A statistical downscaling approach for generating high spatial resolution health risk maps: a case study of road noise and ischemic heart disease mortality in Melbourne, Australia

Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease

Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the "big n" problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10109-021-00368-0.

Dynamic stiffness of road pavements: Construction characteristics-based model and influence on tire/road noise

Tire/pavement noise is produced by the interaction between a tire and the road surface. Complex noise generation mechanisms are involved in this process. Dynamic stiffness (or mechanical impedance) is considered as a related reduction/amplification mechanism. Despite its potential importance, in conventional hot bituminous mixtures this mechanism has little influence on tire/pavement noise. This is because the dynamic stiffness of tire treads is significantly lower than that of the hot bituminous mixes (conventional road surfaces). This paper presents a field assessment of the dynamic stiffness of five experimental sections with different characteristics: from the environmentally friendly mixes such as the Cold-In place-Recycled (CIR) pavement to the gap graded hot bituminous mixtures with crumb rubber (CR) from end of life tires (ELTs). The obtained dynamic stiffness spectra are fitted using a Boltzmann Sigmoid function. The relationships between the fitting coefficients and a few pavement construction characteristics are assessed in order to model the pavement dynamic stiffness as a function of these characteristics. In addition to dynamic stiffness, several surface characteristics such as the Close ProXimity (CPX) sound levels from the tire/pavement interaction, macrotexture in terms of the Mean Profile Depth (MPD), unevenness in terms of the International Roughness Index (IRI), and acoustic absorption are studied. According to our results, lower dynamic stiffness values are related to lower noise levels at high frequencies. No relationship was observed between the low dynamic stiffness values and noise attenuation at medium frequencies in this research work.