COVID-19 Mobility Data
This article is originally published at https://rviews.rstudio.com/
We are in the middle of a mind-boggling natural experiment here in the United States. In spite of the advice from the CDC and dire warnings from our nation’s health care experts, millions of Americans will travel over the long holiday weekend. Although the number of people flying is significantly down from last year, there are still large numbers of Americans on the move. The TSA reported that more than two million people went through airport checkpoints last weekend, and the AAA is forecasting as many as fifty-million people may travel.
No matter what the outcome, it is a pretty safe bet that the mobility data collected this weekend will be studied by epidemiologists and public health experts for years to come. In addition to the anecdotal reports linking travel to increased COVID-19 transmission a number of studies including this recent PNAS Report which suggests a “positive relationship between mobility inflow and the number infections”, and this Lancet Correspondence which concludes that a “concomitant increases in mobility will be correlated with an increased numbers cases”, the experts are just beginning to understand the dynamics of mobility and the spread of infection. (See, for example, this Nature paper that claims a “relatively simple SEIR model” informed by the hourly movements of 98 million people can “accurately fit the real case trajectory”).
Acquiring mobility data requires access to large scale infrastructure. Fortunately, several sites are providing access to large scale data sets. The COVIDcast site from the Delphi group provides both R and Python APIs to access the SafeGraph Mobility Data. Click here to see a time-lapse animation of “away from home” data that shows how the cycles of travel vary from before the pandemic up through the middle of this month.
Click here
for another classy dashboard from the University Maryland and the Maryland Transportation Institute that shows how mobility data tracks with COVID cases.
To get your hands on some mobility data in addition to what is available with the Delphi API, try out the covid19mobility package which scrapes mobility data from Google and Apple and look here for the data and R code behind PNAS report mentioned above.
For an in depth look at the issues relating to mobility data and the COVID-19 pandemic, please sign up for the next COVID-19 Data Forum event which will be held at 9 AM Pacific Time on Thursday, December 10th.
Chris Volinsky, Associate vice-president of Big Data Research at ATT Labs will moderate presentations and a panel discussion with Caroline Buckee, Associate Professor of Epidemiology and Associate Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, Dr. Andrew Schoeder, Vice-president Research & Analytics for Direct Relief, and Christophe Fraser, Professor of Pathogen Dynamics at University of Oxford and Senior Group Leader at Big Data Institute, Oxford University, UK.
Finally, wherever your are: please assess the risks of travel for yourself, for your family, and for anyone with whom you may share the air. Stay safe!
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This article is originally published at https://rviews.rstudio.com/
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