Network-side digital contact tracing on a large university campus

ABSTRACT

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 over 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 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 if chosen at random, a relative risk ratio of 12.6). A cumulative exposure score is computed to account for exposure to multiple individuals that test positive. Over the duration of the study, the exposure score predicts positive cases with a true positive rate of 16.5% and missed detection rate of 79% at a specified operating point. © 2022 ACM.