Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging.

ABSTRACT:

Background

There is a need for comprehensive evaluations of the underlying local factors that contribute to residual malaria in sub-Saharan Africa. However, it is difficult to compare the wide array of demographic, socio-economic, and environmental variables associated with malaria transmission using standard statistical approaches while accounting for seasonal differences and nonlinear relationships. This article uses a Bayesian model averaging (BMA) approach for identifying and comparing potential risk and protective factors associated with residual malaria.

Results

The relative influence of a comprehensive set of demographic, socio-economic, environmental, and malaria intervention variables on malaria prevalence were modelled using BMA for variable selection. Data were co

SUBMITTER: Millar J 

PROVIDER: S-EPMC6162921 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature