JUE Insight: The Geography of Travel Behavior in the Early Phase of the COVID-19 Pandemic

Global Challenges After a Global Challenge: Lessons Learned from the COVID-19 Pandemic

Coronavirus disease 2019 (COVID-19) has affected not only individual lives but also the world and global systems, both natural and human-made. Besides millions of deaths and environmental challenges, the rapid spread of the infection and its very high socioeconomic impact have affected healthcare, economic status and wealth, and mental health across the globe. To better appreciate the pandemic's influence, multidisciplinary and interdisciplinary approaches are needed. In this chapter, world-leading scientists from different backgrounds share collectively their views about the pandemic's footprint and discuss challenges that face the international community.

Exploring governance policy of marine fishery litter in China: Evolution, challenges and prospects

With great economic loss and environmental hazards, litter derived from marine fishery production has been a worldwide problem and has risen common concerns. China is no exception. The government of China has made an effort in the marine fishery litter governance since 1982 by issuing policy documents. This study reviews relevant policies from 1982 to 2021 and analyzes them to improve marine fishery litter governance. Three stages can be divided: The initial formation period (1982-2006), the exploratory governance period (2006-2016), and the fine development period (2016-present). The marine fishery litter governance policy system has been continuously improved, but the rough governance link joins, vague responsibility partition and insufficient coordination, and technology and knowledge pose challenges to the policy implementation. Finally, prospects on marine fishery litter governance policy formulation and implementation are put forward.

JUE Insight: Urban flight seeded the COVID-19 pandemic across the United States

We document large-scale urban flight in the United States during the COVID-19 pandemic. Regions that saw migrant influx experienced greater subsequent new COVID-19 cases, linking urban flight (as a disease vector) and coronavirus spread in destination areas. Urban residents fled to socially connected areas, consistent with the theory that individuals sheltered with friends and family, or in second homes. Populations that fled were disproportionately younger, whiter, and wealthier. The association between migration and subsequent new cases persists when instrumenting for migration with social networks.

Investigating the two-way relationship between mobility flows and COVID-19 cases

Following a pandemic disease outbreak, people travel to areas with low infection risk, but at the same time the epidemiological situation worsens as mobility flows to those areas increase. These feedback effects from epidemiological conditions to inflows and from inflows to subsequent infections are underexplored to date. This study investigates the two-way relationship between mobility flows and COVID-19 cases in a context of unrestricted mobility without COVID-19 vaccines. To this end, we merge data on COVID-19 cases in Spain during the summer of 2020 at the province level with mobility records based on mobile position tracking. Using a control function approach, we find that a 1% increase in arrivals translates into a 3.5% increase in cases in the following week and 5.6% ten days later. A simulation exercise shows the cases would have dropped by around 64% if the Second State of Alarm had been implemented earlier.

Inter-prefectural Travel and Network Connectedness During the COVID-19 Pandemic in Japan

BACKGROUND

Increases in human mobility have been linked to rises in novel coronavirus disease 2019 (COVID-19) transmission. The pandemic era in Japan has been characterized by changes in inter-prefectural mobility across state of emergency (SOE) declarations and travel campaigns, but they have yet to be characterized.

METHODS

Using Yahoo Japan mobility data extracted from the smartphones of more than 10 million Japanese residents, we calculated the monthly number of inter-prefectural travel instances, stratified by residential prefecture and destination prefecture. We then used this adjacency matrix to calculate two network connectedness metrics, closeness centrality and effective distance, that reliably predict disease transmission.

RESULTS

Inter-prefectural mobility and network connectedness decreased most considerably during the first SOE, but this decrease dampened with each successive SOE. Mobility and network connectedness increased during the Go To Travel campaign. Travel volume between distant prefectures decreased more than travel between prefectures with geographic proximity. Closeness centrality was found to be negatively correlated with the rate of COVID-19 infection across prefectures, with the strength of this association increasing in tandem with the infection rate. Changes in effective distance were more visible among geographically isolated prefectures (Hokkaido and Okinawa) than among metropolitan, central prefectures (Tokyo, Aichi, Osaka, and Fukuoka).

CONCLUSION

The magnitude of reductions in human mobility decreased with each subsequent state of emergency, consistent with pandemic fatigue. The association between network connectedness and rates of COVID-19 infection remained visible throughout the entirety of the pandemic period, suggesting that inter-prefectural mobility may have contributed to disease spread.

High-speed railway and the intercity transmission of epidemics: Evidence from COVID-19 in China

In the context of the COVID-19 outbreak in Wuhan, we investigate the effect of intercity high-speed railway (HSR) connections on intercity transmission of epidemics in the absence of government intervention. Intercity HSR connections increase the number of COVID-19 patients per 10,000 population by 0.029, accounting for 45% of the total infections. Our results remain intact in several robustness assessments. The total economic loss owing to HSR connections to Wuhan is estimated to be USD 0.62 billion. The internal mechanism demonstrates that intercity HSR connections increase intercity COVID-19 transmission by facilitating human mobility between cities. Based on intercity transportation connections, our findings can help the government predict the direction and scope of virus transmission and control the intercity transmission of epidemics.

Data analytics during pandemics: a transportation and location planning perspective

The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.

Regional and sectorial impacts of the Covid-19 crisis: Evidence from electronic payments

We use novel and comprehensive monthly data on electronic payments, by municipality and sector, together with cash withdrawals, to study the impact of Covid-19 in Portugal. Our difference-in-differences event study identifies a causal decrease of 17 and 40 percentage points on the year-on-year growth rate of overall purchases in March and April 2020. We document a stronger impact of the crisis in more central and more urban municipalities, due to a combination of the sectorial composition effect of the local economy and the sharper confinement behavioral effect in these locations. We discuss the importance of tourism for the results.

Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic

We consider a model of network interactions where the outcome of a unit depends on the outcomes of the connected units. We determine the key network link, i.e., the network link whose removal results in the largest reduction in the aggregate outcomes, and examine a measure that quantifies the contribution of a network link to the aggregate outcomes. We provide an example examining the spread of Covid-19 in China. Travel restrictions were imposed to limit the spread of infectious diseases. As uniform restrictions can be inefficient and incur unnecessarily high costs, we examine the design of restrictions that target specific travel routes. Our approach may be generalized to multiple countries to guide policies during epidemics ranging from ex ante route-specific travel restrictions to ex post health measures based on travel histories, and from the initial travel restrictions to the phased reopening.

JUE insight: Demand for transportation and spatial pattern of economic activity during the pandemic

Using traffic data from Taiwan for 2020, we quantify how the COVID-19 outbreak affected demand for public and private transportation. Despite there being no governmental restrictions, substantial shifts in travel modes were observed. During the peak of the pandemic in Taiwan within the study period (mid-March 2020), railway ridership declined by 40% to 60%, while highway traffic volume increased by 20%. Furthermore, railway ridership was well below pre-pandemic levels, though there were no locally transmitted cases in the eight-month period from mid-April to December. These changes in traffic patterns had implications for spatial patterns of economic activity: retail sales and nighttime luminosity data show that during the pandemic, economic activity shifted away from areas in the vicinity of major railway stations.

COVID-19, commuting flows, and air quality

Fossil-fuel burning transportation methods significantly contribute to air pollution. During the COVID-19 pandemic, South Korea experienced a 10-20% decline in commuting flows, even without government-mandated stay-at-home orders. This paper quantifies the impact that decreased commuting flows have on PM_2.5, PM₁₀, NO₂, CO, and SO₂, using municipality level commuting data. We find that a 1% decrease in commuting flows decreases air pollutants by 0.08-0.17%, after controlling for seasonality and time-varying local production. The effect was higher in regions with high initial pollution, and people recognized air quality improvements. These results emphasize the importance of encouraging cleaner transportation methods after the pandemic.