A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping

Abstract

Spatial connectivity is an important consideration when modelling infectious disease data across a geographical region. Connectivity can arise for many reasons, including shared characteristics between regions and human or vector movement. Bayesian hierarchical models include structured random effects to account for spatial connectivity. However, conventional approaches require the spatial structure to be fully defined prior to model fitting. By applying penalized smoothing splines to coordinates, we create two-dimensional smooth surfaces describing the spatial structure of the data while making minimal assumptions about the structure. The result is a non-stationary surface which is setting specific. These surfaces can be incorporated into a hierarchical modelling framework and interpreted similarly to traditional random effects. Through simulation studies, we show that the splines can be applied to any symmetric continuous connectivity measure, including measures of human movement, and that the models can be extended to explore multiple sources of spatial structure in the data. Using Bayesian inference and simulation, the relative contribution of each spatial structure can be computed and used to generate hypotheses about the drivers of disease. These models were found to perform at least as well as existing modelling frameworks, while allowing for future extensions and multiple sources of spatial connectivity.

Calculation Method and Application of Time-Varying Transmission Rate via Data-Driven Approach

“…Mathematical modeling has been extensively used in many different fields of study including medicine, physics, economics, and so on [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Medical research issues, especially high-level medical research achievements, often rely on the establishment of reasonable medical mathematical models [7][8][9][10][11][12][13][14][15][16][17][18][19][20]. For example, Tripathi et al [7] provided several examples of designing various mathematical models that can help us better comprehend dynamics at the single-cell and population levels.…”

“…Gupta et al [8] proposed a dynamic Boolean network to categorize gene regulation between two non-coding RNAs (ncRNAs) in gastric cancer, which opens up a new avenue for gastric cancer treatment in response to DNA damage caused by these ncRNAs. Lee et al [9] utilized Bayesian inference and simulation to determine the relative contribution of each spatial structure and used it to generate hypotheses concerning the drivers of disease. The results demonstrated that Bayesian hierarchical models performed at least as well as existing modeling frameworks while permitting extensions in the future and multiple sources of spatial connectedness.…”

Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

“…The model includes the fixed effects of the covariates as well as spatial and temporal random effects and the spatiotemporal interaction [7]. The principal focuses of disease-mapping models are disease map reconstruction, model evaluation, and the quantification of multiple risk factors for spatiotemporal variation in disease risk [8,9].…”