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Using Eggs from Schistosoma mansoni as an In vivo Model of Helminth-induced Lung Inflammation

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Published on: June 5, 2012

Statistical methodological issues in mapping historical schistosomiasis survey data.

Frédérique Chammartin¹, Eveline Hürlimann, Giovanna Raso

¹Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland; University of Basel, P.O. Box, CH-4003 Basel, Switzerland.

|May 8, 2013

Summary

This summary is machine-generated.

Bayesian geostatistical modeling enhances spatial targeting for schistosomiasis control by identifying key infection risk predictors. This approach improves resource allocation for neglected tropical diseases, leading to more effective interventions.

Keywords:

Bayesian geostatistics Block of covariates Côte d’Ivoire Geostatistical variable selection Mapping Schistosomiasis

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Mass Isolation and In Vitro Cultivation of Intramolluscan Stages of the Human Blood Fluke Schistosoma Mansoni

Mass Isolation and In Vitro Cultivation of Intramolluscan Stages of the Human Blood Fluke Schistosoma Mansoni

Published on: January 14, 2018

Area of Science:

Epidemiology
Biostatistics
Spatial analysis

Background:

Limited resources for neglected tropical diseases necessitate efficient control strategies.
Model-based risk maps are crucial for prioritizing spatial targeting of interventions.
Bayesian geostatistical modeling is effective for generating empirical risk maps.

Purpose of the Study:

To review key issues in schistosomiasis risk modeling.
To present advanced Bayesian geostatistical variable selection methods.
To demonstrate improved spatial targeting of schistosomiasis control.

Main Methods:

Review of Bayesian geostatistical modeling for disease risk.
Application of Bayesian geostatistical variable selection using historical Schistosoma mansoni prevalence data from Côte d'Ivoire.
Utilizing a "parameter expanded normal mixture of inverse-gamma" prior for regression coefficients.

Main Results:

The implemented Bayesian geostatistical variable selection rigorously identified key predictors of S. mansoni infection risk.
This method resulted in a more parsimonious model compared to traditional approaches.
The study demonstrated the utility of advanced statistical methods for disease mapping.

Conclusions:

Statistical advances in Bayesian geostatistical modeling provide opportunities to address inherent characteristics of Schistosoma infection.
These models can effectively guide the spatial targeting of control interventions for schistosomiasis.
Improved risk mapping leads to more efficient resource allocation for neglected tropical diseases.