Dependence analysis of social contact behaviors under the impacts of COVID-19 based on a copula approach.

The spread of the SARS-CoV-2 virus during the COVID-19 pandemic was intricately linked with contact between people, but many of the policies designed to encourage safe contact behaviors were unsuccessful. One reason was that the determinants of social contact decisions have not been thoroughly investigated using scientifically sound methodologies. To fill this gap, a unique survey was designed which sought data on social contact behaviors and their determinants. Second, a copula-based behavior model was developed to jointly represent the choices of contact modes (including direct and indirect contact) and the number of contacted persons. The survey was conducted in six countries from March to May 2021 and collected valid responses from >7000 people. A comparison of five key copula functions found that the Frank function outperformed the others. The results of a Frank-based model showed that indirect contacts were significantly and positively associated with the number of contacted persons. Then the influence of various determinants, including activity attributes (e.g., frequency and travel distance), protective measures, safety level of activity settings, and psychological factors related to activity participation and risk perception, were extensively analyzed. In particular, the various heterogeneous influences in different social contact settings were examined. The findings provide scientific evidence for policymakers to promote safe social distancing, even for the post-pandemic era.

Building a system dynamics model to analyze scenarios of COVID-19 policymaking in tourism-dependent developing countries: A case study of Cambodia

To control the COVID-19 pandemic, various policies have been implemented to restrict the mobility of people. Such policies, however, have resulted in huge damages to many economic sectors, especially the tourism sector and its auxiliary services. Focusing on Cambodia, this study presents a system dynamics (SD) model for assessing and selecting effective policy responses to contain the spread of COVID-19, while maintaining tourism development. Policies targeted in this study include international and domestic transportation bans, quarantine policy, tourist-centered protection measures, and enterprise-led protection measures. Two types of scenario analyses are conducted: one targets each policy separately and the other combines different policies. Among all scenarios, quarantine policy is evaluated to be the most effective policy as it balances the containment of the spread of COVID-19 and support for tourism development. This study provides a new way of guiding COVID-19 policymaking and exploring effective policies in the context of tourism.

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities and associating them with land use and inherent socio-economic patterns as well as points of interests (POIs). Massive multi-source and multi-scale data include mobile phone signaling data (500 m × 500 m), aerial images (0.49 m × 0.49 m), night light satellite data (500 m × 500 m), land use data (street-block), and POIs data. Methods of convolutional neural network, guided gradient-weighted class activation mapping, bivariate local indicator of spatial association, Elbow and K-means are jointly applied. It is found that the recovery in central areas was slower than in suburbs, especially in terms of working and night-life activities, showing a donut-shaped spatial pattern. Residential areas with mixed land uses seem more resilient to the pandemic shock. More than 60% of open spaces are highly associated with recovery in areas with high-level pre-pandemic social-economic activities. POIs of sports and recreation are crucial to the recovery in all areas, while POIs of transportation and science/culture are also important to the recovery in many areas. Policy implications are discussed from perspectives of open spaces, public facilities, neighborhood units, spatial structures, and anchoring roles of POIs.

Quantifying the Effects of Different Containment Policies on Urban NO2 Decline: Evidence from Remote Sensing and Ground-Station Data

Cities exposed their vulnerabilities during the COVID-19 pandemic. Unprecedented policies restricted human activities but left a unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to explore the underlying urban development issues behind these restrictions and support a sustainable transition. The data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a 'dynamic zero-COVID';policy with adjusted restrictions, which allowed for better tracking of the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The deviation between the observations and predictions was identified and attributed to the policy interventions, and spatial stratified heterogeneity statistics were used to quantify the effects of different policies. Workplace closures (54.8%), restricted public transport usage (52.3%), and school closures (46.4%) were the top three restrictions that had the most significant impacts on NO2 anomalies. These restrictions were directly linked to mismatched employment and housing, educational inequality, and long-term road congestion issues. Promoting the transformation of urban spatial structures can effectively alleviate air pollution.

Leveraging the COVID-19 crisis for better public transport services in Asian cities

In 2020, government policies to contain the SARS-CoV-2 virus and fear of the virus led people across the world to stop using public transport. While some researchers in Europe and North America predicted that the pandemic would result in a long-term shift away from public transport, such narratives did not reflect the experiences of many Asian cities. Despite dramatic falls in passengers, the numbers appeared to recover once infection levels declined. However, the pandemic had other impacts on both governments and transport operators, as it forced them to take bold actions to deal with transmission risks and the economic fallout of low service demand. This chapter reflects on the impact of COVID-19 policies on public transport services in selected Asian countries, focusing on the themes of governance and financing;quality of services;access control and digital exclusion;modal integration;and collaboration with different stakeholders. Although it is too soon to predict the long-term impacts of the pandemic, it concludes with general observations on how the experience may affect public transport policies and planning in the future.

Social contact patterns and changes at leisure/tourism activity settings during COVID-19 period: An international comparison

Social contacts are an important indicator to track the spread of pandemics and evaluate the effectiveness of policy interventions in specific settings. Using a retrospective survey, this chapter compared the reported contact patterns during the COVID-19 outbreak to contact patterns during the influenza period in leisure/tourism settings of four developed countries. Changes in social contact patterns across demographic and other factors at different locations and regions are identified, which are helpful for classifying the heterogeneity of contact patterns and potential post-lockdown transmission patterns. This analysis can assist policymakers to implement more evidence-based interventions to guide the economic recovery of the tourism sector.

Assessing the impacts of COVID-19 on carbon emissions from the road transport sector in China

The outbreak and spread of the COVID-19 pandemic has altered transport patterns in China, leading to significant changes in energy consumption and carbon dioxide (CO2) emissions. This study assessed the spatiotemporal characteristics of transport-related CO2 emissions at the provincial level in China during the COVID-19 pandemic. Provincial-level time-series emissions were estimated based on monthly transport demand data, including both passenger and freight transport demand in China’s 31 provinces, as well as mode share, technology mix, energy intensity, and emission factor data obtained from an energy system model. Spatial autocorrelation and hot spot analyses of CO2 emissions were then conducted to detect the regional disparities and spatial clusters of the impacts of the COVID-19 pandemic on CO2 emissions. By assessing how transport emissions responded to the outbreak of the COVID-19 pandemic, a series of policy implications were devised that could provide a future decarbonization pathway.

The public health challenge of COVID-19

The world is battling the coronavirus disease 2019 (COVID-19) pandemic. However, pandemics such as COVID-19 are not entirely new phenomena in human history. Humans have always been fighting against infectious diseases since the Neolithic revolution, 12,000 years ago. Over the past few decades, however, new infectious diseases have been caused by sudden environmental changes. To protect against increasing threats from infectious diseases, human society has established public health systems against infectious diseases and has promoted various countermeasures against infectious diseases. In this chapter, past public health efforts in the context of fighting against infectious diseases are described, and the risk factors, such as social and environmental factors, of the burden of the disease are also reviewed. Then, the necessity to make a paradigm shift to a new scientific field, “Planetary Health,” is proposed to achieve a sustainable society after (or with) COVID-19.

COVID-19 and transport: Recording the history of fights against pandemics

This book is a systematic collection about COVID-19 and various modes of transport (railways, road, air, waterway, walking and biking, shared mobility, etc.), logistics, and supply chains, economy and daily life, covering different geographical territories and cultures, from both academic and practical perspectives. Impacts of COVID-19 are widely investigated, and policy measures are examined from various angles. Best practices in emergency and immediate measures (especially from a cross-sectoral perspective) against pandemics are included to provide immediate guidelines to policymakers, supported by various case studies. Effective governance is examined in order to put all policymaking innovations into practice. This book consists of six main parts. Part I overviews historical pandemics. Part II investigates the overall impacts of COVID-19. Part III focuses on logistics and supply chains. Part IV examines responses to distancing policies and public transport. Part V explores recovery issues. Part VI looks to the future, followed by a summary of key policy recommendations and future challenges.

The impacts of the built environment factors and population mobility on the spread of COVID-19 during its initial stage of the COVID-19 pandemic: A case of China

The COVID-19 pandemic has greatly disrupted human mobility and economic development globally. The built environment (BE) can contribute to the spread of COVID-19, as well as other infectious diseases, by facilitating human mobility and social contacts between infected and susceptible individuals. It can also provide a space that directly transmits pathogens to the people. Thus more attention should be given to preventing the spread of such diseases through urban planning and management. At present, knowledge about how the built environment affects the COVID-19 spread is limited. In this chapter, we firstly introduce how the built environment can affect public health, then objectively evaluate the influence of key built environment factors on the spread of COVID-19 through a random forest approach across 2994 townships in China in the initial stages of the pandemic. To represent the spread of COVID-19, the ratio of cumulative infection cases (RCIC) and the coefficient of variation of infection cases (CVIC) that reflects the policy effects in the initial stages of the pandemic are selected.

COVID-19 and big data technologies: Experience in China

The experience of COVID-19 has shown that big data combined with advanced algorithms have a huge potential in supporting the fight against infectious diseases and pandemics. In China, big data on human mobility derived from smart sensors, integrated with detailed epidemiological data from patient interviews, have played an important role in the efficient and effective control of the pandemic via nonpharmaceutical interventions. Two official big data applications, namely “Health Code” and “Instrument for Measuring Close Contacts,” have been promoted to detect infected people with the potential to infect and conduct risk assessments in a timely manner during the pandemic. We explored the relationship between big data technologies and applications in virus transmission, risk assessment, and recovery decision making. In the future, the process of social recovery is likely to require the support of big data technology. The experience of using big data in China is expected to bring new insights into policymaking to control the COVID-19 pandemic in other countries and prevent future pandemics.

Tourism policy responses to COVID-19 and first-stage tourism recovery in China

In order to find a coordinated approach to support tourism recovery following the impacts of COVID-19, this research examines the experiences of mainland China, the first country whose domestic tourism recovered in the first stage (the first year after the pandemic outbreak). Through the content analysis of tourism policy documents at national, provincial, and city levels, we generated the features of the policy responses from the supply and demand sides, and the policy trends before and after the first peak of the recovery. Next, we summarized the three steps which make up the first stage, and describe the effective policy focus for each step. This process-oriented policy analysis can guide other countries in how to cope with tourism recovery during the first stage.

Governance for post-COVID-19 carbon reduction: A case study of the transport sector

This chapter argues that carbon reduction goals in the transport sector should be achieved through the management of policy making and implementation processes, based on a transport-sector carbon emissions identity and the DIRECT approach. Based on the above identity, it is argued that carbon emissions from the transport sector can be reduced from the following six connected domains: (1) carbon intensity of energy consumption from transport, (2) energy consumption from transport, (3) transport pressures from life and business activities, (4) high-carbon life and business activities, (5) changing the needs in life and business, and (6) population policy. The DIRECT approach includes six steps: (1) Detect, (2) Inform/Intervene, (3) React, (4) Enlighten/Enforce/Evaluate, (5) Collaborate, and (6) Transfer, which should be applied to manage each of the above six domains. The “6-domain and 6-step” is proposed as an integrated policy framework for reducing carbon emissions from the transport sector in a seamless way.

Collective thoughts about the COVID-19 pandemic and transport from a worldwide expert survey

The WCTRS (World Conference on Transport Research Society) COVID-19 Task Force implemented a worldwide expert survey between the end of April and late May 2020. Of 357 experts who participated in the survey, more than 100 experts provided their open opinions. Although time has passed since this survey, the situation of the COVID-19 pandemic at the time of writing suggests that these opinions are still relevant for policymakers. This chapter discusses what can be learned from those experts’ open opinions, especially in association with both immediate and long-term policymaking.

COVID-19's impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis.

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students' online learning behavior before and after the outbreak. We collected review data from China's massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses' interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic's role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%–8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%–5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = −1.3%, range of −0.2% to −3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%–75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%–86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

Building a system dynamics model to analyze scenarios of COVID-19 policymaking in tourism-dependent developing countries: A case study of Cambodia

To control the COVID-19 pandemic, various policies have been implemented to restrict the mobility of people. Such policies, however, have resulted in huge damages to many economic sectors, especially the tourism sector and its auxiliary services. Focusing on Cambodia, this study presents a system dynamics (SD) model for assessing and selecting effective policy responses to contain the spread of COVID-19, while maintaining tourism development. Policies targeted in this study include international and domestic transportation bans, quarantine policy, tourist-centered protection measures, and enterprise-led protection measures. Two types of scenario analyses are conducted: one targets each policy separately and the other combines different policies. Among all scenarios, quarantine policy is evaluated to be the most effective policy as it balances the containment of the spread of COVID-19 and support for tourism development. This study provides a new way of guiding COVID-19 policymaking and exploring effective policies in the context of tourism.

"What should be computed" for supporting post-pandemic recovery policymaking? A life-oriented perspective.

The COVID-19 pandemic has caused various impacts on people's lives, while changes in people's lives have shown mixed effects on mitigating the spread of the SARS-CoV-2 virus. Understanding how to capture such two-way interactions is crucial, not only to control the pandemic but also to support post-pandemic urban recovery policies. As suggested by the life-oriented approach, the above interactions exist with respect to a variety of life domains, which form a complex behavior system. Through a review of the literature, this paper first points out inconsistent evidence about behavioral factors affecting the spread of COVID-19, and then argues that existing studies on the impacts of COVID-19 on people's lives have ignored behavioral co-changes in multiple life domains. Furthermore, selected uncertain trends of people's lives for the post-pandemic recovery are described. Finally, this paper concludes with a summary about "what should be computed?" in Computational Urban Science with respect to how to catch up with delays in the SDGs caused by the COVID-19 pandemic, how to address digital divides and dilemmas of e-society, how to capture behavioral co-changes during the post-pandemic recovery process, and how to better manage post-pandemic recovery policymaking processes.