  The spread of an infectious disease through a population can be modeled using
a network or a graph. In digital advertising, internet device graphs are graph
data sets that organize identifiers produced by mobile phones, PCs, TVs, and
tablets as they access media on the internet. Characterized by immense scale,
they have become ubiquitous as they enable targeted advertising, content
customization and tracking. This paper posits that internet device graphs, in
particular those based on IP colocation, can provide significant utility in
predicting and modeling the spread of infectious disease. Starting the week of
March 16th, 2020, in the United States, many individuals began to
`shelter-in-place' as schools and workplaces across the nation closed because
of the COVID-19 pandemic. This paper quantifies the effect of the
shelter-in-place orders on a large scale internet device graph with more than a
billion nodes by studying the graph before and after orders went into effect.
The effects are clearly visible. The structure of the graph suggests behavior
least conducive to transmission of infection occurred in the US between April
12th and 19th, 2020. This paper also discusses the utility of device graphs for
i) contact tracing, ii) prediction of `hot spots', iii) simulation of
infectious disease spread, and iv) delivery of advertisement-based warnings to
potentially exposed individuals. The paper also posits an overarching question:
can systems and datasets amassed by entities in the digital ad ecosystem aid in
the fight against COVID-19?
