Spatio-temporal data mining in ecological and veterinary epidemiology

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