Challenges in Modeling the Dynamics of Infectious Disease at the Wildlife-Human Interface
Many infectious diseases recorded in the last century were of zoonotic origin and are spread by transmission from animals through diverse routes of spillover.
Most emerging infectious diseases recorded in the last century were of zoonotic origin. Their transmission from wildlife or domestic animals encompasses diverse routes of spillover, through direct contact and aerosol to vector-borne. While some pathogens have been known for decades to cause recurrent spillover events (e.g. rabies virus, Borrelia burgdorferi and Yersinia pestis), new pathogens are discovered sporadically following outbreaks. For example, the Hendra and Nipah viruses were identified 20 years ago, and are now recognized as members of the family Paramyxoviridae, comprising viruses infecting mammals, birds and reptiles with various levels of host specificity. The growing pace of research in this field, fueled by the ability to combine and mine global medical, genomic, ecological and environmental datasets, has generated statistical models and risk maps of increasing complexity, either for emerging diseases as a whole or for specific pathogens such as the Ebola virus. However, many spillover events remain unobserved or unreported, and our ability to predict or prevent zoonotic spillover is in its infancy.
Method and definitions
This article describes the challenges that arise in modeling the dynamics of infectious disease spillover and host shifting at the interface between humans, wildlife and domestic animals. It shows that progress has been made on some challenges, but not all, and new challenges have also arisen. The article refers to host shifting as the infection of a novel host species (including the expansion of a pathogen’s host range) and defines the interface as a biological system in which direct or indirect interactions between animal species and humans may result in cross-species transmission and the sharing of pathogens. The interface involves at least three species: the human host, an animal host, and the pathogen. Many more species may be involved, either directly or indirectly.
Conclusion
This article has reviewed the challenges involved in modeling the dynamics of infectious diseases at the wildlife-human interface and argued that the precise nature of the interface is not well known, as it is rarely detected and never observed directly. More information is needed on the diversity of pathogens at the interface, especially viruses, and the scales and frequencies at which they transmit. This can only be achieved through increased data collection and surveillance. The major drivers of the emergence of zoonoses are anthropogenic. These include the global change in climate modifying the ranges of hosts and pathogens, as well as changes in land use that increases contact between human and animal hosts. Models will have a significant role to play in predicting the impact of these changes on disease dynamics. Ecological processes can move pathogen transmission toward tipping points, facilitating transmission. Eco-epidemiological models are required to understand the transmission of infections between host species in an ecosystem, to illustrate the influence of the wider ecosystem species on host and pathogen dynamics, and to suggest the potential for spillover events that may occur. In some cases, domestic animals may act as an intermediate host, in the sense that they contact infected wild animals and have close contact with humans. Modeling infection dynamics in domestic animals requires a different representation of host population dynamics and contact structures than that of wild animal populations. Once a pathogen has infected a human host, it is not necessarily the case that a zoonotic disease will establish itself in the population. Apparently, pathogen spillover and host-shifting are governed by complex and dynamic interactions among animal and human hosts at different organizational levels, challenging modelers to deal with sources of uncertainty and to find generalizations for robust predictions.
This article is by Mick Roberts, Andrew Dobson, Olivier Restif and Konstans Wells.