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Throughout the COVID-19 pandemic, nonpharmaceutical interventions, such as mobility restrictions, have been globally adopted as critically important strategies to curb the spread of infection. However, such interventions come with immense social and economic costs and the relative effectiveness of different mobility restrictions are not well understood. Some recent works have used telecoms data sources that cover fractions of a population to understand behavioral changes and how these changes have impacted case growth. This study analyzed uniquely comprehensive datasets in order to examine the relationship between mobility and transmission of COVID-19 in the country of Andorra. The data consisted of spatiotemporal telecoms data for all mobile subscribers in the country, serology screening results for 91% of the population, and COVID-19 case reports. A comprehensive set of mobility metrics was developed using the telecoms data to indicate entrances to the country, contact with tourists, stay-at-home rates, trip-making and levels of crowding. Mobility metrics were compared to infection rates across communities and transmission rate over time. All metrics dropped sharply at the start of the country's lockdown and gradually rose again as the restrictions were gradually lifted. Several of these metrics were highly correlated with lagged transmission rate. There was a stronger correlation for measures of indoor crowding and inter-community tripmaking, and a weaker correlation for total trips (including intra-community trips) and stay-at-homes rates. These findings provide support for policies which aim to discourage gathering indoors while lifting the most restrictive mobility limitations.

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra’s serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic.

In 2020, Google announced they would disable third-party cookies in the Chrome browser in order to improve user privacy. In order to continue to enable interest-based advertising while mitigating risks of individualized user tracking, they proposed FLoC. The FLoC algorithm assigns users to "cohorts" that represent groups of users with similar browsing behaviors so that third-parties can serve users ads based on their cohort. In 2022, after testing FLoC in a real world trial, Google canceled the proposal, with little explanation, in favor of another way to enable interest-based advertising. In this work, we offer a post-mortem analysis of how FLoC handled balancing utility and privacy.In particular, we analyze two potential problems raised by privacy advocates: (1) Contrary to its privacy goals, FLoC enables individual user tracking, and (2) FLoC risks revealing sensitive user demographic information. We test these problems by implementing FLoC and compute cohorts for users in a dataset of browsing histories collected from more than 90,000 U.S. devices over a one-year period.For (1) we investigate the uniqueness of users' cohort ID sequences over time. We find that more than 50% of user devices are uniquely identifiable after only 3 weeks, and more than 95% are uniquely identifiable after 4 weeks. We show how these risks increase when cohort IDs are combined with fingerprinting data. While these risks may be mitigated by frequently clearing first-party cookies and increasing cohort sizes, such changes would degrade utility for users and advertisers, respectively. For (2), we find a statistically significant relationship between domain visits and racial background, but do not find that FLoC risks correlating cohort IDs with race. However, alternative clustering techniques could elevate this risk.Our contributions provide insights and example analyses for future novel approaches that seek to protect user privacy while monetizing the web.