  We describe a study conducted at a large public university campus in the
United States which shows the efficacy of network log information for digital
contact tracing and prediction of COVID-19 cases. Over the period of January
18, 2021 to May 7, 2021, more than 216 million client-access-point associations
were logged across more than 11,000 wireless access points (APs). The
association information was used to find potential contacts for approximately
30,000 individuals. Contacts are determined using an AP colocation algorithm,
which supposes contact when two individuals connect to the same WiFi AP at
approximately the same time. The approach was validated with a truth set of 350
positive COVID-19 cases inferred from the network log data by observing
associations with APs in isolation residence halls reserved for individuals
with a confirmed (clinical) positive COVID-19 test result. The network log data
and AP-colocation have a predictive value of greater than 10%; more precisely,
the contacts of an individual with a confirmed positive COVID-19 test have
greater than a 10\% chance of testing positive in the following 7 days
(compared with a 0.79% chance when chosen at random, a relative risk ratio of
12.6). Moreover, a cumulative exposure score is computed to account for
exposure to multiple individuals that test positive. Over the duration of the
study, the cumulative exposure score predicts positive cases with a true
positive rate of 16.5% and missed detection rate of 79% at a specified
operating point.
