SyncOne: A Computational Framework for Standardised Evaluation of One Health Initiatives
1
Medical Laboratory Science, Kwara Statet University, Malete, Nigeria
2
Infectious Diseases Control Centre, Federal Medical Centre Owo, Owo, Ondo, Nigeria
3
Public Health, Kwara State University, Malete, Nigeria
4
Biology, University of Texas at Tyler, Tyler, United States
Popul. Med. 2026;8(Supplement Supplement 1):
ABSTRACT
BACKGROUND:
One Health initiatives are major interventions used to tackle diseases of public health concern that emerge from human, animal, and environmental interactions1. There’s a need to evaluate these One Health initiatives to determine whether they work and whether they are worth the investment1. The Network for Evaluation of One Health (NEOH) framework was introduced as a systems-thinking tool to assess One Health initiatives based on their ecological, social, and institutional dimensions2. However, it has high technical demands and is sensitive, which limits its consistent application across various settings, especially in low- and middle-income countries2.
AIM:
This study introduces an artificial intelligence framework designed to operationalise the NEOH framework using computational intelligence called SyncOne. This SyncOne model will reduce the technical barriers of NEOH and improve the scalability, standardisation, and reproducibility of one health evaluations while preserving the conceptual foundations of the NEOH framework. Model Architecture: The SyncOne model works by merging one health systems theory, NEOH evaluation metrics, and artificial intelligence. It uses five multi-layered architecture that contains (a) a data ecosystem integrated to multiple sources3, (b) an AI-mediation layer that uses natural language processing, knowledge graphs, and machine learning, (c) computational NEOH dimensions3, (d) automated evaluation metrics of One Health Index and One Health Ratio, and (e) knowledge translation and output mechanisms. It also embeds a hybrid reasoning approach that combines logic with machine learning, and supports the transformation of qualitative and quantitative data into interpretable evaluation outputs.
DISCUSSION:
This model was created to make the NEOH framework user-friendly with AI support. SyncOne enables the inclusion of multiple kinds of data, reduces subjectivity in scoring, and improves comparability across one health contexts. However, LMICs may experience infrastructural limitations and decreased data quality2. These challenges reveal the importance of human intervention to drive the tool’s adaptation and learning features.
One Health initiatives are major interventions used to tackle diseases of public health concern that emerge from human, animal, and environmental interactions1. There’s a need to evaluate these One Health initiatives to determine whether they work and whether they are worth the investment1. The Network for Evaluation of One Health (NEOH) framework was introduced as a systems-thinking tool to assess One Health initiatives based on their ecological, social, and institutional dimensions2. However, it has high technical demands and is sensitive, which limits its consistent application across various settings, especially in low- and middle-income countries2.
AIM:
This study introduces an artificial intelligence framework designed to operationalise the NEOH framework using computational intelligence called SyncOne. This SyncOne model will reduce the technical barriers of NEOH and improve the scalability, standardisation, and reproducibility of one health evaluations while preserving the conceptual foundations of the NEOH framework. Model Architecture: The SyncOne model works by merging one health systems theory, NEOH evaluation metrics, and artificial intelligence. It uses five multi-layered architecture that contains (a) a data ecosystem integrated to multiple sources3, (b) an AI-mediation layer that uses natural language processing, knowledge graphs, and machine learning, (c) computational NEOH dimensions3, (d) automated evaluation metrics of One Health Index and One Health Ratio, and (e) knowledge translation and output mechanisms. It also embeds a hybrid reasoning approach that combines logic with machine learning, and supports the transformation of qualitative and quantitative data into interpretable evaluation outputs.
DISCUSSION:
This model was created to make the NEOH framework user-friendly with AI support. SyncOne enables the inclusion of multiple kinds of data, reduces subjectivity in scoring, and improves comparability across one health contexts. However, LMICs may experience infrastructural limitations and decreased data quality2. These challenges reveal the importance of human intervention to drive the tool’s adaptation and learning features.
| eISSN: | 2654-1459 |
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