The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the
transmission of the disease is driven by complex spatial and temporal variations in …

Spatial epidemiology investigates the patterns and determinants of health outcomes over
both space and time. Within this field, Bayesian spatiotemporal models have gained …

A new clustering algorithm for spatio-temporal data is developed. The proposed method
leverages a weighted combination of a spatial haversine distance matrix and a spectral …

The COVID-19 pandemic has caused a significant disruption in the social lives and mental
health of people across the world. This study aims to asses the effect of using internet search …

It is often of primary interest to analyze and forecast the levels of a continuous phenomenon
as a categorical variable. In this paper, we propose a new spatio-temporal model to deal …

The special issue 'Statistical Perspectives on Analytics for COVID-19 Data'of the Journal of
Applied Statistics (JAS), Taylor and Francis, contains papers accepted from those submitted …

Abstract The recently derived Hybrid-Incidence Susceptible-Transmissible-Removed (HI-
STR) prototype is a deterministic compartment model for epidemics and an alternative to the …

Although there is substantial literature on identifying structural changes for continuous spatio-
temporal processes, the same is not true for categorical spatio-temporal data. This work …

We consider the standard compartmental Susceptible-Infected-Recovered (SIR) model of
infectious disease epidemiology with a random effect on the susceptibility of the individuals …

The emergence of infectious diseases in an urban area involves a complex interaction
between the socioecological processes in the neighbourhood and urbanization. As a result …