TITLE:
Dynamic Spatio-Temporal Modeling in Disease Mapping
AUTHORS:
Flavian Awere Otieno, Cox Lwaka Tamba, Justin Obwoge Okenye, Luke Akong’o Orawo
KEYWORDS:
Spatio-Temporal Model, Matern Exponential Covariance Function, Spatial and Temporal Dependencies, Markov Chain Monte Carlo (MCMC)
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.6,
December
22,
2023
ABSTRACT: Spatio-temporal
models are valuable tools for disease mapping and understanding the
geographical distribution of diseases and temporal dynamics. Spatio-temporal
models have been proven empirically to be very complex and this complexity has
led many to oversimply and model the spatial and temporal dependencies
independently. Unlike common practice, this study formulated a new
spatio-temporal model in a Bayesian hierarchical framework that accounts for
spatial and temporal dependencies jointly. The spatial and temporal
dependencies were dynamically modelled via the matern exponential covariance
function. The temporal aspect was captured by the parameters of the exponential with a first-order
autoregressive structure. Inferences about the parameters were obtained
via Markov Chain Monte Carlo (MCMC) techniques and the spatio-temporal maps
were obtained by mapping stable posterior means from the specific location and
time from the best model that includes the significant risk factors. The model
formulated was fitted to both simulation data and Kenya meningitis incidence
data from 2013 to 2019 along with two covariates; Gross County Product (GCP)
and average rainfall. The study found that both average rainfall and GCP had a
significant positive association with meningitis occurrence. Also, regarding
geographical distribution, the spatio-temporal maps showed that meningitis is
not evenly distributed across the country as some counties reported a high
number of cases compared with other counties.