Malaria Modeling for Thailand

8/9/2019 Malaria Modeling for Thailand

1/20

Richard Kiang

NASA Goddard Space Flight Center

Greenbelt, MD 20771

Malaria Modeling for Thailand

— NASA echni!"e# and Call $or %alidation &artner#


2/20

Introduction• The transmission of infectious diseases is influenced by a myriad

of factors. Environmental, meteorological, social, economic, political

and warlike conditions have all been shown to contribute to the

occurrence and outbreaks of a large number of diseases.

• They can be conveniently measured repeatedly using remotesensing in either friendly or hostile territories. Other factors, on the

other hand, often require substantial efforts to measure and can

only be epressed qualitatively

• !alaria is a parasitic disease that infects both humans and

primates, and is endemic in most parts of the tropic, especially in thedeveloping countries. "mong the continents, "frica has nearly ninety

per cent of the malaria cases and deaths. #ut malaria is also a

significant problem in $outh and $outheast "sia.


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Me'ong Malaria and Filaria#i#

Richard(Kiang)na#a(go*

Data

+ teperat"re+ precipitation+ h"idit-+ #"r$ace .ater

+ .ind #peed / direction+ land co*er + *egetation t-pe+ tran#portation net.or'

+ pop"lation den#it-MEASUREMENTS

'ono# ASR 3and#at

M4DS etc(

MODELS

%ector 5abitat Model Malaria ran#i##ion Model Ri#' &rediction Model


4/20

TRANSMISSION MODEL

Local EnvironmentLandcoverSatellite & Meteor.

Data

Population Database

Dwelling

Vector Control

Microepidemiology

Data

Medical Care

Vector Ecology

Host e!aviors

Primary Sc!i"ogony

#se$ual

Eryt!ro.

Cycle

Hypno"oites %elapses

ametocytes

HUMANVECTOR

PARASITE

'ertili"ation

(ocysts

Sporo"oites

• blood meal

• oviposition• eggs• larvae• pupae• adults• destroyed

• pre)patent• incubation• delay• treatment• in*ectious• relapse• immunity

Spatio)+emporal Distribution o* Disease Cases


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6aboo C"p#

Kanchanab"ri


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Step# in &er$oring

Di#crete a*elet ran#$or

iage

lo. pa##

on ro.#

high pa##

on ro.#

do.n

#aple

col#

lo. pa##

on col#

high pa##

on col#

do.n

#aple

ro.#

appro8

*ertical edge#

hori9ontal edge#

diagonal edge#


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e8t"ral Feat"re 8traction

"#ing Di#crete a*elet ran#$or

5ori9ontaldge#

%ertical

dge#

Diagonal

dge#

5

% D A #!"are

neighborhood

in the iager-

data

n+D

entrop-

*ector

Appro8


8/20

Cla## Separabilit- .ith e8t"ral Feat"re#

e8tracted b- Di#crete a*elet ran#$or


9/20

ntrop- Deri*ed $ro D

a# e8t"ral Mea#"re to Aid Cla##i$ication

3a#t :8: neighborhood t# C $ro D

3arge#t entrop- 2nd large#t entrop-Cobined .ith

panchroatic

'ono#

1 re#ol"tion


10/20

From Cook et al. “Ikonos Technical Performance Assessment” 2001 SPIE Proceedings Algorithms for

!"ltis#ectral $%#ers#ectral ... #.&'.


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(0(;


12/20

Detection o$ Ditche# "#ing 1+eter Data@3ar*al 5abitat# o$ An. sinensis


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&o#t+&roce##ing .ith Cla## Fre!"enc- Filter#


14/20

Ano#heles dir"s $ore#tB #haded pool#B hoo$print# in or at theedge o$ $ore#t#B .ith increa#ing de$ore#tation,adapting to orchard#, tea, r"bber and otherplantation#(

An. minim"s $ore#t $ringeB $lo.ing .ater# @$oothill #trea#,#pring#, irrigation ditche#, #eepage#, borro.pit#, rice $ield#B #haded area#B gra##- and#haded ban'# o$ #table, clear, #lo. o*ing#trea#(

An. mac"lat"s #eepage .ater#B #trea# pool#B pond edge#Bditche# and #.ap# .ith inial *egetationB#"nlit area#(


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An. dir"s

An. minim"s


16/20

RISK PREDICTION MODEL

,onparametric model computes

the likelihood of disease outbreak

using meteorological and

epidemiological time series as

input.

-avelet +rans*orm and Hilbert)Huang

+rans*orm Empirical Mode

Decomposition identify the driving

variables that lead to disease outbreaksand provide more accurate predictions.

>0002


17/20

2+ear &rediction o$ Malaria Ca#e#

6a#ed on n*ironental &araeter#@teperat"re, precipitation, h"idit-, *egetation inde8

a', hailand

000

2000

1000

0

20001;;;1;;:1;;71;;=1;;1;;21;;11;;0

CASES

+ITTED

CASES

PREDICTED


18/20

#ctual le*t/ and predicted rig!t/ malaria case rates in +!ailand


19/20

Res#l" and concl#sion

Many *actors including environmental and meteorological conditions

a**ect t!e transmission o* malaria. +!e environmental and meteorological

conditions can indeed be considered t!e driving *actors *or t!ese

diseases w!en ot!er *actors are stable. +!is is especially evident *or vector)

borne diseases w!ere t!e vector propagation is directly in*luenced by t!e

environmental and meteorological conditions. +!is is t!e essential premise

o* w!y remote sensing can be used to predict disease ris0s. 1n general2

statistical and biological models can be used to predict disease ris0s.

ot! types o* models accept remotely sensed environmental and

meteorological parameters as input. 1n some applications2 remotesensing not only can be used *or predicting ris0s2 but also *or

detecting and reducing ris0s. 1n addition2 remote sensing)based model

can be used to pro3ect disease ris0 under t!e impact o* global warming.


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+H#,4 5(6

Documents

Malaria Modeling for Thailand