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The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

The paper sets out to understand the key issues that the various functions and optimal allocation of NGOs (non-governmental organizations) in the circular economy that provide public services depend not only on external quantities or densities but also on their internal size of human resources.

The paper uses different data samples and models to study the influence mechanism of optimal NGO size of human resources and its differentiated effects on governance quality of entrepreneurship.

The authors find that a reduction in transaction costs and an increase in the aggregation degree of public demand lead to increased human capital and lower financial capital intensity. In addition, the authors find that NGO size of human resources has a relationship that is approximately U-shaped (or inverse U-shaped) with the governance quality of entrepreneurship.

The paper discusses the implications for programs that encourage NGOs to optimally determine their internal size of human resources and further improve the governance quality of entrepreneurship in the circular economy.

The paper reveals the significant nonmonotonic relationship between local governance quality and NGO financial size, even after controlling for other NGO, city and provincial characteristics.

Over 2.7 million workers are employed in the UK construction industry and with the fragmented nature of the construction sector; cases of poor mental health of workers are on the increase. This upsurge in the number of workers experiencing poor mental health could directly impact construction safety with significant financial adverse consequences on employers and the UK economy. Studies have identified lapses within the construction sector emphasising the lack of transparency regarding reporting of mental health and well-being of construction workers due to the inadequate engagement from employers and the lack of genuine leadership commitment to tackle mental health.

This study adopted a non-probability purposive sampling strategy, using a self-selected sample. A self-administered questionnaire benchmarked against the mental health core and enhanced standards tools by the “Stevenson/Farmer review of mental health and employers” served as the basis for the methodology. A total of 106 industry managers from highways, construction, maritime, utilities, home building, rail and haulage/fleet were involved in this study.

The findings indicate that the industry is making good strides towards addressing mental health issues; poor mental health have significant financial burdens on businesses and the economy; some contractors have mental health initiatives and programmes in place; there is inconsistency of support available to employees; some contractors now integrate leadership training; the level of engagement vary based on the strategy and action plan adopted by organisations; different mechanisms are adopted for monitoring mental health issues, and there are cross-industry initiatives.

A limitation of this study is the number of participants which is not representative of the entire UK construction workforce. Therefore, the findings from this study as much as it presents some understanding of employee mental health and well-being cannot be overtly generalised across multiple industries, different geographic regions or contexts.

Employers should have a clear representation of the mental health of their employees to help them understand what affects worker’s mental well-being and how they can support them. Disregarding the multifaceted causes of mental ill-health due to the perceived financial implications could be more devastating for the industry.

This article aims to relate investments in human capital to the United Nations Sustainable Development Goals (UN SDGs), and examine the spending levels necessary to achieve high performance in related SDG sectors for Azerbaijan.

Employing data from the World Bank, the empirical approach undertaken in this study relies on peer analysis by examining spending levels for nations exhibiting similar income levels and geographical proximity to Azerbaijan.

This study estimates that total spending in education would need to increase by 0.4 percentage points of GDP by 2030, while total spending in health would need to increase by 5.9 percentage points of GDP by 2030 for Azerbaijan.

This study contributes to the literature by conducting an empirical analysis in which other nations can emulate in measuring their relative progress on human capital investments and related UN SDGs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0137

The purpose of this study is to present a system dynamic (SD)-based remanufacturing economic analysis model of used automobile engine under two recycling modes. The authors will compare the remanufacturing cost, sales profit and sales revenue from time and space dimensions incurred in different recycling modes in the long run.

The remanufacturing economic analysis model is based on SD methodology. The authors can simulate the relations of impact factors on automobile engine recycling and remanufacturing and further analyze and compare the cost, sales profit and sales revenue incurred in different recycling modes in the long term.

Sinotruk Steyr engine remanufacturing in Shandong province is taken as the research case subject. The revenue, cost and profit under the two recycling modes from 2015 to 2035 are analyzed and compared. The results show that different recycling modes have significant varying influence on the economy of engine remanufacturing.

This economic analysis model can provide a method reference to decide the recycling mode for auto components and other product remanufacturing. Moreover, this model can guide and support the sustainable development of remanufacturing industry.

Adopting digital technology could facilitate the public health response to the COVID-19 pandemic. Some analysts argue that countries that adopted digital technology in their health sector have managed to control the virus better (Whitelaw et al., 2020). For instance, countries with more comprehensive contact tracing have significantly lower fatality rates (Yalaman et al., 2021). Moreover, World Health Organization (WHO) believes this technology is a crucial enabler for countries to meet the current challenge (WHO. Regional Office for the Western Pacific & University of Melbourne, 2021). In this regard, this study aims to quantitatively find the relationship between the technological advancement of countries and COVID-19 health outcomes, using seven technological indices that measure technological advancement.

The authors used the multiple linear regression method to answer the research questions. The first analysis focuses on a cross section of all countries worldwide, and the second focuses on European countries for which weekly death statistics exist after the pandemic.

The findings support those countries with more technological abilities managed to control the virus’s mortality better, as evidenced by the negative link between the mortality rate of COVID-19 and the technological factors at the national level. Results also reveal that technology adoption decreases the death risk due to COVID-19 in countries with more elderly people. The authors may argue that technological advancement positively correlates with the number of deaths and diagnosed cases because the authors can better collect data or because the virus spreads due to higher economic and business activities. However, such technological advancement significantly decreases the death risk (lower mortality rate in the first analysis and lower mortality rate for elderly people in the second analysis).

Three important conclusions could be made from the results: a lower mortality rate is generally expected for countries adopting advanced technology; technological advancement significantly decreases the death risk for elderly people; and a higher technology adoption level does not necessarily result in fewer diagnosed cases of/death due to COVID-19.

Although some studies have focused on e-health applications in the public health response to the COVID-19 pandemic, no studies, to the best of the authors’ knowledge, have tried to quantify its efficacy, most especially on the global level.

This study aims to analyze the relationship between the consequences of the pandemic and the housing sector with econometric tests that allow for structural breaks.

Study data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed for Turkey. In the model of the study, housing loans were used as a housing market indicator, and the number of new deaths and new cases were used as data related to the pandemic. The exchange rate, which affects the use of housing loans, was added to the model as a control variable. This study was analyzed to examine the relationship between the pandemic and the housing sector, time series analysis techniques that allow structural breaks were used.

Based on the result of the analyses, it was concluded that there is a long-run relationship between the pandemic stages and housing markets along with structural breaks. As a result of the time-varying causality test developed to determine the causality relationship between the variables and its direction, a bidirectional causality relationship was identified between all variables at certain dates.

Study data were collected weekly between March 9, 2020, and February 4, 2022, and analyzed in the case of Turkey.

Based on results of the study, it is recommended that policy makers and market actors take into account extraordinary situations such as pandemics and create a budget allocation that is always ready to use for this purpose.

The empirical examination of the relationship between the pandemic and the housing sector in Turkey provides originality to this study in terms of its topic, sample, methodology, contribution to the literature and potential policy recommendations.

During the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.

The authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.

The authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.

From the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

This paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.

The proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.

The proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.

The proposed methods can be applied to conduct infectious diseases impacts analysis.

In this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.

• A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

• Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

• Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

• An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

• A real case study is considered to evaluate the performances of the proposed models.

A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;

Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;

Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;

An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;

A real case study is considered to evaluate the performances of the proposed models.

This study has aimed to analyse the role of innovation-sharing collaboration in the large-scale manufacturing of Covid-19 vaccination across the globe and its impact on the mortality rate of the countries where the pharmaceutical manufacturers received such innovation.

The authors have relied upon the difference-in-difference (DID) approach by utilizing the data available on public platforms such as World Health Organization (WHO) databank, organization for economic co-operation and development (OECD) data bank, istat, Indian bureau of statistics and European centre for disease prevention and control (ecdc) from 2020 to 2021 to establish the empirical inference of the analysis.

This study’s results present that after the invention and commercialization of the vaccine, the Covid-19 impact was still intact and people were dying continuously. However, it was impossible to fulfil the demand of the 7 billion population in a short time. In the light of these facts, the WHO encouraged sharing vaccine innovation with other countries to enhance production capacity. The authors found that after vaccine innovation sharing, Covid-19’s devastation slowed: the fatality rate was marginally reduced, and economic conditions started their recovery journey.

This study’s findings present that the Covid-19 vaccine played a pivotal role in tackling the Covid-19’s devastating impact on the entire world. It emphasizes the role of innovation-sharing collaborations in curtailing hazardous consequences, including the mortality rate during a crisis, and such collaborations’ impact on the countries where institutions involved in them reside.