King Abdullah University, Saudi Arabia
Geospatial Data Science for Public Health Surveillance
Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. In this talk, I will give an overview of my research which focuses on the development of geospatial methods and interactive visualization applications for health surveillance. I will present tropical disease models where environmental, demographic and climatic data are used to predict the risk and identify targets for intervention of lymphatic filariasis in sub-Saharan Africa, and leptospirosis in a Brazilian urban slum. I will also show the R packages epiflows for risk assessment of travel-related spread of disease, and SpatialEpiApp for disease mapping and the detection of clusters. Finally, I will describe my future research and how it can inform better surveillance and improve population health globally.
- Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Paula Moraga, 2019, Chapman & Hall/CRC, https://paula-moraga.github.io/book-geospatial/.