title: Editorial: Mathematical modelling of infectious diseases.
abstract: The field of disease ecology - the study of the spread and impact of parasites and pathogens within their host populations and communities - has a long history of using mathematical models. Dating back over 100 years, researchers have used mathematics to describe the spread of disease-causing agents, understand the relationship between host density and transmission and plan control strategies. The use of mathematical modelling in disease ecology exploded in the late 1970s and early 1980s through the work of Anderson and May (Anderson and May, 1978, 1981, 1992; May and Anderson, 1978), who developed the fundamental frameworks for studying microparasite (e.g. viruses, bacteria and protozoa) and macroparasite (e.g. helminth) dynamics, emphasizing the importance of understanding features such as the parasite's basic reproduction number (R 0) and critical community size that form the basis of disease ecology research to this day. Since the initial models of disease population dynamics, which primarily focused on human diseases, theoretical disease research has expanded hugely to encompass livestock and wildlife disease systems, and also to explore evolutionary questions such as the evolution of parasite virulence or drug resistance. More recently there have been efforts to broaden the field still further, to move beyond the standard 'one-host-one-parasite' paradigm of the original models, to incorporate many aspects of complexity of natural systems, including multiple potential host species and interactions among multiple parasite species.
authors.affiliation: Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK.
authors.forename: Andy
authors.initials: A
authors.lastname: Fenton
mesh_terms: D000820:Animal Diseases; D000818:Animals; D003141:Communicable Diseases; D054884:Host-Pathogen Interactions / Q000502:physiology*; D008954:Models, Biological
keywords: Ecology; data integration; evolution; model complexity; model testing; population dynamics; theory