OH-MEMA: An Integrated One Health Mixed-Effects Modeling Approach for Syndromic Surveillance
OH-MEMA provides an interpretable, human-in-the-loop platform for exploratory forecasting and comparative model analysis in syndromic surveillance. The framework effectively bridges data integration, modeling, and interpretation, supporting user-centered analytical reasoning and decision-making in One Health applications.
Authors
- Michael C. Wimberly (ORCID: https://orcid.org/0000-0003-1549-3891)
- David S. Ebert
- Wolfgang Jentner (ORCID: https://orcid.org/0000-0003-1045-6020)
- Parisa Masnadi Khiabani
- Katrin Gaardbo Kuhn (ORCID: https://orcid.org/0000-0003-4905-837X)
- Aaron M. Wendelboe (ORCID: https://orcid.org/0000-0002-8670-7730)
- Aseel Basheer (ORCID: https://orcid.org/0000-0002-6483-3365)
- Jason R. Vogel
- Dean Hougen
Institutions
- University of Arizona (US)
- University of Oklahoma Health Sciences Center (US)
- University of Arkansas for Medical Sciences (US)
- University of Oklahoma (US)
Publication Details
- Journal
- Journal of Clinical Medicine
- Published
- 2026-04-14
- DOI
- https://doi.org/10.3390/jcm15082966
- Primary Topic
- Zoonotic diseases and public health
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation