Saras Windecker
Research Fellow
BES, PhD
saras.windecker@thekids.org.au
Dr. Saras Windecker is a quantitative ecologist with expertise in statistical modelling, data visualisation, and software development. She joins The Kids Research Institute Australia from the University of Melbourne where she did a PhD and worked for several years as a research fellow doing ecological modelling. Dr. Windecker’s ecological research has focused on predicting plant species’ distributions across space and time. She is also interested in spatial risk modelling for disease and public health, previously collaborating with the Doherty Institute, the Department of Health and Human Services, and Menzies Institute of Health.
Dr. Windecker is actively involved in the open research community and interested in promoting reproducible research practices. She enjoys spending time outdoors, especially in wetland ecosystems such as the freshwater systems that were the focus of her PhD research. She is based in Brisbane.
Projects
Australia-Aotearoa Consortium for Epidemic Forecasting & Analytics (ACEFA)
The ACEFA NHMRC Centre of Research Excellence aims to support the timely, effective response to epidemic diseases in Australia through real-time data analytics, modelling, and forecasting.
Published research
Inferring temporal trends of multiple pathogens, variants, subtypes or serotypes from routine surveillance data
Estimating the temporal trends in infectious disease activity is crucial for monitoring disease spread and the impact of interventions. Surveillance indicators routinely collected to monitor these trends are often a composite of multiple pathogens. For example, "influenza-like illness"-routinely monitored as a proxy for influenza infections-is a symptom definition that could be caused by a wide range of pathogens, including multiple subtypes of influenza, SARS-CoV-2, and RSV.
Characterization and individual-level prediction of cognitive state in the first year after ‘mild’ stroke
Mild stroke affects more than half the stroke population, yet there is limited evidence characterizing cognition over time in this population, especially with predictive approaches applicable at the individual-level. We aimed to identify patterns of recovery and the best combination of demographic, clinical, and lifestyle factors predicting individual-level cognitive state at 3- and 12-months after mild stroke.
Education and Qualifications
- Bachelor of Arts, University of Pennsylvania
- Master of Environmental Studies, University of Pennsylvania
- Doctor of Philosophy, University of Melbourne