Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China

A conceptual and computational framework for modeling the complex, adaptive dynamics of epidemics: The case of the SARS-CoV-2 pandemic in Mexico

In the quest to ensure adequate preparedness for health emergencies caused by infectious disease pandemics, there is a need for tools that can address the myriad relevant questions related to the spread and trajectory of pandemics. A hybrid intelligence model that combines human and artificial intelligence may provide a viable solution, as it can process data from models that comprehensively integrate contextual and direct factors, effectively mimicking the social processes surrounding transmission while incorporating human interpretation to enhance our understanding of pandemics. Using data from the COVID-19 pandemic, we demonstrate the implementation of this approach with the publically available EpI-PUMA (Epidemiological Intelligence Platform for the Universidad Nacional Autónoma de México ("PUMA")) project and platforms, where a user may create their own hybrid intelligence Bayesian classifier models for a range of epidemiological indicators of interest. EPI-Puma integrates data from various public sources (including the national registry of SARS-CoV-2 cases, census data, poverty indicators, climate data and data related to atmospheric contaminants), enabling the deployment of models that predict a range of relevant outcomes. The main criteria for the data included was its coverage (at least at the municipality level) and availability (public data). EPI-Puma was able to identify both the differential predictive value of the different sets of factors related to the epidemic path and well as anticipate with a high probability the path of the pandemic (typical areas under the ROC curve for the associated classifiers being 0.8-0.9).

Deciphering the COVID-19 density puzzle: A meta-analysis approach

The COVID-19 pandemic has sparked widespread efforts to mitigate its transmission, raising questions about the role of urban density in the spread of the virus. Understanding how city density affects the severity of communicable diseases like COVID-19 is crucial for designing sustainable, pandemic-resilient cities. However, recent studies on this issue have yielded inconsistent and conflicting results. This study addresses this gap by employing a comprehensive meta-analytic approach, synthesizing data across diverse regions and urban contexts to offer a broader, more nuanced perspective on the impact of city density. A systematic meta-analysis was conducted, initially screening 2,452 studies from Google Scholar, Scopus, and Avery Index databases (up to August 31, 2023), and narrowing down to 63 eligible studies. Using the restricted maximum likelihood (REML) method with a random effects model, the study accounted for variations across different studies. Statistical tests, file drawer analysis, and influence measure analysis were performed, along with assessments of heterogeneity and publication bias through forest and funnel plots. Despite this extensive analysis, the findings indicate that city density has a negligible effect on the severity of COVID-19, challenging the prevailing assumptions in the literature.

A comparative analysis of influenza and COVID-19: Environmental-ecological impacts, socioeconomic implications, and future challenges

In the last century, global pandemics have been primarily driven by respiratory infections, which consistently rank among the top 20 causes of death worldwide. The coronavirus disease 2019 (COVID-19) pandemic has underscored the intricate nature of managing multiple health crises simultaneously. In recent years, climate change has emerged as a major biosafety and population health challenge. Global warming and extreme weather events have intensified outbreaks of climate-sensitive infectious diseases, especially respiratory diseases. Influenza and COVID-19 have emerged as two of the most significant respiratory pandemics, each with unique epidemic characteristics and far-reaching consequences. Our comparative analysis reveals that while both diseases exhibit high transmission rates, COVID-19's longer incubation period and higher severity have led to more profound and prolonged socioeconomic disruptions than influenza. Both pandemics have highlighted the exacerbating effects of climate change, with extreme weather events intensifying the spread and impact of these diseases. The COVID-19 pandemic exposed vulnerabilities in global healthcare systems and economies on an unprecedented scale, outstripping the strain caused by influenza outbreaks. Importantly, the COVID-19 pandemic has not only reshaped global public health strategies but also significantly impacted the epidemiology of influenza. Despite these differences and associations, both diseases underscore the urgent need for robust pandemic preparedness and adaptable public health strategies. This review delineates the overlaps and distinctions between influenza and COVID-19, offering insights into future challenges and the critical steps needed to enhance healthcare system resilience and improve global responses to pandemics.

Innovative public strategies in response to COVID-19: A review of practices from China

The COVID-19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top-down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence-based treatment, and well-planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge-sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.

Prospective Association Between 24-Hour Movement Behaviors and Fundamental Movement Skills in Chinese Preschoolers During the COVID-19 Pandemic: A Compositional and Reallocation Analysis

BACKGROUND

This prospective observation study explored the association between 24-hour movement behaviors and fundamental movement skills (FMS) in Chinese preschoolers during the COVID-19 pandemic.

METHODS

Four hundred and eighteen preschoolers (226 males; 4.0 [0.6] y old) from Zhuhai, China, completed the device-based physical activity (PA) and sedentary behavior measures, and sleep duration was reported by parents at baseline (October 2021 to December 2021). FMS was assessed using the Test of Gross Motor Development: Third Edition at 1-year follow-up (October 2022 to December 2022). The compositional analysis and isotemporal substitution were used.

RESULTS

Moderate to vigorous PA (MVPA) was positively associated with locomotor skills and ball skills (P < .05), and light physical activity was negatively associated with locomotor skills (P < .05) during the COVID-19 pandemic. FMS improvements were linked to the addition of MVPA at the expense of light physical activity, sedentary behavior, and sleep. The estimated detriments to FMS were larger in magnitude than the estimated benefits of time reallocation from MVPA to light physical activity, sedentary behavior, and sleep.

CONCLUSIONS

This study provides evidence about 24-hour movement behaviors and FMS during the COVID-19 pandemic and highlights the importance of participating in MVPA to improve preschoolers' FMS development during the COVID-19 era.

Do COVID-19 Infectious Disease Models Incorporate the Social Determinants of Health? A Systematic Review

Objectives

To identify COVID-19 infectious disease models that accounted for social determinants of health (SDH).

Methods

We searched MEDLINE, EMBASE, Cochrane Library, medRxiv, and the Web of Science from December 2019 to August 2020. We included mathematical modelling studies focused on humans investigating COVID-19 impact and including at least one SDH. We abstracted study characteristics (e.g., country, model type, social determinants of health) and appraised study quality using best practices guidelines.

Results

83 studies were included. Most pertained to multiple countries (n = 15), the United States (n = 12), or China (n = 7). Most models were compartmental (n = 45) and agent-based (n = 7). Age was the most incorporated SDH (n = 74), followed by gender (n = 15), race/ethnicity (n = 7) and remote/rural location (n = 6). Most models reflected the dynamic nature of infectious disease spread (n = 51, 61%) but few reported on internal (n = 10, 12%) or external (n = 31, 37%) model validation.

Conclusion

Few models published early in the pandemic accounted for SDH other than age. Neglect of SDH in mathematical models of disease spread may result in foregone opportunities to understand differential impacts of the pandemic and to assess targeted interventions.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020207706], PROSPERO, CRD42020207706.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

Influences of public health emergency and social isolation on older adults' wellbeing: evidence from a longitudinal study

Previous research has identified social isolation as a significant detriment to the wellbeing of older adults. However, studies that consider endogenous issues are scarce. The present paper examines the impact of the recent exogenous shock, the COVID-19 pandemic on the wellbeing of the older adult population using a longitudinal dataset from China for the period 2016-2020. The results of this study indicate that the life satisfaction of Chinese older adults was negatively affected, e particularly in regions where social distancing measures were more strictly enforced. Declines in physical and mental health were found to be attributable to declines in life satisfaction. Those who experienced greater exposure to the pandemic were more likely to suffer from chronic disease, illness, and insomnia, and many found it challenging to complete tasks during the lockdown. Furthermore, heterogeneity estimation shows that these effects are stronger among the rural older adult, females, those without a spouse, and those with less education.

A COMPREHENSIVE REVIEW ON THE MULTIFACETED INTERACTIONS BETWEEN HOST IMMUNITY AND VIRAL PATHOGENESIS IN COVID-19

The Corona Virus Disease (COVID-19) pandemic has presented unparalleled challenges, marked by a wide array of clinical presentations spanning from asymptomatic carriage to severe respiratory compromise and multi-organ dysfunction. It is crucial to comprehend the intricate interplay between host immunity and viral pathogenesis to elucidate disease mechanisms and guide therapeutic strategies. This review delves into the multifaceted interactions between host immunity and viral pathogenesis in COVID-19, with a particular focus on the impact of host factors such as age, sex, comorbidities, and genetic predisposition on disease severity. Utilizing state-of-the-art methodologies, including multiomics approaches, has yielded an expansive molecular portrayal of COVID-19, furnishing innovative perspectives on host immune reactions, viral pathogenicity, and disease advancement. Establishing standardized methodologies for data analysis and interpretation while concurrently addressing ethical considerations and promoting interdisciplinary collaboration are crucial steps in advancing our comprehension of COVID-19 pathogenesis. Despite obstacles like complexities in data integration, this review highlights the imperative of persistent endeavors in deciphering the complex interactions between hosts and pathogens to alleviate the global health ramifications of COVID-19.

A tale of lockdown policies on the transmission of COVID-19 within and between Chinese cities: A study based on heterogeneous treatment effect

During the early outbreak phase of COVID-19 in China, lockdowns prevailed as the only available policy tools to mitigate the spread of infection. To evaluate the impact of lockdown policies in the context of the first phase of COVID-19 pandemic, we leverage data on daily confirmed cases per million people and related characteristics of a large set of cities. The study analyzed 369 Chinese cities, among which 188 implemented lockdowns of varying severity levels from January 23 to March 31, 2020. We use nationwide Baidu Mobility data to estimate the impact of lockdown policies on mitigating COVID-19 cases through reducing human mobility. We adopt a heterogeneous treatment effect model to quantify the effect of lockdown policies on containing confirmed case counts. Our results suggest that lockdowns substantially reduced human mobility, and larger reduction in mobility occurred within-city compared to between-city. The COVID-19 daily confirmed cases per million people decreased by 9% - 9.2% for every ten-percentage point fall in within-city travel intensity in t+7 timeframe. We also find that one city's lockdowns can effectively reduce the spillover cases of the traveler's destination cities. We find no evidence that stricter lockdowns are more effective at mitigating COVID-19 risks. Our findings provide practical insights about the effectiveness of NPI during the early outbreak phase of the unprecedented pandemic.

Socioeconomic and Health Determinants of the Prevalence of COVID-19 in a Population of Children with Respiratory Diseases and Symptoms

BACKGROUND

Most epidemiological studies indicate that bronchial asthma is not a risk factor for COVID-19, but previous analyses have not additionally focused on the socioeconomic determinants of SARS-CoV-2 infection in children with asthma, bronchitis, and respiratory symptoms.

AIMS

This research aimed to investigate the correlation between the socioeconomic status of families and the prevalence of respiratory conditions such as asthma, bronchitis, and respiratory symptoms in children, in addition to exploring their association with the prevalence of COVID-19. The study involved a cross-sectional epidemiological investigation conducted in 2022, encompassing 2454 students from elementary schools in Poland. The parents of the students completed a questionnaire modeled after the International Study on Asthma and Allergies in Childhood (ISAAC). Socioeconomic status (SES) indicators were determined based on parental education, self-reported economic status, and housing conditions. To assess the impact of social factors and health on the occurrence of COVID-19, odds ratios (ORs) were calculated. The findings revealed several COVID-19 risk factors, including higher maternal (OR 2.2; 95%CI: 1.3-3.0) and paternal education (OR 1.9; 95%CI: 1.3-2.4), urban residence (OR 1.7; 95%CI: 1.3-2.1), the presence of mold in residences (OR 1.7; 95%CI: 1.0-2.3), bronchitis (OR 1.5; 95%CI: 1.2-2.0), and chronic cough (OR 1.8; 95%CI: 1.3-2.4). Further analysis, stratifying children based on their baseline health status (i.e., presence or absence of asthma, bronchitis, and chronic cough), indicated that higher parental education increased the risk of COVID-19 solely for children without pre-existing conditions. The occurrence of SARS-CoV-2 infections was found to be notably associated with mold exposure in children who did not have bronchial asthma. Rigorous multivariate analyses substantiated the collective impact of factors such as residential environment, the existence of mold and moisture, and a history of bronchitis. This study's conclusions highlight a higher frequency of SARS-CoV-2 infections in cases where bronchitis had been diagnosed previously and chronic cough was prevalent. Interestingly, the initially hypothesized higher prevalence of COVID-19 among children with bronchial asthma did not receive confirmation in our findings. This study highlights the importance of urban residence, exposure to mold or dampness, and higher parental education in the incidence of COVID-19. Higher parental education was a significant factor in increasing the risk of COVID-19 among children without bronchitis, chronic cough, and asthma.

Development of New Stringency Indices for Nonpharmacological Social Distancing Policies Implemented in Korea During the COVID-19 Pandemic: Random Forest Approach

BACKGROUND

In the absence of an effective treatment method or vaccine, the outbreak of the COVID-19 pandemic elicited a wide range of unprecedented restriction policies aimed at mitigating and suppressing the spread of the SARS-CoV-2 virus. These policies and their Stringency Index (SI) of more than 160 countries were systematically recorded in the Oxford COVID-19 Government Response Tracker (OxCGRT) data set. The SI is a summary measure of the overall strictness of these policies. However, the OxCGRT SI may not fully reflect the stringency levels of the restriction policies implemented in Korea. Korea implemented 33 COVID-19 restriction policies targeting 4 areas: public facilities, public events, social gatherings, and religious gatherings.

OBJECTIVE

This study aims to develop new Korea Stringency Indices (KSIs) that reflect the stringency levels of Korea's restriction policies better and to determine which government-implemented policies were most effective in managing the COVID-19 pandemic in Korea.

METHODS

The random forest method was used to calculate the new KSIs using feature importance values and determine their effectiveness in managing daily COVID-19 confirmed cases. Five analysis periods were considered, including November 01, 2020, to January 20, 2021 (Period 1), January 20, 2021, to June 27, 2021 (Period 2), November 01, 2020, to June 27, 2021 (Period 3), June 27, 2021, to November 01, 2021 (Period 4), and November 01, 2021, to April 24, 2022 (Period 5).

RESULTS

Among the KSIs, public facilities in period 4, public events in period 2, religious gatherings in periods 1 and 3, and social gatherings in period 5 had the highest importance. Among the public facilities, policies associated with operation hour restrictions in cinemas, restaurants, PC rooms, indoor sports facilities, karaoke, coffee shops, night entertainment facilities, and baths or saunas had the highest importance across all analysis periods. Strong positive correlations were observed between daily confirmed cases and public facilities, religious gatherings, and public events in period 1 of the pandemic. From then, weaker and negative correlations were observed in the remaining analysis periods. The comparison with the OxCGRT SI showed that the SI had a relatively lower feature importance and correlation with daily confirmed cases than the proposed KSIs, making KSIs more effective than SI.

CONCLUSIONS

Restriction policies targeting public facilities were the most effective among the policies analyzed. In addition, different periods call for the enforcement of different policies given their effectiveness varies during the pandemic.

Spatial co-location patterns between early COVID-19 risk and urban facilities: a case study of Wuhan, China

Introduction

COVID-19, being a new type of infectious disease, holds significant implications for scientific prevention and control to understand its spatiotemporal transmission process. This study examines the diverse spatial patterns of COVID-19 within Wuhan by analyzing early case data alongside urban infrastructure information.

Methods

Through co-location analysis, we assess both local and global spatial risks linked to the epidemic. In addition, we use the Geodetector, identifying facilities displaying unique spatial risk characteristics, revealing factors contributing to heightened risk.

Results

Our findings unveil a noticeable spatial distribution of COVID-19 in the city, notably influenced by road networks and functional zones. Higher risk levels are observed in the central city compared to its outskirts. Specific facilities such as parking, residence, ATM, bank, entertainment, and hospital consistently exhibit connections with COVID-19 case sites. Conversely, facilities like subway station, dessert restaurant, and movie theater display a stronger association with case sites as distance increases, hinting at their potential as outbreak focal points.

Discussion

Despite our success in containing the recent COVID-19 outbreak, uncertainties persist regarding its origin and initial spread. Some experts caution that with increased human activity, similar outbreaks might become more frequent. This research provides a comprehensive analytical framework centered on urban facilities, contributing quantitatively to understanding their impact on the spatial risks linked with COVID-19 outbreaks. It enriches our understanding of the interconnectedness between urban facility distribution and transportation flow, affirming and refining the distance decay law governing infectious disease risks. Furthermore, the study offers practical guidance for post-epidemic urban planning, promoting the development of safer urban environments resilient to epidemics. It equips government bodies with a reliable quantitative analysis method for more accurately predicting and assessing infectious disease risks. In conclusion, this study furnishes both theoretical and empirical support for tailoring distinct strategies to prevent and control COVID-19 epidemics.

Impact of human mobility on the epidemic spread during holidays

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures.

Knowledge, psychological impacts, and protective behaviours during the first wave of the COVID-19 pandemic among Chinese residents in Canada with dependent school-age children: a cross-sectional online study

BACKGROUND

The purpose of this study was to describe the knowledge, protective behaviours, and psychological impact of COVID-19 on Chinese residents in Canada, as the emotional and behavioural impacts of the pandemic have not been intensively studied amongst these populations. It was important to determine whether having dependent school-age children (DSAC) aged 16 or under was associated with adverse psychological impacts amongst the Chinese residents living in the country.

METHODS

In April 2020, 757 eligible participants were recruited through a snowball sampling to complete an online survey related to the COVID-19 pandemic. Psychological, behavioural, and sociodemographic variables were collected and first analyzed using descriptive and univariate statistics. Multiple logistic regression analyses were performed to further confirm the observed significant associations in bivariate analyses for selected psychological outcome variables.

RESULTS

Seven hundred forty-two participants who responded to the "dependent school-age children" question were included in the analysis. Most of them identified as females (65.8%) and 77.2% included receiving a university degree or higher. There were no significant differences in COVID-19 knowledge between those living with or without DSAC. However, participants with DSAC were more likely to perceive themselves as being at greater risk of contracting COVID-19 (p = .023); therefore, having a higher chance of adopting protective behaviours (e.g., hand washing, sanitizing frequently or disinfecting work and living spaces (p < .05), elevated risks of depression (p = .007), and stress (p = .010), compared to those without DSAC.

CONCLUSIONS

Predominantly, the Chinese residents in Canada with dependent school-age children were more likely to report the negative psychological impacts of the pandemic. These findings warrant further investigations that may contribute to informing key stakeholders about the identification and implementation of policies and interventions to support the needs of parents with young children, during and after the pandemic.

The micro-economic effects of COVID-19 containment measures: A simple model and evidence from China

Optimizing the trade-off between economic growth and public health is a major goal of public administration, especially during public health events. Although containment measures are widely used to combat the Covid-19 outbreak, it is still debated how the measures affect the economy. Using a simplified susceptible-infected-recovered (SIR) model, this study investigates the dynamic impact of lockdown policy on social costs during the epidemic and the underlying mechanism, revealing that the lockdown policy has both a "shutdown effect" and an "anti-epidemic effect", and should be implemented and lifted in a timely manner. Based on a micro-level dataset of 57,547 private enterprises in China in 2020, this study provided empirical evidence for the presence of negative "shutdown effect" and positive "anti-epidemic effect" of lockdown on reopening, both of which are in part mediated by labor input, factor mobility, and market demand recovery. Furthermore, the shutdown effect is weaker in regions with sufficient testing and quarantine resources, government capacity and preference for targeted response, whereas the anti-epidemic effect is stronger in densely populated areas with relatively low public compliance. Additionally, digital measures can aid in the containment of epidemics. The findings not only contribute to a better understanding of the rationality and effectiveness of the lockdown policy, but also provides practical evidence and implications for the government to improve the synergistic efficiency of epidemic control tools and strengthen the resilience of local economic growth.

Spatio-temporal dynamic of the COVID-19 epidemic and the impact of imported cases in Rwanda

INTRODUCTION

Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions.

RESULTS

The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda.

CONCLUSION

The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system.

Evaluating the Impact of COVID-19 on Hospital Profit Compensation Activities: A Difference-in-Differences Event Study Analysis in China

The impact of the 2019 coronavirus disease (COVID-19) pandemic is still being revealed, and little is known about the effect of COVID-19-induced outpatient and inpatient losses on hospital operations in many counties. Hence, we aimed to explore whether hospitals adopted profit compensation activities after the 2020 first-wave outbreak of COVID-19 in China. A total of 2,616,589 hospitalization records from 2018, 2019, and 2020 were extracted from 36 tertiary hospitals in a western province in China; we applied a difference-in-differences event study design to estimate the dynamic effect of COVID-19 on hospitalized patients' total expenses before and after the last confirmed case. We found that average total expenses for each patient increased by 8.7% to 16.7% in the first 25 weeks after the city reopened and hospital admissions returned to normal. Our findings emphasize that the increase in total inpatient expenses was mainly covered by claiming expenses from health insurance and was largely driven by an increase in the expenses for laboratory tests and medical consumables. Our study documents that there were profit compensation activities in hospitals after the 2020 first-wave outbreak of COVID-19 in China, which was driven by the loss of hospitalization admissions during this wave outbreak.

Association of the corona virus (Covid-19) epidemic with environmental risk factors

The current outbreak of the novel coronavirus SARS-CoV-2 (coronavirus disease 2019; previously 2019-nCoV), epicenter in Hubei Province (Wuhan), People's Republic of China, has spread too many other countries. The transmission of the corona virus occurs when people are in the incubation stage and do not have any symptoms. Therefore, the role of environmental factors such as temperature and wind speed becomes very important. The study of Acute Respiratory Syndrome (SARS) indicates that there is a significant relationship between temperature and virus transmission and three important factors, namely temperature, humidity and wind speed, cause SARS transmission. Daily data on the incidence and mortality of Covid-19 disease were collected from World Health Organization (WHO) website and World Meter website (WMW) for several major cities in Iran and the world. Data were collected from February 2020 to September 2021. Meteorological data including temperature, air pressure, wind speed, dew point and air quality index (AQI) index are extracted from the website of the World Meteorological Organization (WMO), The National Aeronautics and Space Administration (NASA) and the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Statistical analysis carried out for significance relationships. The correlation coefficient between the number of infected people in one day and the environmental variables in the countries was different from each other. The relationship between AQI and number of infected was significant in all cities. In Canberra, Madrid and Paris, a significant inverse relationship was observed between the number of infected people in one day and wind speed. There is a significant positive relationship between the number of infected people in a day and the dew point in the cities of Canberra, Wellington and Washington. The relationship between the number of infected people in one day and Pressure was significantly reversed in Madrid and Washington, but positive in Canberra, Brasilia, Paris and Wuhan. There was significant relationship between Dew point and prevalence. Wind speed showed a significant relationship in USA, Madrid and Paris. AQI was strongly associated with the prevalence of covid19. The purpose of this study is to investigate some environmental factors in the transmission of the corona virus.

COVID-19: Financial Stress and Well-Being in Families

In all areas of knowledge, research has shown the devastating effects of COVID-19, and the impact on families' financial stress and well-being is one of them. Crises are predictors of families' financial stress as they produce changes in their income and negative feelings, such as fear and demotivation, which affect well-being. This study analyses the financial and social impact of COVID-19 on families, supported by the ABCE-WB model, with data collection being the result of snowball sampling. The results obtained allow the conclusion that the current pandemic crisis has caused financial stress in families, to a greater or lesser degree, and caused feelings of fear and demotivation as consequences of the general lockdown. The empirical evidence also shows that these effects are positively associated with the perception of their level of well-being. The contribution of the study lies in corroborating the model used. Final considerations are presented together with the limitations and suggestions for future research.

Do COVID-19 pandemic-related policy shocks flatten the bid-rent curve? Evidence from real estate markets in Shanghai

The COVID-19 pandemic has drastically affected the socioeconomic activities and peoples' daily life, resulting in a change in locational preferences in the real estate markets. Although enormous efforts have been devoted to examining the housing price impacts of the COVID-19 pandemic, little is known about the responses of the real estate markets to the evolving pandemic control measures. This study investigates the price gradient effects of various pandemic-related policy shocks using a hedonic price model on the district-level property transaction data in Shanghai, China over a 48-month period from 2018 to 2021. We found that these shocks have significantly altered the bid-rent curves. The price gradient for residential property units decreased in absolute value to - 0.433 after Wuhan's lockdown, demonstrating peoples' preferences to avoid the high infection risks in districts closer to the city center. However, in the post-reopening and post-vaccine periods, the price gradient increased to - 0.463 and - 0.486, respectively, implying rational expectations of a recovering real estate market for the low infection and mortality rates. In addition, we discovered that Wuhan's lockdown has steepened the price gradient for commercial property units, suggesting a decline in business volumes and an increase in operating costs in the low-density districts imposed by the strict pandemic control measures. This study contributes to the empirical literature on the price gradient effects of the COVID-19 pandemic by extending the study period to the post-vaccine era.

Population Older than 69 Had Similar Fatality Rates Independently If They Were Admitted in Nursing Homes or Lived in the Community: A Retrospective Observational Study during COVID-19 First Wave

The aim of this study is to assess the influence of living in nursing homes on COVID-19-related mortality, and to calculate the real specific mortality rate caused by COVID-19 among people older than 20 years of age in the Balaguer Primary Care Centre Health Area during the first wave of the pandemic. We conducted an observational study based on a database generated between March and May 2020, analysing COVID-19-related mortality as a dependent variable, and including different independent variables, such as living in a nursing home or in the community (outside nursing homes), age, sex, symptoms, pre-existing conditions, and hospital admission. To evaluate the associations between the independent variables and mortality, we calculated the absolute and relative frequencies, and performed a chi-square test. To avoid the impact of the age variable on mortality and to assess the influence of the "living in a nursing home" variable, we established comparisons between infected population groups over 69 years of age (in nursing homes and outside nursing homes). Living in a nursing home was associated with a higher incidence of COVID-19 infection, but not with higher mortality in patients over 69 years of age (p = 0.614). The real specific mortality rate caused by COVID-19 was 2.27⁰/₀₀. In the study of the entire sample, all the comorbidities studied were associated with higher mortality; however, the comorbidities were not associated with higher mortality in the infected nursing home patients group, nor in the infected community patients over 69 years of age group (except for neoplasm history in this last group). Finally, hospital admission was not associated with lower mortality in nursing home patients, nor in community patients over 69 years of age.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

Pandemic related changes in social interaction are associated with changes in automatic approach-avoidance behaviour

People's natural tendencies to either approach or avoid different stimuli in their environment are considered fundamental motivators of human behaviour. There is a wealth of research exploring how changes in approach and avoidance motivational orientations impact behaviour with consequences for wellbeing. However, research has seldom explored this relationship in reverse. The COVID-19 pandemic offered a unique opportunity to explore whether widespread changes in social behaviour are associated with changes in automatic approach-avoidance tendencies over time. We gathered online survey data on people's adherence to 7 of the prescribed social restrictions set out by the UK government and people's automatic approach-avoidance tendencies in response to different stimuli (sad/happy faces and social scenes) at three time points during the COVID-19 pandemic. Reduced-overall-interaction (digital and in person) was found to be significantly associated with faster avoidance relative to approach of sad faces. The results suggest that automatic approach-avoidance tendencies may function to protect people against the typically negative experience of reduced social interaction, with important implications for understanding public resilience during times of crisis, and beyond.

Relative deprivation, inequality and the Covid-19 pandemic

There is a growing concern that inequalities are hindering health outcomes. This paper's primary objective is to investigate the role of relative deprivation and inequality in explaining the daily spread of the Covid-19 pandemic. For this purpose, we use secondary cross-sectional data across 119 (developed and developing) countries from January 2020 – to April 2021. For the empirical analysis, we use a recent dynamic panel data modelling approach that allows us to identify the role of time-invariant variables such as degree of globalisation, political freedom and income inequality on the dynamics of the pandemic and fatality rates across countries. We find that new cases per million and fatality rates are highly persistent processes. After controlling for time-varying mobility statistics from the Google mobility database and region-specific dummy variables, the two significant factors that explain the severity of Covid-19 spread in a country are per-capita Gross Domestic Product (GDP) and Yitzhaki's relative income deprivation index. Lagged value of new cases per million significantly explains cross-country variations in the daily case fatality rates. A higher proportion of the older population and pollution increased fatality rates while better medical infrastructure reduced it.

Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia

In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)-based on publicly available national statistics-founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots-aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

A mixed effects changepoint quantile regression model for longitudinal data with application on COVID-19 data

Introduction

Longitudinal individual response profiles could exhibit a mixture of two or more phases of increase or decrease in trend throughout the follow-up period, with one or more unknown transition points (changepoints). The detection and estimation of these changepoints is crucial. Most of the proposed statistical methods for detecting and estimating changepoints in literature rely on distributional assumptions that may not hold. In this case, a good alternative is to use a robust approach; the quantile regression model. There are methods in the literature to deal with quantile regression models with a changepoint. These methods ignore the within-subject dependence of longitudinal data.

Methods

We propose a mixed effects quantile regression model with changepoints to account for dependence structure in the longitudinal data. Fixed effects parameters, in addition to the location of the changepoint, are estimated using the profile estimation method. The stochastic approximation EM algorithm is proposed to estimate the fixed effects parameters exploiting the link between an asymmetric Laplace distribution and the quantile regression. In addition, the location of the changepoint is estimated using the usual optimization methods.

Results and discussion

A simulation study shows that the proposed estimation and inferential procedures perform reasonably well in finite samples. The practical use of the proposed model is illustrated using COVID-19 data. The data focus on the effect of global economic and health factors on the monthly death rate due to COVID-19 from 1 April 2020 to 30th April 2021. the results show a positive effect on the monthly number of patients with COVID-19 in intensive care units (ICUs) for both 0.5th and 0.8th quantiles of new monthly deaths per million. The stringency index, hospital beds, and diabetes prevalence have no significant effect on both 0.5th and 0.8th quantiles of new monthly deaths per million.

Analysis of economic forecasting in the post-epidemic era: evidence from China

This paper presents a predictive analysis of the Chinese economy in the post-epidemic era. Five major public health emergencies historically similar to the COVID-19 epidemic are used as the control group, and a fuzzy mathematical model is applied to forecast and analyze China's economy after the COVID-19 epidemic. The forecast results show that China's overall economy will have recovered to the pre-epidemic level in about 1 year, with the fastest recovery in individual economic indicators, followed by government final consumption and imports, then CPI, fiscal revenue, exports and money supply, and the slowest recovery in employment. Finally, a combination of all the parties makes policies and recommendations for China's economic and social development in the post-epidemic era.

Environment and COVID-19 incidence: A critical review

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented worldwide health crisis. Many previous research studies have found and investigated its links with one or some natural or human environmental factors. However, a review on the relationship between COVID-19 incidence and both the natural and human environment is still lacking. This review summarizes the inter-correlation between COVID-19 incidence and environmental factors. Based on keyword searching, we reviewed 100 relevant peer-reviewed articles and other research literature published since January 2020. This review is focused on three main findings. One, we found that individual environmental factors have impacts on COVID-19 incidence, but with spatial heterogeneity and uncertainty. Two, environmental factors exert interactive effects on COVID-19 incidence. In particular, the interactions of natural factors can affect COVID-19 transmission in micro- and macro- ways by impacting SARS-CoV-2 survival, as well as human mobility and behaviors. Three, the impact of COVID-19 incidence on the environment lies in the fact that COVID-19-induced lockdowns caused air quality improvement, wildlife shifts and socio-economic depression. The additional value of this review is that we recommend future research perspectives and adaptation strategies regarding the interactions of the environment and COVID-19. Future research should be extended to cover both the effects of the environment on the COVID-19 pandemic and COVID-19-induced impacts on the environment. Future adaptation strategies should focus on sustainable environmental and public policy responses.

Psychological Impact of COVID-19 Lockdown and Its Evolution: A Case Study Based on Internet Searching Data during the Lockdown of Wuhan 2020 and Shanghai 2022

It has been three years since the initial outbreak of COVID-19 in Wuhan, China, which incurred huge damage both physically and psychologically on human's normal life. As a prevention measure, the lockdown was first adopted by Wuhan, then by a long list of Chinese cities and many other major cities around the world. Lockdown is the most restrictive social distancing strategy, turning out effective in mitigating the spreading of COVID-19 on the community level, which, however, cuts off all social interactions and isolates healthy people from each other. The isolated nature of the lockdown could induce severe mental health issues, forming one major source of depression and domestic violence. Given the potential side effect, a comprehensive investigation based on reliable data sources is needed to evaluate the real psychological impact of COVID-19 lockdown and its evolution over time, particularly in the time when the Omicron variant, known for its low death risk, dominates the pandemic. Based on the Baidu Searching Index data collected for Wuhan and Shanghai, two major cities in China that suffered from long-lasting (over two months) lockdowns in 2020 and 2022, respectively, it is found that the major psychological issue during the lockdown period is not induced by the spreading of COVID-19, but by the execution of lockdown. With the deepening of knowledge about COVID-19 and the decrease in the death risk, the psychological impact of lockdown keeps increasing, while the impact of virus spreading becomes less important and even irrelevant to depression and domestic violence issues. The findings reveal that from the psychological perspective, the negative effect of lockdown already overweighs the positive one, which is especially true for the Omicron variant provided its almost ignorable death risk. Therefore, it is necessary to re-evaluate the yield and cost of lockdown for those countries where the COVID-19 pandemic has not yet come to an end.

Economic geography of contagion: a study of COVID-19 outbreak in India

We propose a mechanism based on regional inequality in economic activity to explain the heterogeneity in the spread of COVID-19 and test it using data from India. Contagion is expected to spread at a higher rate in regions characterized by greater movement of goods and services. We argue that mobility is higher in regions with greater degree of intra-regional inequality in economic activity. Such regions are usually characterized by a core-periphery economic structure in which the periphery is dependent on the core for the supply of jobs, goods, and services. Such dependence leads to a greater degree of mobility between the core and periphery, which in turn leads to higher rate of contagion. Using nightlight data to measure regional inequality, we find evidence in support of our hypothesis. Using mobility data, we provide direct evidence in support of our proposed channel; the positive relationship between regional inequality and COVID-19 infection is driven by mobility. Our findings suggest that policy responses to contain COVID-19 contagion need to be heterogeneous across India, where the priority areas can be chosen ex ante based on a regional inequality-based criterion.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00148-022-00935-9.

JUE Insight: COVID-19 and household preference for urban density in China

This paper investigates the effect of COVID-19 on both housing prices and housing price gradients in China using transaction level data from 60 Chinese cities. After using a difference-in-differences (DID) specification to disentangle the confounding effects of China's annual Spring Festival, we find that housing prices decreased by two percent immediately after the COVID-19 outbreak but gradually recovered by September 2020. Moreover, our findings suggest that COVID-19 flattens the horizontal housing price gradient, reduces the price premium for living in tall buildings, and changes the vertical gradient within residential buildings. This is likely explained by the changing household preferences towards low-density areas associated with lower infection risk.

High-Speed railways and the spread of Covid-19

High-speed railways (HSRs) greatly decrease transportation costs and facilitate the movement of goods, services, and passengers across cities. In the context of the Covid-19 pandemic, however, HSRs may contribute to the cross-regional spread of the new coronavirus. This paper evaluates the role of HSRs in spreading Covid-19 from Wuhan to other Chinese cities. We use train frequencies in 1971 and 1990 as instrumental variables. Empirical results from gravity models demonstrate that one more HSR train originating from Wuhan each day before the Wuhan lockdown increases the cumulative number of Covid-19 cases in a city by about 10 percent. The empirical analysis suggests that other transportation modes, including normal-speed trains and airline flights, also contribute to the spread of Covid-19, but their effects are smaller than the effect of HSRs. This paper's findings indicate that transportation infrastructures, especially HSR trains originating from a city where a pandemic broke out, can be important factors promoting the spread of an infectious disease.

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

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

The Effects of Financial Stress and Household Socio-Economic Deprivation on the Malnutrition Statuses of Children under Five during the COVID-19 Lockdown in a Marginalized Region of South Punjab, Pakistan

The lockdown after the COVID-19 pandemic not only caused public health crises and income stress but also put millions at risk of food insecurity and malnutrition across the globe, especially in low and middle-income countries [LMICs]. This study evaluated the effects of financial stress and household socio-economic deprivation on the nutritional status of 1551 children under the age of five during COVID-19 in Pakistan. A self-administered questionnaire was used between November 2020 and April 2021 to collect information on age, height, children's weight, and socio-economic status from 1152 rural households from underdeveloped regions in Punjab, Pakistan. With the help of the proportionate simple random sampling method, this study employed a model (binary logistic regression) to calculate the likelihood of malnourishment. The findings showed that the stunting, underweight, and wasting prevalence rates during COVID-19 were 58.86%, 41.89%, and 8.11%, respectively, in the Bahawalpur region. According to the binary logistic regression analysis, among the household deprivation status (HDS) categories, the risks of childhood malnutrition were lesser in HDS-2 (OR = 0.05, 95% CI: 0. 005-0.879) and HDS-3 (OR = 0.04, 95% CI: 0.008-0.193). Similar to this, within the financial stress index (FSI) categories, the children in homes with medium financial stress had reduced odds of malnutrition (OR = 0.10, 95% CI: 0.018-0.567), and the children in households with low financial stress had reduced risks of malnutrition (OR = 0.006, 95% CI: 0.005-0.061). The proposed research found that stunting and underweight increased by 17.26% and 12.29% during the COVID-19 lockdown in the Bahawalpur region. Additionally, financial stress and socio-economic deprivation strongly affected children's nutritional statuses during the COVID-19 lockdown in the Bahawalpur region of Southern Punjab.

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance," "effort," and "consistency." We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.

The Impact of the COVID-19 Pandemic on the Mental Health of Urban Residents-Evidence from China

Based on a nationwide micro-survey in China from 2018 to 2021, this paper empirically estimates the causal impact of the COVID-19 pandemic on the mental health of Chinese residents, by exploiting the distribution of the outflow population from Wuhan as an instrumental variable (IV). Our findings suggest that for every 10% increase in the cumulative confirmed cases, the number of mentally unhealthy days reported by urban residents in the past 30 days will increase by 2.19, an increase of 46.90% compared with the mean value. The impact is more significant among females, people aged 30 or above, and private-sector employees. Further evidence highlights the negative impact of the COVID-19 pandemic on residents' expectations of future income and confidence in macroeconomic development, both of which we interpret as mechanisms related to economic concerns. In addition, application of the multi-period difference-in-differences (DID) strategy revealed that the negative impact still exists two years post-pandemic, but it has been dramatically alleviated since the initial stage.

Effects of the built environment and human factors on the spread of COVID-19: A systematic literature review

Soon after its emergence, COVID-19 became a global problem. While different types of vaccines and treatments are now available, still non-pharmacological policies play a critical role in managing the pandemic. The literature is enriched enough to provide comprehensive, practical, and scientific insights to better deal with the pandemic. This research aims to find out how the built environment and human factors have affected the transmission of COVID-19 on different scales, including country, state, county, city, and urban district. This is done through a systematic literature review of papers indexed on the Web of Science and Scopus. Initially, these databases returned 4264 papers, and after different stages of screening, we found 166 relevant papers and reviewed them. The empirical papers that had at least one case study and analyzed the effects of at least one built environment factor on the spread of COVID-19 were selected. Results showed that the driving forces can be divided into seven main categories: density, land use, transportation and mobility, housing conditions, demographic factors, socio-economic factors, and health-related factors. We found that among other things, overcrowding, public transport use, proximity to public spaces, the share of health and services workers, levels of poverty, and the share of minorities and vulnerable populations are major predictors of the spread of the pandemic. As the most studied factor, density was associated with mixed results on different scales, but about 58 % of the papers reported that it is linked with a higher number of cases. This study provides insights for policymakers and academics to better understand the dynamic roles of the non-pharmacological driving forces of COVID-19 at different levels.

Spatiotemporal heterogeneity of SARS-CoV-2 diffusion at the city level using geographically weighted Poisson regression model: The case of Bologna, Italy

This paper aimed to analyse the spatio-temporal patterns of the diffusion of SARS-CoV-2, the virus causing coronavirus 2019 (COVID-19, in the city of Bologna, the capital and largest city of the Emilia-Romagna Region in northern Italy. The study took place from February 1st, 2020 to November 20th, 2021 and accounted for space, sociodemographic characteristics and health conditions of the resident population. A second goal was to derive a model for the level of risk of being infected by SARS-CoV-2 and to identify and measure the place-specific factors associated with the disease and its determinants. Spatial heterogeneity was tested by comparing global Poisson regression (GPR) and local geographically weighted Poisson regression (GWPR) models. The key findings were that different city areas were impacted differently during the first three epidemic waves. The area-to-area influence was estimated to exert its effect over an area with 4.7 km radius. Spatio-temporal heterogeneity patterns were found to be independent of the sociodemographic and the clinical characteristics of the resident population. Significant single-individual risk factors for detected SARS-CoV-2 infection cases were old age, hypertension, diabetes and co-morbidities. More specifically, in the global model, the average SARS-CoV-2 infection rate decreased 0.93-fold in the 21-65 years age group compared to the >65 years age group, whereas hypertension, diabetes, and any other co-morbidities (present vs absent), increased 1.28-, 1.39- and 1.15-fold, respectively. The local GWPR model had a better fit better than GPR. Due to the global geographical distribution of the pandemic, local estimates are essential for mitigating or strengthening security measures.

Close encounters during a pandemic: Social habits and inter-generational links in the first two waves of COVID-19

Social habits are ingrained in a community and affect human behaviour. Have they played any role in the spread of the pandemic? We use high-frequency data for 220 regions in 15 European countries from March to December 2020 to compare the association between social contacts outside the family and within inter-generational families, on the one hand, and cases and excess mortality on the other. We find that a standard deviation increase in the percentage of people having daily face-to-face contacts outside the household is associated with 5 new daily cases and 2.6 additional weekly deaths, while the incidence of inter-generational households exhibits a less robust association with both COVID-19 transmission and mortality. We compare results across the first and the second wave of pandemic and show that differences are related to the average age of the most affected groups. Our findings are robust to the inclusion of a number of controls, fixed effects, the chosen sample of countries, and the estimation method. We argue that type and frequency of social interactions are interweaved with a region culture and habits and are informative on the potential transmission of contagion and on its lethality.

The correlation between attack rates and urban health indicators during the third wave of the COVID-19 outbreak in Turkey

This study aims to analyze the inter-provincial variation in the increase of attack rates in the third wave of the COVID-19 outbreak in Turkey and to determine their relationship with potential urban health indicators. In this ecological study, dependent variables were selected as the COVID-19 attack rates of provinces before the third wave and during the third peak and the attack rate increase ratio. Urban health indicators that can function as determinants of health were calculated for each province under five headings: demographic, health capacity, economic, environmental, and socio-cultural. The epidemiologic maps were produced to show the spatial distribution of COVID-19 attack rates pre- and during the third wave. The associations with urban indicators were conducted using bivariate analysis, including Pearson or Spearman correlation analysis. A multiple linear regression model was run with variables significantly associated with increased attack rates. The results of our study show significant regional variations in COVID-19 attack rates both at the beginning and during the third wave of the COVID-19 pandemic in Turkey. Among the provinces, the attack rate increase ratio has only shown significant correlations to education level and some economic indicators, such as income, employment, industrial activity measured by electric consumption, and economic activity in the manufacturing industry. The multivariate analysis determined that the indicator of economic activity in the manufacturing industry is related to the increase of the attack rate in the third wave. Our results show that the COVID-19 cases are higher in more developed cities with more manufacturing sector activity. It makes us think that it is mainly related to inequalities arising from access to health institutions and testing. It can be determined that the partly lockdown strategy, which excluded the industrial activity in the country, concluded the higher increase in the attack rates in highly industrialized provinces.

Cities in a pandemic: Evidence from China

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID-19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city-specific and time-specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle.

Factors Related to the Possibility of Accepting COVID-19 Vaccines: A Population-Based Cross-Sectional Study

Background: The reluctance to accept vaccination could severely affect global efforts to control the COVID-19 pandemic. Objectives: The present study investigated the willingness to accept COVID-19 vaccines and related factors among Isfahan University of Medical Sciences students. Methods: This study was conducted in June-July 2021 at Isfahan University of Medical Sciences. A total of 324 students completed the designed questionnaire in this population-based, web-based, cross-sectional study. SPSS 25.0 software was used to analyze the data. Results: The estimate of willingness to accept the COVID-19 vaccine in the study participants was 91.7%. The highest percentage of acceptance was related to medical students. Education level (P = 0.002) and acceptability (P

COVID-19 transmission in cities

Do cities accelerate COVID-19 transmission? Increased transmission arising from population density prompts spatial policies for financial support and containment, and poorer prospects for recovery. Using daily case counts from over 3,000 counties in the U.S. from February to September 2020, I estimate a compartmental transmission equation. Rational sheltering behavior plausibly varies by location, so I propose two instruments that exploit unanticipated variation in exposure to potential infection. In the first month of local infections, an additional log point of population density raises the expected transmission parameter estimate by around 3%. After the first month, the relation vanishes: density effects occur only in the outbreaks. Public transport, work-from-home jobs and income explain additional variation in transmission but do not account for the density effects. Consistent with location-varying optimal sheltering behavior, I document stronger mobility declines in denser areas, but only after the first month of infections. These results suggest that differences in transmission between cities and other places do not motivate spatial policies for recovery or containment, or poorer prospects after the pandemic.

Modeling Key Strategies for Reducing Socio-Economic and Health Crisis: Perspective from COVID-19 Pandemic

The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that "strong leadership and control" and "awareness on social media" play significant roles in reducing pandemic challenges, while "promoting online purchase behavior" and "online education" are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.

The effectiveness and costs of nonpharmaceutical interventions for COVID-19 containment: A border discontinuous difference-in-difference approach

We examine the effectiveness and costs of alternative nonpharmaceutical interventions (NPIs) for COVID-19 containment. Using a border discontinuous difference-in-difference approach, we find that the enforcement of rigid NPIs reduces the number of new COVID-19 cases by 10.8% in China, comparied with cities with less NPIs. Among the three NPIs, contact tracing is much more effective than the other two NPIs, namely, public information provision and social distancing. The connections of mayors to the upper-level politicians reinforce the city's implementation of rigid NPIs. These networks also serve as an informal signaling channel to the neighboring cities, encouraging the adjacent cities to impose strict NPIs to curb the spread of COVID-19. We further estimate the long-term costs of the NPIs - a net present value of 2153 yuan per child in the human capital loss attributed to more prolonged school closure alone.

The impact of COVID-19 on employment and income of vocational graduates in China: Evidence from surveys in January and July 2020

The COVID-19 pandemic shocked the economy of China in early 2020. Strict lockdown measures were implemented nationwide to prevent the further spread of the virus. During the lockdown period, many economic activities were affected, which had repercussions for the nation's overall employment. Vocational graduates were among the most affected by the crisis. To estimate the causal effects of COVID-19 on the full-time employment of vocational high school graduates as well as their monthly income and hours worked by week, we exploit variations in the intensity of the pandemic in time and across space using survey data from vocational schools from six provinces in China. The results of the difference-in-differences (DID) estimates indicate that being located in counties with high pandemic intensity significantly reduced both the employment in full-time jobs of vocational graduates as well as their monthly income. Our study's analysis demonstrates that the effects of COVID-19 on the labor market can be attributed to the large-scale contraction of labor demand of the enterprises that were hiring vocational graduates. To cope with this situation, vocational graduates took various measures, including reducing consumption, drawing on their savings, searching for new jobs, taking on part-time jobs, borrowing money, and attending new training programs. In addition, the empirical analysis finds that there were heterogeneous effects with respect to gender, family social capital, the industry in which the vocational graduate was participating, and whether the individual was in a management position.

Causal connections between socioeconomic disparities and COVID-19 in the USA

With the increasing use of machine learning models in computational socioeconomics, the development of methods for explaining these models and understanding the causal connections is gradually gaining importance. In this work, we advocate the use of an explanatory framework from cooperative game theory augmented with do calculus, namely causal Shapley values. Using causal Shapley values, we analyze socioeconomic disparities that have a causal link to the spread of COVID-19 in the USA. We study several phases of the disease spread to show how the causal connections change over time. We perform a causal analysis using random effects models and discuss the correspondence between the two methods to verify our results. We show the distinct advantages a non-linear machine learning models have over linear models when performing a multivariate analysis, especially since the machine learning models can map out non-linear correlations in the data. In addition, the causal Shapley values allow for including the causal structure in the variable importance computed for the machine learning model.