How Open Data & Collaborative Analytical Tools Can Help With Decision-Making In Public Health
Permanent link to this recordhttp://hdl.handle.net/10754/669635
MetadataShow full item record
Paula Moraga is an Assistant Professor of Statistics at KAUST, and her research has directly informed strategic policy in reducing disease burden in several countries. In this lecture, she will discuss the importance of open and reliable data and robust analytical tools for real-time surveillance and response to emerging public health threats. Infectious disease outbreaks cause significant suffering and mortality and damage the health, social, and economic wellbeing of the families affected, and produce substantial economic costs for local and national governments. In this lecture, Paula will explain how traditional surveillance systems rely on data gathered via periodic reports often reported with a considerable delay from a medical diagnosis. Unfortunately, they are ineffective for real-time surveillance. New data sources are available in real-time that may complement traditional data sources and provide valuable disease prediction information. These data include digital information such as health-related social media posts and search engine query logs and demographic and environmental factors influencing disease dynamics. Moreover, it is crucial surveillance systems use robust analytical tools and predictive models that can integrate complex data from different sources and at different geographic and temporal resolutions taking into account potential data biases. Finally, open and collaborative research and dissemination are essential to enable broad access to data and new methodology for developing and implementing appropriate population health policies and achieving global sustainable development.Speaker Bio
Paula Moraga (https://www.paulamoraga.com/) is an Assistant Professor of Statistics at the King Abdullah University of Science and Technology (KAUST) and the Principal Investigator of the Geospatial Statistics and Health Surveillance Research Group. Paula's research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance, and the impact of her work has directly informed strategic policy in reducing disease burden in several countries. She has developed modeling architectures to understand the spatial and Spatio-temporal patterns and identify targets for intervention of diseases such as malaria in Africa, leptospirosis in Brazil, and cancer in Australia, and has worked on the development of a number of R packages for Bayesian risk modeling, detection of disease clusters, and risk assessment of the travel-related spread of disease. Paula has published extensively in leading journals and is the author of the book 'Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny' (2019, Chapman & Hall/CRC). Paula received her Ph.D. degree in Mathematics from the University of Valencia, and her Master's degree in Biostatistics from Harvard University.