COVID-19 and Income Profile: How People in Different Income Groups Responded to Disease Outbreak, Case Study of the United States

Due to immature treatment and rapid transmission of COVID-19, mobility interventions play a crucial role in containing the outbreak. Among various non-pharmacological interventions, community infection control is considered to be a quite promising approach. However, there is a lack of research on improving community-level interventions based on a community's real conditions and characteristics using real-world observations. Our paper aims to investigate the different responses to mobility interventions between communities in the United States with a specific focus on different income levels. We produced six daily mobility metrics for all communities using the mobility location data from over 100 million anonymous devices on a monthly basis. Each metric is tabulated by three performance indicators: "best performance," "effort," and "consistency." We found that being high-income improves social distancing behavior after controlling multiple confounding variables in each of the eighteen scenarios. In addition to the reality that it is more difficult for low-income communities to comply with social distancing, the comparisons between scenarios raise concerns on the employment status, working condition, accessibility to life supplies, and exposure to the virus of low-income communities

Introducing Frameworks to Analyze Human Mobility Behavior with Advanced Computational Algorithms and Machine Learning Methods Using Mobile Device Location Data

The emergence of mobile device location data (MDLD) provides new opportunities to analyze human mobility behaviors. The large penetration rate and the possibility of observing human mobility behaviors continuously are among the most important features of the passively collected mobile device location data. However, to utilize MDLD in mobility behavior analysis, comprehensive computational algorithms need to be developed to carefully process the data.This research proposes novel sets of frameworks to extract mobility context from the raw MDLD. First, this study introduces a set of algorithms to construct the travel behavior of mobile device owners along with the non-observable attributes of both trips and travelers by extracting trips, identifying significant activity locations of the travelers such as their home and work locations, and imputing the travel mode. The proposed algorithms in this study were tested against the state-of-practice and state-of-art algorithms developed in the literature. The proposed algorithms were shown to have superior performance compared to other methods. Next, this study further examines the usefulness of the proposed framework in providing near real-time insights on the evolution of human mobility behavior during the Coronavirus disease 2019 (COVID-19) pandemic. As a part of this study, a new metric has also been introduced to measure the social distancing practices from the mobility perspective. Additional investigations are also conducted to understand the linkage between the outbreak of COVID-19 and the mobility behavior of the communities. Lastly, this study seeks to develop a framework to investigate the evacuation behavior of individuals during a natural disaster and construct the evacuation evolution patterns and decisions based on the MDLD. This dissertation evaluates the importance of the historical mobility behavior of the device owners in their decision-making procedure during natural disasters using statistical discrete choice models

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: “best performance,” “effort,” and “consistency.” We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.https://doi.org/10.1111/rsp3.1259