معرفی
Hannah Worthington is a Lecturer in Statistics at the University of St Andrews, affiliated with the Statistics Centre for Research into Ecological & Environmental Modelling. She holds a PhD in Statistical Development of Integrated Multi-State Stopover Models from the University of St Andrews (2016).
Her research focuses on developing and applying statistical ecology methods to animal conservation and population management. Key interests include capture-recapture techniques, spatial capture-recapture, passive acoustic/visual monitoring, and workflow pipelines for new technologies. She also investigates public health issues such as antibiotic misuse in East Africa through mixed-methods approaches, linking multidimensional poverty to healthcare behaviors. Her work contributes to UN Sustainable Development Goals related to environmental protection and global health.
Recent publications highlight her contributions to citizen science data analysis, spatial modeling bias reduction, and antibiotic resistance studies in East Africa. Her earlier work includes foundational research on hidden Markov models for birth cohort recruitment and therapy termination in clinical psychology.
- Scientific Awards:
- Conference Award (2012), Conference Award (2013)
- Summer Studentship (2009, 2010)
- Travel Bursary (2014)
She actively participates in academic activities such as conferences (e.g., International Statistical Ecology Conference 2020) and public engagement (e.g., EXPLORATHON @ The Shops in 2023). Her research collaborations span institutions in East Africa (Kenya, Tanzania, Uganda) and Mongolia. She supervises postgraduate research students, offering projects on topics like movement ecology and statistical inference integration with machine learning.
Hannah leads the development of statistical tools for ecological modeling, including a publicly available R package for seal population analysis (Zenodo, 2020). She collaborates with the HATUA Consortium on antimicrobial resistance research and maintains expertise in Hidden Markov Models and multidisciplinary data integration.
