https://www.econstor.eu/bitstream/10419/223647/1/dp13205.pdf

Trust and Compliance to Public Health Policies in Times of COVID-19

Various measures were recommended or imposed by the governments to control the spread of COVID-19. Travel behaviors are significantly influenced due to such measures. However, people have various travel needs ranging from grocery shopping to work. This study examines the changes that occurred in travel behavior due to the COVID-19 pandemic. Data were collected through an online questionnaire survey that included questions on trip purpose, mode choice, distance traveled, and frequency of trips before and during COVID-19. 1203 responses were collected from various countries around the world. Results explained that trip purpose, mode choice, distance traveled, and frequency of trips for the primary travel were significantly different before and during the pandemic. Further, the majority of trips were made for shopping during the pandemic. There was a significant shift from public transport to private transport and non-motorized modes. People placed a higher priority on the pandemic related concerns while choosing a mode during the pandemic as compared to the general concerns. Gender, car ownership, employment status, travel distance, the primary purpose of traveling, and pandemic-related underlying factors during COVID-19 were found to be significant predictors of mode choice during the pandemic. Outcomes of this study could be useful in transport planning and policymaking during pandemics based on the travel needs of people. In particular, government authorities could utilize such knowledge for planning smart and partial lockdowns. Service providers, e.g., taxi companies and retailers, could use such information to better plan their services and operations.

Exploring the Impacts of COVID-19 on Travel Behavior and Mode Preferences

Voluntary physical distancing is essential for preventing the spread of COVID-19. We assessed the role of political partisanship in individuals' compliance with physical distancing recommendations of political leaders using data on mobility from a sample of mobile phones in 3,100 counties in the United States during March 2020, county-level partisan preferences, information about the political affiliation of state governors, and the timing of their communications about COVID-19 prevention. Regression analyses examined how political preferences influenced the association between governors' COVID-19 communications and residents' mobility patterns. Governors' recommendations for residents to stay at home preceded stay-at-home orders and led to a significant reduction in mobility that was comparable to the effect of the orders themselves. Effects were larger in Democratic- than in Republican-leaning counties, a pattern more pronounced under Republican governors. Democratic-leaning counties also responded more strongly to recommendations from Republican than from Democratic governors. Political partisanship influences citizens' decisions to voluntarily engage in physical distancing in response to communications by their governor.

https://www.pnas.org/content/pnas/117/39/24144.full.pdf

Political partisanship influences behavioral responses to governors' recommendations for COVID-19 prevention in the United States.

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions

COVID-19 has caused unprecedented challenges to our lives. Many governments have forced people to stay at home, leading to a radical shift from on-site to virtual collaboration for many knowledge workers. Existing remote working literature does not provide a thorough explanation of government-enforced working from home situations. Using an affordance lens, this study explores the sudden and enforced issues that COVID-19 has presented, and the technological means knowledge workers use to achieve their team collaboration goals. We interviewed 29 knowledge workers about their experiences of being required to work from home and introduced the term “enforced work from home”. This paper contributes to the affordance theory by providing an understanding of the substitution of affordances for team collaboration during COVID-19. The shifting of affordances results in positive and negative effects on team collaboration as various affordances of technology were perceived and actualised to sustain “business as usual”.

https://www.tandfonline.com/doi/pdf/10.1080/0960085X.2020.1800417

An affordance perspective of team collaboration and enforced working from home during COVID-19

We combine GPS data on changes in average distance traveled by individuals at the county level with COVID-19 case data and other demographic information to estimate how individual mobility is affected by local disease prevalence and restriction orders to stay-at-home. We find that a rise of local infection rate from 0% to 0.003% is associated with a reduction in mobility by 2.31%. An official stay-at-home restriction order corresponds to reducing mobility by 7.87%. Counties with larger shares of population over age 65, lower share of votes for the Republican Party in the 2016 Presidential Election, and higher population density are more responsive to disease prevalence and restriction orders.

Anson Zhou

Papers

Staying at Home: Mobility Effects of COVID-19

Bounding fertility elasticities

Intergenerational Mobility Begins Before Birth