Paula Moraga

Assistant Professor, Statistics

Computer, Electrical and Mathematical Science and Engineering Division


Education Profile

  • ​Ph.D. Mathematics, University of Valencia, 2012
  • M.Sc. Biostatistics, Harvard University, 2011
  • B.Sc. Mathematics, University of Valencia, 2006

Research Interests

​Professor Moraga's research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance including methods to understand the geographic and temporal patterns of diseases, assess their relationship with potential risk factors, detect clusters, and evaluate the impact of interventions. She is also interested in the development of statistical software including R packages and interactive visualization applications for reproducible research and communication. Professor Moraga's work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries.

Selected Publications

  • ​P. Moraga. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Chapman & Hall/CRC Press, ISBN 978-0367357955, 2019.
  • P. Moraga, I. Dorigatti, Zhian N. Kamvar, P. Piatkowski, Salla E. Toikkanen, VP Nagraj, C. A. Donnelly, and T. Jombart. epiflows: an R package for risk assessment of travel-related spread of disease. F1000Research, 7:1374, 2019.
  • P. Moraga. SpatialEpiApp: A Shiny web application for the analysis of spatial and spatiotemporal disease data. Spatial and Spatio-temporal Epidemiology, 23:47–57, 2017.
  • P. Moraga, S. Cramb, K. Mengersen, and M. Pagano. A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE. Spatial Statistics, 21:27–41, 2017.
  • P. Moraga, J. Cano, R. F. Baggaley, J. O. Gyapong, S. Njenga, B. Nikolay, E. Davies, M. P. Rebollo, R. L. Pullan, M. J. Bockarie, D. Hollingsworth, M. Gambhir, and S. J. Brooker. Modelling the distribution and transmission intensity of lymphatic filariasis in sub-Saharan Africa prior to scaling up interventions: integrated use of geostatistical and mathematical modelling. Parasites & Vectors, 8:560, 2015.