Zoonotic disease classification in wildlife: a theoretical framework for researchers

Abstract

Classification systems are useful tools for aggregating diseases to study them in more detail. The most widespread system for the classification of human diseases is the International Classification of Diseases (ICD); however, the ICD was designed for use in health care and fails to capture many details of zoonotic diseases as a result. We propose a framework for zoonotic disease classification which combines four known classification types (pathogen type, life cycle, transmission direction and ecosystem) into one systematic method. We chose to focus on four specific aspects to provide as broad an overview as possible of zoonoses, in order to inform the risk of transmission to humans. This framework can be applied to the zoonoses occurring in any non-human animal species and supports the generation of systematically collected empirical data that are useful for monitoring the spatio-temporal dynamics of zoonoses. To demonstrate the utility of our framework, we focus on six deer species that are widespread in Eurasia: red deer Cervus elaphus, roe deer Capreolus capreolus, fallow deer Dama dama, sika deer Cervus nippon, Reeves's muntjac Muntiacus reevesi and Chinese water deer Hydropotes inermis. We examined the worldwide occurrence and prevalence of zoonoses in these species through a scoping review of the literature. Altogether, 996 records of zoonotic diseases in the six wild deer species, from 431 peer-reviewed papers, and 120 zoonotic pathogens were found. Within deer populations, the most prevalent zoonoses were bacterial, while the most prevalent life cycles were metazoonoses. We also found that the most prevalent pathogens in deer were those with hosts capable of occupying sylvatic, peridomestic and domestic ecosystems. Baseline data generated using our framework method can provide important starting points for making projections of human disease risk, assisting wildlife managers and policy-makers in making wildlife management decisions, and assisting experts in the development of disease-monitoring and management plans.