Risk Mapping of Schistosomiasis in Minas Gerais, Brazil, Using MODIS and Socioeconomic Spatial Data

Martins-Bedé, Flávia Toledo; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sandri, Sandra; Fonseca, Fernanda Rodrigues; Drummond, Isabela; Guimarães, Ricardo José de Paula Souza e; Amaral, Ronaldo; Carvalho, Omar S.

doi:10.1109/tgrs.2009.2028332

Cited by 13 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the original experiments presented in [7], the disease prevalence data was provided by the Health Secretary of the State of Minas Gerais state. The prevalence is known for 197 municipalities out of the 853 composing the state (see Figure 3.1.a).…”

Section: The Original Experiments: Materials and Methodsmentioning

confidence: 99%

“…In [7], the authors present a classification Schistosomiasis prevalence for the State of Minas Gerais, using remote sensing, climate, socioeconomic and neighborhood related variables. Two approaches were used, a global and a regional one.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of this paper is to check the usefulness of the fuzzy CBR approach to classification proposed in [2] in order to estimate and classify schistosomiasis prevalence, as an alternative to the linear regression model approach developed in [7]. To allow the comparison of the results, we present two approaches for solving the problem, a global and a regional one, following the guidelines in [7].…”

Section: Introductionmentioning

confidence: 99%

“…To allow the comparison of the results, we present two approaches for solving the problem, a global and a regional one, following the guidelines in [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Classification of Schistosomiasis Prevalence Using Fuzzy Case-Based Reasoning

Martins-Bedé

Godo

Sandri

et al. 2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Section: The Original Experiments: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To allow the comparison of the results, we present two approaches for solving the problem, a global and a regional one, following the guidelines in [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Schistosomiasis Prevalence Using Fuzzy Case-Based Reasoning

Martins-Bedé

Godo

Sandri

et al. 2009

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…These studies have provided risk maps for schistosomiasis infection on a municipality basis by using multiple regression analyses that include environmental features, a priori disease prevalence data and other spatial data (Bavia et al 1999, 2008, Martins-Bedê et al 2009). …”

Section: Schistosomiasis Mansoni Is Not Just a Physical Disease But mentioning

confidence: 99%

Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Martins-Bedé

Dutra

Freitas

et al. 2010

Mem. Inst. Oswaldo Cruz

View full text Add to dashboard Cite

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MGThe disease is related to social and behavioural factors, particularly inadequate public and environmental sanitation and a low level of education about health in the populations involved (Doumenge et al. 1987).Once schistosomiasis risk is identified by both environmental and social factors, new computerised analytical tools, known as Geographic Information Systems and Remote Sensing Data Analysis, have been used to map epidemiological data or analyse satellite images .In Brazil, geo-processing tools have been used in the study of schistosomiasis in the states of Bahia (BA) and Minas Gerais (MG). These studies have provided risk maps for schistosomiasis infection on a municipality basis by using multiple regression analyses that include environmental features, a priori disease prevalence data and other spatial data (Bavia et al. 1999, 2008, Martins-Bedê et al. 2009).In the present paper, a standard data-mining technique, the decision tree, was used to identify the severity of disease prevalence. This technique is based on a recursive Predictive models are used to classify different samples whose values or labels are not known. In this paper, a decision tree model is used to classify the schistosomiasis prevalence risk for the whole state. Remote sensing data and spatial sociological indicators are used to map potential risk areas which are not covered by the schistosomiasis control program. This map of potential risk areas can be used by a decision maker to evaluate municipalities outside of the program that have similar environmental and social conditions to the municipalities covered by the program. Moreover, the map can serve as a guide for future disease-control efforts in the state. MATERIALS AND METHODSStudy area -The study area was MG, Brazil. MG is 590,000 square kilometres in size and is politically divided into 853 cities; the area has a tropical climate and includes approximately 18 million inhabitants (IBGE 2008).

show abstract