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Piecewise linear regression (PLR) method is applied to study cumulative cases of COVID-19 evolving everyday in England up to 6th February 2022 just before travel restrictions are removed and people started not to get tested anymore in the UK and factors e.g. the lockdowns behind the spread COVID-19 are also investigated. It is clear that different periods exhibit distinct patterns depending on variants and government-imposed restriction. Therefore, the effectiveness of lockdown measures is evaluated by comparing the rate of increase after a certain period (delay effect of measures) and that of time before as well as how new variants take over as a dominant variant. In addition, autoregression function is studied to show strong effect of cases in the past on today's cases since the disease is highly infectious. Most of work is carried out thorough python built-in libraries such as pandas for preprocessing data and matplotlib which allows us to gain more insight and better visualization into the real scenario. Visualization is conducted by Geoda showing the regional level of infections. © 2023 SPIE.
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Objective: To explore the clinical safety of lopinavir/ritonavir (LPV/r) by mining the risk signals of adverse events (AEs) related to LPV/r for the safe application of the drug in the treatment of novel coronavirus pneumonia (COVID-19). Method(s): The risk signals related to LPV/r in AE reports of US FDA Adverse Event Reporting System (FAERS) from the first quarter of 2010 to the third quarter of 2019 were mined by reporting odds ratio (ROR). An AE with reports more than 3 and 95% confidence interval (CI) lower limit of ROR greater than 1 was defined as a positive signal. AEs were counted and classified using the preferred system organ class (SOC) and preferred term (PT) of Medical Dictionary for Regulatory Activities (MedDRA). The PTs of top 50 adverse event reports and signal strength were selected and analyzed. Result(s): From the first quarter of 2010 to the third quarter of 2019, a total of 13 335 AE reports with LPV/r as the primary suspicious drug were reported in the FAERS database. Four hundred and fifty-five AE risk signals with reports more than 3 and the 95%CI lower limit of ROR greater than 1 were detected, involving 7 718 AE reports. The top 2 system organs involved in AE reports were "injury, poisoning and procedural complications" [13.6% (1 051/7 718)] and "pregnancy, puerperium and perinatal conditions" [11.7% (899/7 718)]. However, 998 (95.0%) of 1051 AE reports involved in "injury, poisoning and procedural complications" were related to drug exposure during pregnancy. The system organ with the highest signals was "congenital, familial and genetic disorders" [16.3% (74/455)]. In addition, 144 AEs caused by drug interactions were detected, which ranked the 7th in the AE reports. Conclusion(s): The risk signals of fetal, neonatal and infant abnormalities related to LPV/r during pregnancy were detected, suggesting that attention should be paid to the risk of using LPV/r in pregnant women and infants. The interaction between LPV/r and other drugs was also worthy of attention.Copyright © 2020 by the Chinese Medical Association.
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We construct a theoretical model to interpret the structural shock from the COVID-19 pandemic and the response of local labour market and industry specialization. The empirical study takes the large-scale online labour market of China to analyse firms' hiring demand for 20 industries across 380 cities with monthly recruitment data from May 2017 to September 2020. Post-event quantitative analysis on job postings and employer demand highlighted that the pandemic resulted in an unemployment shock and industry- and city-level redistribution of the worker. China's local job market resilience also revealed a regional imbalance, correlated with pandemic risk, city scale and industry structure. © 2023 Regional Studies Association.
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The COVID-19 pandemic has emphasized the need for non-contact medical robots to alleviate the heavy workload and emotional stress experienced by healthcare professionals while preventing infection. In response, we propose a non-contact robotic diagnostic system for otolaryngology clinics, utilizing a digital twin model for initial design optimization. The system employs a master-slave robot architecture, with the slave robot comprising a flexible endoscope manipulation robot and a parallel robot arm for controlling additional medical instruments. The novel 4 degrees of freedom (DOF) control mechanism enables the single robotic arm to handle the endoscope, facilitating the process compared to the traditional two-handed approach. Phantom experiments were conducted to evaluate the effectiveness of the proposed flexible endoscope manipulation system in terms of diagnosis completion time, NASA task load index (NASA-TLX), and subjective risk score. The results demonstrate the system's usability and its potential to alternate conventional diagnosis. © 2013 IEEE.
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The rapid expansion of plastic manufacturing industries in last several decades has brought serious concerns over the environmental impacts of plastic wastes. Recent outbreak of Covid-19 drastically increased production, use, and disposal of plastic products. Current management strategies for wasted plastics still rely on landfill and incineration that continue to exacerbate plastic pollution and carbon emissions. Many countries have put forward multifaceted administrative efforts to reduce plastic wastes, but the annual global generation of plastic wastes is still increasing. In techno-society, researchers have been exploring more effective plastic wastes treatment technologies to alleviate environmental impacts of plastic wastes. Such efforts entailed several technical options that can potentially contribute to establishing a circular economy for plastics. Thermochemical process is a prominent example of such techniques. This review presents an overview of the issue of plastic pollution, covering topics including global plastic production, environmental impacts, and toxicity. In addition, the global administrative efforts aimed at reducing plastic pollution are discussed, as well as detection and treatment strategies to establish a circular economy in plastic management. © 2023 Elsevier B.V.
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Digital contact tracing is an effective solution to prevent such a pandemic, but the low adoption rate of a required mobile app hinders its effectiveness. A large collection of cellular trajectories from mobile subscribers can be an out-of-the-box solution that is free from the low adoption issue, but has been overlooked due to its low spatial resolution. In this paper, to increase the resolution of this cellular trajectory, we present a new problem that estimates the user's visited places at the point-of-interest(POI) level, which we call POI-level cellular trajectory reconstruction. We propose a novel algorithm, Pincette, that accomplishes more accurate POI reconstruction by leveraging various external data such as road networks and POI contexts. Specifically, Pincette comprises multi-view feature extraction and GCN-LSTM-based POI estimation. In the multi-view feature extraction, Pincette extracts three complementary features from three views: efficiency, periodicity, and popularity. In the GCN-LSTM-based POI estimation, these three views are seamlessly integrated, where spatio-temporal periodic patterns are captured by graph convolutional networks (GCNs) and an LSTM. With extensive experiments on two real data collections of two cities, we show that Pincette outperforms four POI estimation baselines by up to 21.20%. We believe that our work sheds light on the use of cellular trajectories for digital contact tracing. We release the source code at https://github.com/kaist-dmlab/Pincette. © 2022 IEEE.
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Assessing the impact of the COVID-19 crisis on economies is fundamental to tailor the responses of the governments to recover from the crisis. In this paper, we present a novel approach to assessing the economic impact with a large-scale credit card transaction dataset at a fine granularity. For this purpose, we develop a fine-grained economic-epidemiological modeling framework COVID-EENet, which is featured with a two-level deep neural network. In support of the fine-grained EEM, COVID-EENet learns the impact of nearby mass infection cases on the changes of local economies in each district. Through the experiments using the nationwide dataset, given a set of active mass infection cases, COVID-EENet is shown to precisely predict the sales changes in two or four weeks for each district and business category. Therefore, policymakers can be informed of the predictive impact to put in the most effective mitigation measures. Overall, we believe that our work opens a new perspective of using financial data to recover from the economic crisis. For public use in this urgent problem, we release the source code at https://github.com/kaist-dmlab/COVID-EENet.
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PURPOSE: Serum electrolyte imbalances are highly prevalent in COVID-19 patients. However, their associations with COVID-19 outcomes are inconsistent, and of unknown prognostic value. We aim to systematically clarify the associations and prognostic accuracy of electrolyte imbalances (sodium, calcium, potassium, magnesium, chloride and phosphate) in predicting poor COVID-19 clinical outcome. METHODS: PubMed, Embase and Cochrane Library were searched. Odds of poor clinical outcome (a composite of mortality, intensive-care unit (ICU) admission, need for respiratory support and acute respiratory distress syndrome) were pooled using mixed-effects models. The associated prognostic sensitivity, positive and negative likelihood ratios (LR + , LR-) and predictive values (PPV, NPV; assuming 25% pre-test probability), and area under the curve (AUC) were computed. RESULTS: We included 28 observational studies from 953 records with low to moderate risk-of-bias. Hyponatremia (OR = 2.08, 95% CI = 1.48-2.94, I2 = 93%, N = 8), hypernatremia (OR = 4.32, 95% CI = 3.17-5.88, I2 = 45%, N = 7) and hypocalcemia (OR = 3.31, 95% CI = 2.24-4.88, I2 = 25%, N = 6) were associated with poor COVID-19 outcome. These associations remained significant on adjustment for covariates such as demographics and comorbidities. Hypernatremia was 97% specific in predicting poor outcome (LR + 4.0, PPV = 55%, AUC = 0.80) despite no differences in CRP and IL-6 levels between hypernatremic and normonatremic patients. Hypocalcemia was 76% sensitive in predicting poor outcome (LR- 0.44, NPV = 87%, AUC = 0.71). Overall quality of evidence ranged from very low to moderate. CONCLUSION: Hyponatremia, hypernatremia and hypocalcemia are associated with poor COVID-19 clinical outcome. Hypernatremia is 97% specific for a poor outcome, and the association is independent of inflammatory marker levels. Further studies should evaluate if correcting these imbalances help improve clinical outcome.
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As the world's second largest economy, by the end of 2020, China had fully solved the problem of food and clothing, and the Chinese people are working hard to improve their happiness level. Affected by the COVID-19 epidemic, e-commerce live streaming services represented by Douyin have provided people with various conveniences. People can meet their needs for communication and shopping without leaving their homes. This paper takes the New Oriental live store studio, which is set up on the Douyin e-commerce platform, as the research object. Starting from the basic elements of e-commerce live broadcast, through the typical cases and data in the two and a half months after June 9, 2022, this paper analyzes the sense of happiness built by the content marketing model of the live broadcast room, as well as the wider spread of happiness brought by user interaction and feedback. © 2022 IEEE.
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Objective: To explore the mechanism of Xuanbai chengqi decoction and Sangbei power in the treatment of epidemic-closed lung type COVID-19 by network pharmacology. Method(s): The potential blood active components and gene targets of Xuanbai chengqi decoction and Sangbei power were screened and predicted by TCMSP;The angiotensin converting enzyme 2(ACE2)related gene targets were downloaded;The PPI network of components-targets was plotted by STRING database.The intersection of ACE2-related genes and target genes of Xuanbai chengqi decoction and Sangbei power was extracted;The DAVID database was used to analyze and screen the key targets and mechanisms of Xuanbai chengqi decoction and Sangbei power. Result(s): A total of 496 active ingredients related to Xuanbai chengqi decoction and Sangbei power were retrieved from TCMSP database.According to the pharmacokinetic parameters, 78 active components in blood were screened and 761 targets were retrieved.5 556 ACE2-related genes were downloaded.49 key genes were obtained after the intersection of Chinese medicine component targets and ACE2 related gene targets;The genes affected by Xuanbai chengqi decoction and Sangbei power were mainly involved in cytoketone metabolism, intracellular protein transport, internal peptidase inhibitor activity and others, which were mainly related to the signaling pathway of the Jak-STAT, the intestinal immune network pathway of producing IgA, complement and coagulation cascade pathway, etc. Conclusion(s): Xuanbai chengqi decoction and Sangbei power can act on ACE2 through 49 gene loci, and its mechanism is related to cellular ketone metabolism and inhibition of protein entry into cells. Copyright © 2021, Central Station of Chinese Medicinal Materials Information, National Medical Products Administration. All right reserved.
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We present the first label-free detection and discrimination of SARS-CoV-2 variants having global spread (Wild-type, delta and omicron) by combining terahertz (THz) time-domain spectroscopy and a metamaterial-based sensing chip. THz spectra suggest sensitive discrimination between variants that have only one or two different amino-acid sequences for each virus type, respectively, within just few minutes. © 2022 IEEE.
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Some asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19. Here, we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate the relationship between asymptomatic proportion and age. Epidemiological data obtained before mass vaccination provide valuable insights into the nature of pathogenicity of SARS-CoV-2. The data were collected by multiple rounds of city-wide PCR testing with contact tracing, where each patient was monitored for symptoms through the whole course of infection. The clinical endpoint (asymptomatic or symptomatic) for each patient was recorded (the pre-symptomatic patients were classified as symptomatic). We find that the proportion of infections that are asymptomatic declines with age (coefficient = -0.006, 95% CI: -0.008 to -0.003, p < 0.01), falling from 42% (95% CI: 6-78%) in age group 0-9 years to 11% (95% CI: 0-25%) in age group greater than 60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern, together with the distribution of cases by age, can explain most of the reported variation in asymptomatic proportions among cities. Our analysis suggests that SARS-CoV-2 surveillance strategies should take account of the variation in asymptomatic proportion with age.
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The global scope of pollution from plastic waste is a well-known phenomenon associated with trade, mass consumption, and disposal of plastic products (e.g., personal protective equipment (PPE), viral test kits, and vacuum-packaged food). Recently, the scale of the problem has been exacerbated by increases in indoor livelihood activities during lockdowns imposed in response to the coronavirus disease 2019 (COVID-19) pandemic. The present study describes the effects of increased plastic waste on environmental footprint and human health. Further, the technological/regulatory options and life cycle assessment (LCA) approach for sustainable plastic waste management are critically dealt in terms of their implications on energy resilience and circular economy. The abrupt increase in health-care waste during pandemic has been worsening environmental quality to undermine the sustainability in general. In addition, weathered plastic particles from PPE along with microplastics (MPs) and nanoplastics (NPs) can all adsorb chemical and microbial contaminants to pose a risk to ecosystems, biota, occupational safety, and human health. PPE-derived plastic pollution during the pandemic also jeopardizes sustainable development goals, energy resilience, and climate control measures. However, it is revealed that the pandemic can be regarded as an opportunity for explicit LCA to better address the problems associated with environmental footprints of plastic waste and to focus on sustainable management technologies such as circular bio-economies, biorefineries, and thermal gasification. Future researches in the energy-efficient clean technologies and circular bio-economies (or biorefineries) in concert with a "nexus" framework are expected to help reduce plastic waste into desirable directions.
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COVID-19 , Humanos , COVID-19/epidemiología , Plásticos , Ecosistema , Control de Enfermedades Transmisibles , Energía RenovableRESUMEN
Silty sand in the Yellow River flood area (YRFA) of China exhibits a low cohesive force and water-holding capacity. Its direct use for subgrade filling leads to phenomena such as subgrade bed depression, slurry, and mud. Therefore, from the perspective of waste utilization, this study investigates disposable protective clothing (DPC) and disposable nitrile gloves (DNG) produced due to COVID-19 in combination with silty sand as subgrade filling for the YRFA. Through an unconfined compressive strength and permeability test, we studied the influence of personal protective equipment (PPE) with different moisture content, concentration, and size on the strength and permeability of mixed samples. Further, we discuss its impact on the improvement of subgrade filling in YRFA. Results show that adding DPC improves both the compressive strength and ductility of the sample. However, the impermeability of the sample remains unchanged. In contrast, the addition of DNG does not increase the compressive strength and ductility of the sample at the same time, whereas it significantly improves its impermeability. The size of PPE has a considerable influence on the compressive strength of the mixed sample, whereas it does not affect the sample permeability. This method makes use of the PPE waste produced due to COVID-19, reduces environmental pollution, and provides a novel concept for improving silty sand in the YRFA. Copyright © 2022 Wang, Guo, Liu, Song, Yan and Fu.
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SESSION TITLE: Long COVID: It Can Take Your Breath Away SESSION TYPE: Original Investigations PRESENTED ON: 10/16/2022 10:30 am - 11:30 am PURPOSE: Survivors of COVID-19 hospitaliaztion may be at high risk for interstitial lung disease (ILD). The incidence and natural history of post-COVID ILD may vary in cancer and non-cancer patients, particularly if survival is lower in cancer patients. We sought to determine the incidence of ILD at 3 and 6 months after hospital discharge in cancer and non-cancer patients METHODS: We analyzed a prospective cohort of patients discharged after COVID-19 hospitalization between March 2020 and March 2021. Cancer patients were referred to post-COVID clinics 3 months after discharge, while non-cancer patients self-referred to post-COVID clinics at a tertiary referral center. We classified patients into 4 groups: Group 1, asymptomatic without ILD;Group 2, symptomatic without ILD;Group 3, ILD with spontaneous improvement by 6 months;Group 4, persistent ILD at 6 months. Group 1 patients were not seen after initial visits, while all others returned at 6 and 12 months after discharge. We hypothesized that initial COVID-19 severity, measured by the Radiologic Severity Index (RSI) on admission CT, would be associated with irreversible ILD. RSI measures radiologic severity by measuring percentage of involvement (normal - 0, <25% involvement-1, 25-50% involvement-2, 50-75% involvement-3, >75% involvement–4) and multiplying by a score based on the predominant pattern of infiltrate (normal-1, ground glass-2, consolidation-3) in six zones (left, right;upper, middle, lower) to yield a score between 0-72. We used logistic regression to measure whether admission CT RSI was associated with ILD at 3 months. RESULTS: 609 cancer patients were hospitalized with COVID-19 during the study period, of whom 85 (14%) died in-hospital, and 31 (5%) were sent home to hospice. A further 63 (10%) patients died before post-COVID evaluation. Similar data were not available for non-cancer patients due to self-referral. 98 cancer patients and 75 non-cancer patients were seen in post-COVID clinics. Among cancer patients, 20 were Group 1 (20%);8, Group 2 (8%);42, Group 3 (43%);24, Group 4 (25%);5 (5%) lacked post-COVID imaging. ILD was seen in 68% of patients at 3 months, but only in 25% at 6 months. 6% of all hospitalized cancer patients developed ILD. Among non-cancer patients: Group 1, 2 patients (3%);Group 2, 3 patients (4%);Group 3, 26 patients (35%);Group 4, 14 patients (19%);30 (40%) had no post-COVID imaging. Higher RSI at COVID admission associated with ILD at 3 months in non-cancer patients (OR 1.03, 95% CI 1.00-1.07, p=0.054) but not cancer patients (OR 1.3, 95% CI 0.4-4.5). CONCLUSIONS: Initial COVID-19 severity is associated with ILD 3 months after discharge in non-cancer patients but not cancer patients. CLINICAL IMPLICATIONS: ILD is common in survivors of COVID-19 hospitalization, particularly in non-cancer patients hospitalized for severe infection. These data may guide patient selection for referral to post-COVID clinics. DISCLOSURES: No relevant relationships by Roberto Adachi No relevant relationships by Diwakar Balachandran No relevant relationships by Lara Bashoura No relevant relationships by Christopher Bertini No relevant relationships by Kodwo Dickson Owner/Founder relationship with Pulmotect, Inc Please note: 2010-present by Scott Evans, value=Royalty No relevant relationships by Saadia Faiz no disclosure on file for Bruno Granwehr;no disclosure submitted for Shannon Holloway;No relevant relationships by Maryam Kaous no disclosure on file for Fareed Khawaja;No relevant relationships by Lyndon Lee No relevant relationships by Joanna Manzano No relevant relationships by Isabel Mira-Avendano No relevant relationships by Alyssa Mohammed No relevant relationships by Mayoora Muthu No relevant relationships by Sungryong Noh Research relationship with United Therapeutics;PhaseBio Please note: $5001 - $20000 by Bela Patel, value=Grant/Research No relevant relationships by Vickie Shannon onsultant relationship with Psioxus Therapeutics Please note: 3/1/20-7/1/20 by Ajay Sheshadri, value=Consulting fee Consultant relationship with Enanta Pharmaceuticals Please note: 01/01/21-ongoing by Ajay Sheshadri, value=Consulting fee No relevant relationships by Hui Song
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The COVID-19 pandemic has been the single most important global agenda in the past two years. In addition to its health and economic impacts, it has affected people's psychological states, including a rise in depression and domestic violence. We traced how the overall emotional states of individual Twitter users changed before and after the pandemic. Our data, including more than 9 million tweets posted by 9,493 users, suggest that the threat posed by the virus did not upset the emotional equilibrium of social media. In early 2020, COVID-related tweets skyrocketed in number and were filled with negative emotions;however, this emotional outburst was short-lived. We found that users who had expressed positive emotions in the pre-COVID period remained positive after the initial outbreak, while the opposite was true for those who regularly expressed negative emotions. Individuals achieved such emotional consistency by selectively focusing on emotion-reinforcing topics. The implications are discussed in light of an emotionally motivated confirmation bias, which we conceptualize as emotion bubbles that demonstrate the public's resilience to a global health risk. © 2022 ACM.