Disease mortality in domesticated animals is predicted by host evolutionary relationships

Farrell, M. J. and Davies, T. J. (2019) Disease mortality in domesticated animals is predicted by host evolutionary relationships. Proceedings of the National Academy of Sciences of the United States of America, 116(16), pp. 7911-7915. (doi: 10.1073/pnas.1817323116) (PMID:30926660) (PMCID:PMC6475420)

Abstract

Infectious diseases of domesticated animals impact human well-being via food insecurity, loss of livelihoods, and human infections. While much research has focused on parasites that infect single host species, most parasites of domesticated mammals infect multiple species. The impact of multihost parasites varies across hosts; some rarely result in death, whereas others are nearly always fatal. Despite their high ecological and societal costs, we currently lack theory for predicting the lethality of multihost parasites. Here, using a global dataset of >4,000 case-fatality rates for 65 infectious diseases (caused by microparasites and macroparasites) and 12 domesticated host species, we show that the average evolutionary distance from an infected host to other mammal host species is a strong predictor of disease-induced mortality. We find that as parasites infect species outside of their documented phylogenetic host range, they are more likely to result in lethal infections, with the odds of death doubling for each additional 10 million years of evolutionary distance. Our results for domesticated animal diseases reveal patterns in the evolution of highly lethal parasites that are difficult to observe in the wild and further suggest that the severity of infectious diseases may be predicted from evolutionary relationships among hosts.

• Item Type: Articles
• Glasgow Author(s) Enlighten ID: Farrell, Dr Maxwell
• Authors: Farrell, M. J., and Davies, T. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Proceedings of the National Academy of Sciences of the United States of America
• Publisher: National Academy of Sciences
• ISSN: 0027-8424
• ISSN (Online): 1091-6490
• Published Online: 29 March 2019
• Data DOI: 10.6084/m9.figshare.7497137