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The COVID-19 global health crisis forced border closings, strained resources and tightened funding, forcing humanitarian organisations to innovate. This paper aims to identify gaps in the literature on innovation in humanitarian supply chains, and to develop an appropriate framework for future research through a systematic literature review. We use a systematic literature review approach and synthesis the discussion of innovation in humanitarian supply chains after reviewing 43 papers. The synthesis identifies the different contexts for and outcomes of innovation in humanitarian supply chains. Our findings indicate that research on innovation in humanitarian supply chains is an underdeveloped topic. Gaps we identified in regards to the humanitarian context are: (1) a limited discussion of the contribution by the beneficiary to the supply chain; (2) a limited discussion of reconstruction innovations; (3) a lack of study on field application for complex innovations; (4) the lack of discussion of the role of individual knowledge in humanitarian supply chain innovation and finally (5) a lack of study of position innovations where humanitarian organisations use supply chains as a way to market effectively towards donors. [ABSTRACT FROM AUTHOR]

In recent years, emerging technologies have gained popularity and being implemented in different fields. Thus, critical leading-edge technologies such as artificial intelligence and other related technologies (blockchain, simulation, 3d printing, etc.) are transforming the operations and other traditional fields and proving their value in fighting against unprecedented COVID-19 pandemic outbreaks. However, due to this relation's novelty, little is known about the interplay between emerging technologies and COVID-19 and its implications to operations-related fields. In this vein, we mapped the extant literature on this integration by a structured literature review approach and found essential outcomes. In addition to the literature mapping, this paper's main contributions were identifying literature scarcity on this hot topic by operations-related fields; consequently, our paper emphasizes an urgent call to action. Also, we present a novel framework considering the primary emerging technologies and the operations processes concerning this pandemic outbreak. Also, we provided an exciting research agenda and four propositions derived from the framework, which are collated to operations processes angle. Thus, scholars and practitioners have the opportunity to adapt and advance the framework and empirically investigate and validate the propositions for this and other highly disruptive crisis. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

Digital Contact Tracing (DCT) has been proved to be an effective tool to counteract the new SARS-CoV-2 or Covid-19. Despite this widespread effort to adopt the DCT, less attention has been paid to the organisation of the health logistics system that should support the tracing activities. Actually, the DCT poses a challenge to the logistics of the local health system in terms of number of daily tests to be collected and evaluated, especially when the spreading of the virus is soaring. In this paper we introduce a new optimisation problem called the Daily Swab Test Collection (DSTC) problem, that is the daily problem of collecting swab tests at home in such a way to guarantee a timely testing to people notified by the app to be in contact with a positive case. The problem is formulated as a variant of the team orienteering problem. The contributions of this paper are the following: (i) the new optimisation problem DSTC that complements and improves the DCT approach proposed by Ferretti et al. (Science https://doi.org/10.1126/science.abb6936, 2020), (ii) the DSCT formulation as a variant of the TOP and a literature review highlighting that this variant can have useful application in healthcare management, (iii) new realistic benchmark instances for the DSTC based on the city of Turin, (iv) two new efficient and effective hybrid algorithms capable to deal with realistic instances, (v) the managerial insights of our approach with a special regard on the fairness of the solutions. The main finding is that it possible to optimise the underlying logistics system in such a way to guarantee a timely testing to people recognised by the DCT. [ABSTRACT FROM AUTHOR]

In this paper, we address the controversies of epidemic control planning by developing a novel Simulation-Deep Reinforcement Learning (SiRL) model. COVID-19 reminded constituents over the world that government decision-making could change their lives. During the COVID-19 pandemic, governments were concerned with reducing fatalities as the virus spread but at the same time also maintaining a flowing economy. In this paper, we address epidemic decision-making regarding the interventions necessary given of the epidemic based on the purpose of the decision-maker. Further, we intend to compare different vaccination strategies, such as age-based and random vaccination, to shine a light on who should get priority in the vaccination process. To address these issues, we propose a simulation-deep reinforcement learning (DRL) framework. This framework is composed of an agent-based simulation model and a governor DRL agent that can enforce interventions in the agent-based simulation environment. Computational results show that our DRL agent can learn effective strategies and suggest optimal actions given a specific epidemic situation based on a multi-objective reward structure. We compare our DRL agent's decisions to government interventions at different periods of time during the COVID-19 pandemic. Our results suggest that more could have been done to control the epidemic. In addition, if a random vaccination strategy that allows super-spreaders to get vaccinated early were used, infections would have been reduced by 32% at the expense of 4% more deaths. We also show that a behavioral change of fully quarantining 10% of the risky individuals and using a random vaccination strategy leads to a reduction of the death toll by 14% and 27% compared to the age-based vaccination strategy that was implemented and the New Jersey reported data, respectively. We have also demonstrated the flexibility of our approach to be applied to other locations by validating and applying our model to the COVID-19 case in the state of Kansas. [ABSTRACT FROM AUTHOR]

Disruption from the COVID-19 pandemic has caused major upheavals for manufacturing, and has severe implications for production networks, and the demand and supply chains underpinning manufacturing operations. This paper is the first of its kind to pull together research on both—the pandemic-related challenges and the management interventions in a manufacturing context. This systematic literature review reveals the frailty of supply chains and production networks in withstanding the pressures of lockdowns and other safety protocols, including product and workforce shortages. These, altogether, have led to closed facilities, reduced capacities, increased costs, and severe economic uncertainty for manufacturing businesses. In managing these challenges and stabilising their operations, manufacturers are urgently intervening by—investing in digital technologies, undertaking resource redistribution and repurposing, regionalizing and localizing, servitizing, and targeting policies that can help them survive in this altered economy. Based on holistic analysis of these challenges and interventions, this review proposes an extensive research agenda for future studies to pursue. [ABSTRACT FROM AUTHOR]

The governments' isolation measures to contain the transmission of COVID-19 imposed a dilemma for the people at the bottom of the pyramid. Since these people have very unreliable sources of income, a dilemma arises: they must either work under risky conditions or refrain from work and suffer from income cuts. Emergency donations of food and cleaning supplies in a pandemic context might be overlooked by government and civil society actors. This paper aims to model the effects of donations on mitigating the negative effects of COVID-19 on vulnerable communities. Applying the system dynamics method, we simulated the behaviour of the pandemic in Rio de Janeiro (Brazil) communities and the impacts that donations of food and cleaning supplies have in these settings. We administered surveys to the beneficiaries and local organisations responsible for the final distribution of donations to gather information from the field operations. The results show that increasing access to cleaning supplies in communities through donations can significantly reduce coronavirus transmission, particularly in high-density and low-resource areas, such as slums in urban settings. In addition, we also show that food donations can increase the vulnerable population's ability to afford necessities, alleviating the stress caused by the pandemic on this portion of the population. Therefore, this work helps decision-makers (such as government and non-governmental organisations) understand the impacts of donations on controlling outbreaks, especially under COVID-19 conditions, in a low-resource environment and, thus, aid these hard-to-reach populations in a pandemic setting. [ABSTRACT FROM AUTHOR]

The COVID 19 has brought unprecedented changes in the way we communicate. There is a greater accent on Virtual communication. This paper aims to establish a relationship between Emotional intelligence and the effectiveness of Virtual communication on Decision making. This empirical study is based on a sample drawn from 296 working professionals at five different levels of organizational hierarchy. A standardized questionnaire (ɑ = 0.824) was used to collect the responses of Emotional intelligence, Virtual communication, and Decision-making effectiveness. Hierarchical regression using PROCESS Macro model 1 was used to identify the moderating effect of Emotional intelligence on Virtual communication and Decision making effectiveness. Since the p-value (p ≤.007) is found significant, Emotional intelligence acts as a moderator that affects the strength of the relationship between Virtual communication effectiveness and Decision making. Validation of Task Technology fit theory is the theoretical implication of the study. Manipulation of individual dimensions in the model can reduce the dependence on technology for task completion with enhanced performance effectiveness. The findings are relevant to educators, consultants, and any professional who need to adapt Virtual communication platforms on an ongoing basis. Since work-life balance is projected as a constraint in this study, policymakers can consider policy amendments to reduce the stress caused due to Virtual communication which intrudes into their personal space. This empirical study is the first of its kind to benchmark the organizational practice of Emotional intelligence training to enhance Virtual communication and Decision making effectiveness during unprecedented times of pandemic. [ABSTRACT FROM AUTHOR]

Recent pandemic outbreaks, including the COVID-19 and SARS, have revealed that supply chains (SCs) are unable to respond to such disasters. To mitigate the destructive impacts and improve the performance of SCs, Operations Research (OR) techniques have been applied to address the issues over the last two decades. The objective of this paper is to develop a network data envelopment analysis (NDEA) model to measure the resilience and sustainability of healthcare SCs in response to the COVID-19 pandemic outbreak. In the proposed NDEA model, for the first time, outputs' weak disposability, chance-constrained programming (CCP), the convexity assumption, and the semi-oriented radial approach are aggregated. Moreover, a modified directional distance function (DDF) measure is developed to measure the overall and divisional efficiency scores. Furthermore, the proposed model can deal with different types of data such as integer-valued data, negative data, stochastic data, ratio data, and undesirable outputs. Also, several useful and interesting properties of the novel efficiency measure are presented. Finally, we measure the performance of 28 healthcare SCs to demonstrate the applicability and capability of our proposed approach. [ABSTRACT FROM AUTHOR]

This paper evaluates the stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019, to March 31, 2020). Using 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10–13% higher than those of conventional banks after controlling for a host of the bank- and country-level variables. This study explains the Islamic banks' superior crisis stock performance by exploring the potential role of pre-crisis bank efficiency. In a univariate analysis, we document higher non-parametric Data Envelopment Analysis (DEA) efficiency levels for Islamic banks than conventional banks in the year preceding the COVID-19 crisis. Our multivariate regressions show that the risk-adjusted DEA efficiency scores can explain crisis stock returns for Islamic banks but not conventional banks. The evidence is robust to alternative measures of stock returns, efficiency models, and other empirical strategies. Finally, we present insight on the importance of key bank characteristics in determining the stock returns of conventional banks during the crisis period. [ABSTRACT FROM AUTHOR]

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model's pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress. [ABSTRACT FROM AUTHOR]

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis. [ABSTRACT FROM AUTHOR]

This paper analyses the volatility transmission between European Global Systemically Important Banks (GSIBs) and implied stock market volatility. A Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity model is applied to determine the dynamic correlation between returns of Europe's GSIBs and the world's most prominent measure of market "fear", the CBOE Volatility Index (VIX). The results identify a higher negative co-relationship between the VIX and GSIB returns during the COVID-19 period compared with the Global Financial Crisis (GFC), with one-day lagged changes in the VIX negatively Granger-causing bank returns. The asymmetric impact of changes in implied volatility is examined by quantile regressions, with the findings showing that in the lower quartile–where extreme negative bank returns are present–jumps in the VIX are highly significant. This effect is more pronounced during COVID-19 than during the GFC. Additional robustness analysis shows that these findings are consistent during the periods of the Swine Flu and Zika virus epidemics. [ABSTRACT FROM AUTHOR]

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets. [ABSTRACT FROM AUTHOR]

In the aftermath of the global financial crisis and ongoing COVID-19 pandemic, investors face challenges in understanding price dynamics across assets. This paper explores the performance of the various type of machine learning algorithms (MLAs) to predict mid-price movement for Bitcoin futures prices. We use high-frequency intraday data to evaluate the relative forecasting performances across various time frequencies, ranging between 5 and 60-min. Our findings show that the average classification accuracy for five out of the six MLAs is consistently above the 50% threshold, indicating that MLAs outperform benchmark models such as ARIMA and random walk in forecasting Bitcoin futures prices. This highlights the importance and relevance of MLAs to produce accurate forecasts for bitcoin futures prices during the COVID-19 turmoil. [ABSTRACT FROM AUTHOR]

Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization's decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer's, policymaker, and researchers' viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption. [ABSTRACT FROM AUTHOR]

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308. [ABSTRACT FROM AUTHOR]

In today's business, environment natural and manmade disasters like recent event (Covid 19) have increased the attention of practitioners and researchers to Supply chain vulnerability. Purpose of this paper is to investigate and prioritize the factors that are responsible for supply chain vulnerability. Extant literature review and interviews with the experts helped to extract 26 supply chain vulnerability factors. Further, the relative criticality of vulnerability factors is assessed by analytical hierarchy process (AHP). Critical part supplier; location of supplier; long supply chain lead times; Fixing process owners and mis-aligned incentives in supply chain are identified as the most critical factors among twenty-six vulnerability factors. Research concludes that not only long and complex supply chain but supply chain practices adopted by firms also increase supply chain vulnerability. Relative assessment of vulnerability factors enables professionals to take appropriate mitigation strategies to make the supply chains more robust. This research adds in building a model for vulnerability factors that are internal to supply chain & controllable. [ABSTRACT FROM AUTHOR]

Several recent studies suggest that the home advantage, that is, the benefit competitors accrue from performing in familiar surroundings, was—at least temporarily—reduced in games played without spectators due to the COVID-19 Pandemic. These games played without fans during the Pandemic have been dubbed 'ghost games'. However, the majority of the research to date focus on soccer and no contributions have been provided for indoor sports, where the effect of the support of the fans might have a stronger impact than in outdoor arenas. In this paper, we try to fill this gap by investigating the effect of ghost games in basketball with a special focus on the possible reduction of the home advantage due to the absence of spectators inside the arena. In particular, we test (i) for the reduction of the home advantage in basketball, (ii) whether such reduction tends to disappear over time, (iii) if the bookmakers promptly adapt to such structural change or whether mispricing was created on the betting market. The results from a large data set covering all seasons since 2004 for the ten most popular and followed basketball leagues in Europe show, on the one hand, an overall significant reduction of the home advantage of around 5% and no evidence that suggests that this effect has been reduced at as teams became more accustomed to playing without fans; on the other hand, bookmakers appear to have anticipated such effect and priced home win in basketball matches accordingly, thus avoiding creating mispricing on betting markets. [ABSTRACT FROM AUTHOR]

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream. [ABSTRACT FROM AUTHOR]

Financial models are based on the standard assumptions of frictionless markets, complete information, no transaction costs and no taxes and borrowing and short selling without restrictions. Short-selling bans around the world after the global financial crisis and in several exchanges during the COVID 19 period, become more and more important. This paper bridges the gap by providing for the first time in the literature a model that accounting explicitly and simultaneously for inflation, information costs and short sales in the portfolio performance with regime switching. Our model can be used by portfolio managers to assess the impact of these market imperfections on portfolio decisions. [ABSTRACT FROM AUTHOR]

Tourism is one of the fastest-growing sectors in the world with a shift from mass tourism to personalized travel. Nevertheless, it generates significant environmental impacts. The current events associated with quarantine measures generated by COVID-19 represent, however, a risk for this sector. It is hence necessary to create strategies that allow efficient decision-making for all echelons and actors for a rapid recovery. Tourists are key actors, which makes necessary to facilitate tourism trip planning according to tourists' preferences as a complex process. In this paper, we propose a novel model of tourist trip planning for heterogeneous preferences in a tourist group and selection of transport modes, in the first instance, while a second step seeks at minimizing the level of CO2 emissions. A comparison of the two models is made considering the objectives associated with individual tourist benefits and group profit equity, in contrast to the inclusion of the cost of CO2 emissions. A numerical comparison is carried out with a total of 546 data sets. Results illustrate the conflict between those objectives by generating an inverse relationship between the individual and group profit equity of tourists, in addition to individual benefit and emission minimization. [ABSTRACT FROM AUTHOR]

This research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models. [ABSTRACT FROM AUTHOR]

Although coronavirus disease 2019 (COVID-19) vaccines have been introduced, their allocation is a challenging problem. We propose a data-driven, spatially-specific vaccine allocation framework that aims to minimize the number of COVID-19-related deaths or infections. The framework combines a regional risk-level classification model solved by a self-organizing map neural network, a spatially-specific disease progression model, and a vaccine allocation model that considers vaccine production capacity. We use data obtained from Wuhan and 35 other cities in China from January 26 to February 11, 2020, to avoid the effects of intervention. Our results suggest that, in region-wise distribution of vaccines, they should be allocated first to the source region of the outbreak and then to the other regions in order of decreasing risk whether the outcome measure is the number of deaths or infections. This spatially-specific vaccine allocation policy significantly outperforms some current allocation policies. [ABSTRACT FROM AUTHOR]

Hesitant attitudes have been a significant issue since the development of the first vaccines—the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%. [ABSTRACT FROM AUTHOR]

Humanitarian supply chains (HSC) have vital significance in mitigating different disruptive supply chain risks caused due to natural or man-made activities such as tsunami, earthquakes, flooding, warfare, or the recent COVID-19 pandemic. Each kind of disaster poses a unique set of challenges to the operationalization of HSC. This study attempts to determine the critical barriers to the operationalization of HSC in India during the COVID-19 pandemic. Initially, we determined and validated 10 critical barriers to HSC operationalization through a Delphi method. Further, we analyzed the barriers by computing the driving and dependence power of each barrier to determine the most critical ones. To do so, we coined a distinct form of interpretive structural modeling (ISM) by amalgamating it with the neutrosophic approach, i.e. Neutrosophic ISM. The findings indicate, "lack of Government subsidies and support, lack of skilled and experienced rescuers, and lack of technology usage" are the most critical barriers that influence the streamline operations of HSC during the COVID-19 outbreak, unlike other disruptions. This is the first-of-its-kind research work that has identified and analyzed the critical barriers to HSC operationalization during COVID-19 in the Indian context. The results and recommendations of the study can aid policymakers and HSC professionals in formulating suitable strategies for successful HSC operations. [ABSTRACT FROM AUTHOR]

The world is witnessing a difficult time trying to circumscribe the spread of COVID-19 pandemic. The situation has become even more challenging as several natural disasters and emergencies coincided with this pandemic and created a situation of concurrent emergency. Effectively managing concurrent emergencies are extremely challenging. Only by utilizing and applying the knowledge gathered while combating a real-life concurrent emergency, one can further be prepared to face such emergencies. Hence, this study intends to identify the critical success factors (CSFs) having a major role in effective management of concurrent emergencies and evaluate the indispensable links among the factors. Twelve CSFs are identified from Pareto analysis, for which feedback from 46 experts involved in the management of emergencies is utilized. In addition, the revised rough-decision making trial and evaluation laboratory (rough-DEMATEL) approach is suggested to analyze the indispensable links among the CSFs and further, these factors are ranked based on the average vector length. The recent case of concurrent emergency in Bangladesh- floods amid a pandemic is considered to establish the applicability of this method. The findings of this research reveal that to handle concurrent emergencies 'Incremental improvement of proactive measures', 'Resilient supply chain and logistics network', and 'Government leadership and military cooperation' are the most critical factors to concurrent emergency management (CEM). These CSFs play a major role in ensuring the effectiveness of CEM. Thus, this study can be a building block in developing an effective CEM plan for the policymakers, managers, and practitioners. [ABSTRACT FROM AUTHOR]

The procurement of food grains from farmers is one of the biggest challenges under the COVID-19 outbreak due to country-wise lockdowns. The present study aims to reconfigure the existing food grain supply chain network. The study advances the extant literature by proposing a novel mathematical model that considers the government guidelines issued to procure food grains from farmers under the COVID-19 situation. The model includes personal distancing, a key parameter relevant in the COVID-19 crisis, and has remained unaddressed in the existing literature. The proposed model is tested in India. The effect of different parameters like personal distancing cost, carbon emission cost, fixed cost, and transportation cost is also investigated under a given set of procurement centers. Finally, the procurement schedule for each procurement center is generated, which is especially useful for managing its activities and is also helpful to farmers to streamline the process. Results indicate that the proposed model is highly effective under pandemic emergencies like the current COVID-19 crisis. Policymakers and the government will find this model helpful in drafting relevant policies regarding food grain procurement under emergencies such as the COVID-19 outbreak. The distribution segment of the supply chain network is not part of the present research work. In future studies, this part could be then added to the whole of the procurement process, and both procurement and distribution can be assessed together again. [ABSTRACT FROM AUTHOR]