Crisis management in times of uncertain supply and value chains

Supply chain managers are forced to develop crisis-induced strategies due to the complexity of crises, as opposed to the more traditional strategies that prioritize competitive priorities. The increasing frequency and severity of recent crises, such as the coronavirus outbreaks, widespread product recalls, and financial crises, highlight the need for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. This article's goal is to review the literature on supply chain management during times of crisis, organizing the relevant body of scholarly work in a systematic way, outlining current research methodologies, capturing strategic priorities and themes of complexity in research studies, and highlighting opportunities for additional research. Four factors for restorative priorities are identified by the review, which is based on a systematic analysis of 250 academic publications from 1996 to 2021 and reflects operations strategy in times of crisis: Critical supplies with important services, prompt action with restoration, safety with security, and traceability with transparency are just a few examples. The analysis also reveals that network configurations and business cycle complexity, optimal choices and provisioning system complexity, complicated learning processes and demand forecast are all sources of operational complexity during crises. The build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in emergency situations are suggested with the use of review insights. The article ends with suggestions for future research on supply chain improvements, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection issues on connecting crisis network allocations with cross-functionalities and connecting crisis systems investments with liabilities.

Meeting the demands for bone allografts: Challenges during COVID-19 pandemic

Background: Bone allografts have been an integral part of orthopedic procedures such as revision hip surgery or reconstruction of large bone defects. Femoral heads and necks were harvested from living donors undergoing total hip arthroplasty (THA). They were stored fresh frozen or processed into bone chips by the Innsbruck Bone Bank. The challenge was to meet the demand for bone grafts through in-house manufacturing even during Covid-19 pandemic restrictions. Method(s): Key data from the bone bank's registry and the hospital administration between January 2019 and March 2022 were analysed with regard to collection of fresh frozen allogenic femoral heads /necks and the manufacture of bone chips and their discard. Donor suitability and release criteria were assessed in accordance with EU and national legislation. Result(s): In 1,249 primary THA surgeries 640 femoral heads and necks were harvested. In the mean 51.4% of these yielded the bulk material for bone chips manufacture. Due to Covid-19 pandemic restrictions the number of grafts retrieved was lowest in Q1 2020 and Q4 2021. The proportion of unreleased transplants remained fairly consistent at 14%, but the reasons for discard varied: technical issues were eliminated, but the increase for discard to >30% due to reasons in donor history required the revision of the questionnaire and the collaboration with the team responsible for listing diagnoses in the medical records. By 2020, the bone chip discard rate had increased to 8%. Hence modification of cleaning and gowning brought it back down to 2% . Conclusion(s): Providing high quality bone allografts is a complex task. In this context, it is important to regularly evaluate the registry data and to modify the process accordingly, thus ensuring adequate supply of allografts even in times of significant restrictions.

The power of purpose – lessons in agility from the Ventilator Challenge

Purpose: COVID-19 has shaken views of what is normal and what is possible, raising questions about conventional norms, ways of working and our understanding of agility. This paper aims to respond to calls for empirical research of supply chain capacities in times of crisis and offer a unique perspective on agile procurement and supply chain management from a case study of the Ventilator Challenge. Design/methodology/approach: A descriptive case study was undertaken, adopting an inductive approach. Interviews were conducted with the major stakeholders tasked with the design, sourcing and assembly of ventilators. Findings: Findings are delivered across four key areas: context;procurement and supply chain management;technology and culture;and environment. Key challenges and enablers are discussed, highlighting the critical roles of trust, empowerment and enabling technologies in the construction of an entirely new ventilator supply chain, from scratch, in five weeks. Originality/value: This paper delivers contributions for both academic research and practice. The case study offers rich new insights relating to procurement in times of crisis, contributing to efforts to advance beyond outdated approaches for resilience in literature. Practical contributions arise in highlighting the significance of adapted sourcing and recruitment, technology, collaboration, people and power of purpose in enabling agility and achieving the impossible. © 2021, Emerald Publishing Limited.

Fluorescence Spectral Shape Analysis for COVID-19 Virus Identification: Machine Learning Approach

Fast and reliable tests for the new coronavirus are urgently needed. Current Polymerase Chain Reaction based virus detection approaches are typically time-consuming and expensive. Technologies capable of providing a fast, real-time and non-contact detection of virus contamination and real-time virus classification are not yet available. Here, we demonstrate the potential of a fluorescence detection technique along with machine-learning based classification for virus detection. The ultraviolet (UV) light irradiated virus emits a fluorescent signal with a characteristic spectrum, which is regarded as a fingerprint for the virus. We analyzed eight virus samples including a heat-inactivated SARS-CoV-2 (virus causing COVID-19) and collected a number of emission spectra. Machine learning techniques are applied to discriminate among the candidate viruses via classifying a number of spectra data collected. First, Principle Component Analysis (PCA) was applied to reduce spectra data dimensionality. Then support vector machine (SVM) with various kernel functions (kernel-SVM), k-nearest-neighbor (k-NN) and Artificial Neural Networks (ANN) methods were used to classify these viruses with dimension-reduced data from PCA. We found that dimension-reduced data in 3 principal components (PCs) space performs better than that in 2 PCs space in the machine learning algorithms mentioned above. Variance ratio analysis is able to explain nearly 95% of variance which allows nearly 100% accuracy of predictions for 25% data test set randomly chosen from the whole dataset. Finally, cross validation (CV) analysis is applied to kernel-SVM and k-NN methods.

Recreational Screen Time Behaviors during the COVID-19 Pandemic in the U.S.: A Mixed-Methods Study among a Diverse Population-Based Sample of Emerging Adults

Understanding how screen time behaviors changed during the COVID-19 pandemic is important to inform the design of health promotion interventions. The purpose of this study was to quantify and describe changes in recreational screen time from 2018 to 2020 among a diverse sample of emerging adults. Participants (n = 716) reported their average weekly recreational screen time in 2018 and again during the pandemic in 2020. Additionally, participants qualitatively reported how events related to COVID-19 had influenced their screen time. Weekly recreational screen time increased from 25.9 +/- 11.9 h in 2018 to 28.5 +/- 11.6 h during COVID-19 (p < 0.001). The form of screen time most commonly reported to increase was TV shows and streaming services (n = 233). Commonly reported reasons for changes in screen time were boredom (n = 112) and a desire to connect with others (n = 52). Some participants reported trying to reduce screen time because of its negative impact on their mental health (n = 32). Findings suggest that screen time and mental health may be intertwined during the pandemic as it may lead to poorer mental health for some, while promoting connectedness for others. Health professionals and public health messaging could promote specific forms for screen time to encourage social connection during the COVID-19 pandemic and beyond.

Changes to Physical Activity during a Global Pandemic: A Mixed Methods Analysis among a Diverse Population-Based Sample of Emerging Adults in the U.S

Emerging adults' lives have changed because of the COVID-19 pandemic. Physical activity (PA) behaviors need to be examined to inform interventions and improve health. Responses to the C-EAT (COVID-19 Eating and Activity over Time) survey (N = 720;age = 24.7 +/- 2.0 yrs) were analyzed. This mixed-methods study quantitatively examined changes in self-reported PA (hours/week of mild PA, moderate-to-vigorous PA (MVPA), and total PA) from 2018 to 2020. Qualitative responses on how COVID-19 impacted PA were analyzed using a grounded theory approach. Hours of PA were lower on average for all intensity levels during COVID-19 than in 2018 (p's < 0.0001). Over half of the sample reported a decrease in MVPA (53.8%) and total PA (55.6%);42.6% reported a decrease in mild PA. High SES were more likely to report an increase in total PA (p = 0.001) compared to those of lower SES. Most (83.6%) participants perceived that COVID-19 had influenced their PA. The most common explanations were decreased gym access, effects on outdoor PA, and increased dependence on at-home PA. Results suggest that emerging adults would benefit from behavioral interventions and health promotion efforts in response to the pandemic, with a focus on activities that can be easily performed in the home or in safe neighborhood spaces.

Prevalence of symptomatic and asymptomatic COVID-19 infection in pregnant women and their infants in an urban hospital

Background: COVID-19 is an emerging pathogen that has caused a global pandemic, with New York City as one of its epicenters. Data are still forthcoming if pregnant women are more vulnerable to COVID-19, as they are with influenza. Additionally, it is not known if infants born to COVID-19 positive women are at risk of being infected at birth. Methods: In March 2020, our hospital instituted a policy of testing all pregnant women presenting for active labor and scheduled C-section or induction of labor, with a nasopharyngeal swab that was sent for RT-PCR qualitative SARSCoV- 2 assay (Roche Cobas® 6800). Upon birth, infants were also tested, unless the parent did not give consent. We retrospectively reviewed the COVID-19 test results of all pregnant women and their infants, from March 23 through May 31, 2020 using our infection control surveillance system (VigiLanz®). We also reviewed the electronic medical record (EPIC®) for documentation of any symptoms consistent with COVID-19 infection either prior to hospitalization or during the hospital stay. Results: A total of 415 women and 72 infants were tested for SARS-CoV-2. Of the 415 women tested, 41 (9.9%) were positive. Of the 72 infants tested, 2 (2.8%) were positive and concordant with their birth parent. Only 1 (2.4%) of the women who tested positive was symptomatic. The remaining 40 (97.6%) women did not report any symptoms of COVID-19 during labor. Neither of the two positive infants displayed any signs or symptoms of COVID-19. Of the 41 women who were positive, 5 did not consent to have their infant tested. The one symptomatic woman who tested positive for COVID-19 had an infant who tested negative by PCR. Conclusion: During the first wave of the COVID-19 pandemic, we found 9.9% (41/415) of pregnant women presenting for labor tested positive for SARS-CoV-2. Among the 41 women who tested positive, only 1 (2.4%) had symptoms on presentation and only 2 newborn infants tested positive. Our data suggests that pregnant women may not be at increased risk for complications from COVID-19 disease and are not likely to transmit the disease to their infants during labor.

Unveiling the Molecular Structure of Antimalarial Drugs Chloroquine and Hydroxychloroquine in Solution through Analysis of 1H NMR Chemical Shifts.

Chloroquine (CQ) and hydroxychloroquine (HCQ) have been standard antimalarial drugs since the early 1950s, and very recently, the possibility of their use for the treatment of COVID-19 patients has been considered. To understand the drug mode of action at the submicroscopic level (atoms and molecules), molecular modeling studies with the aid of computational chemistry methods have been of great help. A fundamental step in such theoretical investigations is the knowledge of the predominant drug molecular structure in solution, which is the real environment for the interaction with biological targets. Our strategy to access this valuable information is to perform density functional theory (DFT) calculations of 1H NMR chemical shifts for several plausible molecular conformers and then find the best match with experimental NMR profile in solution (since it is extremely sensitive to conformational changes). Through this procedure, after optimizing 30 trial distinct molecular structures (ωB97x-D/6-31G(d,p)-PCM level of calculation), which may be considered representative conformations, we concluded that the global minimum (named M24), stabilized by an intramolecular N-H hydrogen bond, is not likely to be observed in water, chloroform, and dimethyl sulfoxide (DMSO) solution. Among fully optimized conformations (named M1 to M30, and MD1 and MD2), we found M12 (having no intramolecular H-bond) as the most probable structure of CQ and HCQ in water solution, which is a good approximate starting geometry in drug-receptor interaction simulations. On the other hand, the preferred CQ and HCQ structure in chloroform (and CQ in DMSO-d6) solution was assigned as M8, showing the solvent effects on conformational preferences. We believe that the analysis of 1H NMR data in solution can establish the connection between the macro level (experimental) and the sub-micro level (theoretical), which is not so apparent to us and appears to be more appropriate than the thermodynamic stability criterion in conformational analysis studies.

Responding to the new International Classification of Diseases-11 prolonged grief disorder during the COVID-19 pandemic: a new bereavement network and three-tiered model of care.

The field of bereavement research and care is at a tipping point. The introduction of prolonged grief disorder (PGD) in the International Classification of Diseases (ICD-11) has ignited clinical interest in this new disorder, along with debate over challenges in validating and implementing these new criteria. At the same time, the global COVID-19 pandemic has launched several local and international efforts to provide urgent support and comfort for individuals and communities suffering from grief. Recently, grief experts have called for a collective response to these complicated bereavements and possible increase in PGD due to COVID-19. Here we outline a new European network that aims to unite a community of grief researchers and clinicians to provide accessible, evidence-based support particularly during times of unprecedent crisis. The Bereavement Network Europe (BNE) has been developed with two main aims. Firstly, to develop expert agreed, internationally acceptable guidelines for bereavement care through a three-tiered approach. Secondly, to provide a platform for researchers and clinicians to share knowledge, collaborate, and develop consensus protocols to facilitate the introduction of PGD to diverse stakeholders. This article outlines the current status and aims of the BNE along with the plans for upcoming network initiatives and the three-tiered bereavement care guidelines in response to the COVID-19 pandemic.