Scientists from multiple disciplines, including wildlife biology, public health, veterinary parasitology and biomedical sciences, have compiled a wealth of data on the distribution and impacts of infectious diseases, with most studies focused on particular locations or single host-pathogen interactions. This wealth of data creates the opportunity to address a pressing need, namely to explore the patterns and drivers of infectious disease emergence in humans and natural ecosystems at global scales. This project will create a Research Coordination Network to bring together internationally recognized experts from ecology, conservation medicine, parasitology and computational sciences to quantify and explore the drivers of global scale patterns of pathogen biodiversity. Participants will work together to assemble data sets of unprecedented size, including information about disease occurrence in host species ranging from insects to humans. With help from experts in machine learning (artificial intelligence) and geographic information systems (GIS), they will work to build predictive models that can be used to understand the changing distributions of infectious diseases and identify future hotspots of novel disease emergence in humans and wildlife.
Emerging infectious diseases, especially those that jump from wildlife to livestock and humans, threaten public health around the world. Using state of the art computational methods, the RCN will be able to answer critical questions such as: How and why do certain pathogens successfully move from one host species to another? Are hotspots of pathogen biodiversity in wildlife the same areas as hotspots of disease emergence in humans and livestock? To share its findings, the RCN will develop educational products such as webcasts and workshops aimed at educational levels from high school to post graduate, and will train students from under-represented groups through a summer research program. The RCN will also develop and maintain databases of global infectious disease biodiversity and make these data freely available to the academic community and the general public.
