Bayesian hierarchical models for disease mapping applied to contagious pathologies

Abstract

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to model rare infectious diseases proposing specific and generic variants of this methodology. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. In consequence, we test models including a Negative Binomial distribution (instead of the usual Poisson distribution) to deal with local overdispersion. We also use a specific spatio-temporal link in order to better model the stronger spatial and temporal dependencies due to the transmission of the disease. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).

A Model for Highly Fluctuating Spatio-Temporal Infection Data, with Applications to the COVID Epidemic

“…There have been a considerable number of spatio-temporal studies of disease patterns, generally adopting a Bayesian perspective [ 12 , 14 ]. Spatio-temporal models for infection counts [ 15 , 16 ] are a particular sub-theme. These incorporate the themes of the broader disease mapping literature, such as the gains through borrowing strength and the need to reflect spatial correlation in disease; for example, see Andrews et al [ 17 ] on spatial clustering in COVID rates.…”

A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates

“… 2006 ; Coly et al. 2021 ). Consider, in particular, applications to epidemic time series for sets of administrative areas, which are available in several countries for the COVID-19 epidemic.…”

Bayesian Hierarchical Spatial Modeling of COVID-19 Cases in Bangladesh

“…This work aimed to enhance monitoring procedures and improve government policy. Previous research investigated demographic aspects important for COVID-19 transmission in Bangladesh using conventional statistical models [ 21 ], but no research has examined the spatial dependency of COVID-19 cases across Bangladesh’s 64 districts. The traditional statistical methods assume that the observations are independent and identically distributed.…”