What attracted you to the health metrics field?
In college, I studied the relationship between geography and public health, seeking to situate health outcomes and environments in a place-specific context. I’m interested in the intersection of geographic data and health, and in using place as a tool for understanding population health at local, national, and international scales. At IHME, we’re using increasingly refined geographic data to inform our understanding of how geography impacts the burden of disease and explains differences in health outcomes.
What work are you doing at IHME?
As health data become available at increasingly granular geographic scales, we’re taking advantage of this data to produce health metrics at finer geographic scales. The geospatial team uses population surveys, rasters of covariate data, and statistical modeling to produce continuous surfaces of health outcomes such as under-5 mortality, child growth failure, and malaria at a 5 km x 5 km pixel level. Other examples of our work include predicting the zoonotic niche of Ebola Virus Disease through species distribution modeling to help inform the global health community’s understanding of risk and potential outbreaks.
How do you think your experience at IHME will contribute to your future work?
On the geospatial team, I’m learning about model-based geostatistics, the challenges of using big population-level and geographic data, and the mechanics of running complex models in cluster computing environments, as well as the extension of health metrics to an increasingly refined geographic context. I aim to draw on this statistics and modeling knowledge in the future, using this understanding of research to inform a career in public health.
Pigott DM, Millear AI, Earl L, Morozoff C, Han BA, Shearer FM, Weiss DJ, Brady OJ, Kraemer MUG, Moyes CL, Bhatt S, Gething PW, Golding N, Hay SI. Updates to the zoonotic niche map of Ebola virus disease in Africa. eLife. 2016; 5:e16412. doi: 10.7554/eLife.16412.