Data Science Challenges and Opportunities in One Health

Dear Colleagues,

One Health is an integrated, transdisciplinary approach based on the foundational concept that the health of humans, animals, plants, and the environment are all intercorrelated and impacted by the multiscale global ecosystem. Exemplified by the COVID-19 pandemic, a One Health approach is required to successfully achieve early warning for the prediction, prevention, and mitigation of threats to our public health and food security. However, because of the extraordinary intricacies and differing scales across various ecosystem components, a One Health approach inherently contains a variety of complex data science challenges. The heterogeneity of data, data collection methods, reporting methods, and databases available all provide significant hurdles for making available datasets ‘data science ready’. The nuanced interactions between and varying responses to threats by the environment, plants, humans, and animals—and the different scales of interactions and effects—present significant obstacles when developing data science approaches and thus require innovative integration techniques. At the same time, data science has been transformative across research and industry. It provides the ability to gain situational awareness and predictive insight into short- and long-term integrated health outcomes. In this Special Issue, we invite authors to submit research highlighting specific challenges and unique opportunities arising from the use of a One Health, multi-modal data science approach across a variety of applications.

Dr. Lauren Charles
Dr. Samantha Erwin
Dr. Eva Brayfindley
Guest Editors

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