Researchers at Stanford University have developed a computer model to predict the spread of COVID-19 in cities:
The study traced the movements of 98 million Americans in 10 of the nation’s largest metropolitan areas through half a million different establishments, from restaurants and fitness centers to pet stores and new car dealerships.
The team included Stanford PhD students Serina Chang, Pang Wei Koh and Emma Pierson, who graduated this summer, and Northwestern University researchers Jaline Gerardin and Beth Redbird, who assembled study data for the 10 metropolitan areas. In population order, these cities include: New York, Los Angeles, Chicago, Dallas, Washington, D.C., Houston, Atlanta, Miami, Philadelphia and San Francisco.
SafeGraph, a company that aggregates anonymized location data from mobile applications, provided the researchers data showing which of 553,000 public locations such as hardware stores and religious establishments people visited each day; for how long; and, crucially, what the square footage of each establishment was so that researchers could determine the hourly occupancy density.
The researchers analyzed data from March 8 to May 9 in two distinct phases. In phase one, they fed their model mobility data and designed their system to calculate a crucial epidemiological variable: the transmission rate of the virus under a variety of different circumstances in the 10 metropolitan areas. In real life, it is impossible to know in advance when and where an infectious and susceptible person come in contact to create a potential new infection. But in their model, the researchers developed and refined a series of equations to compute the probability of infectious events at different places and times. The equations were able to solve for the unknown variables because the researchers fed the computer one, important known fact: how many COVID-19 infections were reported to health officials in each city each day.
Read the whole thing. I’ve reproduced a graph illustrating how their model fits with Chicago’s actual results at the top of the post. I found their approach interesting for a number of reasons. First, it relies on source data capture for data which I regard as more reliable than interviews. Second, their model relies on just three variables: where people go in the course of a day, how long they linger; and how crowded the places get at one time. In short behavior is a major predictor of the number of cases of COVID-19.
This is both good news and bad news. The good news is that, as mentioned in the article, using their model would allow policy-makers to craft policies with more granularity than the present meat-axe, one size fits all approach. The bad news is that, although you can limit capacities in places of business you cannot similarly ensure that businesses can turn a profit under those capacity limitations. The underlying problem may simply be cities with high rents.