual classifications significantly
increase landcover mapping accuracy using high
resolution data such as Ikonos.
Identifying key factors that sustain
or intensify transmission
Satellite & meteor.
data
Microepidemiology
data
Local environment
Population database
Landcover
Dwelling
Host behaviors
Vector control
Vector ecology
Medical care
Risk prediction
Nonparametric model computes the likelihood of
disease outbreak using meteorological and
epidemiological time series as input.
4000
Number of Pf & Pv Cases
Habitat identification
Tak
3500
3000
Pf cases
Temperature (deg C) x 100
Rainfall (mm) x 5 + 1000
2500
2000
1500
1000
500
Sporozoites
Discrete Wavelet Transform is used to differentiate
confusion vegetation types.
Oocysts
0
Primary schizogony
Hypnozoites  relapses
0
25
50
75
Month Number
100
125
Wavelet Transform and Hilbert-Huang Transform
Empirical Mode Decomposition identify the driving
variables that lead to disease outbreaks and provide
more accurate predictions.
Asexual
erythrocytic
cycle
VECTOR
HUMAN
Fertilization
Gametocytes
PARASITE
Evaluated Thail military airborne data and established
neural network rectification capability.
 blood meal
 oviposition
 eggs
 larvae
 pupae
 adults
 destroyed
 pre-patent
 incubation
 delay
 treatment
 infectious
 relapse
 immunity
Mode 1
10
Mode 2
1985.0
Mode
2
1987.5
1985.0
1985.0
1990.0
1992.5
Mode 3
1987.5
1990.0
1992.5
1987.5
1990.0
1992.5
0
-10
5
0
-5
Spatio-temporal distribution of disease cases
Mode 1
10
0
-10
Mode 3
[email protected]
Bamboo Cups
Kanchanaburi
Washington, D.C.
Space Imaging’s Ikonos imagery
Steps in Performing
Discrete Wavelet Transform
approx
low pass
on rows
image
down
low pass
on cols
sample
high pass
on rows
cols
down
vertical edges
sample
high pass
on cols
horizontal edges
rows
diagonal edges
Textural Feature Extraction
using Discrete Wavelet Transform
Approx
A square
neighborhood
in the imagery
data
H
Horizontal
Edges
V
D
Vertical
Edges
n-D
entropy
vector
Diagonal
Edges
Class Separability with Textural Features
extracted by Discrete Wavelet Transform
Entropy Derived from DWT
as Textural Measure to Aid Classification
Ikonos
Last 8x8 neighborhood
Largest entropy
1m resolution
Its WC from DWT
2nd largest entropy
Combined with
panchromatic
North Korea – Malaria Transmission
Camp Greaves and Surrounding Area
Kyunggi, South Korea
kr4_truecolor_brightened.jpg
Space Imaging’s Ikonos imagery
Pseudo Ground Truth
Kr34_pseudogt.jpg
(R+G+B)/3
(N+R+B)/3
Panchromatic
Intensity
Space Imaging’s Ikonos imagery
From Cook et al. “Ikonos Technical Performance Assessment” 2001 SPIE Proceedings, Algorithms for
Multispectral, Hyperspectral, ..., p.94.
Classification Accuracy using Pan-Sharpened
Ikonos Data ( 1 meter resolution)
Detection of Ditches using 1-meter Data
(Larval Habitats of An. sinensis)
NDVI from AVHRR Measurements
NDVI = Normalized Difference Vegetation Index
AVHRR = Advanced Very High Resolution Radiometer
 NDVI = (near infrared – red)
÷ (near infrared + red)
 Can be used to infer ground cover
and rainfall.
Compiled by NOAA/NESDIS
for Feb. 13, 2001
 Can be derived from other sensors
as well.
Post-Processing with Class Frequency Filters
Sample Image of Royal Thai
Survey Department’s Airborne Instrument
 From a Beechcraft B200 Super King Air
 Effective surface resolution approx. 1.5m
(b)
(a)
Using Neural Network
to Rectify Aircraft
Measurements
Figure 2. (a) Raw,
and (b) rectified images for Flight 1.
Simulated
Measurements
Open squares are target locations.
Generated by Scanner Model
(a)
Dark pixels are training samples.
Rectified
(b)
open squares = real positions
shaded squares = fitted positions
Figure 3. (a) Raw, and (b) rectified images for Flight 2.
Ban Kong Mong Tha
Sanghlaburi, 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
Satellite & Meteor.
Data
Landcover
Vector Ecology
Microepidemiology
Data
Local Environment
Sporozoites
Primary Schizogony
Hypnozoites  Relapses
Vector Control
Population Database
Host Behaviors
Dwelling
Oocysts
Asexual
Erythro.
Cycle
VECTOR
Medical Care
HUMAN
Fertilization
Gametocytes
PARASITE
 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
NRWELL
100
80
60
40
20
NRINF
0
100
200
300
400
500
200
NMWELL
150
100
NMINF
50
0
100
200
300
400
500
30
20
NBITE
10
NINFBITE
0
100
200
300
400
500
12
10
RATEGAM
8
6
RATESPOR
4
2
100
200
300
400
500
2-Year Prediction of Malaria Cases
Based on Environmental Parameters
(temperature, precipitation, humidity, vegetation index)
Tak, Thailand
5000
CASES
CASES
4000
PREDICTED
3000
FITTED
2000
1000
0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Landsat TM Image over Mae La
Mae La Camp
Sources:
CDC DVBID
Rutgers Univ. Entomology Dept./NJMCA
Airborne
Remote Sensing
In late 19th Century …
ER-2 Fleet
Altair
Proteus
Helios
Neural 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
2000-2003 SIESIP
½°×½° temp, precip
1985-2003 NCEP
2½°×2½° rel. humidity
1998-2003 TRMM
½°×½° precip
1999-2003 MODIS
5×5 km² surface temp,
lifted index, moist., etc.
1985-2000 AVHRR PF
8×8 km² NDVI
1999-2003 MODIS
8×8 km² NDVI
Time-Frequency Decompositions
Dengue Cases – Kuala Lumpur
Hilbert-Huang Transform
Fourier Transform
Wavelet Transform
RISK PREDICTION MODEL
Number of Pf & Pv Cases
4000
Tak
3500
3000
Pf cases
Temperature (deg C) x 100
Rainfall (mm) x 5 + 1000
Nonparametric model computes
the likelihood of disease outbreak
using meteorological and
epidemiological time series as
input.
2500
2000
1500
1000
500
0
0
25
100
125
Wavelet Transform and HilbertHuang Transform Empirical Mode
Decomposition identify the driving
variables that lead to disease outbreaks
and provide more accurate predictions.
Mode 1
10
0
-10
10
50
75
Month Number
1985.0
Mode
1987.52
1990.0
1992.5
1985.0
Mode 3
1987.5
1990.0
1992.5
0
-10
5
0
-5
NASA Goddard Space Flight Center
Landsat-1 MSS
Space Imaging’s Ikonos imagery
NASA/GSFC – Close-Up
Pan: 1m
MS: 4m
Space Imaging’s Ikonos imagery
2-Year Prediction of Malaria Cases
Based on Environmental Parameters
(temperature, precipitation, humidity, vegetation index)
Ratchaburi, Thailand
800
CASES
CASES
600
FITTED
PREDICTED
400
200
0
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Ban Kong Mong Tha
Sanghlaburi, Kanchanaburi, Thailand