What’s next in spatial epidemiology?

EPID 594
Spatial Epidemiology
University of Michigan School of Public Health

Jon Zelner
[email protected]
epibayes.io

Agenda

  • Check in about projects/timeline

  • What does it mean to do spatial epidemiology in the real world?

  • Hands-on activity: Mapping and analyzing spatial clustering in the radon data

  • Wrapping up! 🥲

Final Products

  • Due 3/14 or 3/21?

  • Please remember to include your self-assessment and grade.

  • Happy to check in at any point prior to handing it in.

How do we make spatial analysis useful in the real world?

Spatial cluster of Legoinellosis cases identified by automated system (from Greene et al. (2016))

Small-group discussion

In groups of 3-4:

  • How has your understanding of what spatial epidemiology is changed since the beginning of 592?

  • What are you most interested in going forward?

  • What concepts or ideas are still confusing or bugging you?

library(countdown)
countdown(minutes = 8, warn_when = 60, seconds = 0, play_sound=FALSE)
08:00

Radon…in space! 🪐

Hands-on activity

Wrap-up

Sometimes spatial epidemiology is a perspective more than a specific result

Aggregating across spatial scales obscures clustering of non-vaccination that leads to VPD outbreak risk (from Masters et al. (2020))

Picking the wrong scale of surveillance can lead to predictions that are way off

Predictions of outbreak size get worse as spatial aggregation becomes more extreme (from Masters et al. (2020))

Spatial epidemiology’s strength comes from its integrative nature

Pipeline from data and theory to epidemiological models (from Zelner and Eisenberg (2022))

Effective spatial epidemiology is focused on understanding how community context and ecology impact risk

Characterizing the social and ecological determinants of spatial mobility (from Buckee, Noor, and Sattenspiel (2021))

Thanks!

Stay in touch - don’t hesitate to reach out ([email protected])!

References

Buckee, Caroline, Abdisalan Noor, and Lisa Sattenspiel. 2021. “Thinking Clearly about Social Aspects of Infectious Disease Transmission.” Nature 595 (7866): 205–13. https://doi.org/10.1038/s41586-021-03694-x.
Greene, Sharon K., Eric R. Peterson, Deborah Kapell, Annie D. Fine, and Martin Kulldorff. 2016. “Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014–2015.” Emerging Infectious Diseases 22 (10): 1808–12. https://doi.org/10.3201/eid2210.160097.
Masters, Nina B., Marisa C. Eisenberg, Paul L. Delamater, Matthew Kay, Matthew L. Boulton, and Jon Zelner. 2020. “Fine-Scale Spatial Clustering of Measles Nonvaccination That Increases Outbreak Potential Is Obscured by Aggregated Reporting Data.” Proceedings of the National Academy of Sciences, October. https://doi.org/10.1073/pnas.2011529117.
Zelner, Jon, and Marisa Eisenberg. 2022. “Rapid Response Modeling of SARS-CoV-2 Transmission.” Science 376 (6593): 579–80. https://doi.org/10.1126/science.abp9498.