Bayesian Spatial Modelling of Global Public Health Issues based on Complex Survey Data

Dear Colleagues,

The Special Issue will try to bring together many applications of the Bayesian approach on global health issues (HIV, TB, HSV-2, malaria, hypertension, diabetes, malnutrition, Female Genital Mutilation (FGM), and obesity) using different data sources from complex household survey data.

Hierarchical spatial modeling is a common and useful approach for modeling complex spatially correlated data in many settings in epidemiology, public health, and development studies. Most of the data collected by many governments through surveys and sentinel surveillance are georeferenced by districts, counties, provinces or other administrative units.

Because of the complexity of factors associated with survival and health, traditional measures such as household socioeconomic and education may require supplementation with types of data that are both novel and less conventional. Statistical techniques that incorporate spatial analysis using a combination of data sources and spatial covariates offer such a possibility, though broadening the view of environment at both the macrolevel and the microlevel may be required to fully understand the scope of such influences.

Papers focusing on spatial Bayesian models based on complex surveys data will be invited in this Special Issue. A Bayesian framework based on Markov chain Monte Carlo (MCMC) simulation techniques will be encouraged. The models cover a number of well-known model classes as special cases, including generalized additive models (Hastie and Tibshirani 1990), generalized additive mixed models (Lin and Zhang 1999), geoadditive models (Kammann and Wand 2003), varying coefficient models (Hastie and Tibshirani 1993), and geographically weighted regression (Fotheringham, Brunsdon and Charlton (2002).

Prof. Ngianga-Bakwin Kandala
Prof. Dr. Saverio Stranges
Guest Editors

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• global public health issues
• HIV
• TB
• HSV-2
• malaria, hypertension
• diabetes
• malnutrition
• Female Genital Mutilation (FGM) and obesity
• Environment, Bayesian Spatial modelling
• complex survey data