Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

A.L. Araujo Navas (Corresponding Author), Ricardo J. Soares Magalhães, Frank Osei, Raffy Jay C. Fornillos, Lydia R. Leonardo, Alfred Stein

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
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Abstract

Background
Spatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.

Methods
To delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.

Results
High values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R2 = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.

Conclusions
The utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.
Original languageEnglish
Article number465
Pages (from-to)1-15
Number of pages15
JournalParasites & vectors
Volume11
Issue number1
DOIs
Publication statusPublished - 13 Aug 2018

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Schistosoma japonicum
Schistosomiasis
Snails
Infection
Body Water
Helminths
Buffers
Soil
Public Health
Surveys and Questionnaires
Weights and Measures
Costs and Cost Analysis
Water
Research
Pharmaceutical Preparations

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-GOLD

Cite this

Araujo Navas, A.L. ; Soares Magalhães, Ricardo J. ; Osei, Frank ; Fornillos, Raffy Jay C. ; Leonardo, Lydia R. ; Stein, Alfred. / Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment. In: Parasites & vectors. 2018 ; Vol. 11, No. 1. pp. 1-15.
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abstract = "BackgroundSpatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.MethodsTo delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.ResultsHigh values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R2 = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.ConclusionsThe utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.",
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Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment. / Araujo Navas, A.L. (Corresponding Author); Soares Magalhães, Ricardo J.; Osei, Frank; Fornillos, Raffy Jay C.; Leonardo, Lydia R.; Stein, Alfred.

In: Parasites & vectors, Vol. 11, No. 1, 465, 13.08.2018, p. 1-15.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - Modelling local areas of exposure to Schistosoma japonicum in a limited survey data environment

AU - Araujo Navas, A.L.

AU - Soares Magalhães, Ricardo J.

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AU - Fornillos, Raffy Jay C.

AU - Leonardo, Lydia R.

AU - Stein, Alfred

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N2 - BackgroundSpatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.MethodsTo delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.ResultsHigh values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R2 = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.ConclusionsThe utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.

AB - BackgroundSpatial modelling studies of schistosomiasis (SCH) are now commonplace. Covariate values are commonly extracted at survey locations, where infection does not always take place, resulting in an unknown positional exposure mismatch. The present research aims to: (i) describe the nature of the positional exposure mismatch in modelling SCH helminth infections; (ii) delineate exposure areas to correct for such positional mismatch; and (iii) validate exposure areas using human positive cases.MethodsTo delineate exposure areas to Schistosoma japonicum, a spatial Bayesian network (sBN) was constructed. It uses data on exposure risk factors such as: potential sites for snails’ accessibility, geographical distribution of snail infection rate, and cost of the community to access nearby water bodies. Prior and conditional probabilities were obtained from the literature and inserted as weights based on their relative contribution to exposure; these probabilities were then used to calculate joint probabilities of exposure within the sBN.ResultsHigh values of probability of S. japonicum exposure correspond to polygons where snails could potentially be present, for instance in wet soils and areas with low slopes, but also where people can easily access water bodies. Low correlation (R2 = 0.3) was found between the percentage of human cases and the delineated probabilities of exposure when validation buffers are generated over the human cases.ConclusionsThe utility of a probabilistic method for the identification of exposure areas for S. japonicum, with wider application for other water-borne infections, was demonstrated. From a public health perspective, the schistosomiasis exposure sBN developed in this study could be used to guide local schistosomiasis control teams to specific potential areas of exposure, and improve efficiency of mass drug administration campaigns in places where people are likely to be exposed to the infection.

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