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Supplementary Material 3

August 2018

License
CC BY 4.0

Authors:

University of Twente

Ricardo J Soares Magalhães

The University of Queensland

Frank Badu Osei

University of Twente

Raffy Jay Fornillos

University of the Philippines System

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Available via license: CC BY 4.0

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TEXT ABBREVIATION DESCRIPTION

SCH Schistosomiasis

BN Bayesian network

DAG Directed acyclic graph

NPT Node probability tables

GIS Geographic information systems

GPS Global positioning systems

VARIABLES NOTATION

DESCRIPTION

Set of a variable

LU LU - Land use

DWB DWB - Distance to water bodies

SI SI - Snail infection rate

EE- Elevation

SLP SLP - Slope

PAS - - Potential Accesible snail sites

CC - - Community Cost

EX - ex Exposure

X- x Random variables in the network

S-sSchool survey points

V-vNodes

I-iDiscrete variables

π- - Prior probabilities

D- - DAG

P- - Probability distributions

Additional file 3: Table S11. Abbreviations used in the manuscript and variable notations.

Single

variable

No

evidence

inserted

Inserted

evidence

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

Article

August 2018

Andrea Araujo · Ricardo J Soares Magalhães · Frank Badu Osei · Raffy Jay Fornillos · Alfred Stein

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Supplementary Material 2

August 2018

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Supplementary Material 1

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March 2020 · Parasites & Vectors

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) ... [Show full abstract] bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programmes by providing reliable parameter estimates at the same spatial support and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

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Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

January 2020

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on Schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) ... [Show full abstract] bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models. Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics. Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease. Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

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Modeling Schistosoma japonicum Infection under Pure Specification Bias: Impact of Environmental Driv...

January 2019 · International Journal of Environmental Research and Public Health (IJERPH)

Keywords: schistosomiasis; pure specification bias; uncertainty; Bayesian statistics; convolution model