A literature review of the economics of COVID-19

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.

The power of clans: How social capital sheltered firms during the COVID-19 pandemic

Data from 3,555 Chinese listed firms show that firms in cities with greater clan strength faced smaller losses and swifter recovery following COVID-19. Clans were significantly related to individual values facilitating pandemic prevention; these ties guaranteed economic activities and sheltered firms from the shock. Our results frame social capital as a complementarity to formal institutions during crises.

Face mask is an efficient tool to fight the Covid-19 pandemic and some factors increase the probability of its adoption

This study examines the dynamic impact of face mask use on both infected cases and fatalities at a global scale by using a rich set of panel data econometrics. An increase of 100% of the proportion of people declaring wearing a mask (multiply by two) over the studied period lead to a reduction of around 12 and 13.5% of the number of Covid-19 infected cases (per capita) after 7 and 14 days respectively. The delay of action varies from around 7 days to 28 days concerning infected cases but is more longer concerning fatalities. Our results hold when using the rigorous controlling approach. We also document the increasing adoption of mask use over time and the drivers of mask adoption. In addition, population density and pollution levels are significant determinants of heterogeneity regarding mask adoption across countries, while altruism, trust in government and demographics are not. However, individualism index is negatively correlated with mask adoption. Finally, strict government policies against Covid-19 have a strong significant effect on mask use.

Design and generation of mRNAs encoding conserved regions of SARS-CoV-2 ORF1ab for T cell-mediated immune activation

Aim

To generate mRNAs encoding conserved regions within SARS-CoV-2 ORF1ab which can induce strong T-cell responses to overcome the immune invasion of newly emergent variants.

Methods

We selected two conserved regions with a high density of T-cell epitopes using immunoinformatics for mRNA synthesis. The ability of testing mRNAs to activate T cells for IFN-γ production was examined by an ELISpot assay and flow cytometry.

Results

Two synthesized mRNAs were successfully translated in MDA-MB-231 cells and had comparable potency to the spike mRNA to induce CD4+ and CD8+ T-cell responses in peripheral blood mononuclear cells in 29 out of 34 participants.

Conclusion

This study provides a proof-of-concept for the use of SARS-CoV-2 conserved regions to develop booster vaccines capable of eliciting T-cell-mediated immunity.

Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces

The enactment of COVID-19 policies in Canada falls under provincial jurisdiction. This study exploits time-series variation across four Canadian provinces to evaluate the effects of stricter COVID-19 policies on daily case counts. Employing data from this time-period allows an evaluation of the efficacy of policies independent of vaccine impacts. While both OLS and IV results offer evidence that more stringent Non-Pharmaceutical Interventions (NPIs) can reduce daily case counts within a short time-period, IV estimates are larger in magnitude. Hence, studies that fail to control for simultaneity bias might produce confounded estimates of the efficacy of NPIs. However, IV estimates should be treated as correlations given the possibility of other unobserved determinants of COVID-19 spread and mismeasurement of daily cases. With respect to specific policies, mandatory mask usage in indoor spaces and restrictions on business operations are significantly associated with lower daily cases. We also test the efficacy of different forecasting models. Our results suggest that Gradient Boosted Regression Trees (GBRT) and Seasonal Autoregressive-Integrated Moving Average (SARIMA) models produce more accurate short-run forecasts relative to Vector Auto Regressive (VAR), and Susceptible-Infected-Removed (SIR) epidemiology models. Forecasts from SIR models are also inferior to results from basic OLS regressions. However, predictions from models that are unable to correct for endogeneity bias should be treated with caution.

The economic impacts of COVID-19 and city lockdown: Early evidence from China

As the first major developing country heavily struck by the COVID-19 pandemic, China adopted the world's most stringent lockdown interventions to contain the virus spread. Using macro- and micro-level data, this paper shows that both the pandemic and lockdown policies have had negative and significant impacts on the economy. Gross regional product (GRP) fell by 9.5 and 0.3 percentage points in cities with and without lockdown interventions, respectively. These impacts represent a dramatic recession from China's average growth of 6.74% before the pandemic. The results indicate that lockdown explains 2.8 percentage points of the GDP loss. We also document significant spill-over effects of the pandemic in adjacent areas but no such effects of lockdown. Reduced labor mobility, land supply, and entrepreneurship are among the most significant mechanisms underpinning the impacts of the pandemic and lockdown. Cities with higher share of secondary industry, higher traffic intensity, lower population density, lower internet access, and lower fiscal capacity suffered more. However, these cities seem to have recovered well from the recession and quickly closed the economic gap in the aftermath of the pandemic and city lockdown. Our findings have broader implications for the global interventions in pandemic containment.

The social scar of the pandemic: Impacts of COVID-19 exposure on interpersonal trust

This paper employs a difference-in-differences strategy to examine the causal effect of exposure to the COVID-19 pandemic on interpersonal trust amidst zero-COVID policies in China. Using a nationally representative panel survey, we find that COVID-19 exposure leads to a decrease in the levels of generalized trust. We also show that the change in interpersonal trust varies across domains. Specifically, COVID-19 exposure significantly decreases trust in parents, neighbors, and local government officials, but has small and insignificant effects on trust in doctors, strangers, and Americans. Empirical tests suggest that changes in income and physical health status are not likely to be potential channels. We provide some evidence for the mechanism of deteriorated mental health status and pessimistic expectations.

Quantifying the impact of individual and collective compliance with infection control measures for ethical public health policy

Infectious disease control measures often require collective compliance of large numbers of individuals to benefit public health. This raises ethical questions regarding the value of the public health benefit created by individual and collective compliance. Answering these requires estimating the extent to which individual actions prevent infection of others. We develop mathematical techniques enabling quantification of the impacts of individuals or groups complying with three public health measures: border quarantine, isolation of infected individuals, and prevention via vaccination/prophylaxis. The results suggest that (i) these interventions exhibit synergy: They become more effective on a per-individual basis as compliance increases, and (ii) there is often substantial "overdetermination" of transmission. If a susceptible person contacts multiple infectious individuals, an intervention preventing one transmission may not change the ultimate outcome (thus, risk imposed by some individuals may erode the benefits of others' compliance). These results have implications for public health policy during epidemics.

Beyond Pigou: externalities and civil society in the supply-demand framework

The extent of voluntary cooperation in the presence of externalities is shown as an equilibrium outcome in the supply and demand framework. The analysis uses familiar ingredients to provide a new way of understanding the results of the extensive literature beginning with Buchanan, Coase, Ostrom, Shapley, Telser, Tullock, and Williamson showing that a Pigouvian tax is not the only alternative to independently acting individuals who are coordinated merely through distorted market prices. Voluntary cooperation transforms the character of the costs resulting from externalities and may have a far different incidence than Pigouvian taxes and subsidies do. The paper discusses applications including forest management, volume discounts, residential associations, energy policy, the scope of planning of household activities, and the role of workplaces in preventing infectious disease.

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.

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.

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

IT shields: Technology adoption and economic resilience during the COVID-19 pandemic

We study the labor market effects of information technology (IT) during the onset of the COVID-19 pandemic, using data on IT adoption covering almost three million establishments in the US. We find that in areas where firms had adopted more IT before the pandemic, the unemployment rate rose less in response to social distancing. IT shields all individuals, regardless of gender and race, except those with the lowest educational attainment. Instrumental variable estimates-leveraging historical routine employment share as a booster of IT adoption- confirm IT had a causal impact on fostering labor markets' resilience. Additional evidence suggests this shielding effect is due to the easiness of working-from-home and to stronger creation of digital jobs in high IT areas.

Modeling social, economic, and health perspectives for optimal pandemic policy decision-making

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic. The three objectives being pursued include: (i) the epidemiological impact, (ii) the economic impact on the local businesses, and (iii) the economic impact on the trades between industries. The proposed model is implemented on a dataset that includes 11 states, the District of Columbia, and 19 industries in the US. The solved by augmented ε-constraint approach is used to solve the multi-objective model, and a final strategy is selected from the set of Pareto-optimal solutions based on the least cubic distance of the solution from the optimal value of each objective. 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, and it is more effective to close most states while keeping the majority of industries open during the planning horizon.

Policy choices and compliance behavior in pandemic times

In this paper, we model an evolutionary noncooperative game between politicians and citizens that, given the level of infection, describes the observed variety of mitigation policies and citizens' compliance during the COVID-19 pandemic period. Our results show that different stable equilibria exist and that different ways/paths exist to reach these equilibria may be present, depending on the choice of parameters. When the parameters are chosen opportunistically, in the short run, our model generates transitions between hard and soft policy measures to deal with the pandemic. In the long-run, convergence is achieved toward one of the possible stable steady states (obey or not obey lockdown rules) as functions of politicians' and citizens' incentives.

Behavioral changes in the housing market before and after the Covid-19 lockdown

We exploit unique Norwegian day-by-day transaction and hour-by-hour bidding logs data in order to examine how market participants reacted to the spreading news of Covid-19 in early March 2020, the lockdown on March 12, and the re-opening on April 20. We observe changes on the date of the lockdown in transaction volumes, sell-prediction spreads, exploitative bidding behavior, and seller confidence. However, when we compare observed price developments with our estimated counter-factual price developments, we find that about half of the total fall in prices had already occurred before the lockdown was implemented. The re-opening completely reverses the lockdown effect on prices. We show that voluntary behavioral changes, as well as lockdown and re-opening effects, are visible in various measures of social mobility, and that changes in daily news sentiment correlate with the abnormal price movements during this period.

Expenditure responses to the COVID-19 pandemic

We examine how the COVID-19 contagion influences consumer expenditure patterns. We show that the consumption expenditure responses to the spread of the COVID-19 pandemic are significantly different between the older and younger generations. We find that older adults spend less than the younger generation by at least 5% during the pandemic. In fact, those aged above 60 significantly decrease their spending even on food and drink products by 13%. We also find that older adults forgo shopping in favor of the younger generation. These responses might be due to the fear of COVID-19 infection (Immordino et al., 2022).

The impact of COVID-19 infection on labor outcomes of Mexican formal workers

The outbreak of the COVID-19 pandemic had an immediate and severe impact on the economy. However, we do not know whether the disease may have a longer-term effect on people's employment opportunities. In this study, we focus on the effects of COVID-19 infection on labor market outcomes 12 months after diagnosis. We use a unique dataset that includes all formal private sector workers in the Mexican social security system and that links health outcomes with administrative records. We implement two alternative identification strategies to estimate the impact: matching estimators and individual fixed effects models. Our study finds that COVID-19 infection does not harm employment probabilities or wages. On the contrary, we find that workers who had tested positive for COVID had a higher likelihood of keeping their formal sector jobs and higher wages than those who did not. Moreover, our results describe mostly low-income workers.

Travel behaviour changes and risk perception during COVID-19: A case study of Malaysia

The COVID-19 pandemic has disrupted travel behaviours due to the need for movement restrictions. The restrictions adversely affected various aspects of health and the economy. This study aimed to investigate factors affecting trip frequency during the recovery period of the COVID-19 pandemic in Malaysia. An online national cross-sectional survey was conducted to collect data in conjunction with different movement restriction policies. The questionnaire includes socio-demographics, experience with COVID-19, risk perception of COVID-19, and trip frequency on several activities during the pandemic. Mann Whitney U was conducted to determine whether there were statistically significant differences between the socio-demographic factors for the respondents in the first and second surveys. Results show no significant difference in socio-demographic factors except for the level of education. The results indicate that the respondents from both surveys were comparable. Next, Spearman correlation analyses were conducted to find significant correlations between trip frequencies toward socio-demographics, experience with COVID-19 and risk perception. There was a correlation between the frequency of travel and risk perception for both surveys. Regression analyses were performed based on the findings to investigate trip frequency determinants during the pandemic. Perceived risk, gender, and occupation influenced the trip frequencies for both surveys. By understanding the influence of risk perception on the frequency of travel, the government can identify the appropriate policy during a pandemic or health emergency to avoid impeding normal travel behaviour. Thus, people's mental and psychological well-being are not negatively affected.

A 16-month longitudinal investigation of risk and protective factors for mental health outcomes throughout three national lockdowns and a mass vaccination campaign: Evidence from a weighted Israeli sample during COVID-19

BACKGROUND

COVID-19 is an ongoing global crisis, with a multitude of factors that affect mental health worldwide. We explored potential predictors for the emergence and maintenance of depression, anxiety, and posttraumatic stress symptoms (PTSS) in the general population in Israel.

METHODS

Across the span of 16 months, 2478 people completed a repeated self-report survey which inquired psychiatric symptoms and pandemic related stress factors (PRSF). We applied mixed-effects models to assess how each stressor contributes to depression, anxiety and PTSS at each time point, and longitudinally assessed participants who completed at least two consecutive surveys (n = 400). We weighted our sample to increase representativeness of the population.

RESULTS

Fatigue was the strongest predictor for depression, anxiety and PTSS at all time points, and predicted deterioration overtime. Financial concerns associated with depression and anxiety at all time points, and with their deterioration overtime. Health related concerns were uniquely associated with anxiety and PTSS at all time points and their deterioration, but not with depression. Improvement in sense of protection overtime associated with decrease in depression and anxiety. Hesitancy towards vaccination was associated to higher financial concerns and lower sense of protection by the authorities.

CONCLUSIONS

Our findings accentuate the multitude of risk factors for psychiatric morbidity during COVID-19, and the centrality of fatigue in determining mental health outcomes.

Multidimensional Deprivation from Labor Market Opportunities in Armenia: Evidence from 2018 and 2020

This paper explores the multidimensional deprivation from labor market opportunities in Armenia by constructing a Quality of Employment measure. Using Labor Force Survey datasets for the years 2018 and 2020, we conduct a comparative analysis for a group of job-separated individuals. The identified dimensions of deprivation from labor market opportunities prior to and after the onset of COVID-19 are reasons for separating from a job, reasons for not looking for a job, and main obstacles in finding a job. These dimensions enable to study employee-level (supply factors) and job-related characteristics (demand factors). Our study shows that demand factors are the primary drivers of amplified deprivation in times of the pandemic. Also, we observe that the gender gap in the labor market deprivation has been increased during the pandemic, further amplified for married women. Interestingly, gender gap in deprivation is invariant to the occupational composition.

The short-term impact of the 2020 pandemic lockdown on employment in Greece

This paper analyzes the short-term employment impact of the COVID-19 lockdown in Greece during the first few months following the pandemic onset. During the initial lockdown period, aggregate employment was lower by almost 9 percentage points than it would have been expected based on pre-pandemic employment trends. However, due to a government intervention that prohibited layoffs, this was not due to higher separation rates. The overall short-term employment impact was due to lower hiring rates. To uncover the mechanism behind this, we use a difference-in-differences framework, and show that tourism-related activities, which are exposed to seasonal variation, had significantly lower employment entry rates in the months following the pandemic onset compared to non-tourism activities. Our results highlight the relevance of the timing of unanticipated shocks in economies with strong seasonal patterns, and the relative effectiveness of policy interventions to partly absorb the consequences of such shocks.