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The most enduring measure of how individuals make personal decisions affecting their health and safety is the compensating wage differential for job safety risk revealed in the labor market via hedonic equilibrium outcomes. The decisions in turn reveal the value of a statistical life (VSL), the value of a statistical injury (VSI), and the value of a statistical life year (VSLY), which have both mortality and morbidity aspects that we describe and apply here. All such tradeoff rates play important roles in policy decisions concerning improving individual welfare. Specifically, we explicate the recent empirical research on VSL and its related concepts and link the empirical results to the ongoing examinations of many government policies intended to improve individuals' health and longevity. We pay special attention to recent issues such as the COVID pandemic and newly emerging foci on distributional consequences concerning which demographic groups may benefit most from certain regulations.

We study changes in age-specific diabetes-related mortality and annual health care utilization. We find that half of the estimated 16% increase of diabetic mortality falls within employable age groups. We estimate that disease combination-specific increase in case fatality has resulted in premature diabetic mortality costing $3.2 billion annually. The estimated annual direct cost of treating high-risk diabetics reaches $36 billion, of which Medicare and Other Federal Programs compensate 54%. Respiratory conditions among diabetics comprise the same proportion of high-risk diabetics as do the disease combinations including coronary heart diseases. Treating of general diabetic conditions has become more efficient as indicated by the estimated declines in per unit health care costs.

As a developing country, India initiated several labor measures to contain the Covid-19 pandemic. This study analyzes the impact of the Covid-19-induced comprehensive labor measures by Indian states on the Case Fatality Ratios and Recovery Rates. Such an analysis will provide deeper insights into the importance of labor measures during a health emergency. It will also be a reality check to the existing National Health Policy.

The study examines the four major aspects of labor measures on migration, social security, state-specific needs and health and human rights. The authors use these to formulate a composite labor measure index to comprehend the Covid-19-induced various labor measures. Additionally, the authors apply pooled ordinary least squares and panel random-effects models with the state-wise monthly data to examine the impact of Covid-19-induced labor measures on the severity of the pandemic.

Covid-19-induced comprehensive labor measures reduce fatalities and increase recovery rates. In particular, the measures on labor migration, state-specific needs and health and human rights adopted by the Indian states successfully reduce the Covid-19 fatalities and improve the recovery rates. However, the measures taken to address social security have been ineffective in reducing the severity of the pandemic. The study results are robust to various other specifications.

The time period covered in this research is very brief and does not account for the qualitative impact of labor measures on the severity of the pandemic. This study specifically addresses the number of Covid-19-induced labor measures and not the resources allocated to their implementations or the number of people who benefited from the measures.

This study emphasizes the need for subnational comprehensive labor measures to reduce the severity of the pandemic in developing countries. The study confirms the need for effective Covid-19-induced social security measures to cope with the pandemic in India. This study also ascertains the beneficial impact of the measures on migration, state-specific needs and health and human rights.

The authors make a composite labor measure index that captures state-level Covid-19-induced labor measures on diverse aspects, namely migration, social security, state-specific need and health and human rights, hitherto unexplored. In addition, the authors analyze the impact of these labor measures on the severity of the pandemic.

The purpose of this study is to determine the proportion of the population that will be susceptible to the COVID-19 pandemic, as well as the proportions of infections, recoveries and fatalities from the COVID-19 pandemic.

The design was a longitudinal survey of COVID-19 infections, recoveries and fatalities in Nigeria using the data on the daily updates of the Nigeria Centre for Disease Control for the period 1 May to 23 August 2020. Markov chain analysis was performed on the data.

The results showed that in the long run, 8.4% of the population will be susceptible to COVID-19 infections, 26.4% of them will be infected, 61.2% of the infected will recover and 4% will become fatal. Thus, if this pattern of infections and recoveries continue, the majority of the infected people in Nigeria will recover whilst a very small proportion of the infected people will die.

A dearth of the extant literature on the problem, especially from the management science perspective.

Results of the study will facilitate policymakers’ response to the curtailment of the pandemic in Nigeria.

Curtailing the pandemic through the results of this study will assist in easing the social consequences of the pandemic.

The proposed adjustment to the susceptibilities, infections and recoveries model through the introduction of a fourth state (fatality) to get the susceptibilities, infections, recoveries and fatalities model, signalling a point of departure from previous studies.

The purpose of this study was to determine COVID-19 preliminary case fatality rates (CFR) across Southeast Asian (SEA) countries.

The study accessed the data on COVID-19 accumulated cases of fatalities and infections across SEA countries from the World Health Organization (WHO) website, covering the early days of March to May 21, 2020. The approach involved the computation of the CFR using the simple linear regression model. The slope of the regression line was the estimate of the CFR at a 95% confidence interval. The study also reviewed the different approaches of the SEA countries in dealing with the pandemic.

As of May 21, 2020, Singapore, Indonesia and the Philippines were the top three SEA countries with the highest record of COVID-19 infections. Brunei had one fatality, while Cambodia, Laos, Timor-Leste and Viet Nam had nil fatalities. Indonesia and the Philippines had the highest CFR with 6.66 and 6.59%, with R² of 97.95 and 99.43%, respectively. Singapore had the lowest CFR (0.068%) despite high infections.

Increased CFR in Indonesia and the Philippines suggests that COVID-19 in the two countries is rising at an alarming rate. Strict implementation of shared management approaches to control the pandemic is seen to be closely associated with the decrease of CFR.

The paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective.

Multiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany.

The authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development.

This research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination.

This study aims to explore the efficacy of government policy directions in mitigating the effects of the COVID-19 pandemic by employing a panel of 22 countries throughout the 2020-second quarter of 2022.

The panel autoregressive distributed lag (ARDL) model is employed to examine this phenomenon and to investigate the long-run effects of government policy decisions on infection and mortality rates from the pandemic.

The study reveals the following key findings: (1) Income support and debt relief facilities and stringent standards of governments are associated with reduced infection and death rates. (2) The response of governments has resulted in decreased mortality rates while simultaneously leading to an unexpected increase in infection rates. (3) Containment and healthcare practices have led to a decrease in infection rates but an increase in mortality rates, presenting another counterintuitive outcome. Despite the expectation that robust government responses would decrease infection rates and that healthcare containment practices would reduce mortality, these results highlight a lack of health equity and the challenge of achieving high vaccination rates across countries.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This study concludes by suggesting the importance of implementing economic support in terms of income, and debt relief has played a crucial role in mitigating the spread of COVID-19 infections and reducing fatality rates.

To effectively combat the spread of COVID-19, it is crucial to implement containment health practices in conjunction with tracing and individual-level quarantine. Simply implementing containment health measures without these interconnected strategies would be ineffective. Therefore, policy implications derived from containment health measures should be accompanied by targeted, aggressive, and rapid containment strategies aimed at significantly reducing the number of individuals infected with COVID-19.

This research makes a unique contribution to the existing literature by investigating the impact of government responses on reducing COVID-19 infections and fatalities, specifically focusing on the period before COVID-19 vaccinations became available.

This paper aims to analyze the convergence of the testing and positivity rates for the COVID-19 pandemic focusing on Germany and Italy.

The authors employ the two-regime threshold autoregressive (TAR) panel unit root test by using the weekly data in the period of 2020:03-2021:04.

Following finding out that the testing and positivity rates are nonlinear, the authors determine that the transition country between the two regimes is Italy for the testing rates and Germany for the positivity rates. Their findings support the partial convergence for the testing rates for Germany and Italy. On the other hand, the authors could not find any convergence for the positivity rates of these two countries.

This paper contributes to academic literature in several ways. Firstly, to the best of their knowledge, this paper is the first study that analyzes the convergence of testing and positivity data. This paper further focuses on two Euro-Area countries which have suffered significantly from the COVID-19 pandemic. In addition, the authors employ the two-regime threshold autoregressive (TAR) panel unit root proposed by Beyaert and Camacho (2008) in their empirical analysis. This recent panel data methodology aims to test real convergence in a nonlinear framework by incorporating the threshold model, panel data unit root test and the calculation of critical values by bootstrap simulation.

This study aims to examine the role of safety governance in ensuring public–private interface ecosystems in response to the COVID-19 pandemic. It aims to highlight the role of safety governance in the complex interfaces of pandemic response mechanisms. The analysis is conducted at the national level, considering safety governance issues in terms of test, confirmed cases, fatality rates, vaccination rate, medical capabilities, Information and communications technology capability, Hofstede’s cultural index (individualism and power distance scores), public safety, personal privacy and national health capabilities.

Using multigovernance theory and technology, organization and environment theory, the authors present a research model that defines four independent factors and country level performance outcomes and conducted analysis of variance tests.

Research findings suggest that countries are classified by diverse groups using multiple criteria (e.g. country size, Hofstede’s cultural indexes, ICT capabilities and Governance index). Countries indicate differences in their policy approaches according to their private and public ecosystems. COVID-19 response performance indicators are substantially different.

This study provides the relevance of the multigovernance theory. The empirical results suggest that effective crisis governance is characterized by vertically integrated organizational hierarchies with horizontally connected communication channels that seek maximum voluntary participation and a high level of motivation of informed societal members as a whole. Crisis events occur occasionally, and livelihood routines demonstrate incredible human agility. Gaining insight of the findings from this article may be useful to respond to future crisis events.

This significant study highlights the political and social factors that define response patterns of different countries regarding COVID-19 response mechanisms. With the wide vaccination administration, the COVID-19 landscape shows differences in these countries. This study is rare in providing research framework using Hofstede cultural value and examines with actual data provided by each national government, World Health Organization and credible information sources.

This study aims to highlight the critical role case fatality rates (CFR) have played in the emergence and the management of particularly the early phases of the current coronavirus crisis.

The study presents a contrastive map of CFR for the coronavirus (SARS-CoV-2) and influenza (H1N1 and H2N2).

The mapped data shows that current CFR of SARS-CoV-2 are considerably lower than, or similar to those, of hospitalised patients in the UK, Spain, Germany or international samples. The authors therefore infer a possible risk that the virulence of the coronavirus is considerably overestimated because of sampling biases, and that increased testing might reduce the general CFR of SARS-CoV-2 to rates similar to, or lower than, of the common seasonal influenza.

This study concludes that governments, health corporations and health researchers must prepare for scenarios in which the affected populations cease to believe in the statistical foundations of the current coronavirus crisis and interventions.