**Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State - Brazil at Small Communities and Cities Levels**

Ricardo J.P.S. Guimarães, Corina C. Freitas, Luciano V. Dutra, Guilherme Oliveira and Omar S. Carvalho

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53500

**1. Introduction**

Schistosomiasis, caused by *Schistosoma mansoni*, is an endemic disease conditional on the presence of snails of aquatic habits of the genus *Biomphalaria*.

In Brazil, there are eleven species and one subspecies of *Biomphalaria* genus mollusks that have been identified: *B. glabrata* (Say, 1818), *B. tenagophila* (Orbigny, 1835), *B. straminea* (Dunker, 1848), *B. peregrina* (Orbigny, 1835), *B. schrammi* (Crosse, 1864), *B. kuhniana* (Clessin, 1883), *B. intermedia* (Paraense & Deslandes 1962), *B. amazonica* (Paraense 1966), *B. oligoza* (Paraense 1974), *B. occidentalis* (Paraense 1981), *B. cousini* (Paraense, 1966) and *B. tenagophila guaibensis* (Paraense 1984) [1].

In Minas Gerais state, the presence of seven species: *B. straminea*, *B. tenagophila*, *B. peregrina*, *B. schrammi*, *B. intermedia* and *B. occidentalis* was reported [1]. Among these, there are three *Biomphalaria* species (*B. glabrata*, *B. tenagophila* and *B. straminea*) that have been found to be naturally infected with *S. mansoni*. Other three species, *B. amazonica*, *B. peregrina* and *B. cous‐ ini*, were experimentally infected, being considered as potential hosts of this trematode [2-4]. *B. glabrata* is of great importance, due to its extensive geographic distribution, high infection indices and efficiency in the schistosomiasis transmission. In endemic areas, large concentra‐ tions of these snails, together with other risk factors, favor the existence of localities with high prevalence [5-7].

© 2013 Guimarães et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The snails of the *Biomphalaria* genus live in a wide range of habitats, particularly in shallow and slow running waters and with floating or rooted vegetation. As these snails are distrib‐ uted over large geographic areas and their populations are adapted to different environ‐ mental conditions, they can tolerate large variations in physical, chemical and biological environment in which they live [8, 9].

**Vector Species Study Area Satellite-sensor Technical-variables Reference**

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

China NOAA (AVHRR), Lansdat (TM)

*B. straminea S.mansoni* Brazil - spatial distribution [23]

Egypt NOAA (AVHRR), Lansdat (TM)

(TM)

*B. pfeifferi S. mansoni* Ethiopia NOAA (AVHRR) LST, NDVI [31]

*B. pfeifferi S. mansoni* Ethiopia NOAA (AVHRR) NDVI, temperature, logistic

(TM)

*S.mansoni* Brazil - temperature, precipitation,

*S. mansoni* Brazil NOAA (AVHRR) NDVI, dT [29]

Tanzania NOAA (AVHRR) LST, NDVI, DEM,

*B. pfeifferi S.mansoni* Kenya - linear regression, mapping

Landsat (MSS) climate [17, 18]

Egypt NOAA (AVHRR) temperature, NDVI [21]

Southeast Asia NOAA (AVHRR) NDVI [22]

ecological zones [20]

http://dx.doi.org/10.5772/53500

5

techniques, cluster analysis

DEM, soil type, vegetation

type

China Landsat (TM) classification, GIS [26]

Tanzania *-* GIS, logistic regression [27]

dT, NDVI, MDE [25]

dT, BED, NDVI [28]

TNDVI [33]

precipitation, logistic

regression

regression

NOAA (AVHRR) ecology [34]

[24]

[19]

[30]

[32]

Philippines, the Caribbean

*- Schistosoma*

*Oncomelania spp Schistosoma*

*B. alexandrina S. mansoni*, *S.*

*Oncomelania spp Schistosoma*

*spp*

*S.*

*S. mansoni*,

*haematobium*

*haematobium*

*B. alexandrina S. mansoni* Egypt NOAA (AVHRR), Lansdat

*Oncomelania spp S. japonicum* China NOAA (AVHRR), Lansdat

Chad, Cameroon

*B. truncatus*, *B. alexandrina*

*Phlebotomus papatasi*

*B. glabrata, B. straminea, B. tenagophila*

*Bulinus* spp, *Biomphalaria* sp

*B. glabrata, B. straminea*

*Bulinus spp S.*


*spp*

*spp*

*spp*

*S.*

*S. mansoni*,

*haematobium*

*Schistosoma spp*

*haematobium*

The intermediate hosts' distribution of the parasite in Minas Gerais associated with favora‐ ble eco-epidemiological conditions gives the schistosomiasis expansive character not seen even in non-endemic regions [6, 10, 11].

Public health and the environment are influenced by the patterns of space occupation. Therefore, the use of geoprocessing techniques to analyze the spatial distribution of health problems allows one to determine local risks and delimit areas that concentrate the most vulnerable situations (occurrence of disease, characteristics of the environment and habitat of the intermediate host / vector). It is also possible with the use of geographic information systems to plan, schedule, control, monitor, and evaluate the diseases in groups according to their risk of transmission [12].

The use of Geographic Information Systems (GIS) and statistical tools in health has been fa‐ cilitated by access to epidemiological data bases, enabling the production of thematic maps that contribute to the formulation of hypotheses about the spatial distribution of diseases and their relation to the socioeconomic variables [13].

The use of GIS and Remote Sensing (RS) are powerful tools for working complex analysis of a large number of information and viewing the results of this analysis in graphical maps. Since the seventies, RS has been applied to social sciences and health [14]. There are numer‐ ous information collected by RS data, describing some biotic and abiotic factors [15]. Appli‐ cation of RS and GIS techniques for mapping the risk of parasitic diseases, including schistosomiasis, has been performed over the past 15 years [16].

The estimate of schistosomiasis prevalence using GIS was first used in the Philippines and the Caribbean by [17, 18]. In Brazil, the use of GIS in schistosomiasis was first used by [19] in the state of Bahia. The authors constructed maps with environmental characteristics (total precipitation for three consecutive months, the annual maximum and minimum tempera‐ ture and diurnal temperature differences), prevalence of *S. mansoni* and distribution of snails to study the spatial and temporal dynamics of infection and identify the environmental fac‐ tors that influence the distribution of schistosomiasis. The results indicated that the snail population density and duration of annual dry season are the most important determinants for the prevalence of schistosomiasis in the study areas.

Table 1 shows a brief history of the use of GIS techniques in the study of schistosomiasis in several countries.

The main objective of the present study is to establish a relationship between schistosomia‐ sis positivity index and the environmental and socioeconomic variables, in the Minas Gerais State, Brazil, using multiple linear regressions at small communities and cities levels.

The snails of the *Biomphalaria* genus live in a wide range of habitats, particularly in shallow and slow running waters and with floating or rooted vegetation. As these snails are distrib‐ uted over large geographic areas and their populations are adapted to different environ‐ mental conditions, they can tolerate large variations in physical, chemical and biological

The intermediate hosts' distribution of the parasite in Minas Gerais associated with favora‐ ble eco-epidemiological conditions gives the schistosomiasis expansive character not seen

Public health and the environment are influenced by the patterns of space occupation. Therefore, the use of geoprocessing techniques to analyze the spatial distribution of health problems allows one to determine local risks and delimit areas that concentrate the most vulnerable situations (occurrence of disease, characteristics of the environment and habitat of the intermediate host / vector). It is also possible with the use of geographic information systems to plan, schedule, control, monitor, and evaluate the diseases in groups according to

The use of Geographic Information Systems (GIS) and statistical tools in health has been fa‐ cilitated by access to epidemiological data bases, enabling the production of thematic maps that contribute to the formulation of hypotheses about the spatial distribution of diseases

The use of GIS and Remote Sensing (RS) are powerful tools for working complex analysis of a large number of information and viewing the results of this analysis in graphical maps. Since the seventies, RS has been applied to social sciences and health [14]. There are numer‐ ous information collected by RS data, describing some biotic and abiotic factors [15]. Appli‐ cation of RS and GIS techniques for mapping the risk of parasitic diseases, including

The estimate of schistosomiasis prevalence using GIS was first used in the Philippines and the Caribbean by [17, 18]. In Brazil, the use of GIS in schistosomiasis was first used by [19] in the state of Bahia. The authors constructed maps with environmental characteristics (total precipitation for three consecutive months, the annual maximum and minimum tempera‐ ture and diurnal temperature differences), prevalence of *S. mansoni* and distribution of snails to study the spatial and temporal dynamics of infection and identify the environmental fac‐ tors that influence the distribution of schistosomiasis. The results indicated that the snail population density and duration of annual dry season are the most important determinants

Table 1 shows a brief history of the use of GIS techniques in the study of schistosomiasis in

The main objective of the present study is to establish a relationship between schistosomia‐ sis positivity index and the environmental and socioeconomic variables, in the Minas Gerais

State, Brazil, using multiple linear regressions at small communities and cities levels.

environment in which they live [8, 9].

4 Parasitic Diseases - Schistosomiasis

even in non-endemic regions [6, 10, 11].

and their relation to the socioeconomic variables [13].

schistosomiasis, has been performed over the past 15 years [16].

for the prevalence of schistosomiasis in the study areas.

several countries.

their risk of transmission [12].



**Vector Species Study Area Satellite-sensor Technical-variables Reference**

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

and Bayesian spatial models

model, NDVI

immunological data

regression, Bayesian model

Bayesian models, logistic regression, NDVI, elevation,

data, RS, NDVI, temperature,

classification, regionalization and pattern recognition

land cover, classification, Bayesian model, RS, NDVI,

clustering, Bayesian model,

GIS, logistic regression

cluster analysis

regression

slope, LST

data, regression

Africa - ecology, GIS, RS, geostatistics [58]

*S. mansoni* Uganda - spatial analysis [60]

Tanzania - social and ecological data,

[52]

7

http://dx.doi.org/10.5772/53500

[53]

[54]

[55]

[59]

[61]

[62]

[63]

[64]

[65]

[66]

*- S. mansoni* Brazil - logistic regression models

*B. glabrata S.mansoni* Brazil - spatial analysis, GPS,

*- Schistosoma*

*B. sudanica, B. stanleyi*

*Oncomelania hupensis*

*- Schistosoma*

*- S. mansoni*, *S.*

*spp*

*haematobium*

*- S.*

*spp*

*haematobium*

*Biomphalaria sp S.mansoni* Brazil - social and environmental

*Biomphalaria spp S. mansoni* Brazil - GPS and GIS [56] *B. glabrata S. mansoni* Brazil - kernel [57]

*B. pfeifferi S. mansoni* Côte d'Ivoire - socioeconomic data, logistic

*Biomphalaria sp S.mansoni* Brazil MODIS, SRTM social and environmental

*Biomphalaria sp S.mansoni* Brazil MODIS, SRTM linear regression, imprecise

*S. japonicum* China SPOT ecological data, land use,

*Biomphalaria sp S.mansoni* Brazil - kriging, spatial distribution [67, 68] *Biomphalaria sp S.mansoni* Brazil - Fuzzy logic [69]

China - GIS, spatial analysis and

Mali NOAA (AVHRR) Bayesian models, NDVI, LST,

*Biomphalaria sp S.mansoni* Brazil MODIS, SRTM regression, elevation, mixture

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil... http://dx.doi.org/10.5772/53500 7

**Vector Species Study Area Satellite-sensor Technical-variables Reference**

NOAA (AVHRR) SIG [35]

Chad *-* environmental data [36]

Cameroon NOAA (AVHRR), EROS logistic regression [37]

China Landsat (TM) RS [38]

China Landsat (TM) SIG [40]

Kenya NOAA (AVHRR) Tmax [42]

Uganda Landsat (TM) ecological zones [44]

Uganda NOAA (AVHRR) LST [45]

environmental and socioeconomic data [16, 48]

*S. japonicum* China Landsat (TM) LU [39]

*S. japonicum* China Lansdat (TM) TNDVI [41]

*S. japonicum* Japan - PDA [46]

*S. japonicum* China Ikonos, ASTER MDE [47]

(AVHRR), EROS, MODIS

*S. japonicum* China NOAA (AVHRR) LST [50]

*S. japonicum* China Landsat (TM) SAVI [51]

*O. hupensis S. japonicum* China Landsat (TM) NDVI [49]

*B. glabrata S. mansoni* Brazil - GPS [43]

*B. pfeifferi, B. senegalensis*

*Oncomelania hupensis*

*Oncomelania hupensis*

*Oncomelania hupensis*

*Oncomelania hupensis*

*Oncomelania hupensis*

*Oncomelania hupensis*

*Bulinus spp. S.*

*- S. mansoni*, *S.*

*- Schistosoma*

*spp*

*Oncomelania* spp, *Bulinus* spp, *Biomphalaria* spp



6 Parasitic Diseases - Schistosomiasis

*Oncomelania spp Schistosoma*

*spp*

*S.*

*S. mansoni*,

Africa (sub-Saharan Africa)

*haematobium*

*haematobium*

*haematobium*

*S. japonicum*, *S. mansoni*,

*haematobium*

*haematobium*

*haematobium*

*- S. mansoni* Côte d'Ivoire Landsat (ETM), NOAA

*S.*



bles were used as explanatory variables, as well as a variable containing information about

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

Schistosomiasis positivity index (*Ip*) values were obtained in 1,590 localities from the Brazil‐ ian Schistosomiasis Control Program (PCE) through the Annual Reports of the Secretary of Public Health Surveillance (SVS) and the Secretary of Health in the State of Minas Gerais (SESMG). The *Ip* data were obtained from the database SISPCE (Information System of the Brazilian Schistosomiasis Control Program) from 1996 to 2009. The Kato-Katz technique is

presence of intermediate hosts. A brief description of these variables is given below.

the methodology used to determine positivity index, examining one slide per person.

\* 100 *<sup>i</sup> i <sup>r</sup> Ip*

Information about the existence of *Biomphalaria* snails were provided at a municipality basis by the Laboratory of Helminthiasis and Medical Malacology of the Rene Rachou Research

The distribution of Biomphalaria snails used for this study was defined as: *B. glabrata, B. te‐ nagophila, B. straminea, B. glabrata + B. tenagophila, B. glabrata + B. straminea, B. tenagophila + B. straminea, B. glabrata + B. tenagophila + B. straminea* and No *Biomphalaria*. The class "No *Bio‐ mphalaria*" includes information about the non-occurrence of *Biomphalaria* species or infor‐ mation about non-transmitter species in Brazil, such as *B. peregrina, B. schrammi, B.*

The spatial distribution of the schistosomiasis *Ip* and the *Biomphalaria* species data are pre‐

Twenty eight environmental variables were obtained from remote sensing and meteorologi‐

The remote sensing variables were derived from Moderate Resolution Imaging Spectroradi‐

The variables of MODIS sensor used were collected in two seasons, summer (from 17/Jan/ 2002 to 01/Feb/2002 period) and winter (from 28/Jul/2002 to 12/Aug/2002 period). MODIS data were composed by the blue, red, near and middle infrared bands and also the vegeta‐

ometer (MODIS) and from the Shuttle Radar Topography Mission (SRTM) sensor.

*<sup>n</sup>* <sup>=</sup> (1)

http://dx.doi.org/10.5772/53500

9

is the total population in locality *i*.

**2.2. Schistosomiasis positivity index**

where: *ri*

**2.3. Intermediate hosts**

Center (CPqRR/Fiocruz-MG).

*intermedia, B. occidentalis*, etc.

**2.4. Environmental data**

tion indices (NDVI and EVI) [73].

sented in Fig. 1.

cal sources.

These *Ip* were determined for each locality *i* by:

is the number of infected people and *ni*

**Table 1.** Use of geoprocessing techniques in the study of schistosomiasis.

## **2. Material and methods**

#### **2.1. Materials**

The study area includes 4,846 small communities (called localities) in the entire State of Minas Gerais, Brazil. The dependent variable is the schistosomiasis positivity index (*Ip*). *Ip* were obtained from the Brazilian Schistosomiasis Control Program (PCE) through the annu‐ al reports of the Secretary of Public Health Surveillance (SVS) and the Secretary of Health in the State of Minas Gerais (SESMG). From the 4,846 locations mentioned above, only 1,590 of them have information on the positivity of the disease. Since schistosomiasis is a disease characterized by environmental and social factors, environmental and socioeconomic varia‐ bles were used as explanatory variables, as well as a variable containing information about presence of intermediate hosts. A brief description of these variables is given below.

## **2.2. Schistosomiasis positivity index**

**Vector Species Study Area Satellite-sensor Technical-variables Reference**

clustering, kernel

RS, regression

distribution

data, RS

East Africa - Bayesian geostatistics, logistic

NOAA (AVHRR) geostatistics, LST, NDVI,

socioeconomic, sanitation,

data, sanitation, biological, RS, NDVI, temperature, regression, kriging

regression, Markov chain Monte Carlo simulation,

distribution, temperature, precipitation, MaxEnt, soil

elevation, environmental data, LQAS, LpCP

[70]

[71]

[72]

[1, 74]

[75]

[76]

[77]

[78]

*- S. japonicum* China - GIS, spatial analysis,

*Biomphalaria sp S. mansoni* Brazil MODIS meteorological data,

*B. straminea S.mansoni* Brazil - kernel, GPS, spatial

*B. glabrata S.mansoni* Brazil MODIS mixture model [73]

*Biomphalaria sp S.mansoni* Brazil MODIS, SRTM social and environmental

*Biomphalaria sp S.mansoni* Brazil MODIS decision tree, environmental

*Biomphalaria sp S.mansoni* Africa MODIS Climate change, spatial

*Biomphalaria spp S. mansoni* Brazil - GPS, GIS, spatial distribution [79]

The study area includes 4,846 small communities (called localities) in the entire State of Minas Gerais, Brazil. The dependent variable is the schistosomiasis positivity index (*Ip*). *Ip* were obtained from the Brazilian Schistosomiasis Control Program (PCE) through the annu‐ al reports of the Secretary of Public Health Surveillance (SVS) and the Secretary of Health in the State of Minas Gerais (SESMG). From the 4,846 locations mentioned above, only 1,590 of them have information on the positivity of the disease. Since schistosomiasis is a disease characterized by environmental and social factors, environmental and socioeconomic varia‐

Kenya

**Table 1.** Use of geoprocessing techniques in the study of schistosomiasis.

*- S. mansoni*, *S.*

8 Parasitic Diseases - Schistosomiasis

*haematobium*


**2. Material and methods**

**2.1. Materials**

Schistosomiasis positivity index (*Ip*) values were obtained in 1,590 localities from the Brazil‐ ian Schistosomiasis Control Program (PCE) through the Annual Reports of the Secretary of Public Health Surveillance (SVS) and the Secretary of Health in the State of Minas Gerais (SESMG). The *Ip* data were obtained from the database SISPCE (Information System of the Brazilian Schistosomiasis Control Program) from 1996 to 2009. The Kato-Katz technique is the methodology used to determine positivity index, examining one slide per person.

These *Ip* were determined for each locality *i* by:

$$Ip = \frac{r\_i}{n\_i} \text{\* 100} \tag{1}$$

where: *ri* is the number of infected people and *ni* is the total population in locality *i*.

#### **2.3. Intermediate hosts**

Information about the existence of *Biomphalaria* snails were provided at a municipality basis by the Laboratory of Helminthiasis and Medical Malacology of the Rene Rachou Research Center (CPqRR/Fiocruz-MG).

The distribution of Biomphalaria snails used for this study was defined as: *B. glabrata, B. te‐ nagophila, B. straminea, B. glabrata + B. tenagophila, B. glabrata + B. straminea, B. tenagophila + B. straminea, B. glabrata + B. tenagophila + B. straminea* and No *Biomphalaria*. The class "No *Bio‐ mphalaria*" includes information about the non-occurrence of *Biomphalaria* species or infor‐ mation about non-transmitter species in Brazil, such as *B. peregrina, B. schrammi, B. intermedia, B. occidentalis*, etc.

The spatial distribution of the schistosomiasis *Ip* and the *Biomphalaria* species data are pre‐ sented in Fig. 1.

#### **2.4. Environmental data**

Twenty eight environmental variables were obtained from remote sensing and meteorologi‐ cal sources.

The remote sensing variables were derived from Moderate Resolution Imaging Spectroradi‐ ometer (MODIS) and from the Shuttle Radar Topography Mission (SRTM) sensor.

The variables of MODIS sensor used were collected in two seasons, summer (from 17/Jan/ 2002 to 01/Feb/2002 period) and winter (from 28/Jul/2002 to 12/Aug/2002 period). MODIS data were composed by the blue, red, near and middle infrared bands and also the vegeta‐ tion indices (NDVI and EVI) [73].

**2.7. Indicator Kriging**

it was used for localities level.

**2.8. Multiple linear regressions**

on one or more independent variables.

*glabrata*) as explanatory variables.

explain the dependent variable.

improved the correlation with independent variables.

validation.

classes.

Since information about existence of *Biomphalaria* is only available on municipality basis, in‐ dicator kriging was used in this study to make inferences, in a grid basis, about the presence of the *Biomphalaria* species (*B. glabrata*, *B. tenagophila* and/or *B. straminea*), intermediate hosts of *S. mansoni*. The method allows spatialization of the data conditioned to the sample set of

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

http://dx.doi.org/10.5772/53500

11

The categorical attributes (classes) used for this study were defined as: *B. glabrata, B. tenago‐ phila, B. straminea, B. glabrata + B. tenagophila, B. glabrata + B. straminea, B. tenagophila + B. stra‐ minea, B. glabrata + B. tenagophila + B. straminea* and No *Biomphalaria* totalizing eight probable

The snail attributes (class of species and localization) were distributed along the drainage network of 15 River Basins (Buranhém, Doce, Grande, Itabapoana, Itanhém, Itapemirim, Je‐ quitinhonha, Jucuruçu, Mucuri, Paraíba do Sul, Paranaíba, Pardo, Piracicaba/Jaguari, São

In [1], however the indicator kriging was used only at municipalities' level, but in this study

Indicator kriging procedures were applied to obtain an approximation of the conditional distribution function of the random variables. Based on the estimated function, maps of snail spatial distributions along with the corresponding uncertainties for the entire state and

Multiple linear regressions are a form of regression analysis in which data are modeled by a least squares function which is a linear combination of the model parameters and depends

The regression analysis was applied with the schistosomiasis *Ip* as dependent variable, in addition to 93 quantitative variables (28 environmental variables and 65 socioeconomic vari‐ ables), and one qualitative variable resulting from the kriging (presence or absence of *B.*

The dependent variable was randomly divided into two sets: one with 852 cases (locali‐ ties) for variables selection and model definition, and another with 738 cases for model

Due to the high number of independent variables, some procedures were performed for var‐ iables selection. The relations among the dependent and the independent variables were an‐ alyzed in terms of correlation, multi co-linearity, and possible transformations that better

A logarithmic transformation for the dependent variable (denoted by *lnIp*) was made as it

The indicator kriging result was used as a variable in multiple regression models.

categorical attributes, aiming at the spatial distribution and production of maps.

Francisco and São Mateus), according to the methodology used by [67].

also map of estimated prevalence of schistosomiasis were built.

**Figure 1.** Spatial distribution of the (a) schistosomiasis and (b) *Biomphalaria* species in Minas Gerais State, Brazil.

The Linear Spectral Mixture Model (LSMM) is an image processing algorithm that generates fraction images with the proportion of each component (vegetation, soil, and shade) inside the pixel, which is estimated by minimizing the sum of square of the errors. 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 were also used as an input to the regression models [73].

Others variables obtained from SRTM were also used in this study: the digital elevation model (*DEM*) and slope (derived from *DEM*). Based on the SRTM data, a drainage map of Minas Gerais was generated, and the variables: water percentage in municipality (*QTA*) and water accumulation (*WA*) were derived. Six meteorological variables consisting of total pre‐ cipitation (*Prec*), minimum (*Tmin*) and maximum (*Tmax*) temperature average for summer and winter seasons were obtained from the Center for Weather Forecast and Climate Stud‐ ies (CPTEC), in the same date of MODIS images.

#### **2.5. Socioeconomic data**

Socioeconomic variables obtained by The Brazilian Institute of Geography and Statistics (IBGE) census for the year 2000 were also used as explanatory variables. The variables used in this work are those related to the water quality (percentage of domiciles with access to the general net of water supply, access to the water through wells or springheads, and with oth‐ er access forms to the water), and to the sanitary conditions (the percentage of domiciles with bathroom connected to rivers or lakes, connected to a ditch, to rudimentary sewage, to septic sewage, to a general net, to other sewerage type, with bathroom or sanitarium and without bathroom or sanitarium).

#### **2.6. Methods**

Indicator kriging and multiple linear regressions were employed to estimate the presence of the intermediate host and the schistosomiasis disease, respectively.

## **2.7. Indicator Kriging**

**Figure 1.** Spatial distribution of the (a) schistosomiasis and (b) *Biomphalaria* species in Minas Gerais State, Brazil.

to the regression models [73].

10 Parasitic Diseases - Schistosomiasis

**2.5. Socioeconomic data**

without bathroom or sanitarium).

**2.6. Methods**

ies (CPTEC), in the same date of MODIS images.

The Linear Spectral Mixture Model (LSMM) is an image processing algorithm that generates fraction images with the proportion of each component (vegetation, soil, and shade) inside the pixel, which is estimated by minimizing the sum of square of the errors. 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 were also used as an input

Others variables obtained from SRTM were also used in this study: the digital elevation model (*DEM*) and slope (derived from *DEM*). Based on the SRTM data, a drainage map of Minas Gerais was generated, and the variables: water percentage in municipality (*QTA*) and water accumulation (*WA*) were derived. Six meteorological variables consisting of total pre‐ cipitation (*Prec*), minimum (*Tmin*) and maximum (*Tmax*) temperature average for summer and winter seasons were obtained from the Center for Weather Forecast and Climate Stud‐

Socioeconomic variables obtained by The Brazilian Institute of Geography and Statistics (IBGE) census for the year 2000 were also used as explanatory variables. The variables used in this work are those related to the water quality (percentage of domiciles with access to the general net of water supply, access to the water through wells or springheads, and with oth‐ er access forms to the water), and to the sanitary conditions (the percentage of domiciles with bathroom connected to rivers or lakes, connected to a ditch, to rudimentary sewage, to septic sewage, to a general net, to other sewerage type, with bathroom or sanitarium and

Indicator kriging and multiple linear regressions were employed to estimate the presence of

the intermediate host and the schistosomiasis disease, respectively.

Since information about existence of *Biomphalaria* is only available on municipality basis, in‐ dicator kriging was used in this study to make inferences, in a grid basis, about the presence of the *Biomphalaria* species (*B. glabrata*, *B. tenagophila* and/or *B. straminea*), intermediate hosts of *S. mansoni*. The method allows spatialization of the data conditioned to the sample set of categorical attributes, aiming at the spatial distribution and production of maps.

The categorical attributes (classes) used for this study were defined as: *B. glabrata, B. tenago‐ phila, B. straminea, B. glabrata + B. tenagophila, B. glabrata + B. straminea, B. tenagophila + B. stra‐ minea, B. glabrata + B. tenagophila + B. straminea* and No *Biomphalaria* totalizing eight probable classes.

The snail attributes (class of species and localization) were distributed along the drainage network of 15 River Basins (Buranhém, Doce, Grande, Itabapoana, Itanhém, Itapemirim, Je‐ quitinhonha, Jucuruçu, Mucuri, Paraíba do Sul, Paranaíba, Pardo, Piracicaba/Jaguari, São Francisco and São Mateus), according to the methodology used by [67].

In [1], however the indicator kriging was used only at municipalities' level, but in this study it was used for localities level.

Indicator kriging procedures were applied to obtain an approximation of the conditional distribution function of the random variables. Based on the estimated function, maps of snail spatial distributions along with the corresponding uncertainties for the entire state and also map of estimated prevalence of schistosomiasis were built.

The indicator kriging result was used as a variable in multiple regression models.

#### **2.8. Multiple linear regressions**

Multiple linear regressions are a form of regression analysis in which data are modeled by a least squares function which is a linear combination of the model parameters and depends on one or more independent variables.

The regression analysis was applied with the schistosomiasis *Ip* as dependent variable, in addition to 93 quantitative variables (28 environmental variables and 65 socioeconomic vari‐ ables), and one qualitative variable resulting from the kriging (presence or absence of *B. glabrata*) as explanatory variables.

The dependent variable was randomly divided into two sets: one with 852 cases (locali‐ ties) for variables selection and model definition, and another with 738 cases for model validation.

Due to the high number of independent variables, some procedures were performed for var‐ iables selection. The relations among the dependent and the independent variables were an‐ alyzed in terms of correlation, multi co-linearity, and possible transformations that better explain the dependent variable.

A logarithmic transformation for the dependent variable (denoted by *lnIp*) was made as it improved the correlation with independent variables.

The analysis of the correlation matrix showed that some variables had non-significative cor‐ relations with *lnIp* at 95% confidence level, and also some variables were highly correlated among themselves, indicating that those variables could be excluded from future analysis.

**2.9. Simple average interpolator**

equation (3).

where *I* ^ *pi*

the interpolation function.

**3. Results and discussion**

variables used in this study.

above 15% (class with high positivity index).

The simple average interpolator (SAI) algorithm of the software SPRING [82] was used to estimate the value of *Ip* at each point (*x,y*) of the grid. This estimative is based on the sim‐ ple average of the variable values in the eight nearest neighbors of this point, according to

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

8

<sup>1</sup> <sup>ˆ</sup> (,) <sup>8</sup> *<sup>i</sup> i f x y Ip*

1

is the estimated positivity index of the 8 neighbors of the point (*x,y*) and *f*(*x,y*) is

æ ö <sup>=</sup> ç ÷ ç ÷ è ø <sup>å</sup> (3)

http://dx.doi.org/10.5772/53500

13

=

The file generated by interpolation was a grid with spatial resolution of 1 km. The purpose of using this tool was to determine which of the mesoregions presented estimated values

The GeoSchisto Database (http://www.dpi.inpe.br/geoschisto/) was created containing all

The indicator kriging result was a regular grid of 250 x 250 meters with the estimate of *Bio‐ mphalaria* species class for the entire Minas Gerais State. The indicator kriging result is pre‐ sented in Fig. 2a. The variable *B. glabrata* used in regression models is presented in Fig. 2b.

**Figure 2.** a) Kriging and (b) estimated presence of *B. glabrata* by indicator kriging.

Since multi co-linearity effects among the remaining independent variables were detected, variables selection techniques were used in order to choose a set of variables that better ex‐ plain the dependent variable. Variable selection was performed by the R2 criterion using all possible regressions [80].

This selection technique consists in the identification of a best subset with few variables and a coefficient of determination R2 sufficiently close to that when all variables are used in the model.

Interaction effects were also analyzed to be included in the model. After performing the re‐ sidual analysis, the chosen regression model was then validated. The final estimated regres‐ sion function was computed using the entire data set (definition and validation), and it was applied to all localities to build a risk map for schistosomiasis positivity index.

The multiple regressions were developed based on two approaches: a global model (throughout the state) and a regional model (regionalization).

Regionalization is a classification procedure using the SKATER algorithm (Spatial 'K'luster Analysis by Tree Edge Removal) applied to spatial objects with an areal representation (mu‐ nicipalities), which groups them into homogeneous contiguous regions [81].

Regionalization was applied in Minas Gerais to divide the state into four homogeneous re‐ gions. The choice of the number of regions was based on the spatial distribution of localities (Figure 1b) in order to achieve an adequate number of localities in each region.

The regional model was developed by doing a regression model separately in each of the four regions formed by first applying the SKATER algorithm using environmental variables [74].

The models validation was performed using the Root Mean Square Error (RMSE) and the Mean Squared Prediction Error (MSPR), given by [80].

$$RMSE = \sqrt{\frac{\sum\_{i=1}^{n} \left(Ip\_i - \hat{I}p\_i\right)^2}{n}} \tag{2}$$

where *I pi* and *I* ^ *pi* represent, respectively, the observed and predicted positivity index in the *i*-th observation and *n* is the number of observations of the data model definition (*i =* 1*,..., n*).

The RMSE measures the variation of the observed values around the estimated values. Ide‐ ally, the values of RMSE are close to zero. The MSPR is computed the same way as the RMSE, but using validation samples.

The final models were applied in all 4,846 localities to estimate the positivity index.

#### **2.9. Simple average interpolator**

The analysis of the correlation matrix showed that some variables had non-significative cor‐ relations with *lnIp* at 95% confidence level, and also some variables were highly correlated among themselves, indicating that those variables could be excluded from future analysis. Since multi co-linearity effects among the remaining independent variables were detected, variables selection techniques were used in order to choose a set of variables that better ex‐ plain the dependent variable. Variable selection was performed by the R2 criterion using all

This selection technique consists in the identification of a best subset with few variables and a coefficient of determination R2 sufficiently close to that when all variables are used

Interaction effects were also analyzed to be included in the model. After performing the re‐ sidual analysis, the chosen regression model was then validated. The final estimated regres‐ sion function was computed using the entire data set (definition and validation), and it was

The multiple regressions were developed based on two approaches: a global model

Regionalization is a classification procedure using the SKATER algorithm (Spatial 'K'luster Analysis by Tree Edge Removal) applied to spatial objects with an areal representation (mu‐

Regionalization was applied in Minas Gerais to divide the state into four homogeneous re‐ gions. The choice of the number of regions was based on the spatial distribution of localities

The regional model was developed by doing a regression model separately in each of the four regions formed by first applying the SKATER algorithm using environmental variables

The models validation was performed using the Root Mean Square Error (RMSE) and the

<sup>ˆ</sup> *<sup>n</sup> i i*

*i*-th observation and *n* is the number of observations of the data model definition (*i =* 1*,..., n*). The RMSE measures the variation of the observed values around the estimated values. Ide‐ ally, the values of RMSE are close to zero. The MSPR is computed the same way as the

( )


*Ip Ip*

*n*

1

*i*

The final models were applied in all 4,846 localities to estimate the positivity index.

=

=

*RMSE*

2

*pi* represent, respectively, the observed and predicted positivity index in the

å (2)

applied to all localities to build a risk map for schistosomiasis positivity index.

nicipalities), which groups them into homogeneous contiguous regions [81].

(Figure 1b) in order to achieve an adequate number of localities in each region.

(throughout the state) and a regional model (regionalization).

Mean Squared Prediction Error (MSPR), given by [80].

possible regressions [80].

12 Parasitic Diseases - Schistosomiasis

in the model.

[74].

where *I pi* and *I*

^

RMSE, but using validation samples.

The simple average interpolator (SAI) algorithm of the software SPRING [82] was used to estimate the value of *Ip* at each point (*x,y*) of the grid. This estimative is based on the sim‐ ple average of the variable values in the eight nearest neighbors of this point, according to equation (3).

$$f(x, y) = \frac{1}{8} \left(\sum\_{i=1}^{8} \hat{I} p\_i \right) \tag{3}$$

where *I* ^ *pi* is the estimated positivity index of the 8 neighbors of the point (*x,y*) and *f*(*x,y*) is the interpolation function.

The file generated by interpolation was a grid with spatial resolution of 1 km. The purpose of using this tool was to determine which of the mesoregions presented estimated values above 15% (class with high positivity index).

## **3. Results and discussion**

The GeoSchisto Database (http://www.dpi.inpe.br/geoschisto/) was created containing all variables used in this study.

The indicator kriging result was a regular grid of 250 x 250 meters with the estimate of *Bio‐ mphalaria* species class for the entire Minas Gerais State. The indicator kriging result is pre‐ sented in Fig. 2a. The variable *B. glabrata* used in regression models is presented in Fig. 2b.

**Figure 2.** a) Kriging and (b) estimated presence of *B. glabrata* by indicator kriging.

#### **3.1. Global model**

The five variables selected were: presence or absence of the *B. glabrata*, summer precipitation (*PCs*), summer minimum temperature (*TNs*), winter Enhanced Vegetation Index (*EVIw*) and households with a bathroom or toilet and sewage from septic tank type (*V31*).

The final model, with R2 = 0.18, was:

$$\hat{I}p = \stackrel{(-7.34 + 0.51B\text{G} + 0.004PC\_s + 0.37TN\_s + 0.0003EVl\_w + 0.004V\_{31})}{} - 1\tag{4}$$

**3.2. Regional model**

The Minas Gerais State was divided into four regions using the SKATER algorithm. Table 2 presents the number of localities in each region used for model generation and for model

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

Region 1 (R1) 104 66 170 Region 2 (R2) 428 262 690 Region 3 (R3) 220 338 558 Region 4 (R4) 100 72 172 Total 852 738 1590

Regression models were developed for each of the four regions with the same 94 variables used in the global model, and the same selection procedure. Different numbers of variables

> <sup>25</sup> (14.04 0.05 0.01 1.15 ) 2 <sup>1</sup> <sup>1</sup> 0.35 *w s PC V T Ip <sup>R</sup> e*

<sup>25</sup> <sup>261</sup> ( 11.63 0.68 0.59 0.0004 0.03 0.005 ) 2 <sup>2</sup> <sup>1</sup> 0.21 *s w BG TN EVI V V Ip <sup>R</sup> e*

<sup>33</sup> <sup>283</sup> (0.16 0.39 0.0002 0.015 0.002 ) 2

<sup>254</sup> <sup>272</sup> ( 3.54 0.0001 0.006 0.16 0.27 0.0006 0.003 ) 2 <sup>4</sup> 1 0.22 *s ss EVI PC TN QTA V V Ip <sup>R</sup> e*

work general) and *V272* (percentage of households without toilet or sanitation).

**Table 2.** Number of localities in each region used for model generation and for model validation.

were selected in each region to determine the best regression model.

The final models generated for each region (Fig. 4c) and their R2

Model Generation Model Validation Total

+ + -D <sup>=</sup> -Þ = ) (5)


<sup>3</sup> <sup>1</sup> 0.38 *<sup>s</sup> BG NDVI V V Ip <sup>R</sup> <sup>e</sup>* ++ - + <sup>=</sup> -Þ = ) (7)


where: *PCW* (winter precipitation), *V25* (percentage of households with another form of ac‐ cess to water), *ΔTS* (difference of summer maximum and minimum temperature), *BG* (pres‐ ence or not of the *B. glabrata*), *TNS* (summer minimum temperature), *EVIW* (winter Enhanced Vegetation Index), *V261* (percentage of residents in households with another form of water supply), *NDVIS* (summer Normalized Difference Vegetation Index), *V33* (percentage of hous‐ ing with bathroom or toiled connected to a ditch), *V283* (percentage of households without bathrooms), *EVIS* (summer Enhanced Vegetation Index), *PCS* (summer precipitation), *QTA* (water percentage in municipality), *V254* (percentage of households with water supply net‐

were:

http://dx.doi.org/10.5772/53500

15

validation. The regionalization can be seen in Figure 4 a.

Fig. 3a shows the estimated *Ip* for all 4,842 localities in Minas Gerais using the estimated re‐ gression equation (4). Figure 3b shows the plot of the residuals, resulting from the difference between observed and estimated *Ip* from 1,590 locations. In Figure 3b, dark colors (red and blue) represent overestimated values, light colors (red and blue) underestimated ones, and in white are the municipalities where the estimated prevalence differs very little from the true values.

The precipitation, minimum temperature, EVI and sanitation were positively correlated with *Ip*. This is consistent with the adequate environmental conditions for the transmission of schistosomiasis. Also, the transmission depends on the presence of *B. glabrata*.

**Figure 3.** Global model: (a) estimated *Ip* and (b) residuals.

The result of this model has the same variables (*BG*, *TNs*, *Eviw* and sanitation) obtained by [74] when estimatives were done on a municipality basis, indicating a great similarity be‐ tween the two global models. The difference is in the sanitation variable where the variable obtained by [74] was related to the type of water (well or spring) and this study to the type of sewage system (septic tank).

#### **3.2. Regional model**

**3.1. Global model**

14 Parasitic Diseases - Schistosomiasis

The final model, with R2

true values.

The five variables selected were: presence or absence of the *B. glabrata*, summer precipitation (*PCs*), summer minimum temperature (*TNs*), winter Enhanced Vegetation Index (*EVIw*) and


households with a bathroom or toilet and sewage from septic tank type (*V31*).

<sup>31</sup> ( 7.34 0.51 0.004 0.37 0.0003 0.004 ) <sup>1</sup> *ss w BG PC TN EVI V Ip e*

Fig. 3a shows the estimated *Ip* for all 4,842 localities in Minas Gerais using the estimated re‐ gression equation (4). Figure 3b shows the plot of the residuals, resulting from the difference between observed and estimated *Ip* from 1,590 locations. In Figure 3b, dark colors (red and blue) represent overestimated values, light colors (red and blue) underestimated ones, and in white are the municipalities where the estimated prevalence differs very little from the

The precipitation, minimum temperature, EVI and sanitation were positively correlated with *Ip*. This is consistent with the adequate environmental conditions for the transmission

The result of this model has the same variables (*BG*, *TNs*, *Eviw* and sanitation) obtained by [74] when estimatives were done on a municipality basis, indicating a great similarity be‐ tween the two global models. The difference is in the sanitation variable where the variable obtained by [74] was related to the type of water (well or spring) and this study to the type

of schistosomiasis. Also, the transmission depends on the presence of *B. glabrata*.

= 0.18, was:

**Figure 3.** Global model: (a) estimated *Ip* and (b) residuals.

of sewage system (septic tank).

The Minas Gerais State was divided into four regions using the SKATER algorithm. Table 2 presents the number of localities in each region used for model generation and for model validation. The regionalization can be seen in Figure 4 a.


**Table 2.** Number of localities in each region used for model generation and for model validation.

Regression models were developed for each of the four regions with the same 94 variables used in the global model, and the same selection procedure. Different numbers of variables were selected in each region to determine the best regression model.

The final models generated for each region (Fig. 4c) and their R2 were:

$$
\hat{I}p\_1 = \mathcal{C} \begin{array}{c} (14.04 + 0.05PC\_w + 0.01V\_{25} - 1.15\Lambda T\_s) \\ \end{array} - \mathbf{1} \implies \mathcal{R}^2 = 0.35 \tag{5}
$$

$$\hat{\mathbf{I}}p\_2 = \mathbf{e}^{(-11.63 + 0.68 \,\mathrm{BG} + 0.59 \,\mathrm{TN}\_s + 0.0004 \,\mathrm{E} \,\mathrm{V}\_w + 0.03 \,\mathrm{V}\_{25} - 0.005 \,\mathrm{V}\_{264})} - \mathbf{1} \quad \Rightarrow \quad \mathbf{R}^2 = \mathbf{0}.21\tag{6}$$

$$\hat{\mathbf{I}}p\_3 = \mathbf{e}^{(0.16 + 0.39B\mathbf{G} + 0.0002\mathbf{ND}V\_s - 0.015V\_{33} + 0.002V\_{283})} - \mathbf{1} \quad \Rightarrow \quad \mathbf{R}^2 = \mathbf{0}.38 \tag{7}$$

$$\hat{\mathbf{I}}\mathbf{p}\_4 = \mathbf{g}^{\{-3.54 + 0.0001EVI\_s + 0.006PC\_s + 0.16TN\_s + 0.27QTA + 0.0006V\_{24} + 0.003V\_{22}\}} - \mathbf{1} \Rightarrow \quad \mathbf{R}^2 = 0.22 \tag{8}$$

where: *PCW* (winter precipitation), *V25* (percentage of households with another form of ac‐ cess to water), *ΔTS* (difference of summer maximum and minimum temperature), *BG* (pres‐ ence or not of the *B. glabrata*), *TNS* (summer minimum temperature), *EVIW* (winter Enhanced Vegetation Index), *V261* (percentage of residents in households with another form of water supply), *NDVIS* (summer Normalized Difference Vegetation Index), *V33* (percentage of hous‐ ing with bathroom or toiled connected to a ditch), *V283* (percentage of households without bathrooms), *EVIS* (summer Enhanced Vegetation Index), *PCS* (summer precipitation), *QTA* (water percentage in municipality), *V254* (percentage of households with water supply net‐ work general) and *V272* (percentage of households without toilet or sanitation).

The model for Region 4 (R4) shows that *Ip* was associated with vegetation (*Evis*), weather

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

In all models the presence of *B. glabrata*, sanitation, vegetation index and temperature were the most important variables. These characteristics are the same as environmental condi‐ tions for the presence and development of snails (infection of the intermediate host) and sanitation (water contamination - presence of *S. mansoni* cercariae) obtained by [74] which

[29] also showed that the distribution of schistosomiasis in Bahia, at municipalities level, is related to the vegetation index (*NDVI*) and temperature (*ΔTs*) using sensor data from low

Table 3 presents the mean square error (RMSE) and Mean Squared Error of Prediction (MSPR) for the global and regional models, for each region. From this table we can observe that the mean square decreased from 10.739 to 9.979 when we used separate models for each region. It was also noted that the RMSE of the Regional Model was smaller than the RMSE of the Global Model for all four regions, highlighting the importance of using different equa‐ tions and different variables for each region. Since the Regional Model can be considered a better model the Simple Averages Interpolator (SAI), was applied using the known positivi‐ ty index of the 1,590 localities (Fig. 5a), and using the regression estimated positivity index of all 4,842 localities (Fig. 5b). The objective of applying SAI to all estimated *Ip* values is to

**Model nRMSE RMSE nMSPR MSPR**

R1 104 8.078 66 3.421 R2 428 12.369 262 11.741 R3 220 10.042 338 10.048 R4 100 6.164 72 8.282 total 852 10.739 738 10.145

R1 104 7.576 66 3.376 R2 428 11.553 262 11.577 R3 220 9.044 338 9.538 R4 100 6.123 72 8.291 total 852 9.979 738 9.848

indicate current and potential local transmission of schistosomiasis.

**Table 3.** Residual analysis of the dependent variable (*Ip*) for the models.

found for

17

http://dx.doi.org/10.5772/53500

(precipitation and temperature) and sanitation (type of water and sewage). The R2

this model was 0.22.

Global

Regional

n (number of localities)

were obtained at municipalities level.

spatial resolution (AVHRR/NOAA).

**3.3. Simple Averages Interpolator (SAI)**

**Figure 4.** Regional model: (a) estimated *Ip*, (b) residuals. (c) regionalization.

Figure 4a shows the estimated values of *Ip* for all 4,842 localities in the Minas Gerais State using equations (3, 4, 5 and 6). Also, Figure 4b shows the residues from 1,590 locations. In this figure, red and blue represent overestimates, cyan and magenta represent the underesti‐ mated values and in the white localities with good estimate.

The regional model for Region 1 (R1) reflects the effect of sanitation (households with other forms of water than tap water, wells or springs) and the influence of weather (precipitation and temperature of summer). Region 1 achieved a R2 value of 0.35. The model obtained by [74] for the same Region 1 also has the same sanitation variable (percentage of homes with another type of access to water). The relationship between temperature and disease was also obtained by [29] and [55, 74].

The models for Regions 2 and 3 (R2 and R3) show the presence of *B. glabrata* associated with the effect of vegetation (*Eviw*) and sanitation. Among the regional models, Region 2 had the lowest R2 (0.21) and Region 3 had the highest R2 (0.38).

The model for Region 4 (R4) shows that *Ip* was associated with vegetation (*Evis*), weather (precipitation and temperature) and sanitation (type of water and sewage). The R2 found for this model was 0.22.

In all models the presence of *B. glabrata*, sanitation, vegetation index and temperature were the most important variables. These characteristics are the same as environmental condi‐ tions for the presence and development of snails (infection of the intermediate host) and sanitation (water contamination - presence of *S. mansoni* cercariae) obtained by [74] which were obtained at municipalities level.

[29] also showed that the distribution of schistosomiasis in Bahia, at municipalities level, is related to the vegetation index (*NDVI*) and temperature (*ΔTs*) using sensor data from low spatial resolution (AVHRR/NOAA).

#### **3.3. Simple Averages Interpolator (SAI)**

**Figure 4.** Regional model: (a) estimated *Ip*, (b) residuals. (c) regionalization.

mated values and in the white localities with good estimate.

and temperature of summer). Region 1 achieved a R2

(0.21) and Region 3 had the highest R2

obtained by [29] and [55, 74].

16 Parasitic Diseases - Schistosomiasis

lowest R2

Figure 4a shows the estimated values of *Ip* for all 4,842 localities in the Minas Gerais State using equations (3, 4, 5 and 6). Also, Figure 4b shows the residues from 1,590 locations. In this figure, red and blue represent overestimates, cyan and magenta represent the underesti‐

The regional model for Region 1 (R1) reflects the effect of sanitation (households with other forms of water than tap water, wells or springs) and the influence of weather (precipitation

[74] for the same Region 1 also has the same sanitation variable (percentage of homes with another type of access to water). The relationship between temperature and disease was also

The models for Regions 2 and 3 (R2 and R3) show the presence of *B. glabrata* associated with the effect of vegetation (*Eviw*) and sanitation. Among the regional models, Region 2 had the

(0.38).

value of 0.35. The model obtained by

Table 3 presents the mean square error (RMSE) and Mean Squared Error of Prediction (MSPR) for the global and regional models, for each region. From this table we can observe that the mean square decreased from 10.739 to 9.979 when we used separate models for each region. It was also noted that the RMSE of the Regional Model was smaller than the RMSE of the Global Model for all four regions, highlighting the importance of using different equa‐ tions and different variables for each region. Since the Regional Model can be considered a better model the Simple Averages Interpolator (SAI), was applied using the known positivi‐ ty index of the 1,590 localities (Fig. 5a), and using the regression estimated positivity index of all 4,842 localities (Fig. 5b). The objective of applying SAI to all estimated *Ip* values is to indicate current and potential local transmission of schistosomiasis.


**Table 3.** Residual analysis of the dependent variable (*Ip*) for the models.

Figure 5a shows clusters presence in six mesoregions (Norte de Minas, Jequitinhonha, Vale do Mucuri, Vale do Rio Doce, Metropolitana de Belo Horizonte and Zona da Mata) with the highest *Ip* values. In Figure 5b the same six mesoregions can be noticed; however two news clusters in Sul/Sudoeste de Minas and Triângulo Mineiro/Alto Parnaíba mesoregions pre‐ sented, respectively high and middle *Ip* values.

area of Minas Gerais State) and a high agricultural economy, it is a region with high risk of schistosomiasis transmission. Therefore, it would be interesting to do a detailed study in the

Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

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19

Also, it would be interesting to keep surveillance in the municipalities of the Triângulo

This study shows the importance of a joint use of GIS and RS to study the risk of schistoso‐ miasis. Moreover, it can be concluded that the combined use of GIS and statistical techni‐ ques allowed the estimation of schistosomiasis *Ip*. Results of the regression models confirmed the importance of the use of environmental variables to characterize the snail

Results of the regression models show that regionalization improves the estimation of the disease in Minas Gerais. Based on this model, a schistosomiasis risk map was built for Minas Gerais. [74] and [75] also obtained a better model with the use of regionalization when esti‐

The Simple Averages Interpolator is a technique that may indicate possible local to trans‐

It is recommended the use of GPS for field surveys together and the application of this methodology with images of better spatial resolution (10-30m) in other states for validation. Also, we recommend using a smaller area (municipality or mesoregion) estimate for the

The methodology used in this study can be utilized to control schistosomiasis in the areas with occurrence of the disease and also it can be used to take preventive measures to pre‐

Next step will be to utilize data from the PCE by localities to study other diseases such as ascariasis, hookworm, trichuriasis, etc, using data from CBERS and/or Landsat and new methodologies (Geographically Weighted Regression, Generalized Additive Model, etc).

The authors woud like to acknowledge the support of Sandra da Costa Drummond (Funda‐ ção Nacional de Saúde) and the support of CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) (grants # 300679/2011-4, 384571/2010-7, 302966/2009-9,

Sul mesoregion to determine the schistosomiasis *Ip*.

habitat in the endemic area of the state of Minas Gerais.

mating schistosomiasis at a municipality level.

mission and surveillance of schistosomiasis.

schistosomiasis.

vent the disease transmission.

**Acknowledgements**

308253/2008-6).

**4. Conclusions and future work**

Mineiro/Alto Parnaíba mesoregion that presented *B. glabrata* presence.

**Figure 5.** The averages interpolator: (a) *Ip* and (b) estimated *Ip* by Regional Model. (c) Mesoregions of Minas Gerais State.

Thus, the Norte de Minas, Jequitinhonha, Vale do Mucuri, Vale do Rio Doce, Metropolitana de Belo Horizonte and Zona da Mata mesoregions are endemics areas.

Sul/Sudoeste de Minas and Triângulo Mineiro/Alto Parnaíba mesoregions are not endemic areas, but have a schistosomiasis focus (Itajubá municipality in Sul mesoregion). The Sul/ Sudoeste de Minas mesoregion has 146 municipalities representing about 20% of municipal‐ ities in Minas Gerais State and is a non-endemic area for schistosomiasis. Due to the high concentration of cities in an area of 49,523.893 km2 (which represents less than 10% of the area of Minas Gerais State) and a high agricultural economy, it is a region with high risk of schistosomiasis transmission. Therefore, it would be interesting to do a detailed study in the Sul mesoregion to determine the schistosomiasis *Ip*.

Also, it would be interesting to keep surveillance in the municipalities of the Triângulo Mineiro/Alto Parnaíba mesoregion that presented *B. glabrata* presence.

## **4. Conclusions and future work**

Figure 5a shows clusters presence in six mesoregions (Norte de Minas, Jequitinhonha, Vale do Mucuri, Vale do Rio Doce, Metropolitana de Belo Horizonte and Zona da Mata) with the highest *Ip* values. In Figure 5b the same six mesoregions can be noticed; however two news clusters in Sul/Sudoeste de Minas and Triângulo Mineiro/Alto Parnaíba mesoregions pre‐

**Figure 5.** The averages interpolator: (a) *Ip* and (b) estimated *Ip* by Regional Model. (c) Mesoregions of Minas Gerais

Thus, the Norte de Minas, Jequitinhonha, Vale do Mucuri, Vale do Rio Doce, Metropolitana

Sul/Sudoeste de Minas and Triângulo Mineiro/Alto Parnaíba mesoregions are not endemic areas, but have a schistosomiasis focus (Itajubá municipality in Sul mesoregion). The Sul/ Sudoeste de Minas mesoregion has 146 municipalities representing about 20% of municipal‐ ities in Minas Gerais State and is a non-endemic area for schistosomiasis. Due to the high

(which represents less than 10% of the

de Belo Horizonte and Zona da Mata mesoregions are endemics areas.

concentration of cities in an area of 49,523.893 km2

sented, respectively high and middle *Ip* values.

18 Parasitic Diseases - Schistosomiasis

State.

This study shows the importance of a joint use of GIS and RS to study the risk of schistoso‐ miasis. Moreover, it can be concluded that the combined use of GIS and statistical techni‐ ques allowed the estimation of schistosomiasis *Ip*. Results of the regression models confirmed the importance of the use of environmental variables to characterize the snail habitat in the endemic area of the state of Minas Gerais.

Results of the regression models show that regionalization improves the estimation of the disease in Minas Gerais. Based on this model, a schistosomiasis risk map was built for Minas Gerais. [74] and [75] also obtained a better model with the use of regionalization when esti‐ mating schistosomiasis at a municipality level.

The Simple Averages Interpolator is a technique that may indicate possible local to trans‐ mission and surveillance of schistosomiasis.

It is recommended the use of GPS for field surveys together and the application of this methodology with images of better spatial resolution (10-30m) in other states for validation. Also, we recommend using a smaller area (municipality or mesoregion) estimate for the schistosomiasis.

The methodology used in this study can be utilized to control schistosomiasis in the areas with occurrence of the disease and also it can be used to take preventive measures to pre‐ vent the disease transmission.

Next step will be to utilize data from the PCE by localities to study other diseases such as ascariasis, hookworm, trichuriasis, etc, using data from CBERS and/or Landsat and new methodologies (Geographically Weighted Regression, Generalized Additive Model, etc).

## **Acknowledgements**

The authors woud like to acknowledge the support of Sandra da Costa Drummond (Funda‐ ção Nacional de Saúde) and the support of CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) (grants # 300679/2011-4, 384571/2010-7, 302966/2009-9, 308253/2008-6).

## **Author details**

Ricardo J.P.S. Guimarães1\*, Corina C. Freitas2 , Luciano V. Dutra2 , Guilherme Oliveira3 and Omar S. Carvalho3

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Multiple Regression for the Schistosomiasis Positivity Index Estimates in the Minas Gerais State – Brazil...

http://dx.doi.org/10.5772/53500

21

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**Chapter 2**

**Epidemiological Survey of**

I.S. Akande and A.A. Odetola

http://dx.doi.org/10.5772/53523

**1.1. Incidence of schistosomiasis**

**1. Introduction**

**Human and Veterinary Schistosomiasis**

Schistosomiasis is one of the fifteen neglected tropical diseases (NTDS) namely: schistosomiasis, ascariasis, buruli ulcer, chagas disease, cysticercosis, food borne trematodiases, hookworm dis‐ ease, leprosy,lymphatic filariasis, trachoma, trichuriasis, leishmaniasis, guinea worm, trypano‐ somiasis and oncocerciasis. It is a resurgent disease and *Schistosoma* sp. Infects well over 250 million people worldwide beside livestock [1]. Schistosomiasis (also called Bilharzias after the German tropical disease specialist, Theodore M. Bilharz, 1829 – 1862) is second only to malaria in parasitic disease morbidity. Despite control programmes in place, the distribution and the num‐ ber of people estimated to be infected or at risks have not reduced. Approximately, over 600 mil‐ lion people in tropical and subtropical countries are at risk and of those infected120 million are symptomatic with 20 million having severe manifestations. Schistosomiasis is endemic in many countries, not only in sub-Saharan Africa, but the Middle East, Far East, South and Central Amer‐ ica and the Caribbean. It is endemic in about 76 countries of the world including Nigeria. Present‐

ly, an estimated 3 million Nigerian children aged between 5 and 14 years are infected.

for increasing agricultural production in developing countries.

**Endemic distribution**: Ten species of schistosomes can infect humans out of seventeen recognized species, but a vast majority of infections are caused by *Schistosomamansoni*, S. *japonicum* and S. *haematobium*. Today, 85% of the numbers of infected people live in sub-Saharan Africa due to ignorance, cultural beliefs and practices and water contact patterns where S. *mansoni*, S. *haematobium* and S *intercalatum* are endemic. Livestock such as cattle harbour *Schistosoma* bovis; sheep harbour *Schistosoma curassoni* among others. The crucial agent perpetuating this disease is the water based snail intermediate host, flourishing in slow moving waters of man-made lakes, dams, irrigations channels and other fresh water bodies important

and reproduction in any medium, provided the original work is properly cited.

© 2013 Akande and Odetola; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Additional information is available at the end of the chapter


## **Chapter 2**
