Spatial epidemiology is all about relationships

EPID 684
Spatial Epidemiology
University of Michigan School of Public Health


Jon Zelner
[email protected]
epibayes.io

Agenda

  • Overview of the Roadmap project

  • Tobler’s first law and other words to live by

  • Road-testing project topics with insights from Tobler, Miller and Goodchild

The “First Law” of Geography

“Everything is related to everything else. But near things are more related than distant things.”
-Waldo Tobler, 1969

What makes the first law useful?

“[I]magine a world in which [TFL] is not true. In such a world, the full range of conditions could be encountered in every minute portion of the world. Every room, for example, might contain the full observed range of the Earth’s topographic variation, from the bottom of the Marianas Trench to the summit of Mount Everest[.] (1, p.301)

No first law \(\to\) “A world of white noise”

“White noise”, like TV static, is characterized by high variation but minimal autocorrelation.

But wait…there’s more!

Goodchild argues that:

  • Variation is more fundamental than autocorrelation.

  • Without spatial variation there is only perfect autocorrelation.

  • Maybe the omnipresence of variation should be the First Law?

  • Doesn’t discount role of correlation, just puts them in order.

How do these ‘laws’ relate to your own interests?

In a few minutes I’ll ask you to:

  • Think about the epidemiological system you want to focus on for your project.

  • What kinds of mechanisms do you think are important as drivers of spatial variation?

  • How about clustering?

  • At what scales are these patterns likely to emerge?

Different mechanisms ⚙️ play out at different spatial scales

Global patterns of malaria are likely to be impacted by climate change

Warming temperatures will result in changing habitat suitability for malaria-transmitting mosquitoes 🦟

Differences in accesibility to HIV care across Sub-Saharan Africa reflect local and regional variation epidemiological conditions and policies

Number of people living with HIV within a 60 min non-motorized trip to an HIV clinic (from (2))

North American dispersal of HIV reflects international travel and local sexual networks

Spread of HIV through North America reconstructed using whole-genome sequence data (from (3))

County-level clustering of diabetes incidence reflects variation in state and local policy and wealth

From (4)
## Between- and within-city variation in outcomes

Outbreaks of measles in two cities with very different vaccination coverages. (From (5))

Clustering of cases within a neighborhood containing an environmental point source

John Snow’s 1854 London Cholera outbreak map

Social connectivity with an institutional amplifier drives neighborhood risk

Spillover of multi-drug resistant TB from a jail in Lima, Peru (From (6))

⚡ Jump-starting the roadmap project ⚡

For the balance of the time today:

  • Think about which epidemiological systems you are interested in focusing on for your project.

  • What kinds of mechanisms do you think are important as drivers of spatial variation in these systems?

  • How about clustering or spatial autocorrelation?

  • At what scales are these patterns likely to emerge?

Next Time

  • Stay tuned!

References

1.
Goodchild MF. The Validity and Usefulness of Laws in Geographic Information Science and Geography. Annals of the Association of American Geographers [electronic article]. 2004;94(2):300–303. (https://doi.org/10.1111/j.1467-8306.2004.09402008.x). (Accessed November 2, 2020)
2.
Kim H, Musuka GN, Mukandavire Z, et al. When distance matters: Mapping HIV health care underserved communities in sub-Saharan Africa. PLOS Global Public Health [electronic article]. 2021;1(11):e0000013. (https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000013). (Accessed January 11, 2022)
3.
Worobey M, Watts TD, McKay RA, et al. 1970s and “Patient 0” HIV-1 genomes illuminate early HIV/AIDS history in North America. Nature [electronic article]. 2016;539(7627):98–101. (http://www.nature.com/articles/nature19827). (Accessed August 31, 2020)
4.
Brazil N. The multidimensional clustering of health and its ecological risk factors. Social Science & Medicine [electronic article]. 2022;295:113772. (https://www.sciencedirect.com/science/article/pii/S0277953621001040). (Accessed February 28, 2022)
5.
Landrigan PJ. Epidemic Measles in a Divided City. JAMA [electronic article]. 1972;221(6):567–570. (https://jamanetwork.com/journals/jama/fullarticle/343787). (Accessed February 4, 2020)
6.
Warren JL, Grandjean L, Moore DAJ, et al. Investigating spillover of multidrug-resistant tuberculosis from a prison: A spatial and molecular epidemiological analysis. BMC Medicine [electronic article]. 2018;16(1):122. (https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1111-x). (Accessed December 15, 2019)