Operationalizing “One Health” as “One Digital Health” Through a Global Framework That Emphasizes Fair and Equitable Sharing of Benefits From the Use of Artificial Intelligence and Related Digital Technologies

Abstract

The operationalization of One Health (OH) through digitalization is a means to deploy digital technologies (including Artificial Intelligence (AI), big data and related digital technologies) to better capacitate us to deal with growing climate exigency and related threats to human, animal and plant health. With reference to the concept of One Digital Health (ODH), this paper considers how digital capabilities can help to overcome ‘operational brakes’ in OH through new and deeper insights, better predictions, and more targeted or precise preventive strategies and public health countermeasures. However, the data landscape is fragmented and access to certain types of data is increasingly restrictive as individuals, communities and countries seek to assert greater control over data taken from them. This paper proposes for a dedicated global ODH framework—centered on fairness and equity—to be established to promote data-sharing across all the key knowledge domains of OH and to devise data-driven solutions to challenges in the human-animal-ecosystems interface. It first considers the data landscape in relation to: (1) Human and population health; (2) Pathogens; (3) Animal and plant health; and (4) Ecosystems and biodiversity. The complexification from the application of advance genetic sequencing technology is then considered, with focus on current debates over whether certain types of data like digital (genetic) sequencing information (DSI) should remain openly and freely accessible. The proposed ODH framework must augment the existing access and benefit sharing (ABS) framework currently prescribed under the Nagoya Protocol to the Convention on Biological Diversity (CBD) in at least three different ways. First, the ODH framework should apply to all genetic resources and data, including DSI, whether from humans or non-humans. Second, the FAIRER principles should be implemented, with focus on fair and equitable benefit-sharing. Third, the ODH framework should adopt multilateral approaches to data sharing (such as through federated data systems) and to ABS. By operationalizing OH as ODH, we are more likely to be able to protect and restore natural habitats, secure the health and well-being of all living things, and thereby realize the goals set out in the post-2020 Global Biodiversity Framework under the CBD.

Leveraging Data and Technology to Enhance Interdisciplinary Collaboration and Health Outcomes

“…Along with those reported in the previous section, we could observe other major trends being further continued. They mainly relate to: (i) the assessment of transparency and trustworthiness of medical artificial intelligence tools to deal with issues originated within a broad and comprehensive digital landscape [28,29], and (ii) the importance of data collection, management, and analysis in the One (Digital) Health domain, and its integration with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles to create an innovative framework [6,16,24,30]. These aspects leave a broad room for further investigations.…”

“…The IMIA Yearbook Editorial Committee selection meeting was held in a blended form on May 5, 2023. In this meeting, the paper entitled "Operationalizing "One Health" as "One Digital Health" through a global framework that emphasizes fair and equitable sharing of benefits from the use of artificial intelligence and related digital technologies" authored by Ho [16] was finally selected as the best paper for the Yearbook special section. Besides that, although "Climate change, human health, and health informatics: a new view of connected and sustainable digital health" by Gray [17] was not selected as best paper as well -being it technically a mini review -it was agreed to highlight it as a necessary "honorable mention", so a total of two papers are distinguished (Table 1).…”

“…Many authors emphasize the importance of high-quality data and digital technologies, including artificial intelligence, in monitoring healthcare from various perspectives: a more specific reference to their capacity to address One Health-related complex challenges can be found in Ho [16], who explores the potential of digital tools via the collection and processing of real-time data, in various domains such as agriculture and conservation biology, towards, e.g., early detection of infections. A similar point of view is also tackled by the survey paper of the special section, authored by Scott et al, in describing the role of digital technologies in surveillance systems as a pre-requisite for a "new wave" of public education (the so-called citizen science: see, e.g., [19]) to ensure societal preparedness for necessary tracing in future pandemics [20].…”

“…However, data collection and integration challenges remain, including information gaps, limited granular data, and biases. This translates, at a higher level, into a lack of a unifying framework for data in One Health [16], which leads to several single projects that only enable environmental monitoring and data sharing on a small scale. It is the case of the OP-ERA project, as reported by Tamburis & Benis [24], whose goal is to determine the optimal evacuation route for animals in case of fire in the "Mount Vesuvius' red zone" in South Italy [25].…”

After 2 years of the COVID-19 pandemic, translating One Health into action is urgent

“…28 Overcoming hurdles related to this protocol will require international collaboration and mutual trust and will have to be endorsed by policy makers. 29 , 30 , 31 …”