S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTHIKONOS-DERIVED MALARIA TRANSMISSION RISK INNORTHWESTERN THAILAND
Ratana Sithiprasasna1, Donald M Ugsang2, Kiyoshi Honda2, James W Jones1
and Pratap Singhasivanon3
1
Department of Entomology, US Army Medical Component, Armed Forces Research Institute ofMedical Sciences, Bangkok; 2Space Technology Applications and Research Program, Asian Center
for Research on Remote Sensing, Asian Institute of Technology, Pathum Thani; 3Department of
Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand

Abstract. We mapped overall malaria cases and located each field observed major malaria vector
breeding habitat using Global Positioning System (GPS) instruments from September 2000 to October 2003 around the three malaria-endemic villages of Ban Khun Huay, Ban Pa Dae, and Ban ThamSeau, Mae Sod district, Tak Province, Thailand. The land-use/land-cover classifications of the threevillages and surrounding areas were performed on IKONOS satellite images acquired on 12 November 2001 with a spatial resolution of 1 x 1m. Stream network was delineated and displayed. Proximity analysis was performed on the locations of the houses with and without malaria cases within a
1.5 km buffer from An. minimus immature mosquito breeding habitats, mainly stream margins. The
1.5 km used in our proximity analysis was arbitrarily estimated based on the An. minimus flight
range. A statistical t-test at 5% significance level was performed to evaluate whether houses withmalaria cases have higher proximities to streams than houses without malaria cases. The resultshows no significant difference between proximity to streams between houses with malaria casesand houses without malaria cases. We suspect that the actual flight range of An. minimus may begreater than 1.5 km. The An. minimus larval habitat deserves more detailed investigation. Further
studies on human behavior contrary to that required for adequate malaria control among these threevillages are also recommended.

INTRODUCTION
Malaria transmission is associated with location since the disease is focused around specific mosquito breeding sites and can be transmitted within certain distances from them. Thereis also a clustering of people with malaria parasites and clinical symptoms at particular locations. In low endemicity areas, the level of malaria risk may vary between households becauseof their location and other characteristics that
affect contact between humans and vectors.
Information on the distribution of malaria on the
ground can permit controls to be targeted towards the foci of transmission and the locationsof high malaria risk. Such targeting can greatly
Correspondence: Pratap Singhasivanon, Departmentof Tropical Hygiene, Faculty of Tropical Medicine,
Mahidol University, 420/6 Rajvithi Road, Bangkok
10400, Thailand.
Tel: 66 (0) 2644-7483
E-mail: tmpsh@mahidol.ac.th
14

increase the effectiveness of control measures.
Exclusion of these locations can cause the failure of control measures. Computerized mappingand management of location data using Geographical Information Systems (GIS) and remotesensing can assist the targeting of control measures against malaria at the focal and household levels, leading to effective control.
Control of malaria in Thailand presents asignificant challenge. Targeting the populations
at greatest risk can increase the effectivenessof interventions. Risk of malaria, as measured
by parasite prevalence, estimated annual malaria incidence, and risk of clinical illness, hasbeen shown to increase with mosquito exposure(Trape et al, 1992, 1993; Thompson et al, 1997;
Staedke et al, 2003). Some data suggest thatparasite prevalence (Thomas and Lindsay, 2000)
and clinical illness (Clarke et al, 2002) are lesscommon in more frequently exposed children,
possibly due to acquired immunity. GPS and GIS
technology coupled with clinical data can be
Vol 36 No. 1 January 2005

I KONOS AND MALARIA R ISK IN THAILAND

used to identify geographic areasat greatest malaria risk (Carter etal, 2000). In this study, GPS and
GIS were used to map the location of households in three villages in northwestern Thailand in
Mae Sot district, Tak Province:
Ban Khun Huay, Ban Pa Dae,and Ban Tham Seau. The three
Karen villages are located about
20 km east of the city of Mae Sot
near the Myanmar border. We
examined the relationship between the location of the residence and the incidence of clinical episodes of malaria.
This study is part of a longitudinal study described elsewhere (Sithiprasasna et al, 2003a,b). An. minimus has been implicated as a vector for malaria
based on the detection of sporozoites using ELISA for P.
falciparum and P. vivax. The vectorial capacities and entomological inoculation rates were calculated. An. dirus was encountered
rarely and probably played little
part in transmission in thesethree villages during the study
period. Information obtained provided on nightly biting activity,
parity rate, infectivity, and adultand larval bionomics. Spatial andtemporal comparisons were displayed on different satellite images, including the Normalized
Difference Vegetation Index datafrom the National Oceanographic
and Atmospheric Administration
satellites (NOAA/NDVI), the
LANDSAT satellite Thematic
Mapper and the IKONOS satellite in a GIS. We sampled 291
bodies of water for Anopheles
larvae around the three malariaendemic villages and modeled
surface slope and wetness indices to identify the extent andVol 36 No. 1 January 2005

Fig 1–Locations of each house (black dot) in three villages utilizing Global Positioning System data overlaid on LANDSAT 5 Thematic Mapper data
dated 22 January 1999 (band combination 4, 5, 2).
Land_classes.shp
Rice
Corn
Bare
OrchardRoad
ForestWater
Mine
Built-up
Grass
Other cultivated land
Dam embankment
No data

Fig 2–Classified IKONOS image showing land-use/land-cover of the study area.

15

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

Malaria is a complex disease. Its severity isa function of the interaction between the Anopheles mosquito vector, the parasite, the host, andthe environment. Different mosquito species
have different habitat preferences, such as rice
fields, plantations, forests, forest fringes, andfoothills. Many of these features can be identified on satellite images (Sithiprasasna et al,
2003a,b). Immature stages of mosquitos arefound in a variety of aquatic habitats, but larval
surveys are expensive, time-consuming, andcumbersome. As an alternative to the traditional
ground-based survey approach to vector surveillance, this study used a combination of remotesensing (RS) and GIS technologies to develop
landscape predictors of anopheline abundance,
which is considered an indicator of potential human vector contact and malaria transmission
risk. Satellite data can be used to map the extent and condition of environmental factors pertinent to malaria transmission over space andtime.
Passive instruments on satellites measure
only radiation (reflected light or emitted energy),
therefore observations include the vegetation index, surface temperature, and cloud temperature. These are used to determine the ecological conditions on the ground and assess information regarding ecological variables relevant tomalaria control. Low spatial resolution satellitedata, such as from NOAA (National Oceanographic and Atmospheric Administration) andMeteosat, are available at a scale and frequencyof observation which can cover a large area.
NOAA satellite data are commonly used to monitor changing ecological conditions over multiple
years since data are available from 1981 to thepresent (Linthicum et al, 1999; Anyamba et al,2002). High spatial resolution satellite data (suchas LANDSAT, IKONOS) can provide up-to-datedetailed spatial mapping which can be used asa source of spatial baseline mapping (Pope etal, 1992; Sithiprasasna et al, 2003a,b). Fine spatial resolution data acquired by remote sensing
16

6,000

5,000

4,000

Area (ha)spatial pattern of potential breeding habitats ofthe major malaria vectors An. dirus, An. maculatus, An. minimus, and An. sawadwongporni
by digitizing base topographical maps of thestudy site.

3,000

2,000

1,000

0
Area (ha)Forest5283.95

Corn
1324.33

Rice
451.8

Orchard Cultivated105.58
45.69

Bareland
31.19

Road
23.97

Builtup
37.92

Mine
35.16

Water
54.85

Grass
1.11

Land-useFig 3–Land-use/land-cover and their respective total areain hectares.

has been used in several studies to estimate
adult mosquito numbers (Beck et al, 1994, 2000;
Rejmankova et al, 1995; Roberts et al, 1996;
Sithiprasasna et al, 2003a,b). We adopted thisapproach to estimate variations in cases in geographical area. This allowed us to examine variation in the relationship between exposure andinfection. The spatial resolution is important because the user must determine which data areappropriate for a particular application. IKONOS,
with a high resolution of 1 m (pixel size of 1 by 1m), and LANDSAT Thematic Mapper, with a highspatial resolution of 30 m (pixel size of 30 by 30m), can be used to map objects in detail andcharacterize land use in an area. Recent developments in the access of remotely sensed vegetation and weather data, and their analysis
along with other data sources within a GIS, haveopened new possibilities in malaria stratification,
monitoring, and early warning.

MATERIALS AND METHODS
Study area
Mosquitos were collected in 3 villages in
Mae Sot district, Tak Province (Fig 1). Ban Khun
Huay, Ban Pa Dae, and Ban Tham Seau are
Karen villages, located about 20 km east of thecity of Mae Sot near the Myanmar border withThailand.
Mosquito collection
Adult mosquito collections were conductedmonthly from August 2001 to December 2003
on human bait made between 18 00 and 06 00
hours. All anopheline mosquitos were captured
Vol 36 No. 1 January 2005

I KONOS AND MALARIA R ISK IN THAILAND

and the time of collection recorded. All collections wereconducted using a standard
method to permit temporal andspatial comparisons between
collections and statisticalevaluation. All mosquitos were
tested for P. falciparum and P.
vivax CS antigen using the enzyme-linked immunosorbent
assays (ELISA). Larval collections were made in and aroundthe study area to determine thetypes and abundance of habitats where anophelines occur.
Geographical coordinates for
all larval habitats were recorded using a Trimble GPS
GeoExplorer III. Mosquito larvae were reared to adults, andidentified as to species.
Satellite data
LANDSAT Thematic Mapper, (dated 22 January 1999)
with a spatial resolution of 30 x
30 m, and IKONOS images,(dated 12 November 2001) witha spatial resolution of 1 x 1 m,of the three villages and surrounding area were acquired
and used in GIS using ERDAS(2002) and ENVI (2000) software.
Thematic maps weregenerated using ArcGIS 8.3(ESRI, 2001-2002) software.
RS-based GIS included spatial
and temporal data in the formof geographic coverage anddescriptive information in theform of relational databases
associated with the mappedfeatures.
Land-use/land-cover classification
Visual interpretation of the
IKONOS images was based on
the results of a ground survey
and other ancillary data. A toVol 36 No. 1 January 2005

1.5 km buffer
StreamAn. minimus

Fig 4–Stream network within 1.5 km buffer of An. minimus larval habitats displayedin yellow dots.

Stream network
An. minimus locations
Houses without cases
Houses with cases

Fig 5–Stream network (blue line) with locations of An. minimus larval habitats displayed in red dots, houses with no malaria patients displayed in green dots,
and houses with malaria patients displayed in yellow dots.
17

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

from the SRTM grid file. The extracted stream
network was edited using the IKONOS image
as a base map. Editing was done within a 1.5
km buffer zone of the locations where An.
minimus larvae were collected (Figs 4-5).

Number of sample locations

140
120
100
80
60
40
20
0

No.

StreammarginStream pool

Swamp

133

92

22

Rock pool Wheel track
10

SeepagespringSand pool

3

1

7

LocationsFig 6–Types of An. minimus larval habitats.
Proximity of houses to stream
90

Number of houses

80
70
60
50

Proximity analysis
The edited stream network shapefile in
ArcGIS was imported to ENVI 3.4 image processing software as an ENVI vector file andrasterized with a pixel size of 5 x 5 m. The resulting raster stream network was used as an
input to calculate proximity to streams. Proximity was calculated by 1/(R*R), where R is the distance from the target pixel (center of the window) to other pixels in the window. Proximity isnormalized in the window, which means that if
all pixels in the window are streams, proximity
becomes 1. A window size of 1.5 by 1.5 km wasused based on the estimated mosquito flight
range.

40
30

RESULTS20
1000.00 - 0.02

0.02 - 0.04

0.04 - 0.06

0.06 - 0.08

Proximity

0.08 - 0.10

0.10 - 0.12

> 0.02
NO PatientWith PatientFig 7–Proximity of houses with and without malaria cases
to stream.

tal of 27 ground-truth sampling locations were
made, covering Ban Khun Huay, Ban Pa Dae,and Ban Tham Seau villages. The IKONOS image was imported to ArcGIS and, at a minimum
display scale of 1: 5,000, land-use/land-coverclasses were screen digitized. Within built-up areas, digitizing details were increased to a scaleof 1: 2,000 or larger.
Stream network delineation
Streams in the study area are the breeding
habitats of the major malaria-transmitting vector, An. minimus (Sithiprasasna et al, 2003a). Delineating stream networks was important in mapping the locations of these potential breedingsites. One-arc second (30 meters) Shuttle Radar Topography Mission (SRTM) data of thestudy area was imported into ArcGIS as a grid
file. Using the hydrological modeling extensionof ArcGIS, the stream network was extracted
18

Locations of Ban Khun Huay, Ban Pa Dae,and Ban Tham Seau are displayed on LANDSAT
data in Fig 1. Visual interpretation was performed
to classify the pan-sharpened IKONOS image
into land-use/land-cover classes (Fig 2). Fig 3
showed the dominant land-use of the study area
as forest (71.45%), followed by corn (17.91%).
Large tracts of what were formerly tree-covered
hills and slopes are now cultivated for corn production. Forest to corn land-use conversion wasprevalent in Ban Khun Huay and Ban Tham Seau.
Other forest areas are converted to orchard plantations (banana, fruit trees, teak, etc). Areas converted to rice fields comprised 6.2% of the total. In Ban Khun Huay and Ban Tham Seau, rice
areas are located in small valleys, where streams
serve as a source of water for irrigation. Large
tracts of rice fields are found in Ban Pa Dae.
Other than a large water reservoir, water bodies
were found in small ponds and in runningstreams. Fig 4 showed stream network (blue line)
within 1.5 km buffer of An. minimus larval habitats (yellow dots) with locations of Ban Khun
Huay (in the middle of yellow dots at top), Ban
Pa Dae (at bottom left) and Ban Tham Seau (atbottom right) displayed on a 100 km2 true color
Vol 36 No. 1 January 2005

I KONOS AND MALARIA R ISK IN THAILAND

0.0
0.0-0.004
0.004-0.008
0.008-0.012
0.012-0.016
0.016-0.020
0.020-0.024
0.024-0.028
0.028-0.032>0.032
No patientWith patient

Fig 8–Proximity to stream showing the locations of houses with and withoutmalaria cases.

of IKONOS satellite image.
Stream network with locations of An. minimus larval
habitats displayed in red dots,houses with no malaria patients displayed in green dots,
and houses with malaria patients displayed in yellow
dots, were displayed in Fig 5.
An. minimus larvae were observed in 268 locations. Of
this total, 84% of the specimens were collected fromstream margins and stream
pools (Fig 6). The remaining
16% of the samples were collected from swamps, rockpools, wheel tracks, seepage
springs, and sand pools. The
proximities of streams tohouses with and without malaria cases were summarized(Fig 7). Locations of houses
with and without malaria cases
were overlaid on the proximity
image (Figs 8-11). A statistical test at 5% significance level
was performed to evaluate
whether houses with malaria
cases have higher proximities
to streams than houses without malaria cases. The results
show no significant differencein proximity means between
the 2 groups (Table 1).

DISCUSSION

Fig 9–Thematic map showing locations of houses by Global Positioning Systemoverlaid on IKONOS (spatial resolution 1 by 1 m) dated 12 November 2001
displayed in true color of Ban Khun Huay with malaria patients in red dots
and without malaria patients in yellow dots. Stream network is displayedin yellow lines.
Vol 36 No. 1 January 2005

There are many studies
of malaria transmission andproximity to vector larval habitats, since this disease is focused around specific mosquito breeding sites. Malaria
risk in a number of countries
increases with proximity to larval habitats including: Uganda(Staedke et al, 2003), Senegal(Trape et al, 1992, 1993),
Mozambique (Thompson et19

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

al, 1997), Gambia (Thomas andLindsay, 2000; Clarke et al, 2002),
Uganda (Njama et al, 2003), andSri Lanka (Gunawardena et al,
1998; Van Der Hoek et al, 1998,
2003; Konradsen et al, 2003).

Fig 10–Thematic map showing locations of houses by Global Positioning
System overlaid on IKONOS (spatial resolution 1 by 1 m) dated 12
November 2001, displayed in true color, of Ban Pa Dae, with- malaria patients in red dots and without-malaria patients in yellow dots.
Stream network is displayed in yellow lines.

Fig 11–Thematic map showing locations of houses by Global Positioning
System overlaid on IKONOS (spatial resolution 1 by 1 m) dated 12
November 2001, displayed in true color, of Ban Tham Seau, with-malaria patients in red dots and without-malaria patients in yellow dots.
Stream network is displayed in yellow lines.
20

In our study, in order to determine the type and abundanceof habitats where An. minimus occurs, larval surveys and collections
were made within a 0.5-1 km radius around each village. We observed a high proportion of larvae
close to human settlements. The
1.5 km used in our proximity
analysis was arbitrarily estimatedbased on the An. minimus flight
range. Since we could not find any
significant difference in malaria risk
and proximity to streams when wecompared houses with malaria
cases and houses without malaria
cases, we suspect that the actual
flight range An. minimus may begreater than 1.5 km. We suggestthat the An. minimus larval habitat deserves a more detailed investigation. Further studies on human behavior contrary to that required for adequate malaria control, including: 1) failure to use
mosquito nets, insect repellents,
house-spraying; 2) delay in seeking treatment; and 3) failure tocomply with prescribed drug regimens, are recommended for thesethree villages.
The identification of foci ofmalaria transmission requires
methods of locating vector breeding sites, recording mosquito densities, rates of sporozoites and oocysts, human malaria case incidence rates, and locating andcharacterizing houses and the domestic environment. Locations are
greatly assisted by the use ofhand-held, satellite-dependentGPS. RS also provides high-resoVol 36 No. 1 January 2005

I KONOS AND MALARIA R ISK IN THAILAND

Table 1
Statistical t-test (two-samples assuming unequal variances) of proximity to streams.

Houses with no malaria patientHouses with malaria patient

n

Mean

Standard deviationt

p-value

227
40

0.040
0.039

0.032
0.032

0.203

0.420

lution images that reveal patterns of vegetationand other environmental features and conditions
on the ground. All forms of geographical data
can be assembled using computer-assisted
management of spatial information, such as GIS.
These can be applied to national malaria control
programs. Once developed, GIS can be exported to the regional or local levels in order totarget more precisely limited resources for sustainable malaria control. The major objective in
the application of GIS to malaria control is the
mapping of the distribution of malaria risk, where
possible, down to the household level. GIS can
also be used to generate predictive models ofmalaria risk and transmission in specific situations and locations. In studies in Belize andMexico, RS and GIS were used to develop predictive models for the presence and abundanceof malaria vectors in rural villages and houses(Rejmankova et al, 1995, 1998; Beck et al, 1994;
Roberts et al, 1996). The distance between villages and mosquito larval habitats was an important parameter in the predictive models(Rejmankova et al, 1995). The satellite data wereused to locate specific types of vector breedingsites (Sithiprasasna et al, 2003b). The GIS wasthen used to examine the relationships between
breeding sites and distances to human habitations. Predictive models for two of the three vectors in Belize have proved particularly accurate(Lindsay et al, 1991; Rejmankova et al, 1995).
Properly used, GPS, RS, and GIS could allow the location and quantification of malaria risk
to be determined in a more accurate, time andcost-effective way. Affordable hardware and software for using GIS and RS are now available.
These systems are valuable tools, allowing information on malaria transmission and malaria
risk to be processed and used to guide the management of malaria control campaigns.
Vol 36 No. 1 January 2005

REFERENCES
Anyamba A, Linthicum KJ, Mahoney R, Tucker CJ, Kelley
PW. Mapping potential risk of Rift Valley fever outbreaks in African savannas using vegetation index
time series data. Photogrammet Engineer Remote
Sens 2002; 68: 137-45.
Beck LR, Rodriguez MH, Dister SW, et al. Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am J
Trop Med Hyg 1994; 51: 271-80.
Beck LR, Lobitz BM, Wood BL. Remote sensing and human health: new sensors and new opportunities.
Emerg Infect Dis 2000; 6: 217-27.
Carter R, Mendis K, Roberts D. Spatial targeting of interventions against malaria. Bull WHO 2000; 78:
1401-11.
Clarke SE, Bogh C, Brown RC, Walraven GE, Thomas
CJ, Lindsay SW. Risk of malaria attacks in Gambianchildren is greater away from malaria vector breeding sites.Trans R Soc Trop Med Hyg 2002; 96: 499506.
ENVI software version 3.4, ENVI user’s guide. Boulder,
Colorado: ENVI Research Systems, 2000.
ERDAS software version 8.5 of ERDAS IMAGINE tour
guide, field guide, and virtualGIS. Atlanta, GA:
ERDAS, 2002.
ESRI. ArcGIS 8.3 of ArcMap, ArcCatalog, ArcToolbox andspatial analyst user’s guide. Redlands, CA. 20012002.
Gunawardena DM, Wickremasinghe AR, Muthuwatta L,
et al. Malaria risk factors in an endemic region of Sri
Lanka, and the impact and cost implications of risk
factor-based interventions. Am J Trop Med Hyg
1998; 58: 533-42.
Konradsen F, Amerasinghe P, Van Der Hoek W,
Amerasinghe F, Perera D, Piyaratne M. Strong association between house characteristics and ma-

21

S OUTHEAST ASIAN J T ROP MED P UBLIC H EALTH

laria vectors in Sri Lanka. Am J Trop Med Hyg 2003;
68: 177-81.

Southeast Asian J Trop Med Public Health 2003a;
34: 505-16.

Lindsay SW, Wilkins HA, Zieler HA, Daly RJ, Petrarca V,
Byass P. Ability of Anopheles gambiae mosquitos
to transmit malaria during the dry and wet seasons
in an area of irrigated rice cultivation in The Gambia.
J Trop Med Hyg 1991; 94: 313-24.

Sithiprasasna R, Linthicum KJ, Liu GJ, Jones JW,
Singhasivanon P. Use of GIS-based spatial modeling approach to characterize the spatial patterns ofmalaria mosquito breeding habitats in North-western Thailand. Southeast Asian J Trop Med Public
Health 2003b; 34: 517-28.

Linthicum KJ, Anyamba A, Tucker CJ, Kelley PW, Myers
MF, Peters CJ. Climate and satellite indicators toforecast Rift Valley fever epidemics in Kenya. Science 1999; 285: 397-400.
Njama D, Dorsey G, Guwatudde D, et al. Urban malaria:
primary caregivers’ knowledge, attitudes, practices
and predictors of malaria incidence in a cohort of
Ugandan children. Trop Med Int Health 2003; 8:
685-92.
Pope KO, Sheffner EJ, Linthicum KJ, et al. Identificationof central Kenyan Rift Valley fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar. Remote
Sens Environ 1992; 40: 185-96.
Rejmankova E, Roberts DR, Pawley A, Manguin S,
Polanco J. Predictions of adult Anopheles
albimanus densities in villages based on distances
to remotely sensed larval habitats. Am J Trop Med
Hyg 1995; 53: 482-8.

Staedke SG, Nottingham EW, Cox J, Kamya MR,
Rosenthal PJ, Dorsey G. Proximity to mosquito
breeding sites as a risk factor for clinical malaria
episodes in an urban cohort of Ugandan children.
Am J Trop Med Hyg 2003; 69: 244-6.
Thompson R, Begtrup K, Cuamba N, et al. Malaria
Project: a temporal and spatial study of malariatransmission and disease in a suburban area of
Maputo, Mozambique. Am J Trop Med Hyg 1997;
57: 550-9.
Thomas CJ, Lindsay SW. Local-scale variation in malaria
infection amongst rural Gambian children estimatedby satellite remote sensing. Trans R Soc Trop Med
Hyg 2000; 94: 159-63.
Trape JF, Lefebvre-Zante E, Legros F et al. Vector density gradients and the epidemiology of urban malaria in Dakar, Senegal. Am J Trop Med Hyg 1992;
47: 181-9.

Rejmankova E, Pope KO, Roberts DR, et al. Characterization and detection of Anopheles vestitipennis
and Anopheles punctimacula (Diptera: Culicidae)
larval habitats in Belize with field survey and SPOT
satellite imagery. J Vector Ecol 1998; 23: 74-88.

Trape JF, Lefebvre-Zante E, Legros F, et al. Malaria morbidity among children exposed to low seasonaltransmission in Dakar, Senegal and its implications
for malaria control in tropical Africa. Am J Trop Med
Hyg 1993; 48: 748-56.

Roberts DR, Paris JF, Manguin S, et al. Predictions ofmalaria vector distribution in Belize based on multispectral satellite data. Am J Trop Med Hyg 1996;
54: 304-8.

Van Der Hoek W, Konradsen F, Dijkstra DS, Amerasinghe
PH, Amerasinghe FP. Risk factors for malaria: a
microepidemiological study in a village in Sri Lanka.
Trans R Soc Trop Med Hyg 1998; 92: 265-9.

Sithiprasasna R, Linthicum KJ, Liu GJ, Jones JW,
Singhasivanon P. Some entomological observations on temporal and spatial distribution on malaria
vectors in three villages in North-western Thailandusing a Geographic Information System (GIS).

Van Der Hoek W, Konradsen F, Amerasinghe PH, Perera
D, Piyaratne MK, Amerasinghe FP. Towards a risk
map of malaria for Sri Lanka: the importance ofhouse location relative to vector breeding sites. Int
J Epidemiol 2003; 32: 280-5.

22

Vol 36 No. 1 January 2005