@article{https://doi.org/10.17863/cam.77196,
doi = {10.17863/CAM.77196},
url = {https://www.repository.cam.ac.uk/handle/1810/329750},
author = {Carlson,
Colin J and Farrell,
Maxwell J and Grange,
Zoe and Han,
Barbara A and Mollentze,
Nardus and Phelan,
Alexandra L and Rasmussen,
Angela L and Albery,
Gregory F and Bett,
Bernard and Brett-Major,
David M and Cohen,
Lily E and Dallas,
Tad and Eskew,
Evan A and Fagre,
Anna C and Forbes,
Kristian M and Gibb,
Rory and Halabi,
Sam and Hammer,
Charlotte C and Katz,
Rebecca and Kindrachuk,
Jason and Muylaert,
Renata L and Nutter,
Felicia B and Ogola,
Joseph and Olival,
Kevin J and Rourke,
Michelle and Ryan,
Sadie J and Ross,
Noam and Seifert,
Stephanie N and Sironen,
Tarja and Standley,
Claire J and Taylor,
Kishana and Venter,
Marietjie and Webala,
Paul W},
keywords = {access and benefit sharing,
epidemic risk,
global health,
machine learning,
viral ecology,
zoonotic risk,
Animals,
Animals,
Wild,
COVID-19,
Disease Reservoirs,
Ecology,
FOS: Biological sciences,
Global Health,
Humans,
Laboratories,
Machine Learning,
Pandemics,
Risk Factors,
SARS-CoV-2,
Viruses,
Zoonoses},
language = {en},
title = {The future of zoonotic risk prediction.},
publisher = {The Royal Society},
year = {2021},
copyright = {Creative Commons Attribution 4.0 International} }