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1.
BMJ Open ; 12(12):e064135, 2022.
Article in English | MEDLINE | ID: covidwho-2193776

ABSTRACT

OBJECTIVES: To evaluate the benefits of vaccination on the case fatality rate (CFR) for COVID-19 infections.

2.
PLoS Global Public Health ; 2(1), 2022.
Article in English | CAB Abstracts | ID: covidwho-1854931

ABSTRACT

How COVID-19 vaccine is distributed within low- and middle-income countries has received little attention outside of equity or logistical concerns but may ultimately affect campaign impact in terms of infections, severe cases, or deaths averted. In this study we examined whether subnational (urban-rural) prioritization may affect the cumulative two-year impact on disease transmission and burden of a vaccination campaign using an agent-based model of COVID-19 in a representative COVID-19 Vaccines Global Access (COVAX) Advanced Market Commitment (AMC) setting. We simulated a range of vaccination strategies that differed by urban-rural prioritization, age group prioritization, timing of introduction, and final coverage level. Urban prioritization averted more infections in only a narrow set of scenarios, when internal migration rates were low and vaccination was started by day 30 of an outbreak. Rural prioritization was the optimal strategy for all other scenarios, e.g., with higher internal migration rates or later start dates, due to the presence of a large immunological naive rural population. Among other factors, timing of the vaccination campaign was important to determining maximum impact, and delays as short as 30 days prevented larger campaigns from having the same impact as smaller campaigns that began earlier. The optimal age group for prioritization depended on choice of metric, as prioritizing older adults consistently averted more deaths across all of the scenarios. While guidelines exist for these latter factors, urban-rural allocation is an orthogonal factor that we predict to affect impact and warrants consideration as countries plan the scale-up of their vaccination campaigns.

4.
Supply Chain Management ; 2021.
Article in English | Scopus | ID: covidwho-1311002

ABSTRACT

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.

5.
Remote Sensing for Agriculture, Ecosystems, and Hydrology Xxii ; 11528, 2020.
Article in English | Web of Science | ID: covidwho-1242187

ABSTRACT

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.

6.
International Journal of Environmental Research & Public Health [Electronic Resource] ; 18(9):27, 2021.
Article in English | MEDLINE | ID: covidwho-1209892

ABSTRACT

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.

7.
International Journal of Environmental Research & Public Health [Electronic Resource] ; 18(7):01, 2021.
Article in English | MEDLINE | ID: covidwho-1208602

ABSTRACT

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.

8.
Open Forum Infectious Diseases ; 7(SUPPL 1):S337, 2020.
Article in English | EMBASE | ID: covidwho-1185900

ABSTRACT

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.

9.
J Phys Chem B ; 125(13): 3321-3342, 2021 04 08.
Article in English | MEDLINE | ID: covidwho-1147824

ABSTRACT

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.


Subject(s)
Chloroquine/chemistry , Hydroxychloroquine/chemistry , Molecular Structure , Proton Magnetic Resonance Spectroscopy
10.
Public Health ; 191: 85-90, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1125690

ABSTRACT

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.


Subject(s)
COVID-19 , Community Networks , Delivery of Health Care/organization & administration , Grief , International Classification of Diseases , Bereavement , Europe/epidemiology , Humans , Models, Organizational , Practice Guidelines as Topic
11.
Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII 2020 ; 11528, 2020.
Article in English | ProQuest Central | ID: covidwho-908189

ABSTRACT

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 (kernelSVM), 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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

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