Preserving Sustainability Gains of the COVID-19 Pandemic: A Case Study of MIT Campus Commuting

COVID-19 profoundly affected how communities live, work, and commute. In particular, reductions in commutes caused reductions in commuter-related emissions. The pandemic also prompted businesses and institutions to consider longer-term adjustments, including work-from-home policies that continue beyond pandemic protocols. If properly evaluated and implemented, these policies may have long-term beneficial impacts in reducing commuter-related emissions. This work presents a case study of the Massachusetts Institute of Technology campus community, using data from a prepandemic transportation survey with responses from approximately 50% of the community, as well as data on the academic groups and home locations of the broader population. These data were used to estimate car commuter miles by various academic groups and to model and evaluate interventions in relation to reducing car commuter miles. The interventions include staff work-from-home policies, changes that increase use of alternative transit, and improving access to housing. The analysis used differences in how groups commute to inform the potential interventions. For example, there was an estimated 16% reduction in car commuter miles if staff worked from home 1?day/week on average (excluding service staff whose work is on-site), and the same estimated reduction could be achieved were just one staff group that is well-suited to working from home allowed to do so 2?days/week. This analysis is intended to establish groundwork for further studies to assess how potential campus policies and land use might affect sustainable transportation use and parking demand, as well as to provide a case study for other institutions considering similar changes.

Using mobile phone data to estimate dynamic population changes and improve the understanding of a pandemic: A case study in Andorra.

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.

Mobility and COVID-19 in Andorra: Country-Scale Analysis of High-Resolution Mobility Patterns and Infection Spread.

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 spatio-temporal 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 trip-making, 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.