Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut Using the Cellular Device Network

Abstract

Epidemiologic studies have established associations between various air pollutants and adverse health outcomes for adults and children. Due to high costs of monitoring air pollutant concentrations for subjects enrolled in a study, statisticians predict exposure concentrations from spatial models that are developed using concentrations monitored at a few sites. In the absence of detailed information on when and where subjects move during the study window, researchers typically assume that the subjects spend their entire day at home, school or work. This assumption can potentially lead to large exposure assignment bias. In this study, we aim to determine the distribution of the exposure assignment bias for an air pollutant (ozone) when subjects are assumed to be static as compared to accounting for individual mobility. To achieve this goal, we use cell-phone mobility data on approximately 400,000 users in the state of Connecticut during a week in July, 2016, in conjunction with

Identifying Loci in Mobility Networks with Applications in New Zealand Work Commutes : A Statistical Test for Identifying Extreme Stationary Distribution Values in Markov Transition Matrices

“…While development of methods for understanding human mobility is interesting on its own, it is also fundamental to areas of research including social science, epidemiology, and infrastructure planning. Epidemiology in particular has recently made use of mobility methods with [6] using mobility to both validate existing pollution exposure models as well as identify demographics for which exposure estimates are heavily biased -a question previously posed in [7]. Other examples, like [8][9][10][11], highlight the need to incorporate mobility methods into those of existing outbreak models to better evaluate the effect of interventions in the current COVID-19 pandemic.…”

A Statistical Method for Identifying Areas of High Mobility Applied to Commuting Data for the Country of New Zealand

“…While development of methods for understanding human mobility is interesting on its own, it is also fundamental to areas of research including social science, epidemiology, and infrastructure planning. Epidemiology in particular has recently made use of mobility methods with Gilani et al (2020) using mobility to both validate existing pollution exposure models as well as identify demographics for which exposure estimates are heavily biaseda question previously posed in Park & Kwan (2017). Other examples, like Alessandretti (2022), Bonaccorsi et al (2020), Kane & Gilani (2021), Kraemer et al (2020), highlight the need to incorporate mobility methods into those of existing outbreak models to better evaluate the effect of interventions in the current COVID-19 pandemic.…”