Modeling Movement in a Pandemic

This project is exploring the use of “volunteered” mobile device data to better understand the network of human contact over which Covid-19 (and much of human culture) spreads within an American city.  Specifically, we are looking at a set of 2.2 billion “pings” in the Portland, Ore. Metropolitan region between January and April 2020. These data were obtained from UberMedia – a company that aggregates, analyzes, and resells position information from over 150,000 smart phone apps where the user has agreed to share their position with the app developer and its partners.  We have applied big-data methods (primarily PySpark on RAND’s internal cluster) to process these points and explore several approaches to building a contact network.  We find that the data are sparser than anticipated, making a simple transmission network unworkable.  In light of this limitation, we are developing a method relying on some basic behavioral assumptions and composing data over time to produce a network that has many of the properties of the full contact network.

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