  Understanding the spread of any disease is a highly complex and
interdisciplinary exercise as biological, social, geographic, economic, and
medical factors may shape the way a disease moves through a population and
options for its eventual control or eradication. Disease spread poses a serious
threat in animal and plant health and has implications for ecosystem
functioning and species extinctions as well as implications in society through
food security and potential disease spread in humans. Space-time epidemiology
is based on the concept that various characteristics of the pathogenic agents
and the environment interact in order to alter the probability of disease
occurrence and form temporal or spatial patterns. Epidemiology aims to identify
these patterns and factors, to assess the relevant uncertainty sources, and to
describe disease in the population. Thus disease spread at the population level
differs from the approach traditionally taken by veterinary practitioners that
are principally concerned with the health status of the individual. Patterns of
disease occurrence provide insights into which factors may be affecting the
health of the population, through investigating which individuals are affected,
where are these individuals located and when did they become infected. With the
rapid development of smart sensors, social networks, as well as digital maps
and remotely-sensed imagery spatio-temporal data are more ubiquitous and richer
than ever before. The availability of such large datasets (Big data) poses
great challenges in data analysis. In addition, increased availability of
computing power facilitates the use of computationally-intensive methods for
the analysis of such data. Thus new methods as well as case studies are needed
to understand veterinary and ecological epidemiology. A special issue aimed to
address this topic.
