What determines city's resilience against epidemic outbreak: evidence from China's COVID-19 experience

Physical Chemistry Lab for Data Analysis of COVID-19 Spreading Kinetics in Different Countries

The COVID-19 pandemic has passed. It gives us a real-world example of kinetic data analysis practice for our undergraduate physical chemistry laboratory class. It is a great example to connect this seemingly very different problem to the kinetic theories for chemical reactions that the students have learned in the lecture class. At the beginning of the spring 2023 semester, we obtained COVID-19 kinetic data from the "Our World in Data" database, which summarizes the World Health Organization (WHO) data reported from different countries. We analyzed the effective spreading kinetics based on the susceptible-infectious-recovered-vaccinated (SIR-V) model. We then compared the effective rate constants represented by the real-time reproduction numbers (R t ) underlining the reported data for these countries and discussed the results and the limitations of the model with the students.

Urban resilience governance mechanism: Insights from COVID-19 prevention and control in 30 Chinese cities

Due to the pervasive uncertainty in human society, super large and megacities are increasingly prone to becoming high-risk areas. However, the construction of urban resilience in this new era lacks sufficient research on the core conditions and complex interactive mechanisms governing it. Hence, this study proposes a specialized event-oriented framework for governing urban resilience in China based on the pressure-state-response (PSR) theory. We examined COVID-19 cases in 30 cities across China and analyzed the distribution of prevention and control achievements between high-level and non-high-level conditions. Our findings reveal the following key points: (1) High-level achievements in COVID-19 prevention and control rely on three condition configurations: non-pressure-responsive type, pressure-state type, and pressure-responsive type. (2) High economic resilience may indicate a robust state of urban systems amid demographic pressures. In cities experiencing fewer event pressure factors, the application of digital technology plays a crucial role in daily urban management. (3) The implementation of flexible policies proves beneficial in mitigating the impact of objective pressure conditions, such as environmental factors, on urban resilience.

Assessing urban resilience based on production-living-ecological system using degree of coupling coordination: A case of Sichuan

The issue of urban resilience plays great significance and value for the sustainable development of cities, which has attracted increasing attention from scholars and governments, especially in the western region of China. Based on the Production-Living-Ecological (PLE) system, this study attempts to describe urban resilience by the combination system that contains with P,L,E subsystem. The integrated approach including FAHP-EM,GRA-TOPSIS, CCDM, and ODM is proposed to reveal the urban resilience level and seek out the key constraints' indicators. Then, an empirical analysis of panel data of 18 cities in Sichuan Province from 2011 to 2021 was conducted to analyze the development process. The valuation results suggested that:(1)for urban resilience level, most cities at the moderate imbalance level and basically maintained at this level, only Chengdu is reaching the basic coordination level since in 2013.(2)The insufficient development of P,L,E subsystem is the reason for the moderate imbalance development, especially the key limiting factor is the P subsystem's low development level.(3)the most prominent obstacle indicators are x1(per capita local financial expenditure on science and technology), x2(per capita of R&D spending), x8(total export-import per capita), x14(number of people with basic medical insurance), x22(length of urban drainage pipeline), x23(number of public toilets per person) and the contribution values reach 7.56%,7.49%,11.02%, 9.14%,12.53%, 12.60% respectively. The detailed reference suggestions and effective measures put forwarded for policy makers and planners to promote urban resilience in Western China.

Evaluating the metropolitan public health preparedness for pandemics using entropy-TOPSIS-IF

Introduction

Metropolitan governance's efficacy is regularly gauged by its capability for public health preparedness, a critical component, particularly in the post-pandemic climate, as global cities reassess their mitigation abilities. This process has broader implications, curbing mortality rates and amplifying sustainability. Current methodologies for preparedness assessment lean primarily on either Subjective Evaluation-Based Assessment (SBA), predicated on experts' input on various capacity indicators, or they opt for Data-Based quantitative Assessments (DBA), chiefly utilizing public statistic data.

Methods

The manuscript discusses an urgent need for integrating both SBA and DBA to adequately measure Metropolitan Public Health Pandemics Preparedness (MPHPP), thus proposing a novel entropy-TOPSIS-IF model for comprehensive evaluation of MPHPP. Within this proposed model, experts' subjective communication is transformed into quantitative data via the aggregation of fuzzy decisions, while objective data is collected from public statistics sites. Shannon's entropy and TOPSIS methods are enacted on these data sets to ascertain the optimal performer after normalization and data isotropy.

Results and discussion

The core contribution of the entropy-TOPSIS-IF model lies in its assessment flexibility, making it universally applicable across various contexts, regardless of the availability of expert decisions or quantitative data. To illustrate the efficacy of the entropy-TOPSIS-IF model, a numerical application is presented, examining three Chinese metropolises through chosen criteria according to the evaluations of three experts. A sensitivity analysis is provided to further affirm the stability and robustness of the suggested MPHPP evaluation model.

Anti-pandemic resilience assessment for countries along the Belt and Road route

Background

The COVID-19 pandemic is sweeping the world, and countries along the Belt and Road (B&R) route have also been hit hard. However, the impact varied greatly from country to country, some severely and others mildly. What factors have led to such a wide variation?

Method

In this paper, we considered institutional, infrastructural, economic, social, and technological resilience as components of overall anti-pandemic resilience, and constructed a set of indicators to evaluate this resilience for B&R countries in 2020. We evaluated the anti-pandemic resilience using the combined empowerment-VIKOR method, and classified the countries into different resilience levels by means of hierarchical clustering. The validity of the evaluation indicator system was verified by analyzing the consistency between the actual performance and the assessed resilience.

Results

The ranking results showed that Israel and Bahrain were representative of countries that had the highest resilience, Hungary and Estonia represented countries with moderate resilience, and Laos and Cambodia represented countries with the lowest resilience. We also found that countries with high resilience had much better institutional and economic resilience than countries with moderate resilience, whereas countries with low resilience lagged behind in both infrastructural and social resilience. Based on these findings, policy recommendations were offered to help B&R countries respond to future pandemics.

Predicting the impact of the COVID-19 pandemic on globalization

The COVID-19 pandemic has significantly influenced the global economy, international travel, global supply chains, and how people interact, and subsequently affect globalization in coming years. In order to understand the impact of COVID-19 on globalization and provide potential guidance to policymakers, the present study predicted the globalization level of the world average and 14 specific countries in scenarios with and without COVID-19 based on a new Composite Indicator method which contains 15 indicators. Our results revealed that the world average globalization level is expected to decrease from 2017 to 2025 under the scenario without COVID-19 by -5.99%, while the decrease of globalization under the COVID-19 scenario is predicted to reach -4.76% in 2025. This finding implies that the impact of COVID-19 on globalization will not be as severe as expected in 2025. Nevertheless, the downward trend of globalization without COVID-19 is due to the decline of the Environmental indicators, whereas the decline under the COVID-19 scenario is attributed to Economic aspects (almost -50%). The impact of COVID-19 on globalization varies across individual countries. Among the countries investigated, COVID-19 had a positive impact on the globalization of Japan, Australia, the United States, the Russian Federation, Brazil, India and Togo. In contrast, the globalization in the United Kingdom, Switzerland, Qatar, Egypt, China and Gabon are expected to decrease. The variation of impact induced by COVID-19 on those countries is attributed to the weighting of economic, environmental and political aspects of globalization is different across these countries. Our results can help governments take suitable measures to balance economic, environmental and political policies, which may better support their decision-making.

Neighborhood Governance and Happiness during the COVID-19 Pandemic: An Empirical Analysis of Wuhan's Lockdown

The outbreak of COVID-19 posed a challenge to global governance, residents' happiness, and economic systems around the world. Since the crux of previous research centers on the reactions of both local and national governments, studies on how governance arrangement at the neighborhood level influences people's happiness during the crisis response remain insufficient. This paper aims to explore the relationship between neighborhood governance and residents' happiness based on first-hand data collected during Wuhan's first lockdown. This study highlights the significance of neighborhood governance in crisis response, which includes providing diverse public services, ensuring access to life's necessities, and offering prompt medical treatment. All of these factors are essential for maintaining overall satisfaction with governance and contributing to the happiness of individuals within the community. However, active governance actions do not always lead to favorable results. For example, increased group participation may lead to social conflicts among those involved, ultimately diminishing one's happiness. Furthermore, the COVID-19 pandemic has acted as a risk 'amplifier', exposing and exacerbating pre-existing hukou-based social inequalities in the governance process. The impact of the pandemic on citizen happiness is the cumulative effect of both the immediate social crisis brought on by the pandemic and long-standing structural inequalities. To improve people's happiness and establish inclusive policies, this paper advocates for a 'people-centered' urban governance that enhances public satisfaction and addresses the needs and priorities of migrant populations.

Metro travel and perceived COVID-19 infection risks: A case study of Hong Kong

The COVID-19 pandemic has exerted unprecedented impacts on travel behaviors because of people's increased health precautions and the presence of various COVID-19 containment measures. However, little research has explored whether and how people changed their travel with respect to their perceived local infection risks across space and time. In this article, we relate elasticity and resilience thinking to the changes in metro travel and perceived infection risks at the station or community level over time. Using empirical data from Hong Kong, we measure a metro station's elasticity as the ratio of changes in its average trip length to the COVID-19 cases' footprints around that station. We regard those footprints as a proxy for people's perceived infection risks when making trips to that station. To explore influencing factors on travel in the ups and downs of perceived infection risks, we classify stations based on their elasticity values and examine the association between stations' elasticities and characteristics of stations and their served communities. The findings show that stations varied in elasticity values across space and different surges of the local pandemic. The elasticity of stations can be predicted by socio-demographics and physical attributes of station areas. Stations serving a larger percentage of population with higher education degrees and certain occupations observed more pronounced trip length decrease for the same level of perceived infection risks. The number of parking spaces and retail facilities significantly explained variations in stations' elasticity. The results provide references on crisis management and resilience improvement amid and post COVID-19.

Urban resilience to socioeconomic disruptions during the COVID-19 pandemic: Evidence from China

The COVID-19 pandemic and the associated restrictions have raised the awareness of building pandemic-resilient cities. Prior studies often evaluated the resilience of one type of urban system while lacking a comparison across various urban subsystems. This study fills this gap by measuring and comparing the adaptive resilience to the pandemic of various urban subsystems in Chinese cities. We propose a novel outcome measurement of the pandemic's socioeconomic impacts on cities, i.e., the citizens' complaints data, and use its temporal changes to measure cities' adaptive resilience to the pandemic. We find a wide range of urban subsystems were severely shocked by the pandemic, including the urban economy, construction-and-housing sector, welfare system, and education system. Different urban subsystems exhibit divergent degrees of adaptive resilience to the pandemic. Using cluster analysis, we also identify three types of cities with different patterns of adaptive resilience: cities whose general economies were the least resilient, cities whose construction-and-housing system was the least resilient, and cities that were mostly affected by restriction measures. Our findings contribute to the understanding of the pandemic's socioeconomic costs and help identify the divergent resilience of different urban subsystems so as to develop targeted policy interventions to improve cities' resilience to the pandemic.

How do urban socio-economic characteristics shape a city's social recovery? An empirical study of COVID-19 shocks in China

The COVID-19 pandemic outbreak significantly challenged the cities' abilities to recover from shocks, and cities' responses have widely differed. Understanding these disparate responses has been insufficient, especially from a social recovery perspective. In this study, we propose the concept of social recovery and develop a comprehensive perspective on how a city's socioeconomic characteristics affect it. The analytical framework is applied to 296 prefecture-level cities in China, with social recovery measured by the changes in intercity intensity between the pre-pandemic baseline (2019 Q1 and Q2) and the period in which the pandemic slightly abated (2020 Q1 and Q2) through anonymized location-based big data. The results indicate that the social recovery of Chinese cities during the COVID-19 pandemic are significantly spatially correlated. Cities with larger populations, a higher proportion of GDP in the secondary industry, higher road density or more adequate medical resources tend to recover socially better. Moreover, these municipal characteristics have significant spatial spillover effects. Specifically, city size, government intervention and industrial structure show negative spillover effects on neighboring areas while information dissemination efficiency, road density, and the number of community health services per capita have positive spillover. This study fills the knowledge gap regarding the different performances of cities when they face pandemic shocks. The assessment of a city's social recovery is an insight into the theoretical framework of vulnerability that aids in translating it into urban resilience. Hence our findings provide practice implications for China and beyond as the interest in urban-resilience development surges around the post-pandemic world.

Post COVID-19 pandemic recovery of intracity human mobility in Wuhan: Spatiotemporal characteristic and driving mechanism

After successfully inhibiting the first wave of COVID-19 transmission through a city lockdown, Wuhan implemented a series of policies to gradually lift restrictions and restore daily activities. Existing studies mainly focus on the intercity recovery under a macroscopic view. How does the intracity mobility return to normal? Is the recovery process consistent among different subareas, and what factor affects the post-pandemic recovery? To answer these questions, we sorted out policies adopted during the Wuhan resumption, and collected the long-time mobility big data in 1105 traffic analysis zones (TAZs) to construct an observation matrix (A). We then used the nonnegative matrix factorization (NMF) method to approximate A as the product of two condensed matrices (WH). The column vectors of W matrix were visualized as five typical recovery curves to reveal the temporal change. The row vectors of H matrix were visualized to identify the spatial distribution of each recovery type, and were analyzed with variables of population, GDP, land use, and key facility to explain the recovery driving mechanisms. We found that the "staggered time" policies implemented in Wuhan effectively staggered the peak mobility of several recovery types ("staggered peak"). Besides, different TAZs had heterogeneous response intensities to these policies ("staggered area") which were closely related to land uses and key facilities. The creative policies taken by Wuhan highlight the wisdom of public health crisis management, and could provide an empirical reference for the adjustment of post-pandemic intervention measures in other cities.

Severe Acute Respiratory Syndrome and Particulate Matter Exposure: A Systematic Review

BACKGROUND

Particulate matter (PM) exposure is responsible for seven million deaths annually and has been implicated in the pathogenesis of respiratory infections such as severe acute respiratory syndrome (SARS). Understanding modifiable risk factors of high mortality, resource burdensome C19 and exposure risks such as PM is key to mitigating their devastating effects. This systematic review focuses on the literature available, identifying the spatial and temporal variation in the role of quantified PM exposure in SARS disease outcome and planning our future experimental studies.

METHODS

The systematic review utilized keywords adhered to the PRISMA guidelines. We included original human research studies in English.

RESULTS

Initial search yielded N = 906, application of eligibility criteria yielded N = 46. Upon analysis of risk of bias N = 41 demonstrated high risk. Studies found a positive association between elevated PM2.5, PM10 and SARS-related outcomes. A geographic and temporal variation in both PM and C19's role was observed.

CONCLUSION

C19 is a high mortality and resource intensive disease which devastated the globe. PM exposure is also a global health crisis. Our systematic review focuses on the intersection of this impactful disease-exposure dyad and understanding the role of PM is important in the development of interventions to prevent future spread of viral infections.

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.

Measurement and Spatial Differentiation of Farmers' Livelihood Resilience Under the COVID-19 Epidemic Outbreak in Rural China

Livelihood resilience is the ability of individuals, families or communities to withstand external shocks based on existing resources. It is an important research paradigm in sustainable development studies. The outbreak of COVID-19 and strict epidemic prevention policies have greatly impacted the production and life of rural farmers in China. The resilience of farmers' livelihoods during the epidemic is crucial to the sustainable development of their livelihoods and regional stability. This study uses classic buffer capacity, self-organization ability, and the capacity for learning a three-dimension livelihood resilience framework using the comprehensive index, OLS, and geographical detector methods based on Hubei province and neighboring Anhui and Chongqing. Rural household survey data investigate the background of epidemic hit the livelihood of farmers resilience and its spatial distribution pattern and identify the key influencing factors. The results show that the livelihood shock faced by farmers was higher than the risk of disease, and the overall level of livelihood resilience was low after the pandemic. Financial capital and social capital can effectively help farmers to eliminate livelihood difficulties. In contrast, natural capital has a limited driving force, and physical and human capital have no obvious impact. The spatial agglomeration differentiation is obvious, indicating that the impact of COVID-19 on livelihoods was closely related to the degree of local socio-economic development and geographical location. The results of this study provide targeted recommendations for the development of epidemic prevention and livelihood resilience policies tailored to local conditions, emphasizing the importance of boosting livelihood recovery at both the government and household levels.

What future are you talking about? Efficacy of Life Design Psy-Lab, as career guidance intervention, to support university students' needs during COVID-19 emergency

The pandemic increased world's uncertainty, and led to deep change in daily habits, generating disorientation, and inevitably affecting people life and professional plans. Young adults and students' future perspective and satisfaction with life were widely affected, especially for those facing school/ work transitions. Nevertheless, it was of primary importance for career guidance psychologists to intervene to support students despite having to navigate "on sight" and with little literature available. This paper will thus describe how the Life Design Psy-Lab, a career guidance service from Bicocca University (Milan, Italy) implemented a thematic online group intervention with 103 students enrolled. The intervention was based on the theoretical model of Life Design and was performed by career counselling psychologists. The intervention's goal was to foster students' development of skills and mindset to face complexity and unpredictability, shifting their time perception from an uncertain present to a more optimistic future scenario. The intervention's impact was assessed using pre and post online questionnaires. Results show how students increased their precepted level in a set of skills to face change and complexity after participating to the intervention. In more detail, students' precepted levels of career adaptability, courage, time perspective and resilience rose. Also, data show how the intervention strengthened students' life satisfaction. This paper will thus contribute to expand the growing yet still newborn set of knowledge about career guidance interventions in times of COVID-19 emergency, specifically addressing the issue of design interventions that can improve young adults' skills to visualize and project present and future for themselves, especially during uncertain times.

Can the establishment of national sanitary cities better resist the impact of COVID-19?

The global spread of COVID-19 has led to profound reflection on building a global public health security system. This paper uses the urban data collected during the COVID-19 epidemic in China in 2020 to evaluate the effect of the National Sanitary City (NSC) policy on the prevention and control of that epidemic at different stages. We found that the NSC policy was able to curb the occurrence and transmission of the epidemic the epidemic effectively after controlling a series of factors such as urban characteristics, population mobility and pathogen transmission. Compared with non-NSCs, the NSCs were better able to control the number of infected people and the infection rate and transmission rate, and this performance was even more impressive when the epidemic gradually entered the sporadic distribution stage. The heterogeneity analysis shows that the impact of the NSC policy on the prevention and control of COVID-19 differs according to the economic development level and population size. To a certain extent, the NSC policy has blocked the spread of viruses by continuously improving the urban medical and health system and strengthening the publicity concerning infectious disease prevention and control knowledge.

Resilience assessment of metro stations against rainstorm disaster based on cloud model: a case study in Chongqing, China

Extremely heavy rainfall has posed a significant hazard to urban growth as the most common and disaster-prone natural calamity. Due to its unique geographical location, the metro system is more vulnerable to waterlogging caused by rainstorm disaster. Research on resilience to natural disasters has attracted extensive attention in recent years. However, few studies have focused on the resilience of the metro system against rainstorms. Therefore, this paper aims to develop an assessment model for evaluating metro stations' resilience levels. Twenty factors are carried out from dimensions of resistance, recovery and adaptation. The methods of ordered binary comparison, entropy weight and cloud model are proposed to build the assessment model. Then, taking Chongqing metro system in china as a case study, the resilience level of 13 metro stations is calculated. Radar charts from dimensions of resistance, recovery, and adaptation are created to propose recommendations for improving metro stations' resilience against rainstorms, providing a reference for the sustainable development of the metro system. The case study of the Chongqing metro system in china demonstrates that the assessment model can effectively evaluate the resilience level of metro stations and can be used in other infrastructures under natural disasters for resilience assessment.

Unequal impact of the COVID-19 pandemic on mental health: Role of the neighborhood environment

The COVID-19 pandemic has taken a significant toll on people's mental wellbeing. Few studies have investigated how the neighborhood environment might help to moderate the mental health impact in a natural disaster context. We aim to investigate the unequal impact of the pandemic on mental health between different population groups, and the role of the neighborhood environment in alleviating this impact. We collected survey data (n=2,741) on mental health, neighborhood environment, and pandemic-related behaviors in Beijing metropolitan region between July 10 and 28, 2020, and then applied the partial proportional odds model. Overall, we found that the pandemic has disproportionately affected the lower-income people. The lower-income residents experienced a greater psychological impact than the higher-income residents. We further found that distance to an urban park was a key built environment variable that moderates mental health impact. Residents who lived near urban parks were 4.2 to 4.6% less likely to report an increase in negative emotions, and therefore are more resilient to the mental health impact. In addition to the built environment, a cohesive neighborhood environment may have also helped to mitigate the negative mental health impacts. These findings can inform planning policies that aim to promote healthy and resilient communities.

The Recovery of China's Industrial Parks in the First Wave of COVID-19

Industrial parks are functional urban areas that carry the capacity to support highly concentrated production activities. The robustness and anti-interference ability of these areas are of great importance to maintaining economic vitality of a country. Focusing on the rate of production recovery (RPR), this paper examines the recovery of 436 major industrial parks in mainland China during the first wave of COVID-19. Leveraging spatio-temporal big data, we measured 14 attributes pertaining to industrial parks, covering four categories, namely spatial location, central city, park development, and public service. We focused on the spatial association and heterogeneity of the recovery patterns and identified the factors that truly affected the recovery of industrial parks with quantitative evaluation of their effects. The results reveal that: (1) RPR of industrial parks are significantly spatially clustered, with an obvious "cold spot" in the early outbreak area of Hubei Province and a prominent "center-periphery" pattern in developed areas, which is highly correlated with the spread of the epidemic. (2) The mechanisms driving the resumption of industrial parks are complex and versatile. All four categories in the variable matrix are related to RPR, including up to eight effective influencing factors. The effect of influencing factors is spatially heterogeneous, and its intensity varies significantly across regions. What is more interesting is that some impact factors show positive effects in some industrial parks while inhibiting the recovery in others. On the basis of the discussion of those findings with practical experiences, the planning and construction strategies of industrial park are suggested to mitigate the impact of similar external shocks.

Evaluation of medical services from the perspective of COVID-19 vaccine demand satisfaction in Hangzhou, China

The outbreak of COVID-19 has had a huge global impact, and it continues to test the resilience of medical services to emergencies worldwide. In the current post-epidemic era, vaccination has become a highly effective strategy to prevent the spread of COVID-19. However, using conventional mathematical models to evaluate the spatial distribution of medical resources, including vaccination, ignore people's behaviors and choices and make simplifications to the real world. In this study, we use an enhanced model based on the Theory of People Behavior (TPB) to perform a macro analysis of the satisfaction ability of medical resources for vaccination in Hangzhou, China, and attribute the city to a three-level structure. According to the allocation, the supply capacity of vaccination sites is calculated and divided into four categories (good, normal, not bad, and bad). Meanwhile, we raise an assumption based on the result and the general development law of the city and analyze the reasons for the impact of personal behavior on the spatial distribution of medical resources, as well as the relationship between the demand distribution and spatial distribution of medical resources and future development strategies. It is considered that the overall medical resources, especially vaccination in Hangzhou, feature the situation of central supply overflow, and are found to hardly meet the needs of population points in surrounding areas, requiring a more flexible strategy to allocate facilities in these areas.

The sources of the Kuznets relationship between the COVID-19 mortality rate and economic performance

This paper discusses the findings of an empirical analysis of the Kuznets, or reverse U-shaped relationship, between the COVID-19 mortality rate and economic performance. In the early stages of economic development, the COVID-19 mortality rate is anticipated to rise with rising economic activity and urbanization. Eventually, the mortality rate decreases at higher economic development levels as people and the government are more capable of investing in disease abatement measures. The quality of political institutions, wealth distribution, urbanization, vaccination rate, and improvements in healthcare systems are hypothesized to affect the COVID-19 mortality rate. Examining this relationship can be effective in understanding the change in the COVID-19 mortality rate at different economic performance stages and in identifying appropriate preventive measures. This study employed the negative binomial regression to model a cross-sectional dataset of 137 countries. Results indicated that the relationship between the per-head gross domestic product (GDP) level and the COVID-19 mortality rate appeared to follow a pattern like the Kuznets curve, implying that changes in institutional quality, healthcare advancements, wealth distribution, urbanization, vaccination rate, and the percentage of the elderly population were significant in explaining the relationship. Improvement of the healthcare system has a notable effect on lowering the COVID-19 mortality rate under more effective government conditions. Additionally, the results suggested that a higher per-head GDP is required to reverse the rising trend of the mortality rate under higher income inequality. Based on these results, preventive measures, and policies to reduce COVID-19 mortalities were recommended in the conclusion section.

Links between the pandemic and urban green spaces, a perspective on spatial indices of landscape garden cities in China

The COVID-19 pandemic has inevitably changed people's lifestyles and demands for urban green space and public open space. The National Landscape Garden Cities in China (NLGCC) policy is one of the key development models in China aimed at building sustainable cities and society. In this paper, the development of the study's selection criteria and the significance and benefits of the NLGCC policy are first summarised. 391 cities were chosen from the NLGCC list to analyse the spatial distribution and construction of driving factors. The results show that the NLGCC's selection criteria have shifted from a focus on quantity to overall habitat quality. During the COVID-19 pandemic, city resilience has been examined more closely. The NLGCC policies have boosted to address ecological and environmental crises and enhanced urban disaster preparedness. The spatial distribution analysis shows that the NLGCC is spatially unevenly distributed and has a clustering trend. A total of 54.96% of the NLGCC is concentrated in China's eastern and central regions. The natural environment and socioeconomics are two main categories of driving factors. This study provides significant value to the understanding of the spatial pattern of the NLGCC offers a reference for decision-making about the construction of urban environments worldwide.

Community resilience to pandemics: An assessment framework developed based on the review of COVID-19 literature

The COVID-19 outbreak in 2019 and the challenges it posed to communities around the world, demonstrated the necessity of enhancing the resilience of communities to pandemics. In this regard, assessment frameworks can play an essential role and guide resilience-building efforts. However, the lack of a comprehensive assessment framework has led to a focus on sectoral evaluation. This study aims to propose an integrated framework for assessing the pandemic resilience of communities. For this purpose, we rely on a systematic review of literature indexed in major academic databases. We have thoroughly analyzed a total number of 115 related documents to extract relevant criteria. Findings show that many criteria and factors affect community resilience to pandemics. By inductive content coding in MAXQDA software, we have categorized these criteria into five dimensions of Institutional, Social, Economic, Infrastructural, and Demographic. Good leadership and management, insurance and governmental support, planning and preparation, expertise and labor, and available equipment and technologies are the most important institutional criteria. Communication and collective identity, mutual support, public safety and protection, public awareness, and social justice are the influential social criteria. Economic sustainability and resource availability are criteria of economic resilience. Sufficiency of services, public spaces, housing tenure, and transportation system are the main criteria related to the built environment and infrastructural dimension. Finally, demographic resilience includes physical health, psychological well-being, life quality, and hygiene. Based on these criteria, this study develops an integrated evaluation framework that researchers can implement along with conventional assessment and ranking methods to determine the level of community resilience to pandemics.

Establishment of hospital resilience framework in urban China: insight from Wuhan City

Since the Corona Virus Disease 2019 (COVID-19) swept the world, many countries face a problem that is a shortage of medical resources. The role of emergency medical facilities in response to the epidemic is beginning to arouse public attention, and the construction of the urban resilient emergency response framework has become the critical way to resist the epidemic. Today, China has controlled the domestically transmitted COVID-19 cases through multiple emergency medical facilities and inclusive patient admission criteria. Most of the existing literature focuses on case studies or characterizations of individual facilities. This paper constructs an evaluation system to measure urban hospital resilience from the spatial perspective and deciphered the layout patterns and regularities of emergency medical facilities in Wuhan, the city most affected by the epidemic in China. Findings indicate that the pattern of one center and two circles are a more compelling layout structure for urban emergency medical facilities in terms of accessibility and service coverage for residents. Meanwhile, the Fangcang shelter hospital has an extraordinary performance in terms of emergency response time, and it is a sustainable facility utilization approach in the post-epidemic era. This study bolsters areas of the research on the urban resilient emergency response framework. Moreover, the paper summarizes new medical facilities’ planning and location characteristics and hopes to provide policy-makers and urban planners with valuable empirical evidence.

Institutional development and the government response to COVID-19 in China

As COVID-19 is pervasive across the globe, governments in different countries face the dilemma of restricting the transmission risk of the virus by social distancing while yet maintaining economic activity. Inadequate social distancing policies lead to more infection cases and deaths, while over stringent social distancing policies have significant economic cost implications. This study investigates the role of local government institutions in striking the balance between saving lives and economic recovery. We based our study on a sample of 28 provincial governments in China during the early outbreak of 2020 when the emergency responses of local governments were synchronous. The findings show that local governments in those provinces with lower degrees of marketization, which were accustomed to directly intervene in the social system, mandatorily quarantined many more close contacts for each confirmed case than those in the more market-oriented provinces whose social distancing policies took economic considerations into account. The 'overdone' (over stringent) social distancing policies in the more state-oriented provinces led to lower human mobility and economic growth. This study highlights the importance of taking economic considerations into account when adopting policies and strategies to combat the spread of COVID-19 and how different institution management cultures lead to different outcomes.

Comparing COVID-19 in the antipodes: Insights from pandemic containment strategies on both sides of the Pacific

That the COVID-19 pandemic is unprecedented in terms of its scale, spread and shocks can be evinced by the myriad of ever-changing responses cities all around the world have rolled out throughout the different waves of outbreaks. Although the threat is similar across the world, it took some time before its reach became global and the waves of outbreak are experienced by cities at different times. While this staggered spread imply that some cities might manage the virus better as they learn from the experiences of cities which had been amongst the earliest to face the virus, the reality is more complicated. In the early stages of the pandemic, the global consensus on the best way to contain the virus swiftly converged in the interlinked strategies of restricting the movement of people and minimizing their social contact. However, the effectiveness of these strategies differ greatly between cities. To that end, this study focuses on COVID-19 responses in two regions (Latin America and Southeast Asia) and examines the evolution of the first wave of COVID-19 outbreaks during 2020 in Singapore, Jakarta (Indonesia), Bogotá (Colombia) and Santiago (Chile). The study is based on a comparative approach and uses a variety of data sources, namely morphology, density, housing concentration, mobility, and governance in the four analyzed cities. The goal is to shed light on the response of city governments in these two different regions in terms of mobility restrictions in order to reduce the cases of new infections. The results show the relevance of urban policies and their territorial approaches, particularly in terms of mobility and public transport networks in the four cities.

Assessing impact of the COVID-19 pandemic on China's TFP growth: Evidence from region-level data in 2020

This paper examines the first wave spread of COVID-19 in China and its impact on TFP growth for 2020, and assess the role of anti-epidemic lockdown policy in suppressing the pandemic. Methodologically, we systematically quantify the disparity in the pandemic's productivity impact and the role of lockdown policies across regions, by combining the prefecture-level TFP growth for 422 regions (including 276 municipal cites and 146 county regions) with the daily statistics on the pandemic. Our results show that the negative impact of the COVID-19 pandemic on TFP growth are more likely to occur in municipal cities, compared to rural areas. Moreover, the anti-epidemic quarantine policy succeeded to bring the COVID-19 pandemic down in China, but it may generate additional costs through dampening TFP growth if overused. Given the regions either with a relative higher resilience level or in the remote rural areas suffered more from the strict regulation. A more flexible policy is required to be designed so as to mitigate the ongoing COVID-19 impacts in future. These findings provide useful insights for China, as well as other Asian developing countries, to cope with its continuing episodes.

Key Influencing Factors and Optimization Strategy of Epidemic Resilience in Urban Communities-A Case Study of Nanjing, China

COVID-19 has posed a significantly severe impact on both people’s lives and the global economic development. Increasing the community epidemic resilience will considerably improve the national public health emergency response capacity from bottom to top. This study identifies the influencing factors of community epidemic resilience through systematic literature review under the 4R framework, then obtains the relationships of influencing factors through Interpretive structural model, and finally assesses the performance of epidemic resilience using PROMETHEE II method through empirical cases in Nanjing, China. The results show that: (1) Eight factors influencing the epidemic resilience of community are identified, and the economic level plays the root role; (2) Community epidemic resilience can be improved from robustness, rapidity, redundancy and resourcefulness aspects; (3) Through the empirical analysis, the epidemic resilience ranking of community can be displayed (Community D > T > S > F); (4) Additionally, the performance and sensitivity analysis of influencing factors in each community can be demonstrated. (5) Finally, four implications are proposed, namely, allocating public resources rationally, significantly increasing the economic level, ensuring the accuracy of information delivery and conducting disaster learning.

A machine learning-driven spatio-temporal vulnerability appraisal based on socio-economic data for COVID-19 impact prevention in the U.S. counties

A mature and hybrid machine-learning model is verified by mature empirical analysis to measure county-level COVID-19 vulnerability and track the impact of the imposition of pandemic control policies in the U.S. A total of 30 county-level social, economic, and medical variables and a timeline of the imposed policies constitutes a COVID-19 database. A hybrid feature-selection model composed of four machine-learning algorithms is developed to emphasize the regional impact of community features on the case fatality rate (CFR). A COVID-19 vulnerability index (COVULin) is proposed to measure the county's vulnerability, the effects of model's parameters on mortality, and the efficiency of control policies. The results showed that the dense counties in which minority groups represent more than 45% of the population and those with poverty rates greater than 24% were the most vulnerable counties during the first and the last pandemic peaks, respectively. Highly-correlated CFR and COVULin scores indicated a close agreement between the model outcomes and COVID-19 impacts. Counties with higher poverty and uninsured rates were the most resistant to government intervention. It is anticipated that the proposed model can play an essential role in identifying vulnerable communities and help reduce damages during long-term alike disasters.

ANN-Based traffic volume prediction models in response to COVID-19 imposed measures

Many countries around the globe have imposed several response measures to suppress the rapid spread of the COVID-19 pandemic since the beginning of 2020. These measures have impacted routine daily activities, along with their impact on economy, education, social and recreational activities, and domestic and international travels. Intuitively, the different imposed policies and measures have indirect impacts on urban traffic mobility. As a result of those imposed measures and policies, urban traffic flows have changed. However, those impacts are neither measured nor quantified. Therefore, estimating the impact of these combined yet different policies and measures on urban traffic flows is a challenging task. This paper demonstrates the development of an artificial neural networks (ANN) model which correlates the impact of the imposed response measure and other factors on urban traffic flows. The results show that the adopted ANN model is capable of mapping the complex relationship between traffic flows and the response measures with a high level of accuracy and good performance. The predicted values are closed to the observed ones. They are clustered around the regression line, with a coefficient of determination ( R 2 ) of 0.9761. Furthermore, the developed model can be generalized to determine the anticipated demand levels resulted from imposing any of the response measures in the post-pandemic era. This model can be used to manage traffic during mega-events. It can be also utilized for disaster or emergency situations, where traffic flow estimates are highly required for operational and planning purposes.

Urban resilience as a mitigating factor against economically driven out-migration during COVID-19: The case of Eilat, a tourism-based city

The COVID-19 pandemic has created an ongoing global crisis. The unprecedented shock has been particularly devastating for tourism-based cities and has tested their resilience. This study addresses the mitigating role of urban resilience in the interplay between acute crises and the phenomenon of urban outmigration. Leveraging a unique dataset collected during the first national lockdown that followed the outbreak of COVID-19 in the city of Eilat (Israel)-a geographically isolated single economic sector-based city with no feasible options to commute-we offer here a new conceptual framework and an empirical framework for measuring perceived resilience. Using validated psychometric questionnaires and employing the nested hierarchical modeling approach, we estimate the impact of perceived resilience on the decision to migrate from the city. We find that even though Eilat has all the attributes to experience significant out-migration, its residents are not inclined towards migration due to its prior investment in resilience measures, which strengthened the local community and created a unique credo shared by its residents. These findings call for policymakers to focus on long-term resilience schemes directed at increasing the appeal that cities have for their residents and ensuring their endurance in times of extreme hardship.

Investigating into the Coupling and Coordination Relationship between Urban Resilience and Urbanization: A Case Study of Hunan Province, China

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide.

Data-driven multiscale modelling and analysis of COVID-19 spatiotemporal evolution using explainable AI

To quantificationally identify the optimal control measures for regulators to best minimize COVID-19′s growth (G-rate) and death (D-rate) rates in today's context, this paper develops a top-down multiscale engineering approach which encompasses a series of systematic analyses, namely: (global scale) predictive modelling of G-rate and D-rate due to COVID-19 globally, followed by determining the most effective control factors which can best minimize both parameters over time via explainable Artificial Intelligence (AI) with SHAP (SHapley Additive exPlanations) method; (continental scale) same predictive forecasting of G-rate and D-rate in all continents, followed by performing explainable SHAP analysis to determine the most effective control factors for the respective continents; and (country scale) clustering the different countries (> 150 in total) into 3 main clusters to identify the universal set of effective control measures. By using the historical period between 2 May 2020 and 1 Oct 2021, the average MAPE scores for forecasting G-rate and D-rate are within 10%, or less on average, at the global and continental scales. Systematically, we have quantificationally demonstrated that the top 3 most effective control measures for regulators to best minimize G-rate universally are COVID-CONTACT-TRACING, PUBLIC-GATHERING-RULES, and COVID-STRINGENCY-INDEX, while the control factors relating to D-rate depend on the modelling scenario.

Understanding regional mobility resilience and its relationship with regional culture during the COVID-19 pandemic: A pathogen-stress theory perspective

The global COVID-19 pandemic has severely impacted the passenger flow. Facing the same pandemic, various regions differ in the resilience of population mobility due to differences in the regional cultural. This study uses mobile big data to quantifies regional mobility resilience of 358 cities in China. Study results reveal the differences in regional mobility resilience of cities through spatial autocorrelation analysis, and verify the effects of regional culture on mobility resilience using a panel logit regression model based on pathogen-stress theory. Spatial heterogeneity and autocorrelation in the regional mobility resilience of Chinese cities are identified through spatial analysis, which are manifested by various hot spots over time. Moreover, the panel regression results indicate that the COVID-19 pandemic has a significant negative effect on regional mobility resilience; and that the negative effect of COVID-19 on regional mobility resilience is amplified in the cities with high degrees of dialect diversity, while it is weakened in the cities with high degrees of cultural tightness (which have strict norms and punishments for deviance). This study provides theoretical implications for mobility resilience in the context of COVID-19 and advances the pathogen-stress theory. Study findings also provide practical recommendations for regions to enhance regional mobility resilience under the challenges of future public health crisis events.

Increased anxiety from fear of Omicron in China as compared to North America and Western Europe: A cross-sectional Kendall's tau-b analysis using the generalized anxiety disorder 7-item questionnaire

BACKGROUND

Policies dealing with the Coronavirus Disease 2019 (COVID-19) pandemic vary across the globe, the different governmental responses then affect the public perception of COVID-19. Many unofficial Chinese media outlets frequently spread misinformation about COVID-19 and exaggerated reports of rare sequelae of Omicron for monetization and attention seeking, leading to panics in the Chinese public. In comparison the attitudes toward Omicron in other countries around the world, especially in North America and Western Europe have shifted to a more relaxed stance.

OBJECTIVE

This article primarily aims to investigate the association between Chinese people's attitudes toward the potential after-effects of Omicron and their anxiety status, as compared to these of people living in North America or Western Europe.

METHODS

We conducted a questionnaire survey via the Credamo and collected valid data from 500 Chinese (not living in Shanghai), another 500 Chinese (living in Shanghai) and 500 people living in North America or Western Europe in June 2022. Kendall's coefficient of rank correlation tau-sub-b was used to examine this association.

RESULTS

The results suggested that subjective attitudes of Chinese participants toward the sequelae of Omicron were positively and significantly associated with their anxiety status [i.e., the Generalized Anxiety Disorder 7-item (GAD-7) scores] in Shanghai (China) (Tb = 0.44, p < 0.01) and other parts of China outside Shanghai (Tb = 0.37, p < 0.01). However, no such significant correlation was found in North America & Western Europe (Tb = -0.01, p > 0.05).

CONCLUSION

Our findings showed that Chinese participants who were more worried about the after-effects of Omicron had higher levels of anxiety. Although it is true that Long COVID-19 should be a concern, exaggerated media reporting can impact negatively on an individual's mental wellbeing. Only through the dissemination of robust scientific studies, the misinformation and the fears that follow it can be put to rest.

From federal transfers and local investments to a potential convergence of COVID-19 and climate change: The case study of São Paulo city

This paper is divided into two parts to explore some aspects of municipal development related to national and subnational investments in disaster risk reduction and urban sustainability related to Covid-19 and climate change response. In Part I, a survey on disasters and national transfers to 45 Brazilian municipalities is presented. In Part II, the local-scale approach enabled to compare the areas most affected by COVID-19 with those impacted by climate change. There are large uncertainties around financial support from the federal government and their impact at local scale. São Paulo city was chosen because it reveals some important aspects of spatial structure carried out through local investments. In this sense, updated information on floods and warmer surfaces were updated to provoke a discussion about a potential confluence with the effects of pandemic. The results highlighted the effects of scarce federal transfers and the maps help us to identify the spatial distribution of people at risk, which can be beneficial for municipal decisions as they highlight a significative relationship between pandemic effects and an uneven social structure. In conclusion, the trade-off between this unequal structure and a necessary and effectively sustainable change leads us to reflect on local investment trends.

What determines urban resilience against COVID-19: City size or governance capacity?

This study analyzed the effects of urban governance and city size on COVID-19 prevention and control measures. Based on real-time data in 276 prefecture-level Chinese cities, we used the ordinary least squares plus robust standard error strategy. It was found that: (1) despite the non-significant effect of city size, urban governance capacity was an important factor affecting the prevention and control of the COVID-19 pandemic; urban governance capacity was particularly significant in the late control of the pandemic, but not significant in the early prevention; for every unit increase of urban governance capacity, the number of recovered COVID-19 cases per capita increased by 2.4%. Moreover, (2) the influence mechanism of anti-pandemic measures in cities could be divided into the workforce, financial, and material effects, and their contribution rates were 26.15%, 32.55%, and 37.20%, respectively; namely, the effective/timely assistance from Chinese central government regarding the workforce, financial, and material resources in key pandemic areas and nationwide played a major role in pandemic control. Additionally, (3) cities with a high level of smart city construction were more capable of enhancing the pandemic prevention and control effect, indicating that smart city construction is conducive to enhanced coping with public crises.

Evaluation of Polycentric Spatial Structure in the Urban Agglomeration of the Pearl River Delta (PRD) Based on Multi-Source Big Data Fusion

The rapid development of the urban city has led to great changes in the urban spatial structure. Thus, analyses of polycentric urban spatial structures are important for understanding these kinds of structures. In order to accurately evaluate the polycentric spatial structure of urban agglomerations and judge the differences between the actual development situation and overall planning of urban agglomerations, this study proposes a new method to identify the polycentric spatial structure of urban agglomerations in the Pearl River Delta based on the fusion of nighttime light (NTL) data, point of interest (POI) data, and Tencent migration data (TMG). In the first step, the NTL, POI, and TMG data are fused via wavelet transform; in the second step, Anselin local Moran’s I (LMI) and geographically weighted regression (GWR) were used to identify the main centers and subcenters, respectively. In the third step, the accuracy of the results of this study was further verified and discussed in the context of overall planning. The results show that the accuracy of urban polycenter identification via LMI and GWR after data fusion was 92.84%, and the Kappa value was 0.8971, which was higher than the results of polycenter identification via the traditional relative threshold. After comparing the identification results with the overall planning, firstly, we see that the fusion of multi-source big data can help to accurately evaluate the polycentric spatial structure within the urban agglomeration. Secondly, the fusion of dynamic data and static data can help identify the polycentric spatial structure of urban space more accurately. Therefore, this study can provide a new design for urban polycentric spatial structures, and further provide a reliable reference for the spatial optimization of urban agglomeration and the formulation of regional spatial development policies.

COVID-19 Pandemic, Urban Resilience and Real Estate Prices: The Experience of Cities in the Yangtze River Delta in China

The COVID-19 pandemic has severely impacted the urban real estate market around the world. This study regards the impact of the pandemic as a quasi-natural experiment, using the Difference in Difference model (DID) to examine the short-term impact of this severe public health crisis on the residential land and housing markets in the Yangtze River Delta. The study found that the COVID-19 pandemic has had a significant inhibitory effect on the average price of urban residential land and houses in the Yangtze River Delta. Although the currency oversupply has caused real estate prices in all cities to rise, the price of urban residential land decreased by 13.7% for each additional unit of epidemic severity. The greater the city’s resilience to the pressure of the COVID-19 pandemic, the faster its residential land prices will recover. Empirical research on the new house samples confirmed this conclusion. Local governments should continue to improve their ability to manage abnormal conditions, not only to prevent the spread of the epidemic, but also to gradually promote the recovery of the urban economy, strengthen urban resilience to better respond to health crises, and achieve sustainable urban development.

Development of a Healthy Assessment System For Residential Building Epidemic Prevention

During the period of COVID-19, the number of residents infected in urban communities continued to rise, implying that most of the current building layouts can't effectively resist the spread of infectious diseases, and the outbreak of COVID-19 has led to the need of changes for the current building environment. Therefore, the epidemic prevention should be considered in the residential building design, and the health design of residential community should be carried out from the perspective of epidemic prevention. In order to improve the ability of epidemic prevention of residential buildings and deal with the sudden pandemic and influenza in the post-epidemic era, a Healthy Assessment System for Residential Building Epidemic Prevention (HASRBEP) was developed according to the epidemic impact on residential buildings, the design and measures of epidemic prevention for residential buildings and the Chinese Assessment standard for healthy building (T/ASC 02-2016). Both entropy weight method and expert scoring method were used to determine the specific weight of the index. The HASRBEP includes control item assessment, preliminary assessment and extension assessment. The newly developed HASRBEP was used to assess the residential buildings of the Yulongzhuang Building Community located in Quanzhou, Fujian Province, China. The results show that the HASRBEP can be used to guide the health and epidemic prevention design of residential buildings.

A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions

In Germany, local health departments are responsible for surveillance of the current pandemic situation. One of their major tasks is to monitor infected persons. For instance, the direct contacts of infectious persons at group meetings have to be traced and potentially quarantined. Such quarantine requirements may be revoked, when all contact persons obtain a negative polymerase chain reaction (PCR) test result. However, contact tracing and testing is time-consuming, costly and not always feasible. In this work, we present a statistical model for the probability that no transmission of COVID-19 occurred given an arbitrary number of negative test results among contact persons. Hereby, the time-dependent sensitivity and specificity of the PCR test are taken into account. We employ a parametric Bayesian model which combines an adaptable Beta-Binomial prior and two likelihood components in a novel fashion. This is illustrated for group events in German school classes. The first evaluation on a real-world dataset showed that our approach can support important quarantine decisions with the goal to achieve a better balance between necessary containment of the pandemic and preservation of social and economic life. Future work will focus on further refinement and evaluation of quarantine decisions based on our statistical model.

Spatial Variability in COVID-19 Mortality

COVID-19 emerged as a global pandemic in the spring of 2020. Since that time, the disease has resulted in approximately 150 million cases and 3 million deaths worldwide. However, there is significant spatial variation in the rate of mortality from COVID-19. Here, we briefly explore spatial variations in COVID-19 mortality by country groupings and propose possible explanations for the differences observed. Specifically, we find that there is a statistically significant difference in COVID-19 mortality between countries grouped into categories based on (1) developed, primarily western diets and healthcare systems; (2) "Scandinavian" countries with advanced healthcare systems and generally anti-inflammatory diets, and (3) developing countries. We do not infer causality but believe that the observed associations provide hypotheses for future research investigations. Moreover, our results add further evidence to support additional exploration of vitamin D exposure/status and COVID-19 mortality.

One-Year Lesson: Machine Learning Prediction of COVID-19 Positive Cases with Meteorological Data and Mobility Estimate in Japan

With the wide spread of COVID-19 and the corresponding negative impact on different life aspects, it becomes important to understand ways to deal with the pandemic as a part of daily routine. After a year of the COVID-19 pandemic, it has become obvious that different factors, including meteorological factors, influence the speed at which the disease is spread and the potential fatalities. However, the impact of each factor on the speed at which COVID-19 is spreading remains controversial. Accurate forecasting of potential positive cases may lead to better management of healthcare resources and provide guidelines for government policies in terms of the action required within an effective timeframe. Recently, Google Cloud has provided online COVID-19 forecasting data for the United States and Japan, which would help in predicting future situations on a state/prefecture scale and are updated on a day-by-day basis. In this study, we propose a deep learning architecture to predict the spread of COVID-19 considering various factors, such as meteorological data and public mobility estimates, and applied it to data collected in Japan to demonstrate its effectiveness. The proposed model was constructed using a neural network architecture based on a long short-term memory (LSTM) network. The model consists of multi-path LSTM layers that are trained using time-series meteorological data and public mobility data obtained from open-source data. The model was tested using different time frames, and the results were compared to Google Cloud forecasts. Public mobility is a dominant factor in estimating new positive cases, whereas meteorological data improve their accuracy. The average relative error of the proposed model ranged from 16.1% to 22.6% in major regions, which is a significant improvement compared with Google Cloud forecasting. This model can be used to provide public awareness regarding the morbidity risk of the COVID-19 pandemic in a feasible manner.

The Use of GIS Technology to Optimize COVID-19 Vaccine Distribution: A Case Study of the City of Warsaw, Poland

The COVID-19 pandemic is a global challenge, and the key to tackling it is vaccinating a specified percentage of the population to acquire herd immunity. The observed problems with the efficiency of the vaccination campaigns in numerous countries around the world, as well as the approach used at the initial stage of the National Immunization Program in Poland, prompted us to analyse the possibility of using GIS technology to optimize the distribution of vaccines to vaccination sites so as to minimize the period needed to vaccinate individual population groups. The research work was carried out on the example of Warsaw, the capital of Poland and the city with the largest population in the country. The analyses were carried out for the 60-70 and 50-60 age groups, in various approaches and for vaccines of different companies (Moderna, BioNTech, AstraZeneca), used to vaccinate people in Poland. The proposed approach to optimize vaccine distribution uses Thiessen's tessellation to obtain information on the number of people in a given population group living in the area of each vaccination site, and then to estimate the time needed to vaccinate that group. Compared to the originally used vaccination scenario with limited availability of vaccines, the proposed approach allows practitioners to design fast and efficient distribution scenarios. With the developed methodology, we demonstrated ways to achieve uniform vaccination coverage throughout the city. We anticipate that the proposed approach can be easily automated and broadly applied to various urban settings.