The future of zoonotic risk prediction.
The future of zoonotic risk prediction.
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DOI：
10.1098/rstb.2020.0358
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发表时间：
2021-11-08
期刊：
影响因子：
--
通讯作者：
Webala PW
中科院分区：
文献类型：
--
作者：
Carlson CJ;Farrell MJ;Grange Z;Han BA;Mollentze N;Phelan AL;Rasmussen AL;Albery GF;Bett B;Brett-Major DM;Cohen LE;Dallas T;Eskew EA;Fagre AC;Forbes KM;Gibb R;Halabi S;Hammer CC;Katz R;Kindrachuk J;Muylaert RL;Nutter FB;Ogola J;Olival KJ;Rourke M;Ryan SJ;Ross N;Seifert SN;Sironen T;Standley CJ;Taylor K;Venter M;Webala PW
鉴于 COVID-19 大流行带来的紧迫性，全球对野生动物病毒学的投资可能会增加，新的监测计划将识别出数百种有朝一日可能对人类构成威胁的新型病毒。为了支持实验室表征的广泛任务，科学家可能越来越依赖数据驱动的标准或机器学习模型，这些模型从已知的人畜共患病中学习，以确定哪些动物病原体有一天可能对全球健康构成威胁。我们综合了人畜共患病风险技术跨学科研讨会的研究结果，以回答以下问题。在开放数据、公平和跨学科合作方面，这些工具的开发和应用有哪些先决条件？该技术会对全球健康产生什么影响？谁将控制该技术，谁将有权使用该技术以及谁将从中受益？会改善疫情防控吗？它会带来新的挑战吗？本文是主题“传染病宏观生态学：全球寄生虫多样性和动态”的一部分。
In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
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影响因子：
4.3
作者：
Dallas T;Park AW;Drake JM
通讯作者：
Drake JM
影响因子：
5.4
作者：
Di Giallonardo, Francesca;Schlub, Timothy E.;Holmes, Edward C.
通讯作者：
Holmes, Edward C.
DOI：
10.1073/pnas.2013102117
发表时间：
2020-10-20
影响因子：
11.1
作者：
Bosco-Lauth, Angela M.;Hartwig, Airn E.;Bowen, Richard A.
通讯作者：
Bowen, Richard A.
影响因子：
5
作者：
Banerjee A;Misra V;Schountz T;Baker ML
通讯作者：
Baker ML
影响因子：
4.8
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通讯作者：
Hall RA