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1.
China Biotechnology ; 42(5):146-153, 2022.
Article in Chinese | Scopus | ID: covidwho-1934650

ABSTRACT

With the increasing exhaustion of global resources, various countries have explored bioeconomy as an economic model that can cope with environmental, climate,resource problems and food security crisis. China recently released the " 14th Five-Year Plan for Bioeconomy Development", raising the bioeconomy to the level of national strategic development for the first time. Based on the innovation of life science and biotechnology, emerging industries have come into being in bioeconomy including biomedicine, bioagriculture, biomanufacturing and bioenergy. Bioeconomy is an economic development model with great potential for sustainable development in the future. This paper summarizes the evolution law of the global bioeconomy, the development of the bioeconomy worldwide and the industrial development of Chinese bioeconomy. Moreover, under the complex situation in a time of unprecedented global changes in a century and the COVID-19 epidemic, the relevant countermeasures to cope with challenges and suggestions on Chinese future are put forward. © 2022, China Biotechnology Press. All rights reserved.

2.
2021 International Conference on Computer Application and Information Security, ICCAIS 2021 ; 12260, 2022.
Article in English | Scopus | ID: covidwho-1932601

ABSTRACT

COVID-19 plays role in every part of the world;especially, it does harm to lives of people. Thus, COVID-19 sounds the alarm that is very important to build an effective mechanism to help prevent pandemic disease. In this work, dynamic network based on status value is built, which aims to help simulate the added danger level by the addition of infected people or close contacts. First, each node of this network is labelled with different kinds of status which has special value to show its danger degree. Then, the weight of the network represents the relationship of nodes;with the value of each node, average length and average spread of danger level is calculated based on the accumulation of dynamic weight. Thus, epidemic speed and scope of the infectious disease can be simulated. Moreover, the experiments compared to other networks have verified the effectiveness of our model. © The Authors.

3.
14th International Conference on Cross-Cultural Design, CCD 2022 Held as Part of the 24th HCI International Conference, HCII 2022 ; 13313 LNCS:230-240, 2022.
Article in English | Scopus | ID: covidwho-1919665

ABSTRACT

Social media is one of the most significant sources of information in modern people’s life. Due to the large quantity of user base and public opinions, when people read a blog post, the different tendencies of comments may affect their views on the event to a certain extent. This paper, taking the COVID-19 epidemic as an example, investigated the impact of Weibo (a popular social software in China) comments on readers’ sentiments. In this paper, text mining technology was adopted to collect data including the blogs and the comments under each blog, and the NLPIR-Parser platform was used to analyze the sentiment of the comments. Finally, the conclusion that the sentiments of other comments tend to follow the sentiments of the first comments was drawn. Based on the research results, this paper also gave some enlightenment on social media management and suggestions of public opinions oversight. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

4.
Chinese Journal of New Drugs ; 31(10):972-977, 2022.
Article in Chinese | EMBASE | ID: covidwho-1894105

ABSTRACT

Objective: To explore the implementation and management measures of drug clinical trials during the period of COVID-19 epidemic, protect the safety and rights of subjects, ensure the smooth implementation of clinical trials, and provide reference and suggestions for the management work of clinical trial institutions. Methods: According to the requirements of COVID-19 epidemic prevention and control policies and the national guiding principles for drug clinical trial management, combining the experience of our hospital, we optimized the working process and proposed management measures in four aspects including project and personnel management, subject follow-up management, drug distribution management, and communication between all parties involved in clinical trials. Results and conclusions: During the period of COVID-19 epidemic, our hospital has taken a series of measures which ensured the smooth implementation of more than 200 drug clinical trials and protected the safety and rights of subjects and researchers.

5.
Journal of International Students ; 12(Special Issue 1):83-90, 2022.
Article in Chinese | Scopus | ID: covidwho-1863667

ABSTRACT

COVID-19 disrupts regular educational order and poses significant challenges to international education. In the face of the uncertain development of the pandemic, Tsinghua University has been actively exploring to build a more resilient education system by further promoting convergence management of the local and international students. Based on the organization system theory, we explain the necessity of convergence management, review Tsinghua’s responses to the COVID-19 crisis, and introduce the practices in building a more resilient education system, and discuss the future for promoting system resilience. © Journal of International Students.

6.
Embase; 2020.
Preprint in English | EMBASE | ID: ppcovidwho-337377

ABSTRACT

Computational approaches for accurate prediction of drug interactions, such as drug-drug interactions (DDIs) and drug-target interactions (DTIs), are highly demanded for biochemical researchers due to the efficiency and cost-effectiveness. Despite the fact that many methods have been proposed and developed to predict DDIs and DTIs respectively, their success is still limited due to a lack of systematic evaluation of the intrinsic properties embedded in the corresponding chemical structure. In this paper, we develop a deep learning framework, named DeepDrug, to overcome the above limitation by using residual graph convolutional networks (RGCNs) and convolutional networks (CNNs) to learn the comprehensive structural and sequential representations of drugs and proteins in order to boost the DDIs and DTIs prediction accuracy. We benchmark our methods in a series of systematic experiments, including binary-class DDIs, multi-class/multi-label DDIs, binary-class DTIs classification and DTIs regression tasks using several datasets. We then demonstrate that DeepDrug outperforms state-of-the-art methods in terms of both accuracy and robustness in predicting DDIs and DTIs with multiple experimental settings. Furthermore, we visualize the structural features learned by DeepDrug RGCN module, which displays compatible and accordant patterns in chemical properties and drug categories, providing additional evidence to support the strong predictive power of DeepDrug. Ultimately, we apply DeepDrug to perform drug repositioning on the whole DrugBank database to discover the potential drug candidates against SARS-CoV-2, where 3 out of 5 top-ranked drugs are reported to be repurposed to potentially treat COVID-19. To sum up, we believe that DeepDrug is an efficient tool in accurate prediction of DDIs and DTIs and provides a promising insight in understanding the underlying mechanism of these biochemical relations. The source code of the DeepDrug can be freely downloaded from https://github.com/wanwenzeng/deepdrug.

7.
Epidemiology ; 70(SUPPL 1):S306, 2022.
Article in English | EMBASE | ID: covidwho-1854027

ABSTRACT

Background: The psychological well-being of older adults may have been negatively affected by the outbreak of the COVID- 19 epidemic due to the presence of comorbidity, increased risk of complications, mortality, and difficulty in adapting to mhealth and social isolation. The study aimed to investigate the anxiety level of community older adults during the COVID-19 epidemic and explore its associated factors, so that there can be more evidence-based advice to improve the mental health status for the older adults. Methods: Online questionnaires and face to face communication were used to investigate 320 community older adults, who were selected randomly. The questionnaires were used to investigate the sociodemographic characteristics, anxiety and resilience level of the participants. One-way ANOVA, correlation and regression analysis were performed to explore the factors associated with the anxiety among the older adults. Results: The mean of the anxiety among the all participants is 44.03±10.89 and 128 persons (40%) suffer from the anxiety (mild anxiety: 84.38%, moderate anxiety: 14.06%, severe anxiety: 14.06%). The mean of resilience is 56.68±18.26, and the three dimensions of CD-RISC is negative correlation with the anxiety. The SAS can be influenced by the chronic disease history (P=0.045), physical health conditions (P=0.024), economic income (P=0.026), the health education of the COVID-19 epidemic (P<0.001) and the level of resilience (P=0.002). Conclusions: The morbidity and score of the anxiety among the community older adults are higher during the COVID-19 epidemic than the usual. While the score of the CD-RISC is lower than the previous studies. Anxiety emerged as a prominent issue for community- dwelling older adults during the COVID-19 epidemic. Interventions that targeted resilience may have the potential to reduce anxiety level and improve the psychological well-being of the older adults.

8.
Zhonghua Kou Qiang Yi Xue Za Zhi ; 57(5): 455-461, 2022 May 09.
Article in Chinese | MEDLINE | ID: covidwho-1818247

ABSTRACT

Today, there is greater awareness on the association between oral diseases and respiration diseases after the outbreak of COVID-19. However, confusion regarding the oral health management and medical risk prevention for patients with chronic airway diseases has been remained among dental clinicians. Therefore, the dental experts of the Fifth General Dentistry Special Committee, Chinese Stomatological Association, combined with the experts of respiratory and critical care medicine, undertook the formation of consensus on the oral health management of patients with chronic airway diseases in order to help dental clinicians to evaluate medical risks and make better treatment decision in clinical practice. In the present consensus report, the relationship of oral diseases and chronic airway diseases, the oral health management and the treatment recommendations of patients with chronic airway diseases are provided.


Subject(s)
COVID-19 , Oral Medicine , Consensus , Humans , Oral Health
9.
Acta Medica Mediterranea ; 38(2):1099-1102, 2022.
Article in English | EMBASE | ID: covidwho-1798617

ABSTRACT

Objective: In this example, the patient accidentally fell from 8 meters high, causing trauma to the patient’s chest with tracheal laceration and ‘white lung’ in both lungs. The patient lost respiratory function and was using a breathing machine with 100% pure oxygen while still maintaining 80% oxygen saturation. Routine tracheal intubation under general anaesthesia could potentially cause patient death during the operation. The objective was to assess the use of extracorporeal membrane oxygenation (ECMO) in surgery to repair the patient’s tracheal laceration. Methods: The thoracic surgery department applied hybrid surgery combined with ECMO to rescue the patient. With the support of ECMO, the patient’s intraoperative vital signs were stable, blood oxygen saturation was 100% and the surgery for repairing the laceration with fibreoptic bronchoscopy was successfully completed. Results: The patient recovered and was discharged from hospital. Conclusion: ECMO has successfully treated many critically ill COVID-19 patients during the pandemic, but this is the first time in China that ECMO has been applied to patients suffering from multiple critical injuries such as chest trauma and tracheal laceration.

10.
Microbiology Spectrum ; 10(1):13, 2022.
Article in English | Web of Science | ID: covidwho-1790201

ABSTRACT

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an unprecedented event requiring frequent adaptation to changing clinical circumstances. Convalescent immune plasma (CIP) is a promising treatment that can be mobilized rapidly in a pandemic setting. We tested whether administration of SARS-CoV-2 CIP at hospital admission could reduce the rate of ICU transfer or 28-day mortality or alter levels of specific antibody responses before and after CIP infusion. In a single-arm phase II study, patients >18 years-old with respiratory symptoms with confirmed COVID-19 infection who were admitted to a non-ICU bed were administered two units of CIP within 72 h of admission. Levels of SARS-CoV-2 detected by PCR in the respiratory tract and circulating anti-SARS-CoV-2 antibody titers were sequentially measured before and after CIP transfusion. Twenty-nine patients were transfused high titer CIP and 48 contemporaneous comparable controls were identified. All classes of antibodies to the three SARS-CoV-2 target proteins were significantly increased at days 7 and 14 post-transfusion compared with baseline (P < 0.01). Anti-nucleocapsid IgA levels were reduced at day 28, suggesting that the initial rise may have been due to the contribution of CIP. The groups were well-balanced, without statistically significant differences in demographics or co-morbidities or use of remdesivir or dexamethasone. In participants transfused with CIP, the rate of ICU transfer was 13.8% compared to 27.1% for controls with a hazard ratio 0.506 (95% CI 0.165-1354), and 28-day mortality was 6.9% compared to 10.4% for controls, hazard ratio 0.640 (95% CI 0.124-3.298). IMPORTANCE Transfusion of high-titer CIP to non-critically ill patients early after admission with COVID-19 respiratory disease was associated with significantly increased anti-SARSCoV-2 specific antibodies (compared to baseline) and a non-significant reduction in Ku transfer and death (compared to controls). This prospective phase II trial provides a suggestion that the antiviral effects of CIP from early in the COVID-19 pandemic may delay progression to critical illness and death in specific patient populations. This study informs the optimal timing and potential population of use for CIP in COVID-19, particularly in settings without access to other interventions, or in planning for future coronavirus pandemics.

11.
IEEE Transactions on Intelligent Transportation Systems ; 2022.
Article in English | Scopus | ID: covidwho-1788788

ABSTRACT

With the increase in inevitable large-scale crowd aggregation, disastrous pedestrian stampedes occurred with increasing frequency over the past decade. To prevent these tragedies, it is significant to assess crowd accident-risk (CAR) and identify high-risk areas to control crowd flow dynamically. The cost function of a conventional fluid dynamics model is improved with new items of Gaussian white noise and protection factor, considering both the abnormal pedestrian movements and social distance control due to epidemic, thereby to establish an improved crowd flow model comprehensively. Different from conventional density-based pedestrian aggregation-risk models, this study proposes a hybrid crowd accident-risk assessment (HCRA) model based on internal energy and information entropy. Using the HCRA model, we can consider not only crowd density but also the modulus and direction of a crowd velocity vector simultaneously. Then this study designs a framework to realize crowd accident risk assessment based on the improved crowd-flow model and HCRA model. To validate the proposed models, case studies of CAR assessment in the large-scale waiting hall of the Shanghai Hongqiao railway station are conducted. The pedestrian social control distance-range of 1.0 m-2.0 m under the COVID-19 epidemic situation is verified numerically. Moreover, a valuable result is that this social control distance-range can be shortened to 1.0 m-1.9 m without increase of crow accident-risk. Subsequently, the down-limit of accommodation-capacity of this large waiting hall can be enhanced to 10.54%under this epidemic. IEEE

12.
Ieee Systems Journal ; : 12, 2022.
Article in English | Web of Science | ID: covidwho-1779145

ABSTRACT

The current COVID-19 pandemic has, perhaps, expedited the move to electronic medical systems (e.g., telemedicine). However, in the digitalization of healthcare services, we have to ensure the security and privacy of (sensitive) healthcare data, often stored locally in the hospital's server or remotely within a trusted cloud server. There have been many attempts to design blockchain-based approaches to support security and privacy in medical systems, and this is the focus of this article where we systematically review the existing literature on blockchain-based medical systems. We then categorize the existing security solutions into three categories, namely, 1) decentralized authentication, 2) access control, and 3) audit, and discuss the privacy protection technologies in blockchain-based healthcare systems. Based on our analysis, we identify a number of challenges, including performance limitations and inflexible audit, as well as future research opportunities (e.g., the need for lightweight security schemes for blockchain-based medical systems).

13.
Journal of Analytical and Applied Pyrolysis ; 163, 2022.
Article in English | Scopus | ID: covidwho-1729852

ABSTRACT

The disposable masks generated in the battle against COVID-19 has attracted wide attention in the world. Pyrolysis can convert the masks into useful chemicals and fuels. In this work, the masks are pyrolyzed at temperatures of 400–580 °C and the volatiles generated are cracked without or with catalysts at 440–580 °C. The catalysts used include metal oxides (Al2O3, kaolin, Fe2O3, CeO2, TiO2) and molecular sieves (HZSM5, HY, β(25H), β(60H)). The yields and composition of gas and liquid products are studied in detail where the tetrahydrofuran (THF) soluble compounds are defined as the liquid product and the n-hexane soluble compounds are defined as the oil. The liquid product and the oil were identified by GC-MS and quantified by GC. Results indicate that 440 °C is sufficient for the masks’ pyrolysis and the yields of gas, liquid product and oil are 23.4, 74.7 and 42.1 wt%, respectively. About 30% of the liquid product are C6-C35 hydrocarbons while about 70% are C36-C70 hydrocarbons trapped in the GC column (termed as column residue). The gas products are mainly C5, propylene and butene, accounting for 54.8%, 22.8% and 14.5% of the total gas product, respectively. Cracking of volatiles over various catalysts converts the liquid product mainly to propylene, butene and smaller organic gases. TiO2, HY and β(60H) are good catalysts, especially β(60H), which increases the yield of gas product to 86.5 wt% with 73.0% being ethylene, propylene and butene at 580 °C. © 2022 Elsevier B.V.

14.
2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; : 58-63, 2021.
Article in English | Scopus | ID: covidwho-1722876

ABSTRACT

the population structure of the newly emerged coronavirus SARS-CoV-2 has significant potential to inform public health management and diagnosis. As SARS-CoV-2 sequencing data accrued, grouping them into clusters is important for organizing the landscape of the population structure of the virus. Due to the limited prior information on the newly emerged coronavirus, we utilized four different clustering algorithms to group 16, S73 SARS-CoV-2 strains, which automatically enables the identification of spatial structure for SARS-CoV-2. A total of six distinct genomic clusters were identified using mutation profiles as input features. Comparison of the clustering results reveals that the four algorithms produced highly consistent results, but the state-of-the-art unsupervised deep learning clustering algorithm performed best and produced the smallest intra-cluster pairwise genetic distances. The varied proportions of the six clusters within different continents revealed specific geographical distributions. In particular, our analysis found that Oceania was the only continent on which the strains were dispersively distributed into six clusters. In summary, this study provides a concrete framework for the use of clustering methods to study the global population structure of SARS-CoV-2. In addition, clustering methods can be used for future studies of variant population structures in specific regions of these fast-growing viruses. © 2021 IEEE.

15.
Circulation ; 144:2, 2021.
Article in English | Web of Science | ID: covidwho-1711026
16.
Acta Medica Mediterranea ; 38(1):619-624, 2022.
Article in English | Scopus | ID: covidwho-1704216

ABSTRACT

Introduction: Covid-19 is a dangerous respiratory disease, and nurses are exposed to this virus at work. Thus, nurses need to understand the process of awareness and proper practice about COVID-19. Nurses' knowledge and readiness are important for the effectiveness of corona treatment. This study was conducted to investigate the knowledge and readiness of nurses in this field. Materials and methods: In this study, 264 nurses of Shanghai Hospital participated in the survey from November to August 2021. The instrument was a modified questionnaire related to nurses' performance and knowledge about COVID-19. Data were analyzed by SPSS software. Results: A total of 253 people answered the questionnaire. The mean age of nurses was 34.8 years. General knowledge of COVID preparation was sufficient. The nurse's readiness for personal protective equipment was moderate. The overall response was 94.3%. The majority of respondents (96.2%) were employed by the Ministry of Health. According to reports of 88.4% of participants, there was adequate ventilation in the workplace. In total, 81.7% of respondents wore enough gloves and gowns. About 22.4 % of participants lacked a cape, 12.9 % had an N95 mask, and 28.5 % lacked facial protection. Conclusion: Nurses were knowledgeable enough and moderate in terms of preparation and response to Covid-19. Paying more attention to these issues can contribute to greater efficiency and health of medical staff in the future. © 2022 A. CARBONE Editore. All rights reserved.

17.
2021 ASEE Virtual Annual Conference, ASEE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1695750

ABSTRACT

The COVID-19 pandemic brought about unprecedented academic disruptions to postsecondary education, including engineering education. A considerable decrease in student motivation became a major issue for online learning during the pandemic. This paper attempts to address these questions: How did the online instruction environment affect engineering students' motivation and self-directed learning? How did these changes, in turn, affect their learning outcomes? We used survey data collected from a large Canadian engineering school and conceptualized self-directed learning from a social cognitive perspective to address these questions. Our findings revealed that students' self-directed learning capabilities mediated the effects of learning environment factors on estimated grades and perceived gains in competency development;and student motivation had both direct and indirect effects on these learning outcomes. In their comments, students ascribed lack of motivation to multiple aspects of the online learning environment and felt that decreased motivation affected their learning. Our analysis demonstrated the significant role of student motivation in an online environment and suggested that the decrease in motivation became a major affective barrier to learning. Thus, the extensive online instruction during the pandemic offered both challenges and opportunities for producing self-directed learners. We recommend that engineering schools implement more interventions to help engineering students enhance their self-directed learning capabilities. © American Society for Engineering Education, 2021

18.
Circulation ; 144(SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1638300

ABSTRACT

Introduction: COVID-19 pneumonia is a heterogeneous disorder with a complex pathogenesis. Underlying subgroups may exist among patients who are admitted to the hospital with COVID-19 infection. Methods: We reviewed the charts of adult patients who were hospitalized primarily for COVID-19 at Greenwich Hospital. We performed latent class analysis using variables based on patient demographics (age, sex, race, and body mass index), comorbidities (hypertension, diabetes, coronary artery disease, chronic heart failure, chronic kidney disease, neurological disease, and pre-existing respiratory disease), laboratory results, and treatment information (medications received during hospitalization, hospital length of stay, maximized oxygen requirement, and requirement for intubation). Results: Two phenotypes were identified: C1 and C2. C1 (n=193) included older individuals with a higher mortality rate (25.4% vs 8.97%, p<0.001) and more comorbidities such as hypertension (88.6% vs 22.8%, p<0.001), coronary artery disease (32.1% vs 0.0%, p<0.001), chronic heart failure (20.7% vs 0%, p<0.001), diabetes mellitus (38.3% vs 17.6%, p<0.001), chronic kidney disease (25.4% vs 2.8%, p<0.001), pre-existing respiratory disease (24.9% vs 11.0%, p=0.004), and preexisting neurological disease (32.1% vs 3.5%, p<0.001). C2 (n=290) consisted of individuals who were younger (53.7 years vs 79.5 years, p<0.001), more likely to be obese (40.3% vs. 28.0%, p=0.007), mostly male (70.3% vs 52.3%, p<0.001), and mostly non-white (69.7% vs. 28.5%, p<0.001), with higher levels of inflammatory markers such as C-reactive protein (18.3±43.4 vs. 10.7±13.7 mg/L, p=0.006) and alanine aminotransferase (209±512 vs. 96.2±204 U/L, p=0.001). Conclusions: Using latent class analysis, we identified two clinical phenotypes of patients who were admitted to the hospital for COVID-19. Our findings may reflect different pathophysiologic processes that lead to moderate to severe COVID-19 and may be useful in the identification of treatment targets and the selection of patients with severe COVID-19 disease for future clinical trials.

19.
Journal of Geo-Information Science ; 23(2):222-235, 2021.
Article in Chinese | Scopus | ID: covidwho-1634798

ABSTRACT

Based on the epidemiological investigation data of 545 COVID-19 cases and mobile phone trajectory data of 15 million users during the epidemic ( from 21 January, 2020 to 24 February, 2020 ), this paper analyzed the spatial-temporal characteristics of COVID-19 and the human mobility changes in Chongqing. Furthermore, the correlation relationship between them was explored to explain these characteristics and changes. The results show that: (1) The epidemic pattern in Chongqing can be divided into three stages ( i.e. imported cases stage, imported cases plus local cases stage, and local cases stage ) and the real time reproduction number (Rt) was high at early stage, but declined significantly after prevention and control measures were taken;The spatial distribution of cases presented a significant clustering, and the high clustering areas were mainly distributed in the northeastern and the southwestern of Chongqing;(2) After the epidemic, the total amount of human mobility decreased to 53.20% and the decrease was mainly concentrated in the main urban area, while that of in the suburbs and rural areas did not change, or even increased;(3) The relationship between human mobility and case occurrence lies in two aspects: The correlation coefficient between daily human mobility and Rt, daily increased number of cases after an average incubation period (7 d) were 0.98, 0.87, revealing the time correlation between human mobility and case growth;The correlation coefficient between total amount of human mobility and total number of cases, number of local cases in each street (township) were 0.40, 0.35, revealing the correlation between human mobility and spatial distribution of cases. The cases clustering area corresponds to the network community of human mobility, revealing the local clustering transmission is the major transmission model. By aggregating the big data and the epidemic data, we suggests that cutting off the connection between different human mobility network communities and blocking the local transmission inside the high risk communities is an effective measure for the prevention and control of epidemics in cities. 2021, Science Press. All right reserved.

20.
Ieee Transactions on Engineering Management ; : 13, 2021.
Article in English | Web of Science | ID: covidwho-1583754

ABSTRACT

In the big data era, managing data-driven hospital operations have become one of the most important tasks for healthcare executives, increasing responsiveness to exceptional disruptions such as those caused by the COVID-19 pandemic. However, they are still facing the challenges of how best to orchestrate the digital medical resources for improving operational performance such as cost, delivery, and quality. Therefore, drawing upon resource orchestration theory, this article investigates how hospitals orchestrate data-driven culture (DDC) and digital technology orientation (DTO) to develop big data analytics capability (BDAC) for operational performance improvement. Survey data were collected from 105 hospitals in China and analyzed using structural equation modeling and ordinary least square regression. The results show that DDC has a significant positive impact on DTO. More interestingly, there is no significant interaction effect between DDC and DTO, indicating that DDC and DTO affect BDAC independently, and not synergistically. The results further reveal that BDAC fully mediates the DTO-operational performance relationship. The findings offer useful and timely guidance on how healthcare executives can manage data-driven hospital operations to improve operational performance during and post the COVID-19 pandemic.

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