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1.
Int J Environ Res Public Health ; 18(24)2021 12 17.
Article in English | MEDLINE | ID: covidwho-1599599

ABSTRACT

Foodborne disease events (FDEs) endanger residents' health around the world, including China. Most countries have formulated food safety regulation policies, but the effects of governmental intervention (GI) on FDEs are still unclear. So, this paper purposes to explore the effects of GI on FDEs by using Chinese provincial panel data from 2011 to 2019. The results show that: (i) GI has a significant negative impact on FDEs. Ceteris paribus, FDEs decreased by 1.3% when government expenditure on FDEs increased by 1%. (ii) By strengthening food safety standards and guiding enterprises to offer safer food, government can further improve FDEs. (iii) However, GI has a strong negative externality. Although GI alleviates FDEs in local areas, it aggravates FDEs in other areas. (iv) Compared with the eastern and coastal areas, the effects of GI on FDEs in the central, western, and inland areas are more significant. GI is conducive to ensuring Chinese health and equity. Policymakers should pay attention to two tasks in food safety regulation. Firstly, they should continue to strengthen GI in food safety issues, enhance food safety certification, and strive to ensure food safety. Secondly, they should reinforce the co-governance of regional food safety issues and reduce the negative externality of GI.


Subject(s)
Foodborne Diseases , China/epidemiology , Food Safety , Foodborne Diseases/epidemiology , Foodborne Diseases/prevention & control , Government , Humans
2.
Teaching Statistics ; : 1, 2021.
Article in English | Academic Search Complete | ID: covidwho-1546412

ABSTRACT

The objective of this study is to present and discuss how data visualization can be incorporated into teaching approaches by business faculty in introductory business statistics to strengthen business students' practical skills. Data visualization lessens difficulties in learning statistics by providing opportunities to illustrate analytical findings in graphic form, which is essential for learners with different learning styles. Familiarizing students with Excel, Python, or other software in introductory business statistics is beneficial in helping them attain statistical literacy by analyzing real‐world data such as COVID‐19 statistics. Using such data equips students with knowledge of statistical implementation—a core skill in the business world. [ FROM AUTHOR] Copyright of Teaching Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

3.
Nat Methods ; 18(12): 1477-1488, 2021 12.
Article in English | MEDLINE | ID: covidwho-1541247

ABSTRACT

Emergence of new viral agents is driven by evolution of interactions between viral proteins and host targets. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV-1 arose in part through rapid evolution along the interface between the spike protein and its human receptor ACE2, leading to increased binding affinity. To facilitate broader exploration of how pathogen-host interactions might impact transmission and virulence in the ongoing COVID-19 pandemic, we performed state-of-the-art interface prediction followed by molecular docking to construct a three-dimensional structural interactome between SARS-CoV-2 and human. We additionally carried out downstream meta-analyses to investigate enrichment of sequence divergence between SARS-CoV-1 and SARS-CoV-2 or human population variants along viral-human protein-interaction interfaces, predict changes in binding affinity by these mutations/variants and further prioritize drug repurposing candidates predicted to competitively bind human targets. We believe this resource ( http://3D-SARS2.yulab.org ) will aid in development and testing of informed hypotheses for SARS-CoV-2 etiology and treatments.

4.
Mathematics ; 9(20):2622, 2021.
Article in English | ProQuest Central | ID: covidwho-1480858

ABSTRACT

In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

5.
J Med Internet Res ; 23(10): e27261, 2021 10 20.
Article in English | MEDLINE | ID: covidwho-1463396

ABSTRACT

BACKGROUND: Health care organizations (HCOs) adopt strategies (eg. physical distancing) to protect clinicians and patients in intensive care units (ICUs) during the COVID-19 pandemic. Many care activities physically performed before the COVID-19 pandemic have transitioned to virtual systems during the pandemic. These transitions can interfere with collaboration structures in the ICU, which may impact clinical outcomes. Understanding the differences can help HCOs identify challenges when transitioning physical collaboration to the virtual setting in the post-COVID-19 era. OBJECTIVE: This study aims to leverage network analysis to determine the changes in neonatal ICU (NICU) collaboration structures from the pre- to the intra-COVID-19 era. METHODS: In this retrospective study, we applied network analysis to the utilization of electronic health records (EHRs) of 712 critically ill neonates (pre-COVID-19, n=386; intra-COVID-19, n=326, excluding those with COVID-19) admitted to the NICU of Vanderbilt University Medical Center between September 1, 2019, and June 30, 2020, to assess collaboration between clinicians. We characterized pre-COVID-19 as the period of September-December 2019 and intra-COVID-19 as the period of March-June 2020. These 2 groups were compared using patients' clinical characteristics, including age, sex, race, length of stay (LOS), and discharge dispositions. We leveraged the clinicians' actions committed to the patients' EHRs to measure clinician-clinician connections. We characterized a collaboration relationship (tie) between 2 clinicians as actioning EHRs of the same patient within the same day. On defining collaboration relationship, we built pre- and intra-COVID-19 networks. We used 3 sociometric measurements, including eigenvector centrality, eccentricity, and betweenness, to quantify a clinician's leadership, collaboration difficulty, and broad skill sets in a network, respectively. We assessed the extent to which the eigenvector centrality, eccentricity, and betweenness of clinicians in the 2 networks are statistically different, using Mann-Whitney U tests (95% CI). RESULTS: Collaboration difficulty increased from the pre- to intra-COVID-19 periods (median eccentricity: 3 vs 4; P<.001). Nurses had reduced leadership (median eigenvector centrality: 0.183 vs 0.087; P<.001), and neonatologists with broader skill sets cared for more patients in the NICU structure during the pandemic (median betweenness centrality: 0.0001 vs 0.005; P<.001). The pre- and intra-COVID-19 patient groups shared similar distributions in sex (~0 difference), race (4% difference in White, and 3% difference in African American), LOS (interquartile range difference in 1.5 days), and discharge dispositions (~0 difference in home, 2% difference in expired, and 2% difference in others). There were no significant differences in the patient demographics and outcomes between the 2 groups. CONCLUSIONS: Management of NICU-admitted patients typically requires multidisciplinary care teams. Understanding collaboration structures can provide fine-grained evidence to potentially refine or optimize existing teamwork in the NICU.


Subject(s)
COVID-19 , Intensive Care Units, Neonatal , Humans , Infant, Newborn , Intensive Care Units , Pandemics , Retrospective Studies , SARS-CoV-2
6.
Front Psychol ; 12: 649180, 2021.
Article in English | MEDLINE | ID: covidwho-1156160

ABSTRACT

This study uses the Planned Risk Information Seeking Model (PRISM) to estimate the public's information seeking and avoidance intentions during the COVID-19 outbreak based on an online sample of 1031 Chinese adults and provides support for the applicability of PRISM framework in the situation of a novel high-level risk. The results indicate that information seeking is primarily directed by informational subjective norms (ISN) and perceived seeking control (PSC), while the main predictors of information avoidance include ISN and attitude toward seeking. Because ISN are the strongest predictor of both information seeking and avoidance, the way the public copes with COVID-19 information may be strongly affected by individuals' social environment. Furthermore, a significant relationship between risk perception and affective risk response is identified. Our results also indicate that people who perceive greater knowledge of COVID-19 are more likely to report greater knowledge insufficiency, which results in less information avoidance.

7.
JMIR Hum Factors ; 8(1): e25724, 2021 Mar 08.
Article in English | MEDLINE | ID: covidwho-1127926

ABSTRACT

BACKGROUND: Few intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety. OBJECTIVE: We aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19. METHODS: In this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics-eigencentrality and betweenness-to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non-COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs. RESULTS: HCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non-COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non-COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non-COVID-19 care (P<.001). Compared to HCWs in non-COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001). CONCLUSIONS: Network analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non-COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.

8.
Applied Soft Computing ; : 107118, 2021.
Article in English | ScienceDirect | ID: covidwho-1039282

ABSTRACT

Network teaching has been widely developed under the influence of COVID-19 pandemic to guarantee the implementation of teaching plans and protect the learning rights of students. Selecting a particular website for network teaching can directly affects the end users’ performance and promote the network teaching quality. Normally, e-learning website selection can be considered as a complex multi-criteria decision making (MCDM) problem, and experts’ evaluations over the performance of e-learning websites are often imprecise and fuzzy due to the subjective nature of human thinking. In this article, we propose a new integrated MCDM approach on the basis of linguistic hesitant fuzzy sets (LHFSs) and the TODIM (an acronym in Portuguese of interactive and multi-criteria decision making) method to evaluate and select the best e-learning website for network teaching. This introduced method deals with the linguistic assessments of experts based on the LHFSs, determines the weights of evaluation criteria with the best-worst method (BWM), and acquires the ranking of e-learning websites utilizing an extended TODIM method. The applicability and superiority of the presented linguistic hesitant fuzzy TODIM (LHF-TODIM) approach are demonstrated through a realistic e-learning website selection example. Results show that the LHF-TODIM model being proposed is more practical and effective for solving the e-learning website selection problem under vague and uncertain linguistic environment.

9.
J Chin Med Assoc ; 83(11): 997-1003, 2020 11.
Article in English | MEDLINE | ID: covidwho-915938

ABSTRACT

BACKGROUND: Ever since coronavirus disease 2019 (COVID-19) emerged in Wuhan, China, in December 2019, it has had a devastating effect on the world through exponential case growth and death tolls in at least 146 countries. Rapid response and timely modifications in the emergency department (ED) for infection control are paramount to maintaining basic medical services and preventing the spread of COVID-19. This study presents the unique measure of combining a fever screening station (FSS) and graded approach to isolation and testing in a Taiwanese medical center. METHODS: An FSS was immediately set up outside the ED on January 27, 2019. A graded approach was adopted to stratify patients into "high risk," "intermediate risk," and "undetermined risk" for both isolation and testing. RESULTS: A total of 3755 patients were screened at the FSS, with 80.3% visiting the ED from home, 70.9% having no travel history, 21.4% having traveled to Asia, and 10.0% of TVGH staff. Further, 54.9% had fever, 35.5% had respiratory symptoms, 3.2% had gastrointestinal symptoms, 0.6% experienced loss of smell, and 3.1% had no symptoms; 81.3% were discharged, 18.6% admitted, and 0.1% died. About 1.9% were admitted to the intensive care unit, 10.3% to the general ward, and 6.4% were isolated. Two patients tested positive for COVID-19 (0.1%) and 127 (3.4%) tested positive for atypical infection; 1471 patients were tested for COVID-19; 583 were stratified as high-risk, 781 as intermediate-risk, and 107 as undetermined-risk patients. CONCLUSION: Rapid response for infection control is a paramount in the ED to confront the COVID-19 outbreak. The FFS helped divide the flow of high- and intermediate-risk patients; it also decreased the ED workload during a surge of febrile patients. A graded approach to testing uses risk stratification to prevent nosocomial infection of asymptomatic patients. A graded approach to isolation enables efficient allocation of scarce medical resources according to risk stratification.


Subject(s)
Betacoronavirus , Coronavirus Infections/prevention & control , Emergency Service, Hospital , Fever/diagnosis , Pandemics/prevention & control , Patient Isolation , Pneumonia, Viral/prevention & control , Adult , Aged , COVID-19 , Coronavirus Infections/diagnosis , Disease Outbreaks , Humans , Middle Aged , Pneumonia, Viral/diagnosis , Retrospective Studies , SARS-CoV-2
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