Activity-Based Epidemic Spread in Auto-Dependent Prototype Cities
BAT HEN NAHMIAS BIRAN (firstname.lastname@example.org), Ariel UniversityShow Abstract
Nishant Kumar, Swiss Federal Institute of Technology (ETH Zurich)
Jimi Oke, University of Massachusetts, Amherst
Given the growth of urbanization and emerging pandemic threats, more sophisticated models are required to understand disease propagation and investigate the impacts of intervention strategies across various city types. We introduce a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages on person-trajectories obtained from integrated mobility demand and supply models in auto-dependent cities largely found in the US and Canada. Simulating COVID-19 evolution in two full-scale prototype cities (one sprawling with minimal transit, and the other denser with moderate transit) using representative synthetic populations and mobility patterns, we analyze activity-based contact networks. We observe that transit contacts are scale-free in both cities, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We also investigate the impact of the transit network, finding that its removal dampens disease propagation, while work is also critical to post-peak disease spreading. Our framework, validated against existing case and mortality data in actual cities, demonstrates the potential for tracking and tracing, along with detailed socio-demographic and mobility analyses of epidemic control strategies.
How Different Age Groups Responded to the COVID-19 Pandemic in Terms of Mobility Behaviors: Case Study of the United States
Aliakbar Kabiri (email@example.com), University of Maryland, College ParkShow Abstract
Aref Darzi, University of Maryland, College Park
Weiyi Zhou, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park
The rapid spread of COVID-19 has affected thousands of people from different socio-demographic groups all over the country. A decisive step in preventing or slowing the outbreak is the use of mobility interventions, such as government stay-at-home orders. However, different socio-demographic groups might have different responses to these orders and regulations. In this paper, we attempt to fill the current gap in the literature by examining how different communities with different age groups performed social distancing by following orders such as the national emergency declaration on March 13, as well as how fast they started changing their behavior after the regulations were imposed. For this purpose, we calculated the behavior changes of people in different mobility metrics, such as percentage of people staying home during the study period (March, April, and May 2020), in different age groups in comparison to the days before the pandemic (January and February 2020), by utilizing anonymized and privacy-protected mobile device data. We also explored whether the difference observed between the communities is statistically significant using the t-test method. Our study indicates that senior communities outperformed younger communities in terms of their behavior change. Senior communities not only had a faster response to the outbreak in comparison to young communities, they also had better performance consistency during the pandemic.
Changes in Traffic Crash Patterns: Before and After the Outbreak of COVID-19 in Southern Florida
Jaeyoung Lee, Central South UniversityShow Abstract
Mohamed Abdel-Aty, University of Central Florida
In the twentieth year of the twenty-first century, humanity is facing an unprecedented global crisis owing to the COVID-19 pandemic. It has brought about drastic changes in the way we live and work, as well as the way we move from one place to another, namely transportation. The objective of this study is to explore changes in traffic crash patterns before and after the outbreak of COVID-19 using crash data from Southern Florida for the first half of the years of 2019 and 2020. Preliminary analyses show a considerable reduction from March to June. Substantial changes are shown in the proportions of crashes by time period, injury severity, and crash types. Two types of statistical models are developed to identify factors of (1) changes in the percentages of crashes by type and (2) the numbers of crashes by type. The developed models reveal various demographic, socioeconomic, and disease factors. After controlling other factors, the total numbers of crashes are 21% lower after the outbreak. The most significant reductions are observed in morning peak-hour (33.3%), alcohol/drug (58.0%), and pedestrian crashes (38.3%) while no significant change is found in fatal and single-vehicle crashes. Although the results from this study does not directly determine whether traffic safety has worsened or improved, they imply enormous changes in transportation and travel patterns due to the pandemic.
Understanding the Relationship between Travel-related Behavior and COVID-19 Spread within the Communities
Yuntao Guo (firstname.lastname@example.org), Tongji UniversityShow Abstract
Hao Yu, University of Hawai'i, Manoa
Guohui Zhang, University of Hawai'i, Manoa
The global COVID-19 virus pandemic has led to the implementation of social distancing measures such as work-from-home orders that have drastically changed people’s travel-related behavior. As countries are easing up these measures and people are resuming their pre-pandemic activities, the second wave of COVID-19 is observed in many countries. This study proposes a Community Activity Level Index (CALI) based on inter-community traffic characteristics (traffic volume and travel distance) to capture the current travel-related activity level compared to the pre-pandemic baseline and study its relationship with confirmed COVID-19 cases. Five other travel-related factors are also considered, including home dwell time, work and non-work trip frequencies, percentage of residents staying at home, and out-of-county arrivals to reflect the likelihood of exposure to the COVID-19 virus. Considering that it usually takes days from exposure to confirming virus, the temporal delay between the time-varying travel-related factors and their impacts on the number of confirmed COV-19 cases are considered in this study. Honolulu County in the State of Hawaii is used as a case study to evaluate the proposed CALI and other factors on confirmed COVID-19 cases with various temporal delays. Four econometric models have been considered and evaluated to analyze the data, including Poisson, Negative Binomial, Zero-inflated Poisson, and Zero-inflated Negative Binomial models. The case study results show that CALI and other travel-related factors can be used to predict confirmed COVID-19 cases at a later date so that policymakers can allocate resources for the possible increase in confirmed COVID-19 cases.
Implications of potential Post-COVID Travel Pattern on Regional transportation and Air Quality in the state of texas
Xiaodan Xu (XiaodanXu@lbl.gov), Lawrence Berkeley National LaboratoryShow Abstract
Josie Decherd, Texas A&M University, College Station
Alexander Meitiv, Texas A&M University
Yanzhi Xu, Texas A&M Transportation Institute
The global outbreak of COVID-19 has resulted in substantial impacts on all aspects of transportation. The reduced passenger travel and air quality improvements observed in March and April of 2020 have attracted worldwide attention and sparked public interest. Some of the traffic impacts may stay well beyond the lockdown period. There will likely be a large shift to working from home. Telecommuting can help reduce congestion, traffic crashes, vehicle emissions, and several other transportation-related negative externalities. It is important to quantify the potential transportation and air quality impact under plausible post-COVID scenarios, and measure if the improvements are adequate for addressing regional air quality issues. In this study, we focus on the potential transportation operation, emission, air quality, and health impact of telecommuting after the pandemic. The changes in passenger travel changes are derived from observed travel data collected during the pandemic by the University of Maryland. The changes in traffic congestion levels and resulting air quality and health impact changes are investigated under optimistic future scenarios to see the potential environmental benefits of post-COVID telecommuting. The results suggest that with 30% of employees telecommuting, the congestion and emission reductions are statistically significant, but regional air quality and health benefits are not readily seen. The results imply that an environmentally sustainable post-COVID recovery would require more measures than telecommuting alone to bring about meaningful outcomes.
How Social Distancing, Mobility, and Preventive Policies Affect COVID-19 Outcomes in Urban Areas: Big Data-driven Evidence from the DMV Megaregion
Jina Mahmoudi, University of Maryland, College ParkShow Abstract
Chenfeng Xiong, University of Maryland, College Park
Lei Zhang, University of Maryland, College Park
Many factors play a role in outcomes of emerging highly contagious diseases such as COVID-19. Identification and a better understanding of these factors are critical for the planning and implementation of effective response strategies during such public health crises.This study uses longitudinal data to examine the impact of factors related to social distancing, human mobility, enforcement strategies, hospital capacity, and testing capacity in COVID-19 infection and mortality rates. The results provide evidence that lower COVID-19 infection and mortality rates are linked with increased levels of social distancing and reduced levels of travel—particularly by public transit modes. Other preventive strategies also prove to be influential in COVID-19 outcomes. Most notably, lower COVID-19 infection and mortality rates are linked with stricter enforcement policies and more severe penalties for violating stay-at-home orders. Also, policies that allow gradual relaxation of social distancing measures and travel restrictions as well as those requiring usage of a face mask are related to lower COVID-19 infection and mortality rates.Additionally, increased access to ventilators and Intensive Care Unit beds, which represent hospital capacity, are linked with lower COVID-19 mortality rates. On the other hand, gaps in testing capacity are related to higher COVID-19 infection rates. The results also show that certain minority groups such as African Americans and Hispanics are disproportionately affected by the COVID-19 pandemic.
An Exploratory Study of Determinants of the Spread Of COVID-19 Before Shelter-In-Place Orders
Chisun Yoo (email@example.com), Georgia Institute of Technology (Georgia Tech)Show Abstract
Catherine Ross, Georgia Institute of Technology (Georgia Tech)
In this study, we explore the impact of relevant characteristics of counties and their relationship with increases in COVID-19 cases before shelter-in-place orders in the U.S. The recent emergence of COVID-19 means there is little understanding of the related factors impacting the growth and spread of the disease. We examine these relationships through an analysis of 675 counties before SIP orders were issued identifying those areas that experienced the greatest transmission of disease and analyzing the characteristics of those areas. We found a significant relationship between the increase of COVID-19 cases and several factors. Along with socio-economic factors such as median house value and proportion of the black population, several transportation-related factors had a significant association with the transmission of the disease. Average commute time and the proportion of commuters using transit had a positive relationship. The change rate of total VMT before and after SIP orders also had a strong and positive relationship with the expansion of the disease. Our findings suggest that elected officials, decision-makers, and transportation service providers must integrate evolving public health considerations into transportation services which affect the increase in the transmission of infectious diseases. Keywords: COVID-19, travel behavior, county-level characteristics
Transport, Social Interactions, and Life Satisfaction During COVID-19
E. Owen Waygood (firstname.lastname@example.org), Ecole Polytechnique de MontrealShow Abstract
Genevieve Boisjoly, Polytechnique Montréal
Kevin Manaugh, McGill University
Ipek Sener, Texas A&M Transportation Institute
Yilin Sun, Zhejiang University
Bobin Wang, Ecole Polytechnique de Montreal
Transport and social interactions have significant influence on the assessment of life satisfaction. During the period of COVID-19, where the relationships could be severely affected by the confinement measures, how these influences might impact assessments of life satisfaction is not known. In such a context, people’s daily travel is considerable restrained, and interactions with others by non-virtual means could be nearly eliminated. However, information and communication technologies that allow people to easily have video calls are common, which may compensate for this loss of travel and personal contact. As travel to see friends and family outside of one’s neighborhood may be difficult, discouraged, or against the rules of the confinement period, the frequency of interactions with local friends may become more important. Although in many cases, travel to work or other indoor destinations may be stressful or forbidden, in many cases walking has been recommended as a means of stress relief and to get some physical activity. Individuals may also seek out destinations that are open and green such as civic parks, nature destinations, or destinations with water. Finally, depending on the person, limits on their travel may be something they miss or do not. This study aims to examine how such different influences might impact people’s personal assessment of their life satisfaction during COVID-19. The survey was completed voluntarily by 881 individuals primarily (587) from the province of Quebec, Canada in both English and French. Quebec was the main hotspot for COVID-19 in Canada. Cluster analysis is used to identify different travel practices during the confinement period, and ordered logit models are used to test the various transportation and social interaction variables on life satisfaction.
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