Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userUnderstanding the Relationship between Social Distancing Policies, Traffic Volume, Air Quality, and the Prevalence of COVID-19 Outcomes in Urban Neighborhoods
74
0
0.0
(
0
)
Authors
Daniel L. Mendoza
Ask ChatGPT about the research
No Arabic abstract
In response to the COVID-19 pandemic, governments have implemented policies to curb the spread of the novel virus. Little is known about how these policies impact various groups in society. This paper explores the relationship between social distancing policies, traffic volumes and air quality and how they impact various socioeconomic groups. This study aims to understand how disparate communities respond to Stay-at-Home Orders and other social distancing policies to understand how human behavior in response to policy may play a part in the prevalence of COVID-19 positive cases. We collected data on traffic density, air quality, socio-economic status, and positive cases rates of COVID-19 for each zip code of Salt Lake County, Utah (USA) between February 17 and June 12, 2020. We studied the impact of social distancing policies across three periods of policy implementation. We found that wealthier and whiter zip codes experienced a greater reduction in traffic and air pollution during the Stay-at-Home period. However, air quality did not necessarily follow traffic volumes in every case due to the complexity of interactions between emissions and meteorology. We also found a strong relationship between lower socioeconomic status and positive COVID-19 rates. This study provides initial evidence for social distancings effectiveness in limiting the spread of COVID-19, while providing insight into how socioeconomic status has compounded vulnerability during this crisis. Behavior restrictions disproportionately benefit whiter and wealthier communities both through protection from spread of COVID-19 and reduction in air pollution. Such findings may be further compounded by the impacts of air pollution, which likely exacerbate COVID-19 transmission and mortality rates. Policy makers need to consider adapting social distancing policies to maximize equity in health protection.
rate research
Read More
Social distancing remains an important strategy to combat the COVID-19 pandemic in the United States. However, the impacts of specific state-level policies on mobility and subsequent COVID-19 case trajectories have not been completely quantified. Using anonymized and aggregated mobility data from opted-in Google users, we found that state-level emergency declarations resulted in a 9.9% reduction in time spent away from places of residence. Implementation of one or more social distancing policies resulted in an additional 24.5% reduction in mobility the following week, and subsequent shelter-in-place mandates yielded an additional 29.0% reduction. Decreases in mobility were associated with substantial reductions in case growth 2 to 4 weeks later. For example, a 10% reduction in mobility was associated with a 17.5% reduction in case growth 2 weeks later. Given the continued reliance on social distancing policies to limit the spread of COVID-19, these results may be helpful to public health officials trying to balance infection control with the economic and social consequences of these policies.
Measuring traffic performance is critical for public agencies who manage traffic and individuals who plan trips, especially when special events happen. The COVID-19 pandemic has significantly influenced almost every aspect of daily life, including urban traffic patterns. Thus, it is important to measure the impact of COVID-19 on transportation to further guide agencies and residents to properly respond to changes in traffic patterns. However, most existing traffic performance metrics incorporate only a single traffic parameter and measure only the performance of individual corridors. To overcome these challenges, in this study, a Traffic Performance Score (TPS) is proposed that incorporates multiple parameters for measuring network-wide traffic performance. An interactive web-based TPS platform that provides real-time and historical spatial-temporal traffic performance analysis is developed by the STAR Lab at the University of Washington. Based on data from this platform, this study analyzes the impact of COVID-19 on different road segments and the traffic network as a whole. Considering this pandemic has greatly reshaped social and economic operations, this study also evaluates how COVID-19 is changing the urban mobility from both travel demand and driving behavior perspectives.
Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.
In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have encouraged and ordered citizens to practice social distancing, particularly by working and studying at home. Intuitively, only a subset of people have the ability to practice remote work. However, there has been little research on the disparity of mobility adaptation across different income groups in US cities during the pandemic. The authors worked to fill this gap by quantifying the impacts of the pandemic on human mobility by income in Greater Houston, Texas. In this paper, we determined human mobility using pseudonymized, spatially disaggregated cell phone location data. A longitudinal study across estimated income groups was conducted by measuring the total travel distance, radius of gyration, number of visited locations, and per-trip distance in April 2020 compared to the data in a baseline. An apparent disparity in mobility was found across estimated income groups. In particular, there was a strong negative correlation ($rho$ = -0.90) between a travelers estimated income and travel distance in April. Disparities in mobility adaptability were further shown since those in higher income brackets experienced larger percentage drops in the radius of gyration and the number of distinct visited locations than did those in lower income brackets. The findings of this study suggest a need to understand the reasons behind the mobility inflexibility among low-income populations during the pandemic. The study illuminates an equity issue which may be of interest to policy makers and researchers alike in the wake of an epidemic.
Understanding human urban dynamics is essential but challenging as cities are complex systems where people and space interact. Using a customer-level data set from a leading Korean accommodation platform, we identify that urban hierarchy, geographical distance, and attachment to a location are crucial factors of social gathering behaviors in urban areas. We also introduce a model that incorporates the factors and reconstructs the key characteristics of the data. Our model and analysis show that COVID-19 leads to significant behavioral changes in social gathering behaviors. After the outbreak, people are more likely to visit familiar places, avoid places at the highest level of the urban hierarchy, and travel close distances, while the total number of accommodation reservations does not change much. Interestingly, these changes facilitate social gathering activities only at other high levels, implying an external shock reduces the centralization of human urban dynamics but worsens the inequality of urban areas at low levels.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
