We analyze the implications of infectious diseases and social distancing in an extended SIS framework to allow for the presence of stochastic shocks with state dependent probabilities. Random shocks give rise to the diffusion of a new strain of the disease which affects both the number of infectives and the average biological characteristics of the pathogen causing the disease. The probability of such shock realizations changes with the level of disease prevalence and we analyze how the properties of the state-dependent probability function affect the long run epidemiological outcome which is characterized by an invariant probability distribution supported on a range of positive prevalence levels. We show that social distancing reduces the size of the support of the steady state distribution decreasing thus the variability of disease prevalence, but in so doing it also shifts the support rightward allowing eventually for more infectives than in an uncontrolled framework. Nevertheless, social distancing is an effective control measure since it concentrates most of the mass of the distribution toward the lower extreme of its support.

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.

The sustainability issues have been surmounted in the last decades. The digital disruption caused by blockchains and other digitally backed currencies has raised several serious concerns for policymakers, governmental agencies, environmentalists, and supply chain managers. Alternatively, sustainable resources are environmentally sustainable and naturally available resources which are employable by several regulation authorities to reduce the carbon footprint and attain energy transition mechanisms to support sustainable supply chains in the ecosystem. Using the asymmetric time-varying parameters vector auto-regressions approach, the current study examines the asymmetric spillovers between blockchain-backed currencies and environmentally supported resources. We find clusters between blockchain-based currencies and resource-efficient metals, highlighting similar-class dominance of spillovers. We portrayed several implications of our study for policymakers, supply chain managers, the blockchain industry, sustainable resources mechanisms, and regulatory bodies to emphasize that natural resources play a significant role in attaining sustainable supply chains servicing the benefits to society at large and to other stakeholders.

The relationship between efficiency, quality, and access in healthcare is far from being well defined. In particular, there is no consensus on whether there is a trade-off between hospital performance and its social dimensions, such as the care appropriateness, safety, and access to proper health care. This study proposes a new approach based on the Network Data Envelopment Analysis (NDEA) to evaluate the existence of potential trade-offs between efficiency, quality, and access. The aim is to contribute for the heated debate around this topic with a novel approach. The suggested methodology combines a NDEA model with the weak disposability of outputs to handle with undesirable outputs related to the poor quality of care or the lack of access to appropriate and safe care. This combination results in a more realistic approach that has not yet been used to investigate this topic. We utilised data of the Portuguese National Health Service from 2016 to 2019, with four models and nineteen variables selected to quantify the efficiency, quality, and access to public hospital care in Portugal. A baseline efficiency score was calculated and compared with the performance scores obtained under two hypothetical scenarios to quantify the impact of each quality/access-related dimension on efficiency. The first scenario considers that each variable, individually, is at its best situation (for example, absence of septicaemia cases), and the second one, at its worst (e.g., all seen inpatients had a septicaemia case). The findings suggest that there might exist meaningful trade-offs between efficiency, quality, and access. Most variables exhibited a considerable and negative impact on the overall hospital efficiency. That is, we may expect a trade-off between efficiency and quality/access.

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

We introduce country-month indices of efficiency of government policy in dealing with the COVID-19 pandemic. Our indices cover 81 countries and the period from May 2020 to November 2021. Our framework assumes that governments impose stringent policies (listed in the Oxford COVID-19 Containment and Health Index) with the single goal of saving lives. We find that positive and significant correlates of our new indices are institutions, democratic principles, political stability, trust, high public spending in health, female participation in the workplace, and economic equality. Within the efficient jurisdictions, the most efficient ones are those with cultural characteristics of high patience.

With growing environmental concerns and the exploitation of ubiquitous big data, smart transportation is transforming logistics business and operations into a more sustainable approach. To answer questions in intelligent transportation planning, such as which data are feasible, which methods are applicable for intelligent prediction of such data, and what are the available operations for prediction, this paper offers a new deep learning approach called bi-directional isometric-gated recurrent unit (BDIGRU). It is merged to the deep learning framework of neural networks for predictive analysis of travel time and business adoption for route planning. The proposed new method directly learns high-level features from big traffic data and reconstructs them by its own attention mechanism drawn by temporal orders to complete the learning process recursively in an end-to-end manner. After deriving the computational algorithm with stochastic gradient descent, we use the proposed method to perform predictive analysis of stochastic travel time under various traffic conditions (especially for congestions) and then determine the optimal vehicle route with the shortest travel time under future uncertainty. Based on empirical results with big traffic data, we show that the proposed BDIGRU method can (1) significantly improve the predictive accuracy of one-step 30 min ahead travel time compared to several conventional (data-driven, model-driven, hybrid, and heuristics) methods measured with several performance criteria, and (2) efficiently determine the optimal vehicle route in relation to the predictive variability under uncertainty.

Under the low-carbon economy environment, downstream retailer advertises upstream manufacturer's reduction to achieve better market performance, which is a common form of cooperation in low-carbon supply chain management. This paper assumes that the market share is dynamically influenced by product emission reduction and the retailer's low-carbon advertising. First, the Vidale-Wolfe model is extended. Second, from the perspective of centralization and decentralization, four differential game models of manufacturer and retailer in the two-level supply chain are constructed, while the optimal equilibrium strategies in various situations are compared. Finally, using Rubinstein bargaining model, the profit obtained by the secondary supply chain system is distributed. The main results are as follows: (1) The unit emission reduction and market share of manufacturer are rising with time. (2) The profit of each member of the secondary supply chain and the whole supply chain is always optimal under the centralized strategy. Although the advertising cost allocation strategy achieves the Pareto optimal under the decentralized situation, it still cannot reach the profit of the centralized strategy. (3) The manufacturer's low-carbon strategy and the retailer's advertising strategy have played a positive role in the secondary supply chain. The profits of the secondary supply chain members and the whole are on the rise. (4) As the leader of the secondary supply chain, it is more dominant in profit distribution. The results can provide theoretical basis for the joint emission strategy of supply chain members in low-carbon environment.

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company's historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company.

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.

Financial markets are exposed to extreme uncertain circumstances escalating their tail risk. Sustainable, religious, and conventional markets represent three different markets with various characteristics. Motivated with this, the current study measures the tail connectedness between sustainable, religious, and conventional investments by employing a neural network quantile regression approach from December 1, 2008 to May 10, 2021. The neural network recognized religious and conventional investments with maximum exposure to tail risk following the crisis periods reflecting strong diversification benefits of sustainable assets. The spots Global Financial Crisis, European Debt Crisis, and COVID-19 pandemic as intensive events yielding high tail risk. The ranks the stock market in the pre-COVID period and Islamic stocks during the COVID sample as the most susceptible markets. Conversely, the nominates Islamic stocks as the chief risk contributor in the system. Given these, we portray various implications for policymakers, regulatory bodies, investors, financial market participants, and portfolio managers to diversify their risk using sustainable/green investments.

In the digital era, third-party food delivery operations are very popular all around the world. However, to achieve a sustainable operation for food delivery businesses is a challenging issue. Motivated by the fact that there is a lack of consolidated view towards the topic in the literature, we conduct a systematic literature review to identify how to achieve a sustainable operation for third-party food delivery and highlight the recent advances in this important area with the discussion of real-world practices. In this study, first, we review the relevant literature and apply the triple bottom line (TBL) framework to classify prior studies into economic sustainability, social sustainability, environmental sustainability, and multi-dimensional sustainability. We then identify three major research gaps, including inadequate investigation on the restaurant's preferences and decisions, superficial understanding on the environmental performance, and limited examination on the multi-dimensional sustainability in the third-party food delivery operations. Finally, based on the reviewed literature and observed industrial practices, we propose five future areas that deserve an in-depth further investigation. They are namely applications of digital technologies, behaviors and decisions of the restaurants, risk management, TBL, and post-coronavirus pandemic.

We examine the connectedness of the COVID vaccination with the economic policy uncertainty, oil, bonds, and sectoral equity markets in the US within time and frequency domain. The wavelet-based findings show the positive impact of COVID vaccination on the oil and sector indices over various frequency scales and periods. The vaccination is evidenced to lead the oil and sectoral equity markets. More specifically, we document strong connectedness of vaccinations with communication services, financials, health care, industrials, information technology (IT) and real estate equity sectors. However, weak interactions exist within the vaccination-IT-services and vaccination-utilities pairs. Moreover, the effect of vaccination on the Treasury bond index is negative, whereas the economic policy uncertainty shows an interchanging lead and lag relation with vaccination. It is further observed that the interrelation between vaccination and the corporate bond index is insignificant. Overall, the impact of vaccination on the sectoral equity markets and economic policy uncertainty is higher than on oil and corporate bond prices. The study offers several important implications for investors, government regulators, and policymakers.

Studies indicate that organizational capability is a key factor in operational performance, and that both sensing and analytics capabilities have a significant influence on operational performance. This study develops a framework to examine the impact of organizational capability on operational performance, with a specific focus on the implementation of sensing and analytics capabilities. We combine strategic fit theory, the dynamic capability view, and the resource-based view to examine how micro, small, and medium enterprises (MSMEs) strategically integrate a data-driven culture (DDC) with their organizational capabilities to enhance operational performance. We carry out empirical research to investigate whether a DDC moderates the influence of organizational capability on operational performance. Structural equation modeling of survey data from 149 MSMEs reveals that both sensing and analytics capabilities have a positive impact on operational performance. The results also suggest that a DDC positively moderates the influence of organizational capability on operational performance. We discuss the theoretical and managerial implications of our findings, the limitations of the study, and opportunities for further research.

In the broad sphere of Analytics, prescriptive analytics is one of the emerging areas of interest for both academicians and practitioners. As prescriptive analytics has transitioned from its inception to an emerging topic, there is a need to review existing literature in order to ascertain development in this area. There are a very few reviews in the related field but not specifically on the applications of prescriptive analytics in sustainable operations research using content analysis. To address this gap, we performed a review of 147 articles published in peer-reviewed academic journals from 2010 to August 2021. Using content analysis, we have identified the five emerging research themes. Through this study, we aim to contribute to the literature on prescriptive analytics by identifying and proposing emerging research themes and future research directions. Based on our literature review, we propose a conceptual framework for studying the impacts of the adoption of prescriptive analytics and its impact on sustainable supply chain resilience, sustainable supply chain performance and competitive advantage. Finally, the paper acknowledges the managerial implications, theoretical contribution and the limitations of this study.

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed.

Burnout rates and dissatisfaction among healthcare workers remain high due to long working hours. One possible solution to this problem is to let them choose their weekly working hours and starting times in order to achieve a work-life balance. Moreover, a scheduling process that responds to changing healthcare demands at different times of the day should increase work efficiency in hospitals. In this study, a methodology and software were developed to schedule hospital personnel, taking into account their preferences regarding working hours and starting time. The software also allows the hospital management to determine the number of staff needed at different times of the day. Three methods and five working-time scenarios characterized by different divisions of working time are proposed to solve the scheduling problem. The Seniority score Priority assignment Method appoints personnel prioritizing seniority, whereas the newly developed Balanced and Fair assignment Method and Genetic Algorithm Method aim for a more nuanced distribution. The methods proposed were applied to physicians in the internal diseases department in a specific hospital. Weekly/monthly scheduling of all employees was carried out with the software. The results of scheduling factoring in work-life balance, and the performances of algorithms are shown for the hospital where the application was trialled.

We consider the problem faced by a central bank which bails out distressed financial institutions that pose systemic risk to the banking sector. In a structural default model with mutual obligations, the central agent seeks to inject a minimum amount of cash in order to limit defaults to a given proportion of entities. We prove that the value of the central agent's control problem converges as the number of defaultable institutions goes to infinity, and that it satisfies a drift controlled version of the supercooled Stefan problem. We compute optimal strategies in feedback form by solving numerically a regularized version of the corresponding mean field control problem using a policy gradient method. Our simulations show that the central agent's optimal strategy is to subsidise banks whose equity values lie in a non-trivial time-dependent region.

We contribute to the literature on statistical robustness of risk measures by computing the index of qualitative robustness for risk measures based on utility functions. This problem is intimately related to finding the natural domain of finiteness and continuity of such risk measures.

Can technology protect investors from extreme losses? This paper investigates the short- and long-run hedging and safe haven properties of Bitcoin for the US dollar over the period 2010-2023, incorporating the COVID-19-related market turmoil. Our findings reveal that (i) Bitcoin acts as a strong hedge for all US dollar currency pairs examined, (ii) Bitcoin functions as a weak safe haven for the US dollar at short investment horizons, as indicated by a limited relationship during acute negative price movements, (iii) Bitcoin, instead of acting as a safe haven may, instead, increase aggregate risk at long horizons during periods of extreme losses. The analysis, performed using a series of horizon-dependent econometric tests, provides evidence of some US dollar risk-reduction benefits from Bitcoin but limited potential for enduring relief from long-run extreme negative US dollar rate movements.

Every day, hundreds of mosquito surveys are carried out around the world to inform policy and management decisions on how best to reduce or prevent the burden of mosquito-borne disease or mosquito nuisance. These surveys are usually time consuming and expensive. Mosquito surveillance is the essential component of vector management and control. However, surveillance is often carried out with a limited if not without a quantitative assessment of the sampling effort which can results in underpowered or overpowered studies, or certainly in overpowered studies when power analyses are carried out assuming independence in the measurements obtained from longitudinal and geographically proximal mosquito surveys. Many free, open-source and user-friendly tools to calculate statistical power are available, such as G*Power, glimmpse, powerandsamplesize.com website or R-cran packages (pwr and WebPower to name few of them). However, these tools may not be sufficient for powering mosquito surveys due to the additional properties of seasonal and spatially clustered repeated measurements required to reflect mosquito population dynamics. To facilitate power analysis for mosquito surveillance, we have developed TIMESS, a deployable browser-based Shiny app that estimates the number of repeated measurements and locations of mosquito surveys for a given effect size, power, significance level, seasonality and level of expected between-location clustering. In this article we describe TIMESS, its usage, strengths and limitations.