Abstract
Geospatial health data are essential to inform public health and policy. These data can be used to understand geographic and temporal patterns, identify risk factors, measure inequalities, and quickly detect outbreaks. In this talk, I will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance. I will discuss data biases and availability issues, and present modeling advancements to integrate complex data from different sources and resolutions to predict disease risk and detect outbreaks. Finally, I will discuss the importance of effective communication and dissemination to inform policymaking and improve global population health.
Brief Biography
Dr. Paula Moraga graduated in Mathematics from the University of Valencia with an Erasmus year abroad at the Johannes Gutenberg University of Mainz. Following graduation, she worked in a technological company developing algorithms for optimal investment strategies. After that, she enrolled in the Ph.D. program in Statistics at the University of Valencia and worked at the office for regional statistics and the national childhood cancer registry. During her doctoral studies, she was awarded the prestigious "la Caixa" Fellowship for studying her Master's degree in Biostatistics at Harvard University, and this complemented her mathematical background with a solid knowledge in biostatistics and epidemiology. Dr. Moraga also received an Ibercaja Research Fellowship to carry out a research project at Harvard Medical School, a stipend from Google Summer of Code to write code for the R project and completed a traineeship at the European Center for Disease Prevention and Control (ECDC). After obtaining her Ph.D. with Extraordinary Award, Dr. Moraga was appointed to academic statistics positions at the University of Bath, Lancaster University, Queensland University of Technology, London School of Hygiene & Tropical Medicine, and Harvard School of Public Health.
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