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Acta Tropica 108 (2008) 234–241

Contents lists available at ScienceDirect

Acta Tropica

journa l homepage: www.e lsev ier .com/ locate /ac ta t ropica

chistosomiasis risk estimation in Minas Gerais State, Brazil, usingnvironmental data and GIS techniques

icardo J.P.S. Guimarãesa,b, Corina C. Freitasc, Luciano V. Dutrac, Ana C.M. Mourad,onaldo S. Amarale, Sandra C. Drummondf, Ronaldo G.C. Scholtea,b, Omar S. Carvalhoa,∗

Centro de Pesquisas René Rachou/Fiocruz-MG, Av. Augusto de Lima, 1715 Barro Preto, CEP 30190-002 Belo Horizonte, MG, BrazilPrograma de Pós-Graduacão da Santa Casa de Misericórdia de Belo Horizonte/MG, Avenida Francisco Sales, 1111 Santa Efigênia,EP 30150-221 Belo Horizonte, MG, BrazilInstituto Nacional de Pesquisas Espaciais/INPE, Av dos Astronautas, 1.758 Jd. Granja, CEP 12227-010 São José dos Campos, SP, BrazilUniversidade Federal de Minas Gerais/UFMG, Av. Antônio Carlos, 6627 Pampulha, CEP 31270-901 Belo Horizonte, MG, BrazilSecretaria de Vigilância em Saúde/MS, Setor Hoteleiro Sul, Q-06, Conjunto A, Bl.C, sala 711, CEP 70 322-915 Brasília, DF, BrazilSecretaria de Estado de Saúde de Minas Gerais, Rua Rio Grande do Norte, 613, 4 andar, Santa Efigênia, CEP 30130-130 Belo Horizonte, MG, Brazil

r t i c l e i n f o

rticle history:vailable online 22 July 2008

eywords:IS

a b s t r a c t

The influence of climate and environmental variables to the distribution of schistosomiasis has beenassessed in several previous studies. Also Geographical Information System (GIS), is a tool that has beenrecently tested for better understanding the spatial disease distribution. The objective of this paper isto further develop the GIS technology for modeling and control of schistosomiasis using meteorological

chistosomiasisultiple linear regression

revalenceixing modelODIS

RTM

and social variables and introducing new potential environmental-related variables, particularly thoseproduced 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 consideredadequate for public health needs. The main variables selected as important for modeling purposes wastopographic elevation, summer minimum temperature, the NDVI vegetation index, and the social index

afcicbidr

HDI91.

. Introduction

.1. Schistosomiasis mansoni

Schistosomiasis mansoni is a major parasitic disease in humansnd it is known to be endemic in approximately 54 countries inmericas and Africa (WHO, 1985; Chitsulo et al., 2000). The etio-

ogical agent is the trematode Schistosoma mansoni which causesaried symptoms ranging from acute to chronic forms but predom-nantly with intestinal manifestations. Severe forms of the disease

ay occur with fibro-obstruction in the liver and portal hyperten-ion, splenomegaly and impairment of the central nervous system.reatment for schistosomiasis is simple due to the ready availabil-ty of drugs that can be administered in a single oral dose (Katz et

∗ Corresponding author at: Centro de Pesquisas René Rachou/Fiocruz-MG, Lab-ratório de Helmintologia e Malacologia Médica, Av. Augusto de Lima, 1715 Barroreto, CEP 30190-002 Belo Horizonte, MG, Brazil. Tel.: +55 31 33497745;ax: +55 31 32953115.

E-mail address: omar@cpqrr.fiocruz.br (O.S. Carvalho).

csTlittbtw1

001-706X/$ – see front matter © 2008 Published by Elsevier B.V.oi:10.1016/j.actatropica.2008.07.001

© 2008 Published by Elsevier B.V.

l., 1989). As for prevalence, the disease has been mainly spreadingrom the periphery to big urban centers and other regions of theountry. The disease is socially determined by behavior, spread-ng from the outskirts of big urban centers to other regions of theountry (Graeff et al., 1999). Schistosomiasis is mostly determinedy lack of basic sanitation in peripheries of large urban centers,

n natura sewage directly released in hydric collections and theevelopment of irrigated agriculture related to the use of hydricesources, which has contributed to the disease spread.

Schistosomiasis control comprises public measures ofhemotherapic treatments, sanitation supply, drinking waterupply, sewage draining, health education and use of molluscicide.he use of molluscicide for snails control has proved to damage theocal ecosystem besides being costly. Out of the three S. mansonintermediate hosts in Brazil, Biomphalaria glabrata, Biomphalariaenagophila and Biomphalaria straminea, the B. glabrata constitutes

he most important transmitter due to its wide geographic distri-ution, high infection rates and high capacity of schistosomiasisransmission; indeed, its occurrence has been always associatedith the disease in endemic areas. Such fact was first reported in

917 when Lutz remarked that the distribution of schistosomiasis

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n Brazil remained a public problem to be addressed. The authoruggested the possibility of correlation with Planorbis olivaceus=B. glabrata), which is a snail species from the north living inreshwater collections with aquatic vegetation.

The presence of this mollusc species has been recorded in 16razilian states, besides Distrito Federal, and 799 municipalities

ocated between the parallels 0◦53′S (Quatipuru, PA) and 29◦51′SEsteio, RS), the meridian 53◦44′S (Toledo, PR) and the coast line. B.enagophila is epidemiologically important in the south of Brazilnd has been reported in 404 municipalities as well as in 12razilian states in a quadrant limited by the parallels 10◦12′ and3◦41′S, the meridian 54◦35′W and the coast line. B. straminea haseen recorded in 1201 municipalities within 24 Brazilian statesnd is predominant in the states of Piaui, Ceara, Rio Grande doorte, Paraiba, Pernambuco, Alagoas, Sergipe and Bahia, in the areaetween parallels 02◦54′ and 31◦00′S, the meridian 44◦43′W andhe coast line (Carvalho et al., 2008).

The mollusc fauna of the genus Biomphalaria in the state of Minaserais is represented by seven species. These molluscs have been

ound in 12 mesoregions, in 286 (33.1%) municipalities, out of the53 existing in the state: B. glabrata, B. straminea, B. tenagophila, B.eregrina, B. schrammi, B. intermedia and B. occidentalis. The species. glabrata, recorded in 192 municipalities, is the main responsi-le for S. mansoni transmission. In endemic areas, high densities ofhis intermediate host associated with other risk factors enable forhigh prevalence of schistosomiasis. The snail wide distribution

ccounts for the spreading characteristic of the disease to non-ndemic areas of the state of Minas Gerais (Katz and Carvalho, 1983;arvalho et al., 1987, 1988, 1989, 1994, 1997, 1998).

Schistosomiasis distribution in the state of Minas Gerais is notegular, since areas of high prevalence are close to non-endemicegions, even to those areas where the transmission is null. The dis-ase is known to be endemic in the regions named as Norte (zone ofédio São Francisco and Itacambira), Oriental and Central regions

zone of Alto Jequitinhonha, West, Alto São Francisco and Metalur-ica). The highest infection rates are found in the northeast andast regions of Minas Gerais, comprising the areas of Mucuri, Riooce and Zona da Mata (Pellon and Teixeira, 1950; Katz et al., 1978;arvalho et al., 1987; Lambertucci et al., 1987).

.2. Utilization of the Geographical Information System (GIS) in

he study of schistosomiasis

Since schistosomiasis is a disease determined in space and timey risk factors, the Geographical Information System is a very pow-rful tool that might be used for better understanding the disease

2

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ig. 1. (a) Spatial distribution for prevalence data: no information (white), prevalence (%)ases for variables definition in blue and 47 cases for model validation in red. (For interpreeb version of the article.)

pica 108 (2008) 234–241 235

revalence and risk factors distributions. The joint use of GIS andtatistical techniques allows the determination of risk factors andeolocalization of risk areas leading to the optimization of theesources and to the choice of better strategies for controlling theisease (Beck et al., 1997, 2000; Bavia et al., 2001).

The prediction of schistosomiasis using GIS was first attemptedn the Philippines and the Caribbean by (Cross et al., 1984). Thenfluence of climate and environmental variables to the distributionf schistosomiasis was documented by Brown (1994) and Appleton1978). The use of GIS for the study of schistosomiasis was also donen several other countries: in Asia (Cross et al., 1996), China (Zhout al., 2001; Seto et al., 2002; Yang et al., 2005), Ethiopia (Kristensent al., 2001; Malone et al., 2001), Egypt (Malone et al., 1994, 1997;bdel-Rahman et al., 2001), Uganda (Kabatereine et al., 2004), Tan-ania (Brooker et al., 2001), Chad (Beasley et al., 2002; Brooker etl., 2002). In Brazil, one of the first studies trying to correlate dis-ase distribution with environmental variables was conducted byBavia et al., 2001), in Bahia state. Other studies using GIS were heldn Brazil, such as, in Pernambuco (Barbosa et al., 2004) and Minaserais (Guimarães et al., 2006).

.3. Objectives

The objective of this paper is to further develop the GIS technol-gy for modeling and control of schistosomiasis in the state of Minaserais, Brazil, using meteorological and social variables and intro-ucing new potential environmental factors, particularly thoseroduced by recently launched orbital sensors like the Moderateesolution Imaging Spectroradiometer (MODIS) and the Shuttleadar Topography Mission (SRTM).

. Data acquisition

.1. Schistosomiasis prevalence data

The historical schistosomiasis prevalence data from 189 munic-palities (dependent variable) was gathered from Brazilian Healthational Foundation and from Health Secretariat of Minas Geraistate Annual Reports. The spatial distribution of these data is pre-ented in Fig. 1a.

.2. Remote sensing products

.2.1. Moderate Resolution Imaging Spectroradiometer (MODIS)The MODIS instrument is operating on both the Terra and Aqua

pacecraft. It has a viewing swath width of 2330 km and views the

0.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red). (b) Sets: 142tation of the references to colour in this figure legend, the reader is referred to the

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ntire surface of the Earth every 1–2 days. Its detectors measure 36pectral bands between 0.405 �m and 14.385 �m, and it acquiresata at three spatial resolutions—250 m, 500 m, and 1000 m. Dataroducts derived from MODIS observations describe features of the

and, oceans and the atmosphere that can be used for studies ofrocesses and trends on local to global scales (Justice et al., 1998).

MODIS MOD13 product, which was used in this study, compriseshe blue, red, near infrared (NIR) and the middle infrared (MIR)ands, the enhanced vegetation index (EVI) and the normalizedifference vegetation index (NDVI) (Huete et al., 1999). The MOD13roduct is delivered as a set of image compositions produced glob-

lly with 1 km, 500 m and 250 m of resolution, in a 16-day period.

.2.2. Shuttle Radar Topography Mission data (SRTM)A near-global scale Digital Elevation Model (DEM) was obtained

y the Shuttle Radar Topography Mission, generating the most com-

emv(i

ig. 2. Prevalence as a function of social and meteorological variables: (a) Observed p.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red); (c) summer minimumdark red); (e) residuals, below −10.001 (magenta), −10.000 to −5.001 (cyan), −5.000 to 5he estimated prevalence, 0.060–0.100 (white), 0.101–0.150 (green), 0.151–0.200 (yellow)egend, the reader is referred to the web version of the article.)

pica 108 (2008) 234–241

lete high-resolution digital topographic database of Earth. SRTMonsisted of a specially modified radar system that flew onboardhe Space Shuttle Endeavour during an 11-day mission in Februaryf 2000. The ground altitude used in this experiment is given by thealue of SRTM DEM for each ground pair of coordinates. The localeclivity was derived from SRTM DEM by an appropriate filteringpproximation of first-order derivative.

.2.3. Spectral Linear Mixing Model (SLMM)The SLMM is the image processing algorithm that generates

he fraction images with the proportion of each component (veg-

tation, soil, and shade) inside the pixel which is estimate byinimizing the sum of square of the errors. The proportion

alues must be nonnegative, and they also must add to unityShimabukuro and Smith, 1991). In this case, the pixel responsen any given spectral band is assumed to be a linear combination

revalence and (b) estimated prevalence—no information (white), prevalence (%)temperature, 23 ◦C (red) to 14 ◦C (blue); (d) 1991 HDI, <0.500 (light red) to >0.701

.000 (white), 5.000–10.000 (blue), and above 10.001 (red); and (f) standard error of, and 0.201–0.320 (red). (For interpretation of the references to colour in this figure

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f the responses of each individual component. For any individualixel, the linear model can be expressed by

i =n∑

j=1

(aijxj) + ei, k = 1, . . . , p

here ri represents the pixel’s mean spectral reflectance in the ithpectral band, aij is the spectral reflectance of the jth componentn the ith spectral band, xj is the proportion of the jth component

ithin the pixel, n is the number of components, ei is the residualor the ith spectral band, and p is the number of spectral bands. Theroportions xj are subjected to two constraints:

xj = 1 and xj ≥ 0, for all components

The least squares techniques can then be applied to estimate theomponents proportions xj (Shimabukuro and Smith, 1991).

iyS

ig. 3. Prevalence as a function of remote sensing variables: (a) observed prevalence angreen), 5.001–15.000 (yellow), and above 15.001 (red); (c) SRTM DEM, 2653 m (brown) tomagenta), −10.000 to −5.001 (cyan), −5.000 to 5.000 (white), 5.000–10.000 (blue), and awhite), 0.101–0.150 (green), 0.151–0.200 (yellow), and 0.201–0.280 (red). (For interpretaeb version of the article.)

pica 108 (2008) 234–241 237

In this work the so-called vegetation, soil, and shade fractionmages were generated using the MODIS data and the estimatedalues for the spectral reflectance components also used as an inputo the model.

.2.4. Meteorological variablesThe meteorological variables consisted of total precipitation and

he minimum and maximum temperature for summer and wintereasons, which were obtained from the Brazilian Weather Forecastenter (CPTEC/INPE).

.3. Social variables

The used social variables consisted of human developmentndex (HDI), HDI-income, HDI-longevity and HDI-education for theears of 1991 and 2000 were obtained from Brazilian SNIU (Nationalystem for Urban Indicators).

d (b) estimated prevalence—no information (white), prevalence (%) 0.001–5.00046 m (blue); (d) winter NDVI, 1 (green) to −1 (purple); (e) residuals, below −10.001bove 10.001 (red); and (f) standard error of the estimated prevalence, 0.055–0.100tion of the references to colour in this figure legend, the reader is referred to the

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. Methodology

In this paper a relationship between schistosomiasis prevalencend the aforementioned variables is established by using multipleegression models.

Since the prevalence data is given on a municipality level, all thenput variables were integrated inside the municipalities’ bound-ries, using GIS systems (ArcGis, ENVI) and exported to a standardpreadsheet for the statistical analysis and modeling.

The final statistical model is built using the data from the 189unicipalities, where prevalence information is available. The fit-

ed model is then used to build the risk map for the entire state ofinas Gerais, by applying the model to the remaining municipali-

ies.

.1. Types of linear regression models for prevalence riskstimation

Three different scenarios were analyzed for risk map estimation.he first scenario establishes a relationship between prevalence ofchistosomiasis and social–meteorological variables (Guimarães etl., 2006); the second scenario estimates prevalence from variableserived from remote sensing data, and the third scenario uses allhe previous selected explicative variables, through multiple linearegressions.

In the first phase of model building process, the schistosomiasisrevalence data, to be used as dependent variable (Fig. 1a), wereandomly divided into two sets: one with 142 cases for variableselection and model definition, and another with 47 cases for modelalidation. The spatial distribution of these sets can be observed inig. 1b.

The meteorological and remote sensing data, used as indepen-ent variables, were observed in two seasons: summer (from 17

anuary 2002 to 01 February 2002 period) and winter (from 28 July002 to 12 August 2002 period).

These variables, plus the social variables, were used as inputariables to establish a multiple regression model for prevalenceisk in each scenario. A logarithmic transformation for the depen-ent variable (prevalence, denoted by Pv) was made as it improvedhe correlation with independent variables.

Since multicollinearity effects among the independent variablesere detected, a variables selection technique were used in order

o choose a set of variables (or transformations of them) that betterxplain the dependent variable. The variables selection was done byhe R2 criterion, using all possible regression procedures (Neter etl., 1996). This selection technique consists of a subset identificationith few variables and a coefficient of determination R2 sufficiently

losed to that when all variables are used in the model. Interactionffects were also included in the model.

After performing the residual analysis on the 142 training points,hich gives the mean square error (MSE), the chosen model was

hen validated using the 47 cases by comparing the MSE with theean square error of prediction (MSPR) (Neter et al., 1996). If both

alues are about of the same order the model is considered robust,nd the final model is then built using all 189 available prevalenceata.

. Results

.1. Prevalence as a function of social and meteorologicalariables

Fourteen quantitative independent variables were used in thetatistical analysis: three meteorological variables (total precipi-

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pica 108 (2008) 234–241

ation, minimum and maximum temperature) in summer and ininter seasons, and four social variables (human development

ndex, HDI-income, HDI-longevity and HDI-education indices)or the years of 1991 and 2000. Besides quantitative variables,wo qualitative variables (binary) were also used, to representhree ecosystem types (biomes): savanna, caatinga, and forestGuimarães et al., 2006).

The chosen model, that was statistically significant at 5% level,onsisted of a model with four variables: summer minimum tem-erature (TNs), 1991 Human Development Index (HDI91) and twoinary variables representing the biomes:

1 ={

1 if vegetation is savanna0 otherwise

and V2 ={

1 if vegetation is forest0 otherwise

After variables selection, the significance of several cross-roduct interaction effects was tested. The final selected model,ith R2 = 0.3568, consisted of the aforementioned variables and the

nteraction between HDI91, and V1, showing that the influence ofDI for the explanation of prevalence is different for savanna whenompared to forest and caatinga.

The final model for the prevalence is

ˆv = e(−0.34+0.21TNs−3.92HDI91+1.12V2+0.33HDI91V1) − 1

This model for each biome can be written as

Forest ⇒ Pv = e(0.78+0.21TNs−3.92HDI91) − 1Savanna ⇒ Pv = e(−0.34+0.21TNs−3.49HDI91) − 1Caatinga ⇒ Pv = e(−0.34+0.21TNs−3.92HDI91) − 1

Fig. 2b shows the estimated prevalence for all Minas Geraistate municipalities using the above estimated regression equa-ion, and Fig. 2c and d the explanatory variables TNs and HDI91.ig. 2e shows the plot of the residuals, resulting from the differenceetween observed (Fig. 2a) and estimated (Fig. 2b) schistosomiasisrevalence. In this Fig. 2e, dark colors (red and blue) represent over-stimated values, light colors (cyan and magenta) underestimatednes, and in white are the municipalities where the estimatives dif-er from the true values by less than 5%, which is considered a goodstimative for our purposes. Fig. 2f shows the standard error of thestimated prevalence.

The result indicates that during the summer season the risk ofontracting schistosomiasis increases, probably due to high con-entrations of the snails in the drainage caused mainly by lack ofanitation, high temperature, among other factors, and by the pop-lation searching for water bodies, either for drinking or bathing.

.2. Prevalence as a function of remote sensing variables

The remote sensing data used were derived from MODIS andRTM. MODIS MOD13 products from two dates (one in summernd another in winter) were taken, and for each date nine variablesere used: blue, red, near infrared (NIR), middle infrared (MIR),

nhanced vegetation index (EVI), normalized difference vegetationndex (NDVI), plus vegetation (VEG), soil (SOIL) and shade (SHD)ndices derived from the mixture model. Two variables from SRTMlevation data were also used: the digital elevation model itself andhe declivity (DEC), derived from the DEM.

An analysis of the correlation matrix showed that some variables

ad non-significative correlations with ln Pv at 95% confidence

evel, and also part of them was highly correlated among them-elves, indicating that the model can be further simplified.

After variables selection using the R2 criterion, the final chosenodel was that with two variables (DEM and NDVIw).

cta Tropica 108 (2008) 234–241 239

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Table 1Review of Linear Regression Models types for prevalence risk estimation

Model Type of input data Variables R2 RMSE

1 Social andmeteorological

TNs, HDI91, V2,HDI91 × V1

0.357 0.747

2 Remote sensing derived DEM, NDVIw 0.286 0.7823 Social, meteorological DEM, TNs × NDVIw 0.305 0.772

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R.J.P.S. Guimarães et al. / A

The final model generated by the regression function based onhe 189 municipalities is

ˆv = e(0.83−0.002DEM+4.99NDVIw) − 1

This model showed a coefficient of determination of 0.2859.ig. 3b presents the estimated prevalence, and Fig. 4c and d the DEMnd NDVIw, respectively. Fig. 3e and f shows the plot of the residuals,nd the standard error of the estimated prevalence, respectively.

The elevation was negatively correlated with schistosomiasisrevalence, while NDVI was positively correlated. This is consistentith the adequate environmental conditions for the development

f the intermediate hosts, since the snails are settled in places withower altitudes and vegetated areas.

.3. Prevalence as a function of social, meteorological and remoteensing variables

The third scenario was assessed using the previously selectedariables, with the exception of the categorical variables represent-ng the biomes. NDVI, which was chosen in the second scenario,hould in principle substitute the information provided by biomespecification.

The chosen model, which is statistically significant at 5% level,onsisted of a model with three variables: summer minimum tem-erature (TNs), the digital elevation model and winter normalizedifference vegetation index (NDVIw). After variables selection, theignificance of several cross-product interaction effects was tested.he final selected model, with R2 = 0.3052, consisted of the DEM

nd the interaction between TNs and NDVIw.

The final model generated by the regression function based onhe 189 municipalities is

ˆv = e(0.72−0.002DEM+0.24TNsNDVIw) − 1

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ig. 4. Prevalence as a function of social, meteorological and remote sensing variables:revalence (%) 0.001–5.000 (green), 5.001–15.000 (yellow), and above 15.001 (red); (c)white), 5.000–10.000 (blue), and above 10.001 (red); and (d) standard error of the estima.201–0.300 (red). (For interpretation of the references to colour in this figure legend, the

and remote sensing

Fig. 4a shows the prevalence and Fig. 4b shows the estimatedrevalence for all Minas Gerais State municipalities. The residualsnd the standard error of the estimated prevalence are presentedn Fig. 4c and d, respectively.

The elevation is negatively correlated with schistosomiasisrevalence, while the interaction winter NDVI and summer min-

mum temperature is positively correlated. This is consistent withhe adequate environmental conditions for the development of thentermediate hosts, since the snails are settled in places with lowerltitudes and vegetated areas, and in the summer season the risk ofontracting schistosomiasis increases, due to high concentrationsf the snails, high temperature, and by the population searching forater bodies. Also, the interaction indicates that the prevalence of

he schistosomiasis is related with the joint effect of temperaturend vegetation.

One can note that from the four (4) variables (two from modeland two from model 2) used in this alternative scenario the

ocial variable HDI was not selected this time. This is due, proba-

ly, because the joint effect of minimum summer temperature andegetation index is negatively correlated with HDI in Minas Geraistate.

(a) observed prevalence and (b) estimated prevalence—no information (white),residuals, below −10.001 (magenta), −10.000 to −5.001 (cyan), −5.000 to 5.000

ted prevalence, 0.060–0.100 (white), 0.101–0.150 (green), 0.151–0.200 (yellow), andreader is referred to the web version of the article.)

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. Discussion and conclusions

Variables from different sources (social, meteorological andemote sensing) were used in this study. The prevalence risk wasstimated by three models. Table 1 summarizes the results fromhese models, presenting the variables used in each model, the coef-cient of determination and the root mean square error (RMSE).

Table 1 shows that vegetation is an important variable, since its present in all models, either as a qualitative variable (biome) ors a qualitative one (NDVI).

It can be seen that the type 1 model (social–meteorologicalodel) presented the higher R2-value. Type 2 model (remote

ensing model) presented the worse result, which shows that tem-erature is an important variable since it is appears on models 1nd 3. It is also noticeable that NDVI seems not to be a proper vari-ble to substitute the biomes specification because type 3 model,hat has all the previous variables but the biome specification, haslower R2-value than that obtained from the type 1 model whichses the biomes specification.

Some important issues, related to the nature and precision of therevalence data, need to be considered when looking at the results:he prevalence data were obtained from historical records (mostccurring before the broad usage of GPS equipment), and the infor-ation is given in a municipality level basis, most of them located

t North and Northeast of the State. Therefore, the data might notepresent current reality of the disease, and the precise geoloca-ion of the data, which is a characteristic of schistosomiasis disease,

ight be lost somehow. These issues might have affected andasked the correlations of the prevalence data with explanatory

ariables.The results, however, led to the determination of significant fac-

ors that are related to the disease and the delimitation of riskreas. The importance of the joint use of geographical informa-ion systems and remote sensing for illness risk estimation wasvidenced.

Therefore, even with a low coefficient of determination,t might be concluded that the joint usage of geographicalnformation systems and statistical techniques, allowed the deter-

ination of related factors and the delimitation of risk areas forchistosomiasis.

. Further work

A very large database was obtained for this project. This databasean be further used for many other modeling studies for schistoso-iasis and even for other diseases.In this paper some results were presented for prevalence risk

stimation using environmental and other variables that are gen-ralized over municipality area. One interest is to improve accuracyoth on geolocation axis and on risk estimation precision. All

ogging methodology for field prevalence estimation is being mod-fied to attend the more precise geolocation accuracy which is ofaramount importance to develop risk models with better spatialesolution. It is expected that this will also improve the coefficientf determination on final model when compared with models gen-rated with municipalities’ level data.

Several other variables related to the water usage, such asanitation, water quality, soil water retention, and existence ofntermediate hosts, might be tested as explanatory variables tomprove the model. Suitable regionalization of the State territory,

aking in account as a criterion the uniformity of the relevant inputactors can also improve the overall accuracy as suggested by betteresult obtained by model type 1.

Also intensive studies are being planned to assess local preva-ence on sites pointed out, by model, as of high risk.

H

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pica 108 (2008) 234–241

cknowledgments

The authors would like to acknowledge the support ofNPq (grants# 305546/2003-1; 380203/2004-9; 304274/2005-4),apemig (EDP 1775/03; EDT 61775/03; CRA 0070/04) and NIH-ogarty (grant# 5D43TW007012).

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