Acta Tropica 108 (2008) 234–241
Contents lists available at ScienceDirect
Acta Tropica
journal homepage: www.elsevier.com/locate/actatropica
Schistosomiasis risk estimation in Minas Gerais State, Brazil, using
environmental data and GIS techniques
Ricardo J.P.S. Guimarães a,b , Corina C. Freitas c , Luciano V. Dutra c , Ana C.M. Moura d ,
Ronaldo S. Amaral e , Sandra C. Drummond f , Ronaldo G.C. Scholte a,b , Omar S. Carvalho a,∗
a
Centro de Pesquisas René Rachou/Fiocruz-MG, Av. Augusto de Lima, 1715 Barro Preto, CEP 30190-002 Belo Horizonte, MG, Brazil
Programa de Pós-Graduação da Santa Casa de Misericórdia de Belo Horizonte/MG, Avenida Francisco Sales, 1111 Santa Efigênia,
CEP 30150-221 Belo Horizonte, MG, Brazil
c
Instituto Nacional de Pesquisas Espaciais/INPE, Av dos Astronautas, 1.758 Jd. Granja, CEP 12227-010 São José dos Campos, SP, Brazil
d
Universidade Federal de Minas Gerais/UFMG, Av. Antônio Carlos, 6627 Pampulha, CEP 31270-901 Belo Horizonte, MG, Brazil
e
Secretaria de Vigilância em Saúde/MS, Setor Hoteleiro Sul, Q-06, Conjunto A, Bl.C, sala 711, CEP 70 322-915 Brasília, DF, Brazil
f
Secretaria de Estado de Saúde de Minas Gerais, Rua Rio Grande do Norte, 613, 4 andar, Santa Efigênia, CEP 30130-130 Belo Horizonte, MG, Brazil
b
a r t i c l e
i n f o
Article history:
Available online 22 July 2008
Keywords:
GIS
Schistosomiasis
Multiple linear regression
Prevalence
Mixing model
MODIS
SRTM
a b s t r a c t
The influence of climate and environmental variables to the distribution of schistosomiasis has been
assessed in several previous studies. Also Geographical Information System (GIS), is a tool that has been
recently tested for better understanding the spatial disease distribution. The objective of this paper is
to further develop the GIS technology for modeling and control of schistosomiasis using meteorological
and social variables and introducing new potential environmental-related variables, particularly those
produced by recently launched orbital sensors like the Moderate Resolution Imaging Spectroradiometer
(MODIS) and the Shuttle Radar Topography Mission (SRTM). Three different scenarios have been analyzed,
and despite of not quite large determination factor, the standard deviation of risk estimates was considered
adequate for public health needs. The main variables selected as important for modeling purposes was
topographic elevation, summer minimum temperature, the NDVI vegetation index, and the social index
HDI91 .
© 2008 Published by Elsevier B.V.
1. Introduction
1.1. Schistosomiasis mansoni
Schistosomiasis mansoni is a major parasitic disease in humans
and it is known to be endemic in approximately 54 countries in
Americas and Africa (WHO, 1985; Chitsulo et al., 2000). The etiological agent is the trematode Schistosoma mansoni which causes
varied symptoms ranging from acute to chronic forms but predominantly with intestinal manifestations. Severe forms of the disease
may occur with fibro-obstruction in the liver and portal hypertension, splenomegaly and impairment of the central nervous system.
Treatment for schistosomiasis is simple due to the ready availability of drugs that can be administered in a single oral dose (Katz et
∗ Corresponding author at: Centro de Pesquisas René Rachou/Fiocruz-MG, Laboratório de Helmintologia e Malacologia Médica, Av. Augusto de Lima, 1715 Barro
Preto, CEP 30190-002 Belo Horizonte, MG, Brazil. Tel.: +55 31 33497745;
fax: +55 31 32953115.
E-mail address: omar@cpqrr.fiocruz.br (O.S. Carvalho).
0001-706X/$ – see front matter © 2008 Published by Elsevier B.V.
doi:10.1016/j.actatropica.2008.07.001
al., 1989). As for prevalence, the disease has been mainly spreading
from the periphery to big urban centers and other regions of the
country. The disease is socially determined by behavior, spreading from the outskirts of big urban centers to other regions of the
country (Graeff et al., 1999). Schistosomiasis is mostly determined
by lack of basic sanitation in peripheries of large urban centers,
in natura sewage directly released in hydric collections and the
development of irrigated agriculture related to the use of hydric
resources, which has contributed to the disease spread.
Schistosomiasis control comprises public measures of
chemotherapic treatments, sanitation supply, drinking water
supply, sewage draining, health education and use of molluscicide.
The use of molluscicide for snails control has proved to damage the
local ecosystem besides being costly. Out of the three S. mansoni
intermediate hosts in Brazil, Biomphalaria glabrata, Biomphalaria
tenagophila and Biomphalaria straminea, the B. glabrata constitutes
the most important transmitter due to its wide geographic distribution, high infection rates and high capacity of schistosomiasis
transmission; indeed, its occurrence has been always associated
with the disease in endemic areas. Such fact was first reported in
1917 when Lutz remarked that the distribution of schistosomiasis
R.J.P.S. Guimarães et al. / Acta Tropica 108 (2008) 234–241
in Brazil remained a public problem to be addressed. The author
suggested the possibility of correlation with Planorbis olivaceus
(=B. glabrata), which is a snail species from the north living in
freshwater collections with aquatic vegetation.
The presence of this mollusc species has been recorded in 16
Brazilian states, besides Distrito Federal, and 799 municipalities
located between the parallels 0◦ 53 S (Quatipuru, PA) and 29◦ 51 S
(Esteio, RS), the meridian 53◦ 44 S (Toledo, PR) and the coast line. B.
tenagophila is epidemiologically important in the south of Brazil
and has been reported in 404 municipalities as well as in 12
Brazilian states in a quadrant limited by the parallels 10◦ 12 and
33◦ 41 S, the meridian 54◦ 35 W and the coast line. B. straminea has
been recorded in 1201 municipalities within 24 Brazilian states
and is predominant in the states of Piaui, Ceara, Rio Grande do
Norte, Paraiba, Pernambuco, Alagoas, Sergipe and Bahia, in the area
between parallels 02◦ 54 and 31◦ 00 S, the meridian 44◦ 43 W and
the coast line (Carvalho et al., 2008).
The mollusc fauna of the genus Biomphalaria in the state of Minas
Gerais is represented by seven species. These molluscs have been
found in 12 mesoregions, in 286 (33.1%) municipalities, out of the
853 existing in the state: B. glabrata, B. straminea, B. tenagophila, B.
peregrina, B. schrammi, B. intermedia and B. occidentalis. The species
B. glabrata, recorded in 192 municipalities, is the main responsible for S. mansoni transmission. In endemic areas, high densities of
this intermediate host associated with other risk factors enable for
a high prevalence of schistosomiasis. The snail wide distribution
accounts for the spreading characteristic of the disease to nonendemic areas of the state of Minas Gerais (Katz and Carvalho, 1983;
Carvalho et al., 1987, 1988, 1989, 1994, 1997, 1998).
Schistosomiasis distribution in the state of Minas Gerais is not
regular, since areas of high prevalence are close to non-endemic
regions, even to those areas where the transmission is null. The disease is known to be endemic in the regions named as Norte (zone of
Médio São Francisco and Itacambira), Oriental and Central regions
(zone of Alto Jequitinhonha, West, Alto São Francisco and Metalurgica). The highest infection rates are found in the northeast and
east regions of Minas Gerais, comprising the areas of Mucuri, Rio
Doce and Zona da Mata (Pellon and Teixeira, 1950; Katz et al., 1978;
Carvalho et al., 1987; Lambertucci et al., 1987).
235
prevalence and risk factors distributions. The joint use of GIS and
statistical techniques allows the determination of risk factors and
geolocalization of risk areas leading to the optimization of the
resources and to the choice of better strategies for controlling the
disease (Beck et al., 1997, 2000; Bavia et al., 2001).
The prediction of schistosomiasis using GIS was first attempted
in the Philippines and the Caribbean by (Cross et al., 1984). The
influence of climate and environmental variables to the distribution
of schistosomiasis was documented by Brown (1994) and Appleton
(1978). The use of GIS for the study of schistosomiasis was also done
in several other countries: in Asia (Cross et al., 1996), China (Zhou
et al., 2001; Seto et al., 2002; Yang et al., 2005), Ethiopia (Kristensen
et al., 2001; Malone et al., 2001), Egypt (Malone et al., 1994, 1997;
Abdel-Rahman et al., 2001), Uganda (Kabatereine et al., 2004), Tanzania (Brooker et al., 2001), Chad (Beasley et al., 2002; Brooker et
al., 2002). In Brazil, one of the first studies trying to correlate disease distribution with environmental variables was conducted by
(Bavia et al., 2001), in Bahia state. Other studies using GIS were held
in Brazil, such as, in Pernambuco (Barbosa et al., 2004) and Minas
Gerais (Guimarães et al., 2006).
1.3. Objectives
The objective of this paper is to further develop the GIS technology for modeling and control of schistosomiasis in the state of Minas
Gerais, Brazil, using meteorological and social variables and introducing new potential environmental factors, particularly those
produced by recently launched orbital sensors like the Moderate
Resolution Imaging Spectroradiometer (MODIS) and the Shuttle
Radar Topography Mission (SRTM).
2. Data acquisition
2.1. Schistosomiasis prevalence data
The historical schistosomiasis prevalence data from 189 municipalities (dependent variable) was gathered from Brazilian Health
National Foundation and from Health Secretariat of Minas Gerais
State Annual Reports. The spatial distribution of these data is presented in Fig. 1a.
1.2. Utilization of the Geographical Information System (GIS) in
the study of schistosomiasis
2.2. Remote sensing products
Since schistosomiasis is a disease determined in space and time
by risk factors, the Geographical Information System is a very powerful tool that might be used for better understanding the disease
2.2.1. Moderate Resolution Imaging Spectroradiometer (MODIS)
The MODIS instrument is operating on both the Terra and Aqua
spacecraft. It has a viewing swath width of 2330 km and views the
Fig. 1. (a) Spatial distribution for prevalence data: no information (white), prevalence (%) 0.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red). (b) Sets: 142
cases for variables definition in blue and 47 cases for model validation in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the
web version of the article.)
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entire surface of the Earth every 1–2 days. Its detectors measure 36
spectral bands between 0.405 ␮m and 14.385 ␮m, and it acquires
data at three spatial resolutions—250 m, 500 m, and 1000 m. Data
products derived from MODIS observations describe features of the
land, oceans and the atmosphere that can be used for studies of
processes and trends on local to global scales (Justice et al., 1998).
MODIS MOD13 product, which was used in this study, comprises
the blue, red, near infrared (NIR) and the middle infrared (MIR)
bands, the enhanced vegetation index (EVI) and the normalized
difference vegetation index (NDVI) (Huete et al., 1999). The MOD13
product is delivered as a set of image compositions produced globally with 1 km, 500 m and 250 m of resolution, in a 16-day period.
2.2.2. Shuttle Radar Topography Mission data (SRTM)
A near-global scale Digital Elevation Model (DEM) was obtained
by the Shuttle Radar Topography Mission, generating the most com-
plete high-resolution digital topographic database of Earth. SRTM
consisted of a specially modified radar system that flew onboard
the Space Shuttle Endeavour during an 11-day mission in February
of 2000. The ground altitude used in this experiment is given by the
value of SRTM DEM for each ground pair of coordinates. The local
declivity was derived from SRTM DEM by an appropriate filtering
approximation of first-order derivative.
2.2.3. Spectral Linear Mixing Model (SLMM)
The SLMM is the image processing algorithm that generates
the fraction images with the proportion of each component (vegetation, soil, and shade) inside the pixel which is estimate by
minimizing the sum of square of the errors. The proportion
values must be nonnegative, and they also must add to unity
(Shimabukuro and Smith, 1991). In this case, the pixel response
in any given spectral band is assumed to be a linear combination
Fig. 2. Prevalence as a function of social and meteorological variables: (a) Observed prevalence and (b) estimated prevalence—no information (white), prevalence (%)
0.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red); (c) summer minimum temperature, 23 ◦ C (red) to 14 ◦ C (blue); (d) 1991 HDI, <0.500 (light red) to >0.701
(dark red); (e) residuals, below −10.001 (magenta), −10.000 to −5.001 (cyan), −5.000 to 5.000 (white), 5.000–10.000 (blue), and above 10.001 (red); and (f) standard error of
the estimated prevalence, 0.060–0.100 (white), 0.101–0.150 (green), 0.151–0.200 (yellow), and 0.201–0.320 (red). (For interpretation of the references to colour in this figure
legend, the reader is referred to the web version of the article.)
R.J.P.S. Guimarães et al. / Acta Tropica 108 (2008) 234–241
of the responses of each individual component. For any individual
pixel, the linear model can be expressed by
ri =
n
(aij xj ) + ei ,
k = 1, . . . , p
j=1
where ri represents the pixel’s mean spectral reflectance in the ith
spectral band, aij is the spectral reflectance of the jth component
in the ith spectral band, xj is the proportion of the jth component
within the pixel, n is the number of components, ei is the residual
for the ith spectral band, and p is the number of spectral bands. The
proportions xj are subjected to two constraints:
xj = 1 and xj ≥ 0,
for all components
The least squares techniques can then be applied to estimate the
components proportions xj (Shimabukuro and Smith, 1991).
237
In this work the so-called vegetation, soil, and shade fraction
images were generated using the MODIS data and the estimated
values for the spectral reflectance components also used as an input
to the model.
2.2.4. Meteorological variables
The meteorological variables consisted of total precipitation and
the minimum and maximum temperature for summer and winter
seasons, which were obtained from the Brazilian Weather Forecast
Center (CPTEC/INPE).
2.3. Social variables
The used social variables consisted of human development
index (HDI), HDI-income, HDI-longevity and HDI-education for the
years of 1991 and 2000 were obtained from Brazilian SNIU (National
System for Urban Indicators).
Fig. 3. Prevalence as a function of remote sensing variables: (a) observed prevalence and (b) estimated prevalence—no information (white), prevalence (%) 0.001–5.000
(green), 5.001–15.000 (yellow), and above 15.001 (red); (c) SRTM DEM, 2653 m (brown) to 46 m (blue); (d) winter NDVI, 1 (green) to −1 (purple); (e) residuals, below −10.001
(magenta), −10.000 to −5.001 (cyan), −5.000 to 5.000 (white), 5.000–10.000 (blue), and above 10.001 (red); and (f) standard error of the estimated prevalence, 0.055–0.100
(white), 0.101–0.150 (green), 0.151–0.200 (yellow), and 0.201–0.280 (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the
web version of the article.)
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3. Methodology
In this paper a relationship between schistosomiasis prevalence
and the aforementioned variables is established by using multiple
regression models.
Since the prevalence data is given on a municipality level, all the
input variables were integrated inside the municipalities’ boundaries, using GIS systems (ArcGis, ENVI) and exported to a standard
spreadsheet for the statistical analysis and modeling.
The final statistical model is built using the data from the 189
municipalities, where prevalence information is available. The fitted model is then used to build the risk map for the entire state of
Minas Gerais, by applying the model to the remaining municipalities.
3.1. Types of linear regression models for prevalence risk
estimation
Three different scenarios were analyzed for risk map estimation.
The first scenario establishes a relationship between prevalence of
schistosomiasis and social–meteorological variables (Guimarães et
al., 2006); the second scenario estimates prevalence from variables
derived from remote sensing data, and the third scenario uses all
the previous selected explicative variables, through multiple linear
regressions.
In the first phase of model building process, the schistosomiasis
prevalence data, to be used as dependent variable (Fig. 1a), were
randomly divided into two sets: one with 142 cases for variables
selection and model definition, and another with 47 cases for model
validation. The spatial distribution of these sets can be observed in
Fig. 1b.
The meteorological and remote sensing data, used as independent variables, were observed in two seasons: summer (from 17
January 2002 to 01 February 2002 period) and winter (from 28 July
2002 to 12 August 2002 period).
These variables, plus the social variables, were used as input
variables to establish a multiple regression model for prevalence
risk in each scenario. A logarithmic transformation for the dependent variable (prevalence, denoted by P̂v) was made as it improved
the correlation with independent variables.
Since multicollinearity effects among the independent variables
were detected, a variables selection technique were used in order
to choose a set of variables (or transformations of them) that better
explain the dependent variable. The variables selection was done by
the R2 criterion, using all possible regression procedures (Neter et
al., 1996). This selection technique consists of a subset identification
with few variables and a coefficient of determination R2 sufficiently
closed to that when all variables are used in the model. Interaction
effects were also included in the model.
After performing the residual analysis on the 142 training points,
which gives the mean square error (MSE), the chosen model was
then validated using the 47 cases by comparing the MSE with the
mean square error of prediction (MSPR) (Neter et al., 1996). If both
values are about of the same order the model is considered robust,
and the final model is then built using all 189 available prevalence
data.
4. Results
4.1. Prevalence as a function of social and meteorological
variables
Fourteen quantitative independent variables were used in the
statistical analysis: three meteorological variables (total precipi-
tation, minimum and maximum temperature) in summer and in
winter seasons, and four social variables (human development
index, HDI-income, HDI-longevity and HDI-education indices)
for the years of 1991 and 2000. Besides quantitative variables,
two qualitative variables (binary) were also used, to represent
three ecosystem types (biomes): savanna, caatinga, and forest
(Guimarães et al., 2006).
The chosen model, that was statistically significant at 5% level,
consisted of a model with four variables: summer minimum temperature (TNs ), 1991 Human Development Index (HDI91 ) and two
binary variables representing the biomes:
V1 =
1
0
if vegetation is savanna
otherwise
and V2 =
1
0
if vegetation is forest
otherwise
After variables selection, the significance of several crossproduct interaction effects was tested. The final selected model,
with R2 = 0.3568, consisted of the aforementioned variables and the
interaction between HDI91 , and V1 , showing that the influence of
HDI for the explanation of prevalence is different for savanna when
compared to forest and caatinga.
The final model for the prevalence is
P̂v = e(−0.34+0.21TNs −3.92HDI91 +1.12V2 +0.33HDI91 V1 ) − 1
This model for each biome can be written as
• Forest ⇒ P̂v = e(0.78+0.21TNs −3.92HDI91 ) − 1
• Savanna ⇒ P̂v = e(−0.34+0.21TNs −3.49HDI91 ) − 1
• Caatinga ⇒ P̂v = e(−0.34+0.21TNs −3.92HDI91 ) − 1
Fig. 2b shows the estimated prevalence for all Minas Gerais
State municipalities using the above estimated regression equation, and Fig. 2c and d the explanatory variables TNs and HDI91 .
Fig. 2e shows the plot of the residuals, resulting from the difference
between observed (Fig. 2a) and estimated (Fig. 2b) schistosomiasis
prevalence. In this Fig. 2e, dark colors (red and blue) represent overestimated values, light colors (cyan and magenta) underestimated
ones, and in white are the municipalities where the estimatives differ from the true values by less than 5%, which is considered a good
estimative for our purposes. Fig. 2f shows the standard error of the
estimated prevalence.
The result indicates that during the summer season the risk of
contracting schistosomiasis increases, probably due to high concentrations of the snails in the drainage caused mainly by lack of
sanitation, high temperature, among other factors, and by the population searching for water bodies, either for drinking or bathing.
4.2. Prevalence as a function of remote sensing variables
The remote sensing data used were derived from MODIS and
SRTM. MODIS MOD13 products from two dates (one in summer
and another in winter) were taken, and for each date nine variables
were used: blue, red, near infrared (NIR), middle infrared (MIR),
enhanced vegetation index (EVI), normalized difference vegetation
index (NDVI), plus vegetation (VEG), soil (SOIL) and shade (SHD)
indices derived from the mixture model. Two variables from SRTM
elevation data were also used: the digital elevation model itself and
the declivity (DEC), derived from the DEM.
An analysis of the correlation matrix showed that some variables
had non-significative correlations with ln P̂v at 95% confidence
level, and also part of them was highly correlated among themselves, indicating that the model can be further simplified.
After variables selection using the R2 criterion, the final chosen
model was that with two variables (DEM and NDVIw ).
R.J.P.S. Guimarães et al. / Acta Tropica 108 (2008) 234–241
239
The final model generated by the regression function based on
the 189 municipalities is
Table 1
Review of Linear Regression Models types for prevalence risk estimation
P̂v = e(0.83−0.002DEM+4.99NDVIw ) − 1
Model
Type of input data
Variables
R2
RMSE
1
Social and
meteorological
Remote sensing derived
Social, meteorological
and remote sensing
TNs, HDI91 , V2 ,
HDI91 × V1
DEM, NDVIw
DEM, TNs × NDVIw
0.357
0.747
0.286
0.305
0.782
0.772
This model showed a coefficient of determination of 0.2859.
Fig. 3b presents the estimated prevalence, and Fig. 4c and d the DEM
and NDVIw , respectively. Fig. 3e and f shows the plot of the residuals,
and the standard error of the estimated prevalence, respectively.
The elevation was negatively correlated with schistosomiasis
prevalence, while NDVI was positively correlated. This is consistent
with the adequate environmental conditions for the development
of the intermediate hosts, since the snails are settled in places with
lower altitudes and vegetated areas.
4.3. Prevalence as a function of social, meteorological and remote
sensing variables
The third scenario was assessed using the previously selected
variables, with the exception of the categorical variables representing the biomes. NDVI, which was chosen in the second scenario,
should in principle substitute the information provided by biomes
specification.
The chosen model, which is statistically significant at 5% level,
consisted of a model with three variables: summer minimum temperature (TNs ), the digital elevation model and winter normalized
difference vegetation index (NDVIw ). After variables selection, the
significance of several cross-product interaction effects was tested.
The final selected model, with R2 = 0.3052, consisted of the DEM
and the interaction between TNs and NDVIw .
The final model generated by the regression function based on
the 189 municipalities is
P̂v = e(0.72−0.002DEM+0.24TNs NDVIw ) − 1
2
3
Fig. 4a shows the prevalence and Fig. 4b shows the estimated
prevalence for all Minas Gerais State municipalities. The residuals
and the standard error of the estimated prevalence are presented
in Fig. 4c and d, respectively.
The elevation is negatively correlated with schistosomiasis
prevalence, while the interaction winter NDVI and summer minimum temperature is positively correlated. This is consistent with
the adequate environmental conditions for the development of the
intermediate hosts, since the snails are settled in places with lower
altitudes and vegetated areas, and in the summer season the risk of
contracting schistosomiasis increases, due to high concentrations
of the snails, high temperature, and by the population searching for
water bodies. Also, the interaction indicates that the prevalence of
the schistosomiasis is related with the joint effect of temperature
and vegetation.
One can note that from the four (4) variables (two from model
1 and two from model 2) used in this alternative scenario the
social variable HDI was not selected this time. This is due, probably, because the joint effect of minimum summer temperature and
vegetation index is negatively correlated with HDI in Minas Gerais
State.
Fig. 4. Prevalence as a function of social, meteorological and remote sensing variables: (a) observed prevalence and (b) estimated prevalence—no information (white),
prevalence (%) 0.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red); (c) residuals, below −10.001 (magenta), −10.000 to −5.001 (cyan), −5.000 to 5.000
(white), 5.000–10.000 (blue), and above 10.001 (red); and (d) standard error of the estimated prevalence, 0.060–0.100 (white), 0.101–0.150 (green), 0.151–0.200 (yellow), and
0.201–0.300 (red). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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5. Discussion and conclusions
Acknowledgments
Variables from different sources (social, meteorological and
remote sensing) were used in this study. The prevalence risk was
estimated by three models. Table 1 summarizes the results from
these models, presenting the variables used in each model, the coefficient of determination and the root mean square error (RMSE).
Table 1 shows that vegetation is an important variable, since it
is present in all models, either as a qualitative variable (biome) or
as a qualitative one (NDVI).
It can be seen that the type 1 model (social–meteorological
model) presented the higher R2 -value. Type 2 model (remote
sensing model) presented the worse result, which shows that temperature is an important variable since it is appears on models 1
and 3. It is also noticeable that NDVI seems not to be a proper variable to substitute the biomes specification because type 3 model,
that has all the previous variables but the biome specification, has
a lower R2 -value than that obtained from the type 1 model which
uses the biomes specification.
Some important issues, related to the nature and precision of the
prevalence data, need to be considered when looking at the results:
the prevalence data were obtained from historical records (most
occurring before the broad usage of GPS equipment), and the information is given in a municipality level basis, most of them located
at North and Northeast of the State. Therefore, the data might not
represent current reality of the disease, and the precise geolocation of the data, which is a characteristic of schistosomiasis disease,
might be lost somehow. These issues might have affected and
masked the correlations of the prevalence data with explanatory
variables.
The results, however, led to the determination of significant factors that are related to the disease and the delimitation of risk
areas. The importance of the joint use of geographical information systems and remote sensing for illness risk estimation was
evidenced.
Therefore, even with a low coefficient of determination,
it might be concluded that the joint usage of geographical
information systems and statistical techniques, allowed the determination of related factors and the delimitation of risk areas for
schistosomiasis.
The authors would like to acknowledge the support of
CNPq (grants# 305546/2003-1; 380203/2004-9; 304274/2005-4),
Fapemig (EDP 1775/03; EDT 61775/03; CRA 0070/04) and NIHFogarty (grant# 5D43TW007012).
6. Further work
A very large database was obtained for this project. This database
can be further used for many other modeling studies for schistosomiasis and even for other diseases.
In this paper some results were presented for prevalence risk
estimation using environmental and other variables that are generalized over municipality area. One interest is to improve accuracy
both on geolocation axis and on risk estimation precision. All
logging methodology for field prevalence estimation is being modified to attend the more precise geolocation accuracy which is of
paramount importance to develop risk models with better spatial
resolution. It is expected that this will also improve the coefficient
of determination on final model when compared with models generated with municipalities’ level data.
Several other variables related to the water usage, such as
sanitation, water quality, soil water retention, and existence of
intermediate hosts, might be tested as explanatory variables to
improve the model. Suitable regionalization of the State territory,
taking in account as a criterion the uniformity of the relevant input
factors can also improve the overall accuracy as suggested by better
result obtained by model type 1.
Also intensive studies are being planned to assess local prevalence on sites pointed out, by model, as of high risk.
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