A literature review of the economics of COVID-19

Optimal social distancing in pandemic preparedness and lessons from COVID-19: Intervention intensity and infective travelers

Our analysis seeks best social distancing strategies optimally balancing the direct costs of a threatening outbreak with its societal-level costs by investigating the effects of different levels of restrictions' intensity and of the continued importation of infective travellers, while controlling for the key dimensions of the response, such as early action, adherence and the relative weight of societal costs. We identify two primary degrees of freedom in epidemic control, namely the maximum intensity of control measures and their duration. In the absence of travellers, a lower (higher) maximum intensity requires a longer (shorter) duration to achieve similar control outcomes. However, uncontrollable external factors, like the importation of undetected infectives, significantly constrain these degrees of freedom so that the optimal strategy results to be one with low/moderate intensity but prolonged in time. These findings underscore the necessity for resilient health systems and coordinated global responses in preparedness plans.

Understanding the Relationships Between Fear of COVID-19, Depression, Loneliness, and Life Satisfaction in Türkiye: Testing Mediation and Moderation Effects

AIM

This study examined the mediating roles of depression and various dimensions of loneliness (family, social, and romantic) in the associations between COVID-19 fear and satisfaction with life. Additionally, the research investigated how the economic impact of COVID-19, family income, gender, and marital status may moderate these associations.

DESIGN

A cross-sectional descriptive design.

METHODS

An online survey was conducted in Türkiye. 1702 participants completed self-report measures assessing COVID-19 fear, depression, social, emotional, and romantic loneliness, and satisfaction with life.

RESULTS

The findings revealed positive associations between COVID-19 fear and depression, along with negative relationships between COVID-19 fear and family, social, and romantic loneliness. Depression was identified as a mediator in the relationship between COVID-19 fear and the different components of loneliness. These loneliness components, in turn, influenced life satisfaction. Furthermore, the study found significant moderation effects related to the economic impact of COVID-19 restrictions, family income, marital status, and gender.

CONCLUSIONS

This study provides evidence about the psychological mechanisms linking COVID-19 fear to life satisfaction. It highlights the mediating role of depression and the moderating effects of demographic and economic factors. The findings emphasise the need for targeted mental health interventions, particularly addressing the varied impact of COVID-19 fear across different social and economic groups. This evidence can inform strategies aimed at improving life satisfaction during periods of social and economic disruption.

PATIENT OR PUBLIC CONTRIBUTION

This study has no direct patient involvement in its design, conduct, or reporting. However, it contributes to public health by providing evidence about the psychological impacts of COVID-19, which may inform future interventions and mental health support strategies.

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study

BACKGROUND

Chronic obstructive pulmonary disease (COPD) ranks among the leading causes of global mortality, and COVID-19 has intensified its challenges. Beyond the evident physical effects, the long-term psychological effects of COVID-19 are not fully understood.

OBJECTIVE

This study aims to unveil the long-term psychological trends and patterns in populations with COPD throughout the COVID-19 pandemic and beyond via large-scale Twitter mining.

METHODS

A 2-stage deep learning framework was designed in this study. The first stage involved a data retrieval procedure to identify COPD and non-COPD users and to collect their daily tweets. In the second stage, a data mining procedure leveraged various deep learning algorithms to extract demographic characteristics, hashtags, topics, and sentiments from the collected tweets. Based on these data, multiple analytical methods, namely, odds ratio (OR), difference-in-difference, and emotion pattern methods, were used to examine the psychological effects.

RESULTS

A cohort of 15,347 COPD users was identified from the data that we collected in the Twitter database, comprising over 2.5 billion tweets, spanning from January 2020 to June 2023. The attentiveness toward COPD was significantly affected by gender, age, and occupation; it was lower in females (OR 0.91, 95% CI 0.87-0.94; P<.001) than in males, higher in adults aged 40 years and older (OR 7.23, 95% CI 6.95-7.52; P<.001) than in those younger than 40 years, and higher in individuals with lower socioeconomic status (OR 1.66, 95% CI 1.60-1.72; P<.001) than in those with higher socioeconomic status. Across the study duration, COPD users showed decreasing concerns for COVID-19 and increasing health-related concerns. After the middle phase of COVID-19 (July 2021), a distinct decrease in sentiments among COPD users contrasted sharply with the upward trend among non-COPD users. Notably, in the post-COVID era (June 2023), COPD users showed reduced levels of joy and trust and increased levels of fear compared to their levels of joy and trust in the middle phase of COVID-19. Moreover, males, older adults, and individuals with lower socioeconomic status showed heightened fear compared to their counterparts.

CONCLUSIONS

Our data analysis results suggest that populations with COPD experienced heightened mental stress in the post-COVID era. This underscores the importance of developing tailored interventions and support systems that account for diverse population characteristics.

The Dynamic and Heterogeneous Effects of COVID-19 Vaccination Mandates in the USA

Mandatory vaccination for COVID-19 has received intense political and ethical debates, while the literature on the causal effects of vaccination mandates on vaccination outcomes is very limited. In this study, we examine the effects of the announcement of vaccine mandates (VMs) for workers working in three sectors, including health, education, and state governments, on the uptake of first-dose and second-dose vaccination across 50 states in the United States of America. We show that VM announcements have heterogeneous effects; hence, standard two-way fixed effects and difference-in-differences estimators are biased. We present evidence for the heterogeneous treatment effects in single and two-treatment settings. In the setting of a single treatment, when treating all VM announcements equally, our results show that VM announcement was associated with an increase of 20.6% first-dose uptake from 1 July to 31 August 2021. In two-treatment settings, our results suggest that VM announcements for workers in health or state government sectors have significant causal effects on first-dose vaccination. Additionally, VM announcements do not have significant causal effects on second-dose uptake. Our results are robust to the choice of differing outcome variables and periods after controlling for state-level covariates, including COVID-19 death, unemployment, and cumulative two-dose vaccination.

Governing in the face of a global crisis: When do voters punish and reward incumbent governments?

The recent COVID-19 pandemic offers a rare opportunity to understand how citizens attribute responsibility for governments' responses to unanticipated negative-and in this case, systemic-exogenous shocks. Classical accounts of responsibility are complicated when crises are pervasive, involve multiple valence dimensions, and where individuals can make relative assessments of performance. We fielded a conjoint experiment in 16 countries with 22,147 respondents. In this experiment, subjects made re-election decisions regarding 178,184 randomly assigned incumbent profiles. We find that incumbents' performance along both health and economic dimensions drives these hypothetical reelection decisions. Using machine learning techniques, we find only muted heterogeneity in the magnitude and distribution of these treatment effects. This result suggests that these widely reported performance signals have consistent political effects across countries. In a complementary analysis, we also find that subjects' intentions to vote for incumbent governments are positively correlated with subjective and relative evaluations of the government's pandemic performance, along both health and economic dimensions. These results provide consistent evidence that evaluations of pandemic performance matter politically.

Gender Disparities in Pandemic-Related Strains, Digital Coping Strategies, and Protective Mechanisms Among Rural-to-Urban Migrant Working Adolescents in China

The COVID-19 pandemic placed significant strains on daily life, particularly affecting vulnerable groups such as rural-to-urban young migrant workers. Based on General Strain Theory (GST), these pandemic-related strains lead to delinquent copings, including excessive Internet use. However, the association between pandemic-related challenges faced by migrant youth and their digital copings has yet to be investigated. GST also posits that some conditioning factors, such as conventional beliefs, internal resilience and life satisfaction, might serve as protective factors, which can help to alleviate the disruptive consequences of the pandemic-related strains. Utilizing the fourth sweep of International Self-Report Delinquency Survey (ISRD4) in China comprising 769 working migrant adolescents aged 16 to 19, who did not attend high school, the present study examines variations in pandemic-related strains, frequent use of the Internet for gaming and social media, and their associations. In addition, this study investigates the moderating effect of three protective factors: conventional beliefs, internal resilience and life satisfaction. Results indicated that economic strain, information strain and health-related strain significantly influenced digital coping strategies, with notable gender differences. Conventional beliefs served as a significant moderator for males, while life satisfaction played a more significant moderating role for females. Relevant policy implications are then discussed.

Eco-sustainable chromatographic method for the determination of favipiravir and nitazoxanide for COVID-19: application to human plasma

Coronavirus disease 2019 (COVID-19), an extremely contagious illness, has posed enormous challenges to healthcare systems around the world. Although the evidence on COVID-19 management is growing, antiviral medication is still the first line of treatment. Therefore, it is critical that effective, safe, and tolerable antivirals be available to treat early COVID-19 and stop its progression. Recently, favipiravir (FAV) has received FDA approval as safe and effective antiviral medication for COVID-19 management. Nitazoxanide (NTZ) also possesses antiviral and immunomodulating activities. Moreover, FAV and NTZ in combination are clinically used in COVID-19 treatment with reported safety, synergistic antiviral and immunomodulating effects. Despite the availability of various clinical studies on both FAV and NTZ, no existing analytical application for the simultaneous estimation of FAV and NTZ exists. As a result, the current work goal is to establish a green HPLC method for their analysis and implementation to human plasma. The developed method utilizes isocratic elution with 0.1% aqueous formic acid: ethanol (55:45, v/v) and dantrolene as internal standard. The bioanalytical validation parameters passed the FDA acceptance criteria. NEMI, eco scale, AGREE and ComplexGAPI approaches were used for qualitative and quantitative evaluation of the method's greenness.

COVID-Related Discrimination and Health Care Access among a Nationally Representative, Diverse Sample of US Adults

BACKGROUND

In the United States, COVID-related discrimination towards racial and ethnic minority populations is well documented; however, its impact on healthcare access during the pandemic has not been assessed.

METHODS

We used data from our nationally representative, online survey of 5,500 American Indian/Alaska Native (AIAN), Asian, Black, Native Hawaiian/Pacific Islander, Latino, White, and multiracial adults conducted between 12/2020-2/2021 (baseline) and 8/2021-9/2021 (6-month follow-up; 35.1% response rate). At baseline, participants were asked how often they experienced discriminatory behaviors "because they think you might have COVID-19" (modified Everyday Discrimination Scale). Participants were asked if they were unable to get needed health care (e.g., cancer screening), or COVID-19 testing at both time-points. Vaccine willingness was assessed at baseline and uptake at follow-up.

RESULTS

Experiencing COVID-related discrimination was associated with not being able to get health care at baseline (OR = 3.66, 95% CI = 2.91-4.59) and follow-up (OR = 1.86, 95% CI = 1.16-2.97) and not being able to get a COVID-19 test at baseline (OR = 2.11, 95% CI = 1.68-2.65) and follow-up (OR = 4.12, 95% CI = 2.20-7.72). Experiencing discrimination was also associated with being less likely to have received a COVID-19 vaccine (OR = 0.52, 95% CI = 0.30-0.90), despite individuals who experienced discrimination being more willing to vaccinate at baseline (OR = 1.56, 95% CI = 1.10-2.22).

CONCLUSIONS

COVID-related discrimination was associated with an increased likelihood of being unable to get health care across all racial and ethnic populations, although associations were strongest among Asian, AIAN, and Latino adults. Healthcare providers should be aware of the impact of discrimination on healthcare utilization, delays, and health-seeking behaviors, especially among racial and ethnic minorities.

COVID and consumers' food strategies in Madrid, Spain: Towards a resilient city-region food system approach

Lockdowns, restrictions on people's movements and gatherings, and other COVID-19 containment measures have had economic, social, and environmental effects. The pandemic resulted in major disruption to food systems, highlighting serious weaknesses in the way such systems are currently configured. However, the pandemic also created important opportunities to develop transformative actions enabling the creation of more resilient and sustainable food systems. In this paper, we delve more deeply into the strategies developed by consumers, as key stakeholders in the food system, through an in-depth survey and statistical analysis of changes in consumers' habits during the pandemic lockdowns. We adopt a city-region food system (CRFS) approach to explore ways to enhance food system resilience to future crises. Data were collected through a telephone survey of a representative sample (600 consumers), conducted in the region of Madrid in May 2021. First, descriptive analyses allowed different consumer strategies to be identified. Differences in food preferences and behaviour changes between individuals in the sample were analysed by age, gender, income, and level of education using exponential family Principal Component Analysis (EPCA), logistic regression, and Multiple Correspondence Analysis (MCA). Lastly, Principal Component Analysis (PCA) was applied to classify the consumer strategies into four discrete profiles. The findings show significant differences in consumers' responses according to age, income, and level of education. Additionally, the results highlight increased consumption of local and organic products, and online shopping, as typical strategies employed by consumers to respond to the COVID-19 pandemic. In this context, alternative food networks were shown to play a key role in enhancing food security. Interestingly, no significant differences were found between urban and rural areas. Our discussion addresses this apparent homogeneity in consumers' habits from a resilient CRFS approach.

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 novel comparison framework for epidemiological strategies applied to age-based restrictions versus horizontal lockdowns

During an epidemic, such as the COVID-19 pandemic, policy-makers are faced with the decision of implementing effective, yet socioeconomically costly intervention strategies, such as school and workplace closure, physical distancing, etc. In this study, we propose a rigorous definition of epidemiological strategies. In addition, we develop a scheme for comparing certain epidemiological strategies, with the goal of providing policy-makers with a tool for their systematic comparison. Then, we put the suggested scheme to the test by employing an age-based epidemiological compartment model introduced in Bitsouni et al. (2024), coupled with data from the literature, in order to compare the effectiveness of age-based and horizontal interventions. In general, our findings suggest that these two are comparable, mainly at a low or medium level of intensity.

Impact of COVID-19 Infection and Related Social Concerns on Depressive Symptoms: Mediating Effects of Negative Changes in Daily Life and Moderating Effects of Age and Gender

OBJECTIVE

This study examined the mediating effect of negative changes in daily life due to the coronavirus disease-2019 (COVID-19) pandemic on depressive symptoms, considering COVID-19 infection and related social concerns. Additionally, comparisons of path coefficients between the groups were conducted based on age and gender.

METHODS

A cross-sectional study design used data from the 2020 Korean Community Health Survey consisting of 229,269 individuals. This study used a self-reported questionnaire, including the Patient Health Questionnaire-9 and three items addressing social concerns related to COVID-19 infection. A single question assessed whether individuals had experienced COVID-19 infection within the last 3 months, and scores of negative changes in daily life due to the COVID-19 pandemic. Correlation analysis was performed on the variables. Structural equation model analysis was conducted to identify the mediating role of negative changes in daily life. Chi-square tests were also performed to compare the path coefficients based on age and gender.

RESULTS

The structural equation models revealed that COVID-19 infection and related social concerns had both significant direct effects on depressive symptoms and indirect effects through negative changes in daily life. When comparing the path coefficients by age and gender, the coefficients related to depressive symptoms were highest in those under 65 years and in females.

CONCLUSION

Negative changes in daily life due to the COVID-19 pandemic serve as a partial mediator of the impact of COVID-19 infection and related social concerns on depressive symptoms. Special attention should be paid to depressive symptoms in those under 65 years of age and in females.

Multiregional, multi-industry impacts of fairness on pandemic policies

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies. This article proposes a novel multiobjective mixed-integer linear programming formulation, which results in the optimal timing of closure and reopening of states and industries in each. The three objectives being pursued include: (i) the epidemiological impact of the pandemic in terms of the percentage of the infected population; (ii) the social vulnerability index of the pandemic policy based on the vulnerability of communities to getting infected, and for losing their job; and (iii) the economic impact of the pandemic based on the inoperability of industries in each state. The proposed model is implemented on a dataset that includes 50 states, the District of Columbia, and 19 industries in the United States. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction.

Patterns of News Consumption during the COVID-19 Pandemic Crisis: A 2.5 Year Longitudinal Study in the Netherlands

During major long-term crises, such as the COVID-19 pandemic, news media are crucial sources of information for the public. This study aimed to explore the frequency of COVID-19-related news consumption based on (1) phase of the pandemic, (2) socio-demographic characteristics, and (3) news information channels. The study used a dynamic cohort design with 18 rounds of data collection, including 306,692 responses from 83,180 unique respondents in the Netherlands from 17 April 2020 to 11 September 2022. Results showed that the frequency of general COVID-19-related news media consumption varied throughout the pandemic, following a general decreasing trend with relative spikes often coinciding with periods of stricter behavioural regulations. TV news, newspapers, and online news websites were the most popular news information channels among the respondents. Furthermore, multilevel regression analyses identified several socio-demographic factors influencing news consumption and preferred channels, namely age, migration background, living status, health status, and trust in government - these results remained stable throughout the pandemic. This study highlights the importance of selecting appropriate news channels to effectively reach different socio-demographic groups and shows that during a prolonged crisis, news consumption about the crisis fluctuates with worsening conditions but generally follows a decreasing trend.

Short, stringent lockdowns halted SARS-CoV-2 transmissions in Danish municipalities

In late 2020, the focus of the global effort against the COVID-19 pandemic centered around the development of a vaccine, when reports of a mutated SARS-CoV-2 virus variant in a population of 17 million farmed mink came from Denmark, threatening to jeopardize this effort. Spillover infections of the new variant between mink and humans were feared to threaten the efficacy of upcoming vaccines. In this study the ensuing short-lived yet stringent lockdowns imposed in 7 of the countries 98 municipalities are analysed for their effectiveness to reduce SARS-CoV-2 infections. Synthetic counterfactuals are created for each of these municipalities using a weighted average combination of the remaining municipalities not targeted by the stringent measures. This allows for a clear overview regarding the development of test-positivity rates, citizen mobility behaviours and lastly daily infection numbers in response to the restrictions. The findings show that these targeted, short-term lockdowns significantly curtailed further infections, demonstrating a marked decrease, first in citizens mobility and then in daily cases when compared to their synthetic counterfactuals. Overall, the estimates indicate average reductions to infection numbers to be around 31%. This study underscores the potential of strict, yet severe lockdowns in breaking ongoing infection dynamics, by utilising a rare quasi-experimental design case that avoids bias introduced through treatment selection.

Trajectories of affective and cognitive well-being at times of COVID-19 containment policies in Italy

This paper draws on a subsample (N=851) of respondents to ITA.LI - Italian Lives - a recently established panel study on a probability sample of individuals aged 16+ living in Italy - to track changes in the affective (positive and negative emotions such as energy and sadness) and cognitive (life satisfaction) components of well-being during different COVID-19 policy phases, classified according to the severity of key government responses. An event-study design is employed, which uses mixed-effects ordered logistic models to investigate the change in SWB scores. Given the nested nature of the data, multilevel modelling is chosen as the most appropriate method of analysis. The results reveal the levels of affective and cognitive well-being were significantly lower during the lockdown period than before the pandemic outbreak potentially reflecting both the direct effects of the confinement and other potential sources of distress, such as trends in infection rates and related media alarm. Once the lockdown was lifted, there was no evidence of an immediate and general improvement in well-being. In the following policy phase, with the lifting of most containment measures, there were significant signs of full recovery concerning energy, but the scores for the other well-being components remained relatively lower than those observed before the onset of COVID-19.

Individual and Organizational Ductility: Conceptualization, Development, and Validation of a New Scale

In this article, we conceptualize a new construct named "ductility" and propose a measurement instrument. We examine psychometric properties-the factorial validity and reliability of the Ductility Scale in Italy. The results of exploratory factor analysis showed that the scale has a two-factor structure, namely, individual and organizational ductility. The scale reliability was excellent for both dimensions (individual ω = 0.82; organizational ω = 0.85). The participants were employees from private and public organizations (n = 466). We tested the construct validity of the Ductility Scale. The invariance of the measurement model tested via multigroup confirmative factor analysis showed that the Ductility Scale was invariant across gender. In addition, we found ductility to be positively related to proactive personality and work engagement. These preliminary results show that the Ductility Scale is a reliable and valid measure. In addition, our findings illustrate the potential usefulness of the ductility construct via the newly developed scale.

The exciting potential and daunting challenge of using GPS human-mobility data for epidemic modeling

Large-scale GPS location datasets hold immense potential for measuring human mobility and interpersonal contact, both of which are essential for data-driven epidemiology. However, despite their potential and widespread adoption during the COVID-19 pandemic, there are several challenges with these data that raise concerns regarding the validity and robustness of its applications. Here we outline two types of challenges-some related to accessing and processing these data, and some related to data quality-and propose several research directions to address them moving forward.

Economic policy uncertainty and enterprise export resilience in China: Does the digital economy matter?

In the new era of international trade, escalating uncertainty and the swift development of the digital economy stand out as two pivotal transformations. These phenomena individually exert significant influences on enterprises' exports; however, their combined effects on export resilience remain underexplored. Hence, drawing on existing theories, this paper analyzes the weakening effect of economic policy uncertainty on export resilience and explores the influence of regional digital economic development on this attenuation effect. Furthermore, empirical tests are conducted using micro-level data from China. The study findings reveal: (1) Economic policy uncertainty weakens firms' export resilience by increasing transaction costs; (2) Digital economic development mitigates this weakening effect by reducing transaction costs, with a more pronounced effect observed among highly efficient firms. The findings suggest that amid escalating uncertainty, vigorously promoting digital economic development holds profound policy significance for the high-quality development of international trade.

A time-varying analysis between economic uncertainty and tourism development in Singapore

Tourism development (TO) is seen as a viable solution to address economic policy uncertainty (EPU) risks. However, previous studies have largely ignored the relationship between short, medium, and long term by decomposing TO and EPU index at different time-frequency scales, especially in Singapore. In this study, the Wavelet tools analysis and a rolling window algorithm are employed to re-visit the causal relationship between EPU, industrial production index (IPI), government revenue (GR), and tourism development (TO) in Singapore from January 2003 to February 2022. The findings revealed the heterogeneous effects of EPU on TO at different time horizons in terms of importance and magnitude over time. A rise in EPU results in a decline in TO at the low frequencies, indicating that EPU has a detrimental effect on TO over the short term. Conversely, in the long term, an increase in TO results in a decrease in EPU. Furthermore, the outcome also indicated that there is a uni-directional causality running from TO to EPU, GR and IPI. Expressly, we confirm that the negative co-movement is more pronounced in the aftermath of the COVID-19 crisis, particularly for EPU, and GR at low-medium frequencies throughout the research period. The findings provide tourism policymakers with insight to develop strategic plans for tourism development that consider the effects of economic policy uncertainty. By understanding how uncertainty impacts tourism, governments can tailor development strategies to mitigate risks and capitalize on opportunities.

A Comparative Study of the Impact of the Covid-19 Pandemic on Mental Health, Healthcare Access, and Pain Levels of Patients with Chronic Pain from Spring 2020 to Spring 2021

Purpose

The short-term impact of the Covid-19 pandemic on patients with chronic pain has been under the microscope since the beginning of the pandemic. This time-lag design study aimed to track changes in pain levels, access to care, mental health, and well-being of Greek chronic pain patients within the first year of the Covid-19 pandemic.

Patients and Methods

101 and 100 chronic pain patients were contacted during the Spring of 2020 and 2021, respectively. A customized questionnaire was used to evaluate the perceived impact of the pandemic on pain levels and healthcare access. Psychological responses, personality characteristics, and overall well-being were evaluated using the Depression, Anxiety, and Stress Scale (DASS-42), the Ten-Item Personality Index (TIPI) and the Personal Wellbeing Index (PWI).

Results

The perceived effect of the pandemic and the Covid-related restrictions affected significantly access to healthcare, pain levels and quality of life. Differences were detected in the PWI sub-scales regarding Personal Safety, Sense of Community-Connectedness, Future Security, Spirituality-Religiousness, and General Life Satisfaction. Marital status, parenthood, education and place of residence were associated with differences in pain levels, emotional and psychological responses.

Conclusion

Changes in chronic pain levels, emotional responses, and overall well-being took place throughout the year. Also, an evident shift took place in the care delivery system. Both tendencies disclose an ongoing adaptation process of chronic pain patients and healthcare services that needs further monitoring.

THE ECONOMIC IMPACTS OF COVID-19: EVIDENCE FROM A NEW PUBLIC DATABASE BUILT USING PRIVATE SECTOR DATA

We build a publicly available database that tracks economic activity in the United States at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its effects across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a V-shaped recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the effects of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.

Compliance in the public versus the private realm: Economic preferences, institutional trust and COVID-19 health behaviors

To what extent do economic preferences and institutional trust predict compliance with physical distancing rules during the COVID-19 pandemic? We reexamine this question by introducing the theoretical and empirical distinction between individual health behaviors in the public and in the private domain (e.g., keeping a distance from strangers vs. abstaining from private gatherings with friends). Using structural equation modeling to analyze survey data from Germany's second wave of the pandemic (N = 3350), we reveal the following major differences between compliance in both domains: Social preferences, especially (positive) reciprocity, play an essential role in predicting compliance in the public domain but are barely relevant in the private domain. Conversely, individuals' degree of trust in the national government matters predominantly for increasing compliance in the private domain. The clearly strongest predictor in this domain is the perception pandemic-related threats. Our findings encourage tailoring communication strategies to either domain-specific circumstances or factors common across domains. Tailored communication may also help promote compliance with other health-related regulatory policies beyond COVID-19.

Riding the waves from epidemic to endemic: Viral mutations, immunological change and policy responses

Nonpharmaceutical interventions (NPI) are an important tool for countering pandemics such as COVID-19. Some are cheap; others disrupt economic, educational, and social activity. The latter force governments to balance the health benefits of reduced infection and death against broader lockdown-induced societal costs. A literature has developed modeling how to optimally adjust lockdown intensity as an epidemic evolves. This paper extends that literature by augmenting the classic SIR model with additional states and flows capturing decay over time in vaccine-conferred immunity, the possibility that mutations create variants that erode immunity, and that protection against infection erodes faster than protecting against severe illness. As in past models, we find that small changes in parameter values can tip the optimal response between very different solutions, but the extensions considered here create new types of solutions. In some instances, it can be optimal to incur perpetual epidemic waves even if the uncontrolled infection prevalence would settle down to a stable intermediate level.

Is society caught up in a Death Spiral? Modeling societal demise and its reversal

Just like an army of ants caught in an ant mill, individuals, groups and even whole societies are sometimes caught up in a Death Spiral, a vicious cycle of self-reinforcing dysfunctional behavior characterized by continuous flawed decision making, myopic single-minded focus on one (set of) solution(s), denial, distrust, micromanagement, dogmatic thinking and learned helplessness. We propose the term Death Spiral Effect to describe this difficult-to-break downward spiral of societal decline. Specifically, in the current theory-building review we aim to: (a) more clearly define and describe the Death Spiral Effect; (b) model the downward spiral of societal decline as well as an upward spiral; (c) describe how and why individuals, groups and even society at large might be caught up in a Death Spiral; and (d) offer a positive way forward in terms of evidence-based solutions to escape the Death Spiral Effect. Management theory hints on the occurrence of this phenomenon and offers turn-around leadership as solution. On a societal level strengthening of democracy may be important. Prior research indicates that historically, two key factors trigger this type of societal decline: rising inequalities creating an upper layer of elites and a lower layer of masses; and dwindling (access to) resources. Historical key markers of societal decline are a steep increase in inequalities, government overreach, over-integration (interdependencies in networks) and a rapidly decreasing trust in institutions and resulting collapse of legitimacy. Important issues that we aim to shed light on are the behavioral underpinnings of decline, as well as the question if and how societal decline can be reversed. We explore the extension of these theories from the company/organization level to the society level, and make use of insights from both micro-, meso-, and macro-level theories (e.g., Complex Adaptive Systems and collapsology, the study of the risks of collapse of industrial civilization) to explain this process of societal demise. Our review furthermore draws on theories such as Social Safety Theory, Conservation of Resources Theory, and management theories that describe the decline and fall of groups, companies and societies, as well as offer ways to reverse this trend.

Proposing lead compounds for the development of SARS-CoV-2 receptor-binding inhibitors

The COVID-19 pandemic has had deleterious effects on the world and demands urgent measures to find therapeutic agents to combat the current and related future outbreaks. The entry of SARS-CoV-2 into the host's cell is facilitated by the interaction between the viral spike receptor-binding domain (sRBD) and the human angiotensin-converting enzyme 2 (hACE2). Although the interface of sRBD involved in the sRBD-hACE2 interaction has been projected as a primary vaccine and drug target, currently no small-molecule drugs have been approved for covid-19 treatment targeting sRBD. Herein structure-based virtual screening and molecular dynamics (MD) simulation strategies were applied to identify novel potential small-molecule binders of the SARS-CoV-2 sRBD from an sRBD-targeted compound library as leads for the development of anti-COVID-19 drugs. The library was initially screened against sRBD by using the GOLD docking program whereby 19 compounds were shortlisted based on docking scores after using a control compound to set the selection cutoff. The stability of each compound in MD simulations was used as a further standard to select four hits namely T4S1820, T4589, E634-1449, and K784-7078. Analyses of simulations data showed that the four compounds remained stably bound to sRBD for ≥ 80 ns with reasonable affinities and interacted with pharmacologically important amino acid residues. The compounds exhibited fair solubility, lipophilicity, and toxicity-propensity characteristics that could be improved through lead optimization regimes. The overall results suggest that the scaffolds of T4S1820, E634-1449, and K784-7078 could serve as seeds for developing potent small-molecule inhibitors of SARS-CoV-2 receptor binding and cell entry.Communicated by Ramaswamy H. Sarma.

The economic impact of international travel measures used during the COVID-19 pandemic: a scoping review

INTRODUCTION

Assessment of the use of travel measures during COVID-19 has focused on their effectiveness in achieving public health objectives. However, the prolonged use of highly varied and frequently changing measures by governments, and their unintended consequences caused, has been controversial. This has led to a call for coordinated decision-making focused on risk-based approaches, which requires better understanding of the broader impacts of international travel measures (ITMs) on individuals and societies.

METHODS

Our scoping review investigates the literature on the economic impact of COVID-19 ITMs. We searched health, social science and COVID-19-specific databases for empirical studies preprinted or published between 1 January 2020 and 31 October 2023. Evidence was charted using a narrative approach and included jurisdiction of study, ITMs studied, study design, outcome categories, and main findings.

RESULTS

Twenty-six studies met the inclusion criteria and were included for data extraction. Twelve of them focused on the international travel restrictions implemented in early 2020. Limited attention was given to measures such as entry/exit screening and vaccination requirements. Eight studies focused on high-income countries, 6 on low-income and middle-income countries and 10 studies were comparative although did not select countries by income. Economic outcomes assessed included financial markets (n=13), economic growth (n=4), economic activities (n=1), performance of industries central to international travel (n=9), household-level economic status (n=3) and consumer behaviour (n=1). Empirical methods employed included linear regression (n=17), mathematical modelling (n=3) and mixed strategies (n=6).

CONCLUSION

Existing studies have begun to provide evidence of the wide-ranging economic impacts resulting from ITMs. However, the small body of research combined with difficulties in isolating the effects of such measures and limitations in available data mean that it is challenging to draw general and robust conclusions. Future research using rigorous empirical methods and high-quality data is needed on this topic.

Impact of COVID-19 Infections among Unvaccinated Patients with Congenital Heart Disease: Results of a Nationwide Analysis in the First Phase of the Pandemic

Background: The outcome data and predictors for mortality among patients with congenital heart disease (CHD) affected by COVID-19 are limited. A more detailed understanding may aid in implementing targeted prevention measures in potential future pandemic events. Methods: Based on nationwide administrative health insurance data, all the recorded in-hospital cases of patients with CHD with COVID-19 in 2020 were analyzed. The demographics, treatment details, as well as 30-day mortality rate were assessed. The associations of the patients' characteristics with death were assessed using multivariable logistic regression analysis. Results: Overall, 403 patients with CHD were treated in- hospital for COVID-19 in 2020. Of these, 338 patients presented with virus detection but no pneumonia whilst, 65 patients suffered from associated pneumonia. The cohort of patients with pneumonia was older (p = 0.04) and presented with more cardiovascular comorbidities such as diabetes mellitus (p = 0.08), although this parameter did not reach a statistically significant difference. The 30-day mortality rate was associated with highly complex CHD (odds ratio (OR) 7.81, p = 0.04) and advanced age (OR 2.99 per 10 years, p = 0.03). No child died of COVID-related pneumonia in our dataset. Conclusions: COVID-19 infection with associated pneumonia chiefly affected the older patients with CHD. Age and the complexity of CHD were identified as additional predictors of mortality. These aspects might be helpful to retrospectively audit the recommendations and guide health politics during future pandemic events.

Optimal social distancing in epidemic control: cost prioritization, adherence and insights into preparedness principles

The COVID-19 pandemic experience has highlighted the importance of developing general control principles to inform future pandemic preparedness based on the tension between the different control options, ranging from elimination to mitigation, and related costs. Similarly, during the COVID-19 pandemic, social distancing has been confirmed to be the critical response tool until vaccines become available. Open-loop optimal control of a transmission model for COVID-19 in one of its most aggressive outbreaks is used to identify the best social distancing policies aimed at balancing the direct epidemiological costs of a threatening epidemic with its indirect (i.e., societal level) costs arising from enduring control measures. In particular, we analyse how optimal social distancing varies according to three key policy factors, namely, the degree of prioritization of indirect costs, the adherence to control measures, and the timeliness of intervention. As the prioritization of indirect costs increases, (i) the corresponding optimal distancing policy suddenly switches from elimination to suppression and, finally, to mitigation; (ii) the "effective" mitigation region-where hospitals' overwhelming is prevented-is dramatically narrow and shows multiple control waves; and (iii) a delicate balance emerges, whereby low adherence and lack of timeliness inevitably force ineffective mitigation as the only accessible policy option. The present results show the importance of open-loop optimal control, which is traditionally absent in public health preparedness, for studying the suppression-mitigation trade-off and supplying robust preparedness guidelines.

COVID-19 Pandemic, Physical Distancing Policies, and the Non-Profit Sector Volunteer Force

Although COVID-19-related physical distancing has had large economic consequences, the impact on volunteerism is unclear. Using volunteer position postings data from Canada's largest volunteer center (Volunteer Toronto) from February 3, 2020, to January 4, 2021, we evaluated the impact of different levels of physical distancing on average views, total views, and total number of posts. There was about a 50% decrease in the total number of posts that was sustained throughout the pandemic. Although a more restrictive physical distancing policy was generally associated with fewer views, there was an initial increase in views during the first lockdown where total views were elevated for the first 4 months of the pandemic. This was driven by interest in COVID-19-related and remote work postings. This highlights the community of volunteers may be quite flexible in terms of adapting to new ways of volunteering, but substantial challenges remain for the continued operations of many non-profit organizations.

Residential mobility restrictions and adverse mental health outcomes during the COVID-19 pandemic in the UK

During the COVID-19 pandemic, several governments tried to contain the spread of SARS-CoV-2, the virus that causes COVID-19, with lockdowns that prohibited leaving one's residence unless carrying out a few essential services. We investigate the relationship between limitations to mobility and mental health in the UK during the first year and a half of the pandemic using a unique combination of high-frequency mobility data from Google and monthly longitudinal data collected through the Understanding Society survey. We find a strong and statistically robust correlation between mobility data and mental health survey data and show that increased residential stationarity is associated with the deterioration of mental wellbeing even when regional COVID-19 prevalence and lockdown stringency are controlled for. The relationship is heterogeneous, as higher levels of distress are seen in young, healthy people living alone; and in women, especially if they have young children.

Assessing the effectiveness of international government responses to the COVID-19 pandemic

This paper examines the effectiveness of non-pharmaceutical measures adopted by governments to control the evolution of the COVID-19 pandemic. Using a Panel VAR model for the OECD countries, we test for Granger causality between the 7-day cumulative incidence, mortality rate, and government response indexes. Granger-type statistics reveal evidence that the evolution of the COVID-19 pandemic influenced the measures taken by governments. However, limited or nonexistent evidence supports the reverse situation. This suggests that government measures were not highly effective in controlling the pandemic. While not implying total ineffectiveness, our results indicate a considerable lack of efficacy, emphasizing a lesson for governments to learn from and correct in preparation for similar events in the future.

Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns

The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan's central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of "multicenter-periphery" gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts.

Impact of sociodemographic and economic determinants of health on COVID-19 infection: incidence variation between reference periods

OBJECTIVES

The COVID-19 pandemic hit Portugal in March 2020, causing widespread disruption to various aspects of society. While extensive research has been conducted on the significance of socio-economic disparities in infection risk, this study aims to enhance our understanding of their evolving relationship over time by analysing four distinct periods in 2020.

STUDY DESIGN AND METHODS

This retrospective observational ecological study included individuals residing in the Primary Healthcare Cluster areas of Almada-Seixal and Western Lisbon and Oeiras, who tested positive for SARS-CoV-2 through a polymerase chain reaction (PCR) test between the 2nd of March and the 8th of November of 2020. Using incidence rates for each specific neighbourhood (n = 29) and period, we explored the relationship between neighbourhood-level socio-economic variables and the risk of infection using negative-binomial regression models.

RESULTS

In the analysed period, a total of 8562 confirmed COVID-19 cases were identified. Overall incidence rates for each period were sequentially 2.74, 5.03, 3.99 and 14.29 COVID-19 cases per 100,000 person-days. Housing overcrowding, illiteracy rate and place of birth were associated with increased risk of infection, while age, congregate living, and employment in the secondary sector exhibited the opposite association. No association was consistent across all time periods.

CONCLUSIONS

Our findings support the idea that the influence of socio-economic determinants of health is not immutable throughout time. In a pandemic context where information, knowledge, beliefs, and behaviours are ever-changing and evolving, a dynamic, inclusive, and adaptable approach to disease control can lead to a more equitable distribution of improved outcomes, benefiting all strata of society.

Range area and the fast-slow continuum of life history traits predict pathogen richness in wild mammals

Surveillance of pathogen richness in wildlife is needed to identify host species with a high risk of zoonotic disease spillover. While several predictors of pathogen richness in wildlife hosts have been proposed, their relative importance has not been formally examined. This hampers our ability to identify potential disease reservoirs, particularly in remote areas with limited surveillance efforts. Here we analyzed 14 proposed predictors of pathogen richness using ensemble modeling and a dataset of 1040 host species to identify the most important predictors of pathogen richness in wild mammal species. After controlling for research effort, larger species geographic range area was identified to be associated with higher pathogen richness. We found evidence of duality in the relationship between the fast-slow continuum of life-history traits and pathogen richness, where pathogen richness increases near the extremities. Taxonomic orders Carnivora, Proboscidea, Artiodactyla, and Perissodactyla were predicted to host high pathogen richness. The top three species with the highest pathogen richness predicted by our ensemble model were Canis lupus, Sus scrofa, and Alces alces. Our results can help support evidence-informed pathogen surveillance and disease reservoir management to prevent the emergence of future zoonotic diseases.

Modeling economic impacts of mobility restriction policy during the COVID-19 pandemic

The economic impacts of pandemics can be enormous. However, lockdown and human mobility restrictions are effective policies for containing the spread of the disease. This study proposes a framework for assessing the economic impact of varying degrees of movement restrictions and examines the effectiveness of this framework in a case study examining COVID-19 control measures in Japan. First, mobile network operators data and total employment statistics on a 500-meter grid scale are used to determine the status of mobility restrictions and impacts on consumption in 30 industrial sectors. Next, the economic impacts are assessed using a spatial computable general equilibrium (CGE) model, proven to yield valuable insights into the total economic impacts of natural disasters. In sectors that implement telework and e-commerce-wholesale/retail, finance/insurance, and communication sectors-estimates of production and GDP are obtained that are close to the actual figures. The current case study is limited to Japan, but similar analysis can be conducted by using the CGE model for each country and open mobility data. Thus, the framework has potential to serve as an effective tool for assessing trade-offs between infection risks and economic impacts to inform policy-making by combining with findings from epidemiology.

Healthcare Systems and COVID-19 Mortality in Selected OECD Countries: A Panel Quantile Regression Analysis

OBJECTIVES

The pandemic caused by coronavirus disease 2019 (COVID-19) has exerted an unprecedented impact on the health of populations worldwide. However, the adverse health consequences of the pandemic in terms of infection and mortality rates have varied across countries. In this study, we investigate whether COVID-19 mortality rates across a group of developed nations are associated with characteristics of their healthcare systems, beyond the differential policy responses in those countries.

METHODS

To achieve the study objective, we distinguished healthcare systems based on the extent of healthcare decommodification. Using available daily data from 2020, 2021, and 2022, we applied quantile regression with non-additive fixed effects to estimate mortality rates across quantiles. Our analysis began prior to vaccine development (in 2020) and continued after the vaccines were introduced (throughout 2021 and part of 2022).

RESULTS

The findings indicate that higher testing rates, coupled with more stringent containment and public health measures, had a significant negative impact on the death rate in both pre-vaccination and post-vaccination models. The data from the post-vaccination model demonstrate that higher vaccination rates were associated with significant decreases in fatalities. Additionally, our research indicates that countries with healthcare systems characterized by high and medium levels of decommodification experienced lower mortality rates than those with healthcare systems involving low decommodification.

CONCLUSIONS

The results of this study indicate that stronger public health infrastructure and more inclusive social protections have mitigated the severity of the pandemic's adverse health impacts, more so than emergency containment measures and social restrictions.

Reducing radiation dose for NN-based COVID-19 detection in helical chest CT using real-time monitored reconstruction

Computed tomography is a powerful tool for medical examination, which plays a particularly important role in the investigation of acute diseases, such as COVID-19. A growing concern in relation to CT scans is the radiation to which the patients are exposed, and a lot of research is dedicated to methods and approaches to how to reduce the radiation dose in X-ray CT studies. In this paper, we propose a novel scanning protocol based on real-time monitored reconstruction for a helical chest CT using a pre-trained neural network model for COVID-19 detection as an expert. In a simulated study, for the first time, we proposed using per-slice stopping rules based on the COVID-19 detection neural network output to reduce the frequency of projection acquisition for portions of the scanning process. The proposed method allows reducing the total number of X-ray projections necessary for COVID-19 detection, and thus reducing the radiation dose, without a significant decrease in the prediction accuracy. The proposed protocol was evaluated on 163 patients from the COVID-CTset dataset, providing a mean dose reduction of 15.1% while the mean decrease in prediction accuracy amounted to only 1.9% achieving a Pareto improvement over a fixed protocol.

Impacts of COVID-19 on mental health in the US: evidence from a national survey

BACKGROUND

Studies have reported substantial effects of the COVID-19 pandemic on mental health, but little is known whether the impacts of COVID on individuals, such as being tested for COVID or experiencing disruptions to healthcare utilization, would affect their mental health differently.

AIMS

To examine the impacts of COVID-19 on depression and anxiety disorders among US adults.

METHODS

We included 8098 adults with no prior mental health problems using data from the National Health Interview Survey (2019-2020). We examined two outcomes: current depression and anxiety; and three COVID-related impact measures: ever COVID test, delayed medical care, and no medical care due to COVID. Multinomial logistic regressions were conducted.

RESULTS

Delayed or no medical care were significantly associated with current depression, with adjusted relative risks (aRRs) of 2.17 (95% CI, 1.48-2.85) and 1.85 (95% CI, 1.33-2.38). All three COVID-related impact measures were significantly associated with current anxiety. The aRRs were 1.16 (95% CI, 1.01-1.32) for ever COVID test, 1.94 (95% CI, 1.64-2.24) for no medical care, and 1.90 (95% CI, 1.63-2.18) for delayed medical care.

CONCLUSIONS

Individuals who were affected by COVID were more likely to experience depression or anxiety disorders. Mental health services need to prioritize these high-risk groups.

Understanding mental health conditions and key coping strategies utilized during major lockdowns in the Caribbean based on Google trends searches

The COVID-19 pandemic has prompted countries to implement extended Shelter in Place Orders (SIPOs) to restrict population movement and mitigate community spread. While these lockdown measures may be effective in containing the virus, they can substantially impact the population's well-being, potentially undermining their overall welfare. This study investigates whether major lockdowns implemented in the Caribbean produced differential changes in mental health among key English-Speaking Caribbean countries. More importantly, unlike past studies, we examine key coping strategies persons utilize during major lockdowns. Finally, this paper utilizes a novel near real-time high-frequency data source in Google Trends data analytics to assess mental health patterns and coping strategies among major Caribbean countries. Based on the results of difference-in-difference and event study models, we find positive and significant increases in searches for fear, depression, and suicide during key lockdown periods, which suggest negative mental health effects. Regarding coping strategies, searches for Zoom, learning, books, exercise, prayer, religion, and meditation increased, together with searches for addiction and marijuana. These results indicate the types of programs health administrators and policymakers can implement during lockdown periods to help local mental health communities, particularly among island communities.

Medical occupation preference under the influence of the COVID-19 pandemic: The role of risk and altruistic preferences

We examine the influence of the COVID-19 pandemic on medical occupation preference, focusing on Wuhan, China. We conducted a survey of 5686 respondents in China regarding the influence of the COVID-19 pandemic on medical occupation preference. We also conducted a complimentary survey in the UK with 1198 respondents, as well as a field experiment in Wuhan with 428 first and second-year medical students. We find a significant negative impact of the pandemic on the willingness to let a loved one choose a medical occupation. Individuals who were heavily influenced by the pandemic, that is, Wuhan residents, especially medical workers, express significantly lower medical occupation preference. Further analysis from Sobel-Goodman mediation tests reveals that around half of the total negative effect can be mediated by enhanced risk aversion and reduced altruism. The UK survey and the field experiment with medical students in Wuhan reinforce these findings. Our results suggest a shift in medical workers' risk- and altruistic-preferences has led to a reduced medical occupation preference. Non-medical workers and students who are more altruistic and risk-seeking are more likely to choose a medical occupation.

Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure

This study investigates the impact of COVID-19 pandemic on the Chinese stock market in 2020. Using daily data of three industries, this study addresses the identification of abnormal stock returns as a multiple hypothesis testing problem and proposes to apply a grouped comparison procedure for better detection. By comparing the numbers of daily signals and numbers of stocks with abnormal positive and negative returns, the empirical result shows that the three industries perform differently under the pandemic. Compared to the non-grouped testing procedure, the signals found by the grouped procedure are more prominent, which is advantageous for some situations when there tends to be abnormal performance clustering at the occurrence of major event. This paper on stock return anomalies gives a new perspective on the impact of major events to the stock market, like the global outbreak disease.

The cost of mass gatherings during a pandemic

In June 2020, the U.S., unlike other regions of the world, faced a surge in cases of COVID-19. Immediately prior to this wave of cases, the largest mass protests in U.S. history took place. We show that when holding other factors constant, COVID-19 cases increased most in places where more demonstrations occurred. We exploit variation in rainfall during the protest period as an exogenous source of variation in attendance. We find that good weather coincides with both more people protesting and more subsequent COVID-19 cases and deaths. Mass gatherings during a pandemic thus lead to more contraction and fatalities of COVID-19, and we quantify these effects.

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

Psychometric properties and psychological correlates of the COVID-19 Anxiety Syndrome Scale: A comprehensive systematic review and meta-analysis

Coronavirus disease-19 (COVID-19) has led to the demise of millions of people worldwide; additionally, it has resulted in a significant economic and mental health burden. Since the onset of the COVID-19 pandemic, various measures have been constructed to evaluate pandemic-related fear and anxiety. The COVID-19 Anxiety Syndrome Scale (C-19ASS) is a promising measure that assesses coping strategies (e.g., avoidance, checking, worrying and threat monitoring), termed 'COVID-19 Anxiety Syndrome', in response to COVID-19 fear and anxiety. The measure has been broadly welcomed, leading to its use in Brazil (Portuguese), China, Greece, Indonesia, the Philippines, Iran (Farsi), Italy, Saudi Arabia (Arabic), Turkey, the United Kingdom and the United States. To gain a better understanding of the relevance of the COVID-19 Anxiety Syndrome, we conducted a systematic review and meta-analysis to explore the psychological correlates and psychometric properties of the C-19ASS. Through the analysis of a total of 17,789 individuals (age range 19-70; female = 33%-85%), the C-19ASS demonstrated a consistent factor structure, measurement invariance across gender and acceptable reliabilities. Furthermore, a significant association with COVID-19 anxiety, depressive symptoms, generalized anxiety, health anxiety, psychological distress and functional impairment (work and social adjustment) during the COVID-19 pandemic was observed. When considering the Big Five personality traits, the C-19ASS and its subscales were only significantly and negatively associated with extraversion; only the total score on the measure was associated with neuroticism. The observed effect sizes ranged from very small to medium. Given that all included studies (K = 24) were cross-sectional, and due to the nature of the COVID-19 Anxiety Syndrome, which may well persist after the pandemic ends, it is recommended to continue screening society for the persistence of this syndrome.

Big data evidence of the impact of COVID-19 hospitalizations on mortality rates of non-COVID-19 critically ill patients

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.e. the additional mortality risk that COVID-19 ICU hospitalizations generate on non-COVID-19 patients receiving intensive care. The data contain the universe of ICU hospitalizations in SUS from February 26, 2020 to December 31, 2021. Spillover estimates are obtained from high-dimensional fixed effects regression models that control for a number of unobservable confounders. Our findings indicate that, on average, the pandemic increased the mortality risk of non-COVID-19 ICU patients by 1.296 percentage points, 95% CI 1.145-1.448. The spillover mortality risk is larger for non-COVID patients receiving intensive care due to diseases of the respiratory system, diseases of the skin and subcutaneous tissue, and infectious and parasitic diseases. As of July 2023, the WHO reports more than 6.9 million global deaths due to COVID-19 infection. However, our estimates of spillover effects suggest that the pandemic's total death toll is much higher.

Financial Hardship and Psychological Distress During the Pandemic: A Nationally Representative Survey of Major Racial-Ethnic Groups in the United States

Introduction

While financial hardship has been consistently linked to psychological distress, little research exists on associations between financial hardship experienced during the pandemic and mental health.

Methods

We conducted a nationally representative, online survey of American Indian/Alaska Native, Asian, Black/African American, Latino (English and Spanish speaking), Native Hawaiian/Pacific Islander, White, and multiracial adults, 12/2020-2/2021 (n=5500). Six financial hardship domains were measured (lost income, debt, unmet expenses, unmet health care expenses, housing insecurity, and food insecurity). Psychological distress measures included anxiety-depression symptoms (Patient Health Questionnaire-4), perceived stress (modified Perceived Stress Scale), and loneliness-isolation ("In the past month, how often have you felt lonely and isolated?"). Associations between financial hardship and psychological distress were estimated using multinomial logistic regression.

Results

Overall, 70.3% of participants reported experiencing financial hardship (substantial hardship: 21.3%; some hardship: 27.4%; and a little hardship: 21.6%), with Spanish-speaking Latino (87.3%) and Native Hawaiian/Pacific Islander (79.2%) adults being most likely. Debt (57.6%), lost income (44.5%), and unmet expenses (33.7%) were the most common. There was a dose-response association between financial hardship and moderate/severe anxiety-depression symptoms (a little hardship: adjusted odds ratio [aOR]=1.42, 95% confidence interval [CI]=1.12-1.80; some hardship: aOR=3.21, 95% CI=2.58-3.98; and substantial hardship: aOR=8.15, 95% CI=6.45-10.29). Similar dose-response trends were observed with perceived stress and loneliness-isolation. No racial-ethnic difference in the association between financial hardship during the pandemic and psychological distress was seen.

Discussion

Financial hardship has had a major impact on psychological distress during the pandemic; moreover, while no racial-ethnic difference in the effect of financial hardship was observed, because racial-ethnic minorities experienced greater hardship, financial hardship may exacerbate psychological distress disparities.

COVID-19 risk perception and public compliance with preventive measures: Evidence from a multi-wave household survey in the MENA region

This study investigates the association between individuals' concern about contracting COVID-19 and their compliance with recommended preventive and mitigation measures, namely wearing face masks, maintaining social distancing and handwashing, in the context of the Middle East and North Africa (MENA) region. The empirical analysis is based on a panel dataset from the Combined COVID-19 MENA Monitor Household Survey, which was carried out in Jordan, Morocco, Sudan, Tunisia and Egypt. Applying a probit estimation technique, a positive and statistically significant association was found between the level of COVID-19 worries and individuals' compliance with the mitigation measures. Notably, the results revealed that this association followed a "first-up-then-down" trend, showing that compliance with the three mitigation measures rose as individuals' worries about contracting the virus increased, and then markedly decreased after they had been infected. Socio-demographic characteristics contributing to lower levels of compliance included being male, being over 60, having lower levels of education and having a lower household income. A cross-country analysis revealed remarkable differences between the five countries, with the strongest association between COVID-19 concerns and adherence to mitigation measures observed in Tunisia and Sudan, and the weakest association seen in Jordan and Morocco. Policy implications are outlined for effective risk communication and management during disease outbreaks and public health emergencies to encourage appropriate public health behaviours.

Gone with the epidemic? The spatial effects of the Covid-19 on global investment network

The outbreak of Covid-19 epidemic has a prolonged impact on global economic activities. In recent years, many scholars have been motivated to estimate the effects of Covid-19 shock on global foreign direct investment (FDI). However, existing studies have not paid enough attention to the spillover effects caused by the epidemic. Although few academic works have explored the geographic-neighboring spillover effects of epidemic shock on global investment, we further extent the understanding of the spillover effects in an economic network. On the basis of country-month greenfield FDI panels, we construct a spatial Durbin model, and figure out that Covid-19 shock may have positive FDI spillover effects in an economic network via global FDI transfers. Furthermore, we find that such spillovers are greatly conditioned by country-level network position and institutional ties among nations. Our research suggests that global FDI transfers may partly offset economic-adverse effects of the Covid-19 shock. While global countries, especially those in the Global South, should be more closely embedded in the global investment network in such an uncertain environment.

The dynamics of SARS-CoV-2 infection in unvaccinated and vaccinated populations in Mumbai, India, between 28 December 2020 and 30 August 2021

The emergence and evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants that could compromise vaccine efficacy (VE) with re-infections in immunized individuals have necessitated continuous surveillance of VE. Here, the occurrence and dynamics of SARS-CoV-2 infections in the context of vaccination during the second wave of infection in Mumbai were evaluated. RT-PCR cycle threshold (Ct) values of the open reading frame (ORF)/envelope (E)/nucleocapsid (N) genes obtained from a total of 42415 samples, comprising unvaccinated (96.88%) and vaccinated cases (3.12%) were analyzed between December 28, 2020, and August 30, 2021. A lower incidence of SARS-CoV-2 infection in fully vaccinated cases (5.07%) compared to partially vaccinated cases (6.5%) and unvaccinated cases (13.453%) was recorded. VE was significant after the first dose of vaccination (ORF gene p-value = 0.003429, and E/N gene p-value = 0.000866). Furthermore, VE was observed to be significant when the post-immunization (first dose) period was stratified to within 30 days (ORF gene p-value = 0.0094 and E/N gene p-value = 0.0023) and to 60 days following the second dose of vaccination (ORF gene p-value = 0.0238). Also, significantly higher efficacy was observed within individuals receiving two doses compared to a single dose (ORF gene p-value = 0.0132 and E/N gene p-value = 0.0387). The emergence of breakthrough infections was also evident (odds ratio= 0.34; 95% confidence interval= 0.27-0.43). Interestingly, viral loads trended towards being higher in some groups of partially vaccinated individuals compared to completely vaccinated and unvaccinated populations. Finally, our results delineated a significantly higher incidence of SARS-CoV-2 acquisition in males, asymptomatic individuals, individuals with comorbidities, and those who were unvaccinated.