A Bayesian space–time model for clustering areal units based on their disease trends

Abstract

Population-level disease risk across a set of non-overlapping areal units varies in space and time, and a large research literature has developed methodology for identifying clusters of areal units exhibiting elevated risks. However, almost no research has extended the clustering paradigm to identify groups of areal units exhibiting similar temporal disease trends. We present a novel Bayesian hierarchical mixture model for achieving this goal, with inference based on a Metropolis-coupled Markov chain Monte Carlo ((MC)$^3$) algorithm. The effectiveness of the (MC)$^3$ algorithm compared to a standard Markov chain Monte Carlo implementation is demonstrated in a simulation study, and the methodology is motivated by two important case studies in the United Kingdom. The first concerns the impact on measles susceptibility of the discredited paper linking the measles, mumps, and rubella vaccination to an increased risk of Autism and investigates whether all areas in the Scotland were equally affected. The second concerns respiratory hospitalizations and investigates over a 10 year period which parts of Glasgow have shown increased, decreased, and no change in risk.

Near-term climate change impacts on sub-national malaria transmission

“…We then ran an analysis on Zambia's malaria trends between 2000 and 2016 first by classifying temporally varying random effects of each district's spatio-temporal trends into declining, increasing, or constant 58 . We used a Bayesian hierarchical random mixture model implemented with an inference through a Metropolis-coupled Markov chain Monte Carlo (MCMCMC) model.…”

“…A more detailed description of this model is given elsewhere 58,61 . The model outputs were used to map the malaria trends of the 72 districts over 17 years of the study period, from which the areas that exhibited an increasing trend or declining trend in malaria incidence risk among both under 5 children and those 5 years and older were selected.…”

Spatiotemporal Data Clustering: A Survey of Methods

“…A linear regression model was built to predict the number of diseases using the proportion of user experiences. Napier, et al [69] proposed a novel Bayesian model to identify the cluster of similar temporal disease trends rather than disease estimation and prediction. Adin, et al [70] proposed a two-stage approach to estimate disease risk maps.…”

Population-weighted exposure to air pollution and COVID-19 incidence in Germany

“…A spatial ecological design can be used to estimate the impacts of air pollution on health by comparing geographical contrasts in air pollution and infection risk across contiguous small areas ( Huang et al, 2018 , Napier et al, 2018 , Rushworth et al, 2014 ). In such studies, the outcome data are counts of disease cases occurring in each areal unit while the pollution concentrations in each areal unit are typically estimated by applying Kriging (see Diggle and Ribeiro, 2007 ), to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model ( Wu et al, 2020 , Maheswaran et al, 2006 , Lee et al, 2009 , Warren et al, 2012 ), or by combining both to obtain a better prediction ( Huang et al, 2018 , Vinikoor-Imler et al, 2014 , Sacks et al, 2014 ).…”