Using mobile phone data to evaluate access to essential services following natural hazards.

Natural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor-intensive and of limited geographic scope. In this article, we develop an approach to understanding facility-level inaccessibility across a broad geographic area based on location-based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017.

COVID-19 aerosol transmission simulation-based risk analysis for in-person learning.

As educational institutions begin a school year following a year and a half of disruption from the COVID-19 pandemic, risk analysis can help to support decision-making for resuming in-person instructional operation by providing estimates of the relative risk reduction due to different interventions. In particular, a simulation-based risk analysis approach enables scenario evaluation and comparison to guide decision making and action prioritization under uncertainty. We develop a simulation model to characterize the risks and uncertainties associated with infections resulting from aerosol exposure in in-person classes. We demonstrate this approach by applying it to model a semester of courses in a real college with approximately 11,000 students embedded within a larger university. To have practical impact, risk cannot focus on only infections as the end point of interest, we estimate the risks of infection, hospitalizations, and deaths of students and faculty in the college. We incorporate uncertainties in disease transmission, the impact of policies such as masking and facility interventions, and variables outside of the college's control such as population-level disease and immunity prevalence. We show in our example application that universal use of masks that block 40% of aerosols and the installation of near-ceiling, fan-mounted UVC systems both have the potential to lead to substantial risk reductions and that these effects can be modeled at the individual room level. These results exemplify how such simulation-based risk analysis can inform decision making and prioritization under great uncertainty.