Peer-Reviewed Articles
137 Mapping Potential Risk of Rift Valley Fever Outbreaks in African
Savannas Using Vegetation Index Time Series Data
Assaf Anyamba, Kenneth J. Linthicum, Robert Mahoney, Compton J. Tucker,
and Patrick W. Kelley
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Rift Valley fever (RVF) outbreaks in East Africa are closely coupled
with above normal rainfall that is associated with the occurrence
of the warm phase of the El Ninõ Southern Oscillation (ENSO)
phenomenon. Outbreaks elsewhere in central and southern Africa are
also linked to elevated rainfall patterns. Major RVF activity has
been reported to occur throughout much of sub-Saharan Africa, except
in areas with extensive tropical forest. In this study we used normalized
difference vegetation index (NDVI) time-series data derived from
the Advanced Very High Resolution Radiometer (AVHRR) instrument on
polar orbiting National Oceanographic and Atmospheric Administration
(NOAA) satellites to map areas with a potential for an RVF outbreak.
A 19-year NDVI climatology was created and used to discriminate between
areas with tropical forest, savanna, and desert. Because most RVF
outbreaks have occurred in regions dominated by savanna vegetation,
we created a mask to identify those areas where RVF would likely
occur within the savanna ecosystems. NDVI anomalies were then calculated
for the entire time series from July 1981 to the July 2000. Subsequently,
we developed a methodology that detects areas with persistent positive
NDVI anomalies (greater than ;pl 0.1 NDVI units) using a three-month
moving window to flag regions at greatest risk. Algorithms were designed
to account for periods of extended above normal NDVI (by inference
rainfall) and to consider the complex life cycle of mosquitoes that
maintain and transmit RVF virus to domestic animals and people. We
present results for different ENSO warm-and cold-event periods. The
results indicate that regions of potential outbreaks have occurred
predominantly during warm ENSO events in East Africa and during cold
ENSO events in southern Africa. Results provide a likely historical
reconstruction of areas where RVF may have occurred during the last
19 years. There is a close agreement between confirmed outbreaks
between 1981 and 2000, particularly in East Africa, and the risk
maps produced in this study. This technique is adaptable to near
real-time monitoring on a monthly basis and may be a useful tool
in RVF disease surveillance.
147 Climatic and Ecological Context of the 1994-1996
Ebola Outbreaks
Compton J. Tucker, James M. Wilson, Robert Mahoney, Assaf Anyamba, Kenneth
Linthicum, and Monica F. Myers
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Ebola hemorrhagic fever outbreaks occurred in 1975-1979 and 1994-1996
within tropical Africa. It was determined from Landsat satellite
data that all outbreaks occurred in tropical forest with a range
of human intrusions. Meteorological satellite data, spanning the
1981 to 2000 time period, showed that marked and sudden climate changes
from drier to wetter conditions were associated with the Ebola outbreaks
in the 1990s. The extent of the marked climate changes suggest that
Ebola outbreaks are possible over large areas of equitorial Africa.
Our analysis is limited by only having one Ebola hemorrhagic fever
outbreak during our period of study.
153 Application of Remote Sensing to Enhance the Control
of Wildlife-Associated Mycobacterium bovis Infection
J.S. McKenzie, R.S. Morris, D.U. Pfeiffer, and J.R. Dymond
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The brushtail possum (Trichosurus vulpecula) is a wildlife vector for
tuberculosis (TB) caused by Mycobacterium bovis in New Zealand. Supervised
automatic classification of a SPOT3 multi spectral image was used
to generate a vegetation map, which was used together with slope
data to model the risk of TB-infected possums being present in habitat
patches. The vegetation data were also used to identify habitat patterns
which, together with other geographic variables, were incorporated
into logistic regression models to identify predictors of possum
TB risk of farms. The impact of the predicted possum TB risk data
on the cost-effectiveness of vector control programs at both individual
farm and larger regional control areas is discussed, plus issues
associated with the uptake of the models by operational managers.
161 Updating Historical Maps of Malaria Transmission
Duration in East Africa Using Remote Sensing
J.A. Omumbo, S.I. Hay, S.J. Goetz, R.W. Snow, and D.J. Rogers
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Remotely sensed imagery has been used to update and improve the spatial
resolution of malaria transmission intensity maps in Tanzania, Uganda,
and Kenya. Discriminant analysis achieved statistically robust agreements
between historical maps of the intensity of malaria transmission
and predictions based on multitemporal meteorological satellite sensor
data processed using temporal Fourier analysis. The study identified
land surface temperature as the best predictor of transmission intensity.
Rainfall and moisture availability as inferred by cold cloud duration
(CCD) and the normalized difference vegetation index (NDVI), respectively,
were identified as secondary predictors of transmission intensity.
Information on altitude derived from a digital elevation model significantly
improved the predictions. ``Malaria-free'' areas were predicted with
an accuracy of 96 percent while areas where transmission occurs only
near water, moderate malaria areas, and intense malaria transmission
areas were predicted with accuracies of 90 percent, 72 percent, and
87 percent, respectively. The importance of such maps for rationalizing
malaria control is discussed, as is the potential contribution of
the next generation of satellite sensors to these mapping efforts.
167 The Use of Remote Sensing for Predictive Modeling
of Schistosomiasis in China
Edmund Seto, Bing Xu, Song Liang, Peng Gong, Weiping Wu, George Davis, Dongchuan
Qiu, Xueguang Gu, and Robert Spear
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The development of predictive models of the spatial distribution of
schistosomiasis are hampered by the existence of different regional
subspecies of the Oncomelania hupensis snail that serve as intermediate
hosts for the disease in China. The habitats associated with these
different subspecies vary considerably, with mountainous habitats
in the west and floodplain habitats in the east. Despite these challenges,
continuing environmental change resulting from the construction of
the Three Gorges Dam and global warming that threaten to increase
snail habitat, as well as limited public health resources, require
the ability to accurately map and prioritize areas at risk for schistosomiasis.
This paper describes a series of ongoing studies that rely on remotely
sensed data to predict schistosomiasis risk in two regions of China.
The first study is a classification of Landsat TM imagery to identify
snail habitats in mountainous regions of Sichuan Province. The accuracy
of this classification was assessed in an independent field study,
which revealed that seasonal flooding may have contributed to misclassification,
and that the incorporation of soil maps may greatly improve classification
accuracy. A second study presents the use of Landsat TM and water
level data to understand seasonal differences in Oncomelania hupensis
habitat in the lower Yangtze River region.
175 Using NOAA-AVHRR Data to Model Human Helminth
Distributions on Planning Disease Control in Cameroon, West Africa
Simon Brooker, Simon I. Hay, Louis-Albert Tchuem Tchuenté, and Raoult
Ratard
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Full Article
Human helminth infections (intestinal nematode infections such as Ascaris
lumbricoides, Trichuris trichiura, and hookworm, and schistosome
infections such as Schistosoma haematobium and S. mansoni) affect
more than a quarter of the world's population, with potential consequences
for the health and nutritional and educational development of infected
individuals. The advent of broad-spectrum anthelminthic drugs that
are cheap, safe, and simple to deliver has meant that control has
become a viable option for many countries. Because helminth infections
patterns are highly heterogeneous, methods to identify priority areas
for intervention against intestinal nematode and schistosome will
enhance the efficacy of control. This paper describes the use of
NOAA-AVHRR data to develop logistic regression models that predict
the probability of infection prevalence greater than 50 percent,
and thus warrant mass treatment for intestinal nematodes and schistosomes,
according to WHO's criteria. Moreover, by overlaying the resulting
risk maps on population surfaces, it is possible to estimate the
school-aged population size requiring mass treatment and also provide
an estimate of program costs.
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