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

Assessing coupling coordination between human-animal-environmental health for advancing uniform progress in One Health

The One Health (OH) approach aims to sustainably balance and optimise the health of people, animals, and ecosystems. However, there is a lack of robustly quantified insights into its spatiotemporal coupling and coordination. This study employs the OH index, which incorporates Sustainable Development Goals (SDGs), to examine the coupling and coordination relationships among three health subsystems, elucidate their four spatiotemporal patterns, and identify key driving factors. Our results indicate that the degree of OH coupling coordination is improving, despite spatial unevenness across SDG regions. Countries with varying economic levels often exhibit similar coupling coordination patterns, suggesting the potential for policy coherence to foster regionally uniform development. Key factors for breaking the cycle of poverty include increased health spending, improved education, and better dietary balance. In regions facing significant economic and environmental pressures, promoting animal and environmental health through biodiversity conservation and habitat preservation is essential for achieving OH coupling coordination. Nevertheless, the absence of governance mechanisms, along with factors such as climate change, military conflicts, and fragile alliances, poses serious obstacles to achieving uniform OH. Therefore, this study underscores the necessity of targeted policy interventions, interdisciplinary collaboration, and comprehensive governance to address this unevenness, promote coordination, and advance global health governance.

Developing a one health data integration framework focused on real-time pathogen surveillance and applied genomic epidemiology

BACKGROUND

The One Health approach aims to balance and optimize the health of humans, animals, and ecosystems, recognizing that shared health outcomes are interdependent. A One Health approach to disease surveillance, control, and prevention requires infrastructure for coordinating, collecting, integrating, and analyzing data across sectors, incorporating human, animal, and environmental surveillance data, as well as pathogen genomic data. However, unlike data interoperability problems faced within a single organization or sector, data coordination and integration across One Health sectors requires engagement among partners to develop shared goals and capacity at the response level. Successful examples are rare; as such, we sought to develop a framework for local One Health practitioners to utilize in support of such efforts.

METHODS

We conducted a systematic scientific and gray literature review to inform development of a One Health data integration framework. We discussed a draft framework with 17 One Health and informatics experts during semi-structured interviews. Approaches to genomic data integration were identified.

RESULTS

In total, 57 records were included in the final study, representing 13 pre-defined frameworks for health systems, One Health, or data integration. These frameworks, included articles, and expert feedback were incorporated into a novel framework for One Health data integration. Two scenarios for genomic data integration were identified in the literature and outlined.

CONCLUSIONS

Frameworks currently exist for One Health data integration and separately for general informatics processes; however, their integration and application to real-time disease surveillance raises unique considerations. The framework developed herein considers common challenges of limited resource settings, including lack of informatics support during planning, and the need to move beyond scoping and planning to system development, production, and joint analyses. Several important considerations separate this One Health framework from more generalized informatics frameworks; these include complex partner identification, requirements for engagement and co-development of system scope, complex data governance, and a requirement for joint data analysis, reporting, and interpretation across sectors for success. This framework will support operationalization of data integration at the response level, providing early warning for impending One Health events, promoting identification of novel hypotheses and insights, and allowing for integrated One Health solutions.

Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health

The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world's countries, mainly low-income ones, still incur substantial trade-offs impeding OH's advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.

Research Progress in Biodiversity and Human Well-Being, Based on CiteSpace

Biodiversity is crucial for human well-being and economic prosperity, representing a significant factor in constructing a global community of life on Earth. This study focuses on 4453 articles indexed in the Web of Science Core Collection, employing the visualization analysis software Citespace 6.2.R7 for bibliometric analysis of research related to biodiversity and human well-being. The findings indicate the following: (1) there is a steady increase in publications on biodiversity and human well-being, covering 167 disciplinary fields, indicating a broad research scope; (2) the literature spans 168 countries and regions, with major contributions from the United States, England, Germany, Australia, and China; (3) COSTANZA R is the most cited author in this field, with Science being the most recognized journal; and (4) current research hot topics include ecosystem services, genetic diversity, marine protected areas, nature-based solutions (NbS), and green spaces, while climate change, urban green space management, and national park system development are identified as important emerging directions. This study, grounded in significant global literature, aims to enrich the breadth and depth of research on biodiversity and human well-being, offering feasibility references for constructing theoretical frameworks in biodiversity studies and providing theoretical guidance for promoting modern harmonious coexistence between humans and nature.

Biodiversity conservation in the context of climate change: Facing challenges and management strategies

Biodiversity conservation amidst the uncertainty of climate change presents unique challenges that necessitate precise management strategies. The study reported here was aimed at refining understanding of these challenges and to propose specific, actionable management strategies. Employing a quantitative literature analysis, we meticulously examined 1268 research articles from the Web of Science database between 2005 and 2023. Through Cite Spaces and VOS viewer software, we conducted a bibliometric analysis and thematic synthesis to pinpoint emerging trends, key themes, and the geographical distribution of research efforts. Our methodology involved identifying patterns within the data, such as frequency of keywords, co-authorship networks, and citation analysis, to discern the primary focus areas within the field. This approach allowed us to distinguish between research concentration areas, specifically highlighting a predominant interest in Environmental Sciences Ecology (67.59 %) and Biodiversity Conservation (22.63 %). The identification of adaptive management practices and ecosystem services maintenance are central themes in the research from 2005 to 2023. Moreover, challenges such as understanding phenological shifts, invasive species dynamics, and anthropogenic pressures critically impact biodiversity conservation efforts. Our findings underscore the urgent need for precise, data-driven decision-making processes in the face of these challenges. Addressing the gaps identified, our study proposes targeted solutions, including the establishment of germplasm banks for at-risk species, the development of advanced genomic and microclimate models, and scenario analysis to predict and mitigate future conservation challenges. These strategies are aimed at enhancing the resilience of biodiversity against the backdrop of climate change through integrated, evidence-based approaches. By leveraging the compiled and analyzed data, this study offers a foundational framework for future research and practical action in biodiversity conservation strategies, demonstrating a path forward through detailed analysis and specified solutions.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

May Artificial Intelligence take health and sustainability on a honeymoon? Towards green technologies for multidimensional health and environmental justice

The application of Artificial Intelligence (AI) in healthcare and epidemiology undoubtedly has many benefits for the population. However, due to its environmental impact, the use of AI can produce social inequalities and long-term environmental damages that may not be thoroughly contemplated. In this paper, we propose to consider the impacts of AI applications in medical care from the One Health paradigm and long-term global health. From health and environmental justice, rather than settling for a short and fleeting green honeymoon between health and sustainability caused by AI, it should aim for a lasting marriage. To this end, we conclude by proposing that, in the upcoming years, it could be valuable and necessary to promote more interconnected health, call for environmental cost transparency, and increase green responsibility. Highlights Using AI in medicine and epidemiology has some benefits in the short term.AI usage may cause social inequalities and environmental damage in the long term.Health justice should be rethought from the One Health perspective.Going beyond anthropocentric and myopic cost-benefit analysis would expand health justice to include an environmental dimension.Greening AI would help to reconcile public and global health measures.

One Health in a Digital World: Technology, Data, Information and Knowledge

OBJECTIVES

To describe the origins and growth of the One Health concept and its recent application in One Digital Health.

METHODS

Bibliometric review and critical discussion of emergent themes derived from co-occurrence of MeSH keywords.

RESULTS

The fundamental interrelationship between human health, animal health and the wider environment has been recognized since ancient times. One Health as a distinct term originated in 2004 and has been a rapidly growing concept of interest in the biomedical literature since 2017. One Digital Health has quickly established itself as a unifying construct that highlights the critical role of technology, data, information and knowledge to facilitate the interdisciplinary collaboration that One Health requires. The principal application domains of One Digital Health to date are in FAIR data integration and analysis, disease surveillance, antimicrobial stewardship and environmental monitoring.

CONCLUSIONS

One Health and One Digital Health offer powerful lenses to examine and address crises in our living world. We propose thinking in terms of Learning One Health Systems that can dynamically capture, integrate, analyse and monitor application of data across the biosphere.

Consumer Informatics and One Health: Shifting the Focus from the Individual to the Globe. Findings from the Yearbook 2023 Section on Education and Consumer Health Informatics

OBJECTIVE

To summarise the state of the art during the year 2022 in consumer health informatics and education, with a special emphasis on "One Health".

METHODS

We conducted a systematic search of articles published in PubMed. We build queries to merge terms related to "consumer health informatics", "one health", and "digital". We retrieved 94 potential articles for review. These articles were screened according to topic relevance and 12 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers were discussed in a consensus meeting. Three papers received the highest score from the expert panel, and these papers were selected to be representative papers on consumer informatics for exploring one health from consumer perspective in the year 2022.

RESULTS

Bibliometrics analysis conducted on words found in abstracts of the 12 candidate papers revealed four clusters of articles, where clustering outcomes explained 96.91% of the dispersion. The first cluster composes three papers related to patient engagement in primary care practices, using digital-delivered diabetes prevention programmes, or exploring citizen involvement in co-designing environmental projects (such as air pollution exposure and health). The second cluster represents four papers related to digital health literacy and consumer behavior, such as digital vaccine literacy, and food labelling influences and whether displaying Nutri- and Eco-Score at food product level led to improved consumer choices. The third cluster consists of two papers exploring strategies to involve citizens in various science projects while analyzing the quality of citizen-collected data (e.g., mosquito bites or gastropod community dataset). The last cluster contains three papers related to the relationships between human behavior with their environment and their contribution to citizen science projects (e.g., biological water quality in the Netherlands distribution, composition, abundance of debris across sandy beaches in Australia and its regions, urbanization and reptile biodiversity across Florida).

CONCLUSION

Traditionally, consumer health informatics focuses on providing individuals with tools and resources to actively manage their own health. By incorporating a global health (or one health) perspective, our field is now at a crossroad, demanding us to think beyond the individual and challenging us to instill the thinking that our actions not only have consequences on the individual but also on the population and the environment. Perhaps this is also a reflective time for the consumer informatics field, to consider shifting the focus from the individual to one that is more aligned with one health, helping consumers gain awareness of how their actions impact on the individual, the population and the environment, and providing them with tools to work collectively to help decide how their actions may bring benefits (as well as harms) across these levels.

Leveraging Data and Technology to Enhance Interdisciplinary Collaboration and Health Outcomes

OBJECTIVE

To give an overview of recent research and propose a selection of best papers published in 2022 in Informatics for One Health.

METHODS

An extensive search using PubMed and Web of Science was conducted to identify peer-reviewed articles published between December 2021 and December 2022, in order to find relevant publications in the 'Informatics for One Health' field. The selection process comprised three steps: (i) eight candidate best papers were first selected by the two section editors; (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (iii) the editorial committee of the Yearbook conducted the final best paper selection.

RESULTS

The candidate best papers represent studies that characterized significant challenges facing Informatics for One Health. Other trends of interest related to the deployment of medical artificial intelligence tools and the implementation of the FAIR principles within the One Health broad scenario. In general, papers identified in the search fell into one of the following categories: 1) Health improvement via digital technology; 2) Climate change/Environment/Biodiversity; and 3) Maturity of healthcare services.

CONCLUSION

The topic turns extremely important in the next future for what concerns the need to understand complex interactions in order to safeguard the health of populations and ecosystems.