Comparative Study of Government Response Measures and Epidemic Trends for COVID-19 Global Pandemic

Travel bubble policies for low-risk air transport recovery during pandemics

Global pandemics restrict long-haul mobility and international trade. To restore air traffic, a policy named "travel bubble" was implemented during the recent COVID-19 pandemic, which seeks to re-establish air connections among specific countries by permitting unrestricted passenger travel without mandatory quarantine upon arrival. However, travel bubbles are prone to bursting for safety reasons, and how to develop an effective restoration plan through travel bubbles is under-explored. Thus, it is vital to learn from COVID-19 and develop a formal framework for implementing travel bubble therapy for future public health emergencies. This article conducts an analytical investigation of the air travel bubble problem from a network design standpoint. First, a link-based network design problem is established with the goal of minimizing the total infection risk during air travel. Then, based on the relationship between origin-destination pairs and international candidate links, the model is reformulated into a path-based one. A Lagrangian relaxation-based solution framework is proposed to determine the optimal restored international air routes and assign the traffic flow. Finally, computational experiments on both hypothetical data and real-world cases are conducted to examine the algorithm's performance. The results demonstrate the effectiveness and efficiency of the proposed model and algorithm. In addition, compared to a benchmark strategy, it is found that the infection risk under the proposed travel bubble strategy can be reduced by up to 45.2%. More importantly, this work provides practical insights into developing pandemic-induced air transport recovery schemes for both policymakers and aviation operations regulators.

The synergy effect of multi-country policy actions announced in reaction to global risk: A network structure perspective

The policy actions of countries reflect adaptive responses of local components within the system to the dynamic global risk landscape. These responses can generate interactions and synergy effects on alleviating the evolution of global risks. Adopting a network perspective, the study proposes a theoretical framework that connects three structural characteristics of policy synergy, namely, synergy scale, alignment intensity, and timing synchronization. Focusing on the Covid-19 pandemic as a typical global risk context, the study finds that policy synergy with a larger scale, stronger alignment intensity, and more synchronized timing has a positive impact on mitigating global risks. The effect of alignment intensity is particularly pronounced when polycentric governance involves 20 countries facing severe risks, whereas the effect of timing synchronization is more significant when the multicenter group comprises more countries. Building upon the concept of an efficient scale of polycentric governance in various dimensions, this study develops a policy synergy index model. Through multiple empirical analyses, this study validates the causal relationship between policy synergy and the future evolution of global pandemic risk. Policymakers can leverage the dynamic changes in the policy synergy to predict future risk situations and implement well-rounded and appropriate policy actions, thereby enhancing the efficacy of the synergy effect of multi-country policy actions for risk governance.

The lesser of two evils: Assessing the public acceptance of AI thermal facial recognition during the COVID-19 crisis

AI thermal facial recognition (AITFR) has been rapidly applied globally in the fight against Coronavirus disease 2019 (COVID-19). However, AITFR has also been accompanied by a controversy regarding whether the public accepts it. Therefore, it is necessary to assess the acceptance of AITFR during the COVID-19 crisis. Drawing upon the theory of acceptable risk and Siegrist's causal model of public acceptance (PA), we built a combined psychological model that included the perceived severity of COVID-19 (PSC) to describe the influencing factors and pathways of AITFR acceptance. This model was verified through a survey conducted in Xi'an City, Shaanxi Province, China, which collected 754 valid questionnaires. The results show that (1) COVID-19 provides various application scenarios for AI-related technologies. However, the respondents' trust in AITFR was found to be very low. Additionally, the public appeared concerned about the privacy disclosure issue and the accuracy of the AITFR algorithm. (2) The PSC, social trust (ST), and perceived benefit (PB) were found to directly affect AITFR acceptance. (3) The PSC was found to have a significant positive effect on perceived risk (PR). PR was found to have no significant effect on PA, which is inconsistent with the findings of previous studies. (4) The PB were found to be a stronger mediator of the indirect effect of the PSC on ST induced by AITFR acceptance.

Evaluating public opinions: informing public health policy adaptations in China amid the COVID-19 pandemic

Public concern regarding safety policies serious consequences is anticipated to persist over an extended duration. A study examining a case of rapid public health policy adaptation in China during the COVID-19 epidemic was conducted by gathering public opinion data from major social media platforms. A systematic approach to comprehend public opinion was developed. Five fundamental elements and four dimensions were delineated. An indicator system was established utilizing the K-means text clustering model. Public prediction, expectation, and their evolution underlying public concern were elucidated employing TF-IDF text mining models. The HMM elucidated the way public opinion influences policy adjustments. The findings underscore that public concern regarding enduring events undergoes temporal shifts, mirroring the evolution of public opinion towards policy. Public opinion aroused by both the original event and derived events collaboratively influence policy adjustments. In China, public opinion serves as a mechanism for policy feedback and oversight; notably, negative public sentiment plays a pivotal role in expediting policy transitions. These findings aid in refining policies to mitigate emergencies through a feedback loop, thereby averting the emergence of safety risks such as social unrest prompted by public opinion.

Lessons from the COVID-19 pandemic: Mortality impacts in Poland versus European Union

With COVID-19 moving toward an endemic phase, it is worthwhile to identify lessons from the pandemic that can promote the effective strengthening of national health systems. We look at a single country, Poland, and compare it with the European Union (EU) to contrast approaches and outcomes. Among possible relevant indices, we examine characteristics of COVID-19-related mortality and excess all-cause mortality from March 2020 to February 2022. We demonstrate that both the numbers of COVID-related deaths and all-cause deaths in Poland were much higher than the EU average for most months in the study period. We juxtapose the percentage of fully vaccinated population and cumulative COVID-19 deaths per million people for EU Member States and show that typically higher vaccination rates are accompanied by lower mortality. We also show that, in addition to medical science, the use of a risk science toolbox would have been valuable in the management of the COVID-19 pandemic in Poland. Better and more widespread understanding of risk perception of the pandemic and the COVID-19 vaccines would have improved managing vaccine hesitancy, potentially leading to more effective pro-vaccination measures.

Governmental anti-pandemic policies, vaccination, population mobility, Twitter narratives, and the spread of COVID-19: Evidence from the European Union countries

We provide large-scale empirical evidence on the effects of multiple governmental regulatory and health policies, vaccination, population mobility, and COVID-19-related Twitter narratives on the spread of a new coronavirus infection. Using multiple-level fixed effects panel data model with weekly data for 27 European Union countries in the period of March 2020-June 2021, we show that governmental response policies were effective both in reducing the number of COVID-19 infection cases and deaths from it, particularly, in the countries with higher level of rule of law. Vaccination expectedly helped to decrease the number of virus cases. Reductions in population mobility in public places and workplaces were also powerful in fighting the pandemic. Next, we identify four core pandemic-related Twitter narratives: governmental response policies, people's sad feelings during the pandemic, vaccination, and pandemic-related international politics. We find that sad feelings' narrative helped to combat the virus spread in EU countries. Our findings also reveal that while in countries with high rule of law international politics' narrative helped to reduce the virus spread, in countries with low rule of law the effect was strictly the opposite. The latter finding suggests that trust in politicians played an important role in confronting the pandemic.

Counterterrorism resource allocation during a pandemic: The effects of dynamic target valuations when facing a strategic terrorist

The outbreak of pandemics such as COVID-19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well-being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender-attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

Global analysis of the COVID-19 policy activity levels and evolution patterns: A cross-sectional study

Background and Aims

Since the beginning of the coronavirus disease 2019 (COVID-19), a large number of government policies have been implemented worldwide in response to the global spread of COVID-19. This paper aims at developing a data-driven analysis to answer the three research questions: (a) Compared to the pandemic development, are the global government COVID-19 policies sufficiently active? (b) What are the differences and characteristics in the policy activity levels at the country level? (c) What types of COVID-19 policy patterns are forming?

Methods

Using the Oxford COVID-19 Government Response Tracker data set, we present a global analysis of the COVID-19 policy activity levels and evolution patterns from January 1, 2020 to June 30, 2022, based on the differential expression-sliding window analysis (DE-SWAN) algorithm and the clustering ensemble algorithm.

Results

Within the period under study, the results indicate that (a) the global government policy responses to COVID-19 are very active, and the policy activity levels are significantly higher than those of global pandemic developments; (b) a high activity of policy is positively correlated to pandemic prevention at the country level; and (c) a high human development index (HDI) score is negatively correlated to the country policy activity level. Furthermore, we propose to categorize the global policy evolution patterns into three categories: (i) Mainstream (152 countries); (ii) China; and (iii) Others (34 countries).

Conclusion

This work is one of the few studies that quantitatively explores the evolutionary characteristics of global government policies on COVID-19, and our results provide some new perspectives on global policy activity levels and evolution patterns.

Danger control and fear control during public health emergencies: Considering the roles of fear and hope in the EPPM across different levels of trust

Public health emergencies pose considerable threats to global health and safety. The control of these emergencies requires the efforts of healthcare professionals and calls for the public to take protective actions. The present study not only puts fear back in the extended parallel process model (EPPM) but also considers another similarly productive emotion: hope. We examined the mechanisms behind the effects of four cognitive perceptions on protective actions (i.e., danger control) and information avoidance (i.e., fear control). A national online survey was conducted with 1676 participants during the outbreak of COVID-19 in China from February 1 to 29, 2020. The results revealed that perceived severity and susceptibility could lead to fear, positively affecting protective actions. On the other hand, perceived response efficacy and self-efficacy induced hope, which was positively associated with protective actions but negatively associated with information avoidance. Furthermore, the mechanisms behind the relationships among cognitions, emotions, and behaviors varied across levels of trust in healthcare systems.

The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events

This study aimed to analyze the impact of hosting large events on the spread of pandemics, taking Tokyo Olympics 2020 as a case study. A risk assessment method for the whole organization process was established, which could be used to evaluate the effectiveness of various risk mitigation measures. Different scenarios for Games participants and Japanese residents during the Tokyo Olympics were designed based on the infection control protocols proposed by the Olympic Committee and local governments. A modified Wells-Riley model considering the influence of social distance, masking and vaccination, and an SIQRV model that introduced the effect of quarantine and vaccination strategies on the pandemic spread were developed in this study. Based on the two models, our predicted results of daily confirmed cases and cumulative cases were obtained and compared with reported data, where good agreement was achieved. The results show that the two core infection control strategies of the bubble scheme and frequent testing scheme curbed the spread of the COVID-19 pandemic during the Tokyo Olympics. Among Games participants, Japanese local staff accounted for more than 60% of the total in positive cases due to their large population and most relaxed travel restrictions. The surge in positive cases was mainly attributed to the high transmission rate of the Delta variant and the low level of immunization in Japan. Based on our simulation results, the risk management flaws for the Tokyo Olympics were identified and improvement measures were investigated. Moreover, a further analysis was carried out on the impact of different preventive measures with respect to minimizing the transmission of new variants with higher transmissibility. Overall, the findings in this study can help policymakers to design scientifically based and practical countermeasures to cope with pandemics during the hosting of large events.

A cross-national study of multilevel determinants on public fully vaccination against COVID-19

The pandemic caused by SARS-CoV-2 (COVID-19) has impacted the world for close to three years and led to substantial costs to public well-being. To mitigate the pandemic's damage, the most effective approach lies in the vaccine. This study aims to investigate multilevel predictors of the public decision to become fully vaccinated against COVID-19. Data from a cross-national survey on representative samples are merged with country-level indicators. Multilevel logistic regression models are estimated on populations from 15 countries. Findings show that people who agree the government handles the pandemic well, believe the health officials would provide an effective vaccine, perceive the virus's danger, and are older are more likely to get fully vaccinated than their counterparts. Meanwhile, the national case rate and vaccination rate also affect one's decision to become fully vaccinated. Furthermore, there are significant cross-level interactions as people are more inclined to become fully vaccinated if they agree with the government's performance, perceive the virus's danger, and also reside in countries with higher case and vaccination rates. This study shows cross-national evidence regarding multilevel determinants of public vaccine uptake. Knowing the profiles among populations who have become fully vaccinated or not helps public health experts leverage factors and maximize vaccination.

Health insurance system and resilience to epidemics

We theoretically analyze the resilience (efficiency) of health insurance systems and diverse factors including trace and test technology, infection and contagion rates, and social distancing/lockdown policy, in coping with contagious diseases like COVID-19. Our findings can be summarized as follows. First, public insurance is more resilient than market insurance, as the former's investment in test technology is made at the social optimum, whereas the latter's investment is less. The decentralized behavior of competing insurers leads to a less resilient outcome. Second, resilience decreases as the market becomes more competitive because the externality effect becomes more severe. Third, a higher contagion rate, a more cost-efficient test technology or a higher initial infection rate unless it is not too high, leads to a higher test accuracy level. Fourth, the socially optimal social distancing/lockdown policy is determined by comparison between its relative costs and the benefit from contagion reduction.

Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China

The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function β(t) to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in β(t) and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.

Global systemic risk and resilience for novel coronavirus in postpandemic era

The ongoing pandemic has evolved and is posing diverse challenges for the world. Countermeasures for risks are needed to address both direct and indirect effects of disease on the healthcare system, economic and industrial sectors, governance, environment, transportation, energy, and communication systems. There are indicators of a forthcoming postpandemic era. The rethinking and reevaluation of policies adopted throughout the pandemic are ongoing to address cascading threats of emerging and reemerging infectious diseases. The first Special Issue introduced the topic. This second Special Issue describes international collaboration and innovation for pandemic risk and resilience, with a focus on future policy and operations of global systems toward a postandemic era.