1: Med Vet Entomol 2000 Sep;14(3):227-44

Environmental information systems for the control of arthropod vectors of
disease.

Thomson MC, Connor SJ

MALSAT Research Group, Liverpool School of Tropical Medicine, Liverpool, U.K.
mthomson@liv.ac.uk

Over the last decade, remote sensing technologies and geographical information
systems have moved from the research arena into the hands of vector control
specialists. This review explains remote sensing approaches and spatial
information technologies used for investigations of arthropod pests and vectors
of diseases affecting humans and livestock. Relevant applications are summarized
with examples of studies on African horse sickness vector Culicoides midges
(Diptera: Ceratopogonidae), malaria vector Anopheles and arbovirus vector
culicine mosquitoes (Diptera: Culicidae), leishmaniasis vector Phlebotomus
sandflies (Diptera: Psychodidae), trypanosomiasis vector tsetse (Diptera:
Glossinidae), loaiasis vector Chrysops (Diptera: Tabanidae), Lyme disease vector
Ixodes and other ticks (Acari: Ixodidae). Methods and their uses are tabulated
and discussed with recommendations for efficiency, caution and progress in this
burgeoning field.

Publication Types:
Review
Review, tutorial

PMID: 11016429
 
 

2: Adv Parasitol 2000;47:173-215

Earth observation, geographic information systems and Plasmodium falciparum
malaria in sub-Saharan Africa.

Hay SI, Omumbo JA, Craig MH, Snow RW

Department of Zoology, University of Oxford, UK.

This review highlights the progress and current status of remote sensing (RS)
and geographical information systems (GIS) as currently applied to the problem
of Plasmodium falciparum malaria in sub-Saharan Africa (SSA). The burden of P.
falciparum malaria in SSA is first summarized and then contrasted with the
paucity of accurate and recent information on the nature and extent of the
disease. This provides perspective on both the global importance of the pathogen
and the potential for contribution of RS and GIS techniques. The ecology of P.
falciparum malaria and its major anopheline vectors in SSA in then outlined, to
provide the epidemiological background for considering disease transmission
processes and their environmental correlates. Because RS and GIS are recent
techniques in epidemiology, all mosquito-borne diseases are considered in this
review in order to convey the range of ideas, insights and innovation provided.
To conclude, the impact of these initial studies is assessed and suggestions
provided on how these advances could be best used for malaria control in an
appropriate and sustainable manner, with key areas for future research
highlighted.

Publication Types:
Review
Review, tutorial

PMID: 10997207
 
 

3: J Med Entomol 1998 Jul;35(4):435-45

Landscape ecology and epidemiology of vector-borne diseases: tools for spatial
analysis.

Kitron U

Department of Veterinary Pathobiology, College of Veterinary Medicine,
University of Illinois, Urbana 61801, USA.

Geographic information systems (GIS), global positioning systems (GPS), remote
sensing, and spatial statistics are tools to analyze and integrate the spatial
component in epidemiology of vector-borne disease into research, surveillance,
and control programs based on a landscape ecology approach. Landscape ecology,
which deals with the mosaic structure of landscapes and ecosystems, considers
the spatial heterogeneity of biotic and abiotic components as the underlying
mechanism which determines the structure of ecosystems. The methodologies of
GIS, GPS, satellite imagery, and spatial statistics, and the landscape
ecology--epidemiology approach are described, and applications of these
methodologies to vector-borne diseases are reviewed. Collaborative studies by
the author and colleagues on malaria in Israel and tsetse flies in Kenya, and
Lyme disease, LaCrosse encephalitis, and eastern equine encephalitis in the
north-central United States are presented as examples for application of these
tools to research and disease surveillance. Relevance of spatial tools and
landscape ecology to emerging infectious diseases and to studies of global
change effects on vector-borne diseases are discussed.

PMID: 9701925
 
 

4: Trans R Soc Trop Med Hyg 1998 Jan-Feb;92(1):12-20

Predicting malaria seasons in Kenya using multitemporal meteorological satellite
sensor data.

Hay SI, Snow RW, Rogers DJ

Trypanosomiasis and Land-use in Africa (TALA) Research Group, Department of
Zoology, University of Oxford, UK. simon.hay@zoo.ox.ac.uk

This article describes research that predicts the seasonality of malaria in
Kenya using remotely sensed images from satellite sensors. The predictions were
made using relationships established between long-term data on paediatric severe
malaria admissions and simultaneously collected data from the Advanced Very High
Resolution Radiometer (AVHRR) on the National Oceanic and Atmospheric
Administrations (NOAA) polar-orbiting meteorological satellites and the High
Resolution Radiometer (HRR) on the European Organization for the Exploitation of
Meteorological Satellites' (EUMETSAT) geostationary Meteosat satellites. The
remotely sensed data were processed to provide surrogate information on land
surface temperature, reflectance in the middle infra-red, rainfall, and the
normalized difference vegetation index (NDVI). These variables were then
subjected to temporal Fourier processing and the fitted Fourier data were
compared with the mean percentage of total annual malaria admissions recorded in
each month. The NDVI in the preceding month correlated most significantly and
consistently with malaria presentations across the 3 sites (mean adjusted r2 =
0.71, range 0.61-0.79). Regression analyses showed that an NDVI threshold of
0.35-0.40 was required for more than 5% of the annual malaria cases to be
presented in a given month. These thresholds were then extrapolated spatially
with the temporal Fourier-processed NDVI data to define the number of months, in
which malaria admissions could be expected across Kenya in an average year, at
an 8 x 8 km resolution. The resulting maps were compared with the only existing
map (Butler's) of malaria transmission periods for Kenya, compiled from expert
opinion. Conclusions are drawn on the appropriateness of remote sensing
techniques for compiling national strategies for malaria intervention.

PMID: 9692138
 
 

5: Trans R Soc Trop Med Hyg 1997 Mar-Apr;91(2):105-6

Remote sensing and disease control: past, present and future.

Hay SI

Department of Zoology, University of Oxford, UK. simon.hay@zoo.ox.ac.uk

PMID: 9196741
 
 

6: Am J Trop Med Hyg 1997 Jan;56(1):99-106

Assessment of a remote sensing-based model for predicting malaria transmission
risk in villages of Chiapas, Mexico.

Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Washino RK, Roberts DR, Spanner
MA

Johnson Controls World Services, National Aeronautics and Space Administration,
Ames Research Center, Moffett Field, California, USA.

A blind test of two remote sensing-based models for predicting adult populations
of Anopheles albimanus in villages, an indicator of malaria transmission risk,
was conducted in southern Chiapas, Mexico. One model was developed using a
discriminant analysis approach, while the other was based on regression
analysis. The models were developed in 1992 for an area around Tapachula,
Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic
information system functions. Using two remotely sensed landscape elements, the
discriminant model was able to successfully distinguish between villages with
high and low An. albimanus abundance with an overall accuracy of 90%. To test
the predictive capability of the models, multitemporal TM data were used to
generate a landscape map of the Huixtla area, northwest of Tapachula, where the
models were used to predict risk for 40 villages. The resulting predictions were
not disclosed until the end of the test. Independently, An. albimanus abundance
data were collected in the 40 randomly selected villages for which the
predictions had been made. These data were subsequently used to assess the
models' accuracies. The discriminant model accurately predicted 79% of the
high-abundance villages and 50% of the low-abundance villages, for an overall
accuracy of 70%. The regression model correctly identified seven of the 10
villages with the highest mosquito abundance. This test demonstrated that remote
sensing-based models generated for one area can be used successfully in another,
comparable area.

PMID: 9063370
 
 

7: Am J Trop Med Hyg 1996 Mar;54(3):304-8

Predictions of malaria vector distribution in Belize based on multispectral
satellite data.

Roberts DR, Paris JF, Manguin S, Harbach RE, Woodruff R, Rejmankova E, Polanco
J, Wullschleger B, Legters LJ

Department of Preventive Medicine/Biometrics, Uniformed Services University of
the Health Sciences, Bethesda, Maryland, USA.

Use of multispectral satellite data to predict arthropod-borne disease trouble
spots is dependent on clear understandings of environmental factors that
determine the presence of disease vectors. A blind test of remote sensing-based
predictions for the spatial distribution of a malaria vector, Anopheles
pseudopunctipennis, was conducted as a follow-up to two years of studies on
vector-environmental relationships in Belize. Four of eight sites that were
predicted to be high probability locations for presence of An.
pseudopunctipennis were positive and all low probability sites (0 of 12) were
negative. The absence of An. pseudopunctipennis at four high probability
locations probably reflects the low densities that seem to characterize field
populations of this species, i.e., the population densities were below the
threshold of our sampling effort. Another important malaria vector, An.
darlingi, was also present at all high probability sites and absent at all low
probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a
riverine species. Prior to these collections at ecologically defined locations,
this species was last detected in Belize in 1946.

PMID: 8600771
 
 

8: Ann N Y Acad Sci 1994 Dec 15;740:396-402

The environment, remote sensing, and malaria control.

Roberts DR, Rodriguez MH

Department of Preventive Medicine/Biometrics, Uniformed Services University of
the Health Sciences, Bethesda, Maryland 20814.

Results of studies in California, Mexico and Belize demonstrate the value of
remote sensing technology for studying vector-borne diseases. These studies have
also shown that it is necessary to fully define the environmental factors
associated with the presence of vectors and disease transmission, and to be able
to detect these environmental factors with image data. These studies, and other
published reports, are demonstrating many potential uses of remotely sensed data
in managing and targeting vector and disease control measures.

Publication Types:
Review
Review, tutorial

PMID: 7840472
 
 

9: Am J Trop Med Hyg 1994 Sep;51(3):271-80

Remote sensing as a landscape epidemiologic tool to identify villages at high
risk for malaria transmission.

Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Rejmankova E, Ulloa A, Meza RA,
Roberts DR, Paris JF, Spanner MA, et al

Johnson Controls World Services, NASA Ames Research Center, Moffett Field,
California.

A landscape approach using remote sensing and geographic information system
(GIS) technologies was developed to discriminate between villages at high and
low risk for malaria transmission, as defined by adult Anopheles albimanus
abundance. Satellite data for an area in southern Chiapas, Mexico were digitally
processed to generate a map of landscape elements. The GIS processes were used
to determine the proportion of mapped landscape elements surrounding 40 villages
where An. albimanus abundance data had been collected. The relationships between
vector abundance and landscape element proportions were investigated using
stepwise discriminant analysis and stepwise linear regression. Both analyses
indicated that the most important landscape elements in terms of explaining
vector abundance were transitional swamp and unmanaged pasture. Discriminant
functions generated for these two elements were able to correctly distinguish
between villages with high and low vector abundance, with an overall accuracy of
90%. Regression results found both transitional swamp and unmanaged pasture
proportions to be predictive of vector abundance during the mid-to-late wet
season. This approach, which integrates remotely sensed data and GIS
capabilities to identify villages with high vector-human contact risk, provides
a promising tool for malaria surveillance programs that depend on
labor-intensive field techniques. This is particularly relevant in areas where
the lack of accurate surveillance capabilities may result in no malaria control
action when, in fact, directed action is necessary. In general, this landscape
approach could be applied to other vector-borne diseases in areas where 1) the
landscape elements critical to vector survival are known and 2) these elements
can be detected at remote sensing scales.

PMID: 7943544

...and "remote sensing and disease"

1: Adv Parasitol 2000;47:289-307

Advances in satellite remote sensing of environmental variables for
epidemiological applications.

Goetz SJ, Prince SD, Small J

Department of Geography, University of Maryland, College Park 2074-8225, USA.

Earth-observing satellites have provided an unprecedented view of the land
surface but have been exploited relatively little for the measurement of
environmental variables of particular relevance to epidemiology. Recent advances
in techniques to recover continuous fields of air temperature, humidity, and
vapour pressure deficit from remotely sensed observations have significant
potential for disease vector monitoring and related epidemiological
applications. We report on the development of techniques to map environmental
variables with relevance to the prediction of the relative abundance of disease
vectors and intermediate hosts. Improvements to current methods of obtaining
information on vegetation properties, canopy and surface temperature and soil
moisture over large areas are also discussed. Algorithms used to measure these
variables incorporate visible, near-infrared and thermal infrared radiation
observations derived from time series of satellite-based sensors, focused here
primarily but not exclusively on the Advanced Very High Resolution Radiometer
(AVHRR) instruments. The variables compare favourably with surface measurements
over a broad array of conditions at several study sites, and maps of retrieved
variables captured patterns of spatial variability comparable to, and locally
more accurate than, spatially interpolated meteorological observations.
Application of multi-temporal maps of these variables are discussed in relation
to current epidemiological research on the distribution and abundance of some
common disease vectors.

Publication Types:
Review
Review, tutorial

PMID: 10997210
 
 

2: Adv Parasitol 2000;47:37-80

Linking remote sensing, land cover and disease.

Curran PJ, Atkinson PM, Foody GM, Milton EJ

Department of Geography, University of Southampton, Highfield, UK.

Land cover is a critical variable in epidemiology and can be characterized
remotely. A framework is used to describe both the links between land cover and
radiation recorded in a remotely sensed image, and the links between land cover
and the disease carried by vectors. The framework is then used to explore the
issues involved when moving from remotely sensed imagery to land cover and then
to vector density/disease risk. This exploration highlights the role of land
cover; the need to develop a sound knowledge of each link in the predictive
sequence; the problematic mismatch between the spatial units of the remotely
sensed and epidemiological data and the challenges and opportunities posed by
adding a temporal mismatch between the remotely sensed and epidemiological data.
The paper concludes with a call for both greater understanding of the physical
components of the proposed framework and the utilization of optimized
statistical tools as prerequisites to progress in this field.

Publication Types:
Review
Review, tutorial

PMID: 10997204
 
 

3: Emerg Infect Dis 2000 May-Jun;6(3):217-27

Remote sensing and human health: new sensors and new opportunities.

Beck LR, Lobitz BM, Wood BL

California State University, Monterey Bay, California 94035-2424, USA.
lrbeck@gaia.arc.nasa.gov

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used
to map features on the earth's surface. An increasing number of health studies
have used remotely sensed data for monitoring, surveillance, or risk mapping,
particularly of vector-borne diseases. Nearly all studies used data from
Landsat, the French Systeme Pour l'Observation de la Terre, and the National
Oceanic and Atmospheric Administration's Advanced Very High Resolution
Radiometer. New sensor systems are in orbit, or soon to be launched, whose data
may prove useful for characterizing and monitoring the spatial and temporal
patterns of infectious diseases. Increased computing power and spatial modeling
capabilities of geographic information systems could extend the use of remote
sensing beyond the research community into operational disease surveillance and
control. This article illustrates how remotely sensed data have been used in
health applications and assesses earth-observing satellites that could detect
and map environmental variables related to the distribution of vector-borne and
other diseases.

PMID: 10827111
 
 

4: Proc Natl Acad Sci U S A 2000 Feb 15;97(4):1438-43

From the cover: climate and infectious disease: use of remote sensing for
detection of Vibrio cholerae by indirect measurement.

Lobitz B, Beck L, Huq A, Wood B, Fuchs G, Faruque AS, Colwell R

Johnson Controls World Services, Center for Health Applications of Aerospace
Related Technologies, Aerospace Related Technologies, National Aeronautics and
Space Administration Ames Research Center, Moffett Field, CA 94035, USA.

It has long been known that cholera outbreaks can be initiated when Vibrio
cholerae, the bacterium that causes cholera, is present in drinking water in
sufficient numbers to constitute an infective dose, if ingested by humans.
Outbreaks associated with drinking or bathing in unpurified river or brackish
water may directly or indirectly depend on such conditions as water temperature,
nutrient concentration, and plankton production that may be favorable for growth
and reproduction of the bacterium. Although these environmental parameters have
routinely been measured by using water samples collected aboard research ships,
the available data sets are sparse and infrequent. Furthermore, shipboard data
acquisition is both expensive and time-consuming. Interpolation to regional
scales can also be problematic. Although the bacterium, V. cholerae, cannot be
sensed directly, remotely sensed data can be used to infer its presence. In the
study reported here, satellite data were used to monitor the timing and spread
of cholera. Public domain remote sensing data for the Bay of Bengal were
compared directly with cholera case data collected in Bangladesh from 1992-1995.
The remote sensing data included sea surface temperature and sea surface height.
It was discovered that sea surface temperature shows an annual cycle similar to
the cholera case data. Sea surface height may be an indicator of incursion of
plankton-laden water inland, e.g., tidal rivers, because it was also found to be
correlated with cholera outbreaks. The extensive studies accomplished during the
past 25 years, confirming the hypothesis that V. cholerae is autochthonous to
the aquatic environment and is a commensal of zooplankton, i.e., copepods, when
combined with the findings of the satellite data analyses, provide strong
evidence that cholera epidemics are climate-linked.

PMID: 10677480
 
 

5: Trop Med Int Health 1999 Jan;4(1):58-71

Deriving meteorological variables across Africa for the study and control of
vector-borne disease: a comparison of remote sensing and spatial interpolation
of climate.

Hay SI, Lennon JJ

Department of Zoology, University of Oxford, UK. simon.hay@zoo.ox.ac.uk

This paper presents the results of an investigation into the utility of remote
sensing (RS) using meteorological satellites sensors and spatial interpolation
(SI) of data from meteorological stations, for the prediction of spatial
variation in monthly climate across continental Africa in 1990. Information from
the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and
Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was
used to estimate land surface temperature (LST) and atmospheric moisture. Cold
cloud duration (CCD) data derived from the High Resolution Radiometer (HRR)
on-board the European Meteorological Satellite programme's (EUMETSAT) Meteosat
satellite series were also used as a RS proxy measurement of rainfall.
Temperature, atmospheric moisture and rainfall surfaces were independently
derived from SI of measurements from the World Meteorological Organization (WMO)
member stations of Africa. These meteorological station data were then used to
test the accuracy of each methodology, so that the appropriateness of the two
techniques for epidemiological research could be compared. SI was a more
accurate predictor of temperature, whereas RS provided a better surrogate for
rainfall; both were equally accurate at predicting atmospheric moisture. The
implications of these results for mapping short and long-term climate change and
hence their potential for the study and control of disease vectors are
considered. Taking into account logistic and analytical problems, there were no
clear conclusions regarding the optimality of either technique, but there was
considerable potential for synergy.

PMID: 10203175
 
 

6: J Vector Ecol 1998 Jun;23(1):54-61

An overview of remote sensing and GIS for surveillance of mosquito vector
habitats and risk assessment.

Dale PE, Ritchie SA, Territo BM, Morris CD, Muhar A, Kay BH

Australian School of Environmental Studies, Griffith University, Brisbane,
Queensland.

This paper provides a brief nontechnical overview of the use of remote sensing
to achieve multiple objectives, focusing on mosquito management. It also shows
how Geographic Information Systems, combined with remote sensing analysis, have
the potential to assist in minimizing disease risk. Examples are used from
subtropical Queensland, Australia, where the salt marsh mosquito, Aedes vigilax,
and the freshwater species, Culex annulirostris, are vectors of human arbovirus
diseases such as Ross River and Barmah Forest virus disease. Culex annulirostris
is also implicated in the transmission of Japanese Encephalitis. Mapping the
breeding habitats of the species facilitates assessment of the risk of
contracting the diseases and also assists in control of the vectors. First, it
considers a simple risk model that is applied to data for the city of Brisbane
in southeast Queensland. This is then linked to computer-aided analysis of
remotely sensed data to map potential ephemeral freshwater breeding sites of Cx.
annulirostris. This has the potential to guide control at critical times, for
example after heavy summer rainfall or when there is an outbreak of Ross River
virus disease. Second, the use of color infrared aerial photography is used to
identify the specific parts of the salt marsh in which larvae and eggs of Ae.
vigilax are found. Finally, we explore novel ways to map the detailed pattern of
water under mangrove forest canopy to identify where mosquitoes are breeding and
as an aid to planning modification. For each we discuss the limitations and
advantages and the possibilities for combining methods and/or using a single
method for multiple objectives.

Publication Types:
Review
Review, tutorial

PMID: 9673930
 
 

7: Science 1996 Dec 20;274(5295):2025-31

Global climate and infectious disease: the cholera paradigm.

Colwell RR

University of Maryland Biotechnology Institute, 4321 Hartwick Road, Suite 550,
College Park, MD 20740, USA.

The origin of cholera has been elusive, even though scientific evidence clearly
shows it is a waterborne disease. However, standard bacteriological procedures
for isolation of the cholera vibrio from environmental samples, including water,
between epidemics generally were unsuccessful. Vibrio cholerae, a marine vibrio,
requiring salt for growth, enters into a dormant, viable but nonculturable stage
when conditions are unfavorable for growth and reproduction. The association of
Vibrio cholerae with plankton, notably copepods, provides further evidence for
the environmental origin of cholera, as well as an explanation for the sporadic
and erratic occurrence of cholera epidemics. On a global scale, cholera
epidemics can now be related to climate and climatic events, such as El Nino, as
well as the global distribution of the plankton host. Remote sensing, with the
use of satellite imagery, offers the potential for predicting conditions
conducive to cholera outbreaks or epidemics.

Publication Types:
Historical article
Review
Review, tutorial

PMID: 8953025
 
 

8: Am J Trop Med Hyg 1994;50(6 Suppl):134-44

Application of remote sensing to arthropod vector surveillance and control.

Washino RK, Wood BL

Department of Entomology, University of California, Davis.

A need exists to further develop new technologies, such as remote sensing and
geographic information systems analysis, for estimating arthropod vector
abundance in aquatic habitats and predicting adult vector population outbreaks.
A brief overview of remote sensing technology in vector surveillance and control
is presented, and suggestions are made on future research opportunities in light
of current and proposed remote sensing systems.

Publication Types:
Review
Review, tutorial

PMID: 8024079