Some determinants of urban viral contagion…

In a prior post, we recapped the IHME Murray Model — the coronavirus forecasting model foundational to the Coronavirus Task Force’s thinking.

And, in a subsequent post, we concluded that — given historical precedents — data modelers would be well served including some measure of urbanization in their models.


Digging a bit deeper, I came across a study by a group called Heartland Forward.

The study report has lots of data, charts and maps.

The study proposes determinants of high rates of contagious infection … some of which are directly related to urbanization.


In essence, the Heartland Forward study proposes these urban-related determinants:

  • Population density … Density can be evaluated at a macro level (state, metro area); at a neighborhood level (how closely are people packed during their day-to-day activities?); and at the residential level (how many people are living in the home or apartment?). The micro levels (neighborhood and residential) are most relevant when it comes to contagions: how many people does somebody come in contact with at home, at work, in transit?
  • Prevalence of mass transit: While mass transit is convenient. efficient and environmentally-sensitive in normal times, it’s potentially a liability at times of contagion. For example, some experts hypothesize that a fundamental difference between California and New York is their primary mode of transportation.  New York has a heavily used mass transit system;    California is infamous for auto usage.  When it comes to these times, isolation in car beats being jammed in a subway car with dozens of potentially contagious fellow passengers.
  • Supply chain interconnectedness … The presence and magnitude of import, airport  and trucking hubs can be a liability in times of contagion. The more “stuff” that is transiting an area raises the risk that some of the stuff may be virus-bearing. That applies to people, too.
  • Reliance on tourism and hospitality … The flow and volume of both domestic and foreign travelers to and through an area raises the risk. Think: Super Bowl in Miami and Mardi Gras in New Orleans which may has triggered the local outbreaks

Again, historical precedents indicate that pandemics naturally thrive most in big cities.

When data modelers are shopping for behavioral measures of urbanization, the above factors are worth considering.


Follow on Twitter @KenHoma

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