Mossa Merhi Reimert

Mossa Merhi Reimert is a Postdoctoral Researcher (Research Fellow) at the Department of Veterinary and Animal Sciences within the Faculty of Health and Medical Sciences, University of Copenhagen. Her work is centered in the Animal Welfare and Disease Control section, focusing on quantitative approaches to livestock disease management and prevention.

Her research interests span veterinary epidemiology, disease modeling, animal disease transmission dynamics, spatial risk assessment, and animal welfare optimization. She employs advanced computational techniques to analyze disease spread patterns, emphasizing data-driven interventions for zoonotic and livestock-specific pathogens. Key methodological approaches include spatial modeling, social network analysis, and registry data mining to address critical challenges in Danish agricultural systems.

Analysis of her 2024-2025 publications reveals a cohesive research trajectory centered on high-impact animal diseases: Highly Pathogenic Avian Influenza (HPAIV) transmission to cattle, Porcine Reproductive and Respiratory Syndrome (PRRS) in swine, and Salmonella control in dairy herds. Her work consistently integrates spatial-temporal modeling with practical intervention assessment, demonstrating strong alignment with national disease control priorities in Denmark.

Scientific recognition includes:

• 1st prize for most innovative Datathon Project (August 2021), highlighting methodological innovation in data science applications for veterinary medicine

While no formal student advising is documented in the provided materials, her collaborative publication patterns indicate active mentorship within research teams. Grant funding details are not specified in the source text, though her work clearly leverages national veterinary registries and collaborative frameworks.

Her research operates within the Animal Welfare and Disease Control ecosystem at the University of Copenhagen, characterized by strong interdisciplinary collaboration across veterinary epidemiology, theoretical biology, and agricultural data science teams. Current projects emphasize real-world applicability through partnerships with Danish agricultural authorities and industry stakeholders.