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1.
Front Public Health ; 10: 843787, 2022.
Article in English | MEDLINE | ID: covidwho-1903205

ABSTRACT

Objective: Risk communication and the degree of trust are major factors that affect the public's behavioral coping strategies and play an important role in emergency risk management. However, the internal formation mechanism involved in the public's psychological behavior remains unclear. This study aimed to investigate the association among risk communication, trust, risk perception, negative emotions, and behavioral coping strategies during the coronavirus disease 2019 (COVID-19) pandemic, and to identify and quantify the factors that influence public behavior. Methods: We launched an online survey through social media from April to July 2020 in China. Relevant data were elicited using a self-designed questionnaire that mainly examined respondent characteristics, risk communication, trust, risk perception, negative emotions, protective coping behavior, and excessive coping behavior in the context of the COVID-19 pandemic. A total of 735 valid responses were obtained. A structural equation model was then used to explore relationship pathways among the components. Results: The higher the degree of risk communication (ß = -0.10, p < 0.05) and trust (ß = -0.22, p < 0.001), the lower the public risk perception. Risk communication and trust had a direct effect on public behavioral coping strategies during the COVID-19 pandemic. The higher the level of risk communication (ß = 0.14, p < 0.001) or trust (ß = 0.48, p < 0.001), the more likely it was that this would encourage the public to adopt protective coping behaviors, while the public was less likely to engage in excessive coping behaviors as the degree of trust increased (ß = -0.12, p < 0.01). Risk perception influenced by poor risk communication and trust generated negative emotions (ß = 0.31, p < 0.001), and such negative emotions further positively influenced public behavioral coping strategies (whether protective [ß = 0.09, p < 0.05] or excessive [ß = 0.24, p < 0.001] behaviors). Conclusion: Risk communication, trust, risk perception, and negative emotions were significantly directly or indirectly related to public behavior. The findings provide useful information for emergency risk management and a theoretical basis for follow-up research on public coping behavior during the COVID-19 pandemic.


Subject(s)
COVID-19 , Pandemics , Adaptation, Psychological , COVID-19/epidemiology , China/epidemiology , Communication , Emotions , Humans , Perception , Trust
2.
Complex Intell Systems ; 8(2): 1369-1387, 2022.
Article in English | MEDLINE | ID: covidwho-1827540

ABSTRACT

The outbreak of COVID-19 has greatly threatened global public health and produced social problems, which includes relative online collective actions. Based on the life cycle law, focusing on the life cycle process of COVID-19 online collective actions, we carried out both macro-level analysis (big data mining) and micro-level behaviors (Agent-Based Modeling) on pandemic-related online collective actions. We collected 138 related online events with macro-level big data characteristics, and used Agent-Based Modeling to capture micro-level individual behaviors of netizens. We set two kinds of movable agents, Hots (events) and Netizens (individuals), which behave smartly and autonomously. Based on multiple simulations and parametric traversal, we obtained the optimal parameter solution. Under the optimal solutions, we repeated simulations by ten times, and took the mean values as robust outcomes. Simulation outcomes well match the real big data of life cycle trends, and validity and robustness can be achieved. According to multiple criteria (spans, peaks, ratios, and distributions), the fitness between simulations and real big data has been substantially supported. Therefore, our Agent-Based Modeling well grasps the micro-level mechanisms of real-world individuals (netizens), based on which we can predict individual behaviors of netizens and big data trends of specific online events. Based on our model, it is feasible to model, calculate, and even predict evolutionary dynamics and life cycles trends of online collective actions. It facilitates public administrations and social governance.

5.
BMC Public Health ; 21(1): 2248, 2021 12 11.
Article in English | MEDLINE | ID: covidwho-1566519

ABSTRACT

BACKGROUND: Since the outbreak started in 2019, COVID-19 pandemic has a significant global impact. Due to the highly infective nature of SARS-CoV-2, the COVID-19 close contacts are at significant risk of contracting COVID-19. China's experience in successfully controlling COVID-19 emphasized the importance of managing close contacts because this strategy helps to limit potential infection sources, prevent the unconscious spread of COVID-19 and thus control this pandemic. As a result, to understand and consider the management of close contacts may be beneficial to other countries. However, managing close contacts is challenging owing to the huge number of close contacts and a lack of appropriate management tools and literature references. METHODS: A new system called the COVID-19 Close Contact Information Management System was developed. Here we introduced the design, use, improvement and achievements of this system. RESULTS: This system was designed from the standpoint of the Centers for Disease Control and Prevention in charge of managing close contacts. Two main functions and eight modules/themes were ultimately formed after two development stages. The system introduces what information need to be collected in the close contact management. Since the system allows information flow across cities, the geographical distance and administrative regional boundaries are no longer obstacles for managing close contacts, which promotes the management of each close contact. Moreover, when this system is used in conjunction with other data tools, it provides data assistance for understanding the COVID-19 characteristics and formulating targeted COVID-19 control policies. To date, the system has been widely used in Guangdong Province for over 1 year and has recorded tens of thousands of pieces of data. There is sufficient practical experience to suggest that the system is capable of meeting the professional work requirements for close contact management. CONCLUSIONS: This system provides a new way to manage close contacts and restrict the spread of COVID-19 by combining information technology with disease prevention and control strategies in the realm of public health. We hope that this system will serve as an example and guide for those anticipating similar work in other countries in response to current and future public health incidents.


Subject(s)
COVID-19 , Humans , Information Management , Organizations , Pandemics/prevention & control , SARS-CoV-2 , United States
6.
Bioengineered ; 12(1): 4054-4069, 2021 12.
Article in English | MEDLINE | ID: covidwho-1348035

ABSTRACT

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = -0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan-Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , Carcinoma, Hepatocellular/genetics , Liver Cirrhosis/genetics , Liver Neoplasms/genetics , Neoplasm Proteins/genetics , Receptors, Virus/genetics , alpha-Fetoproteins/genetics , Age Factors , Aged , Angiotensin-Converting Enzyme 2/metabolism , Area Under Curve , COVID-19/virology , Carcinoma, Hepatocellular/diagnosis , Carcinoma, Hepatocellular/mortality , Carcinoma, Hepatocellular/pathology , Databases, Genetic , Datasets as Topic , Female , Gene Expression Regulation, Neoplastic , Humans , Liver Cirrhosis/diagnosis , Liver Cirrhosis/mortality , Liver Cirrhosis/pathology , Liver Neoplasms/diagnosis , Liver Neoplasms/mortality , Liver Neoplasms/pathology , Male , Middle Aged , Neoplasm Proteins/classification , Neoplasm Proteins/metabolism , Protective Factors , Protein Interaction Mapping , ROC Curve , Receptors, Virus/metabolism , SARS-CoV-2/pathogenicity , Survival Analysis , alpha-Fetoproteins/metabolism
7.
J Med Internet Res ; 23(1): e24619, 2021 01 21.
Article in English | MEDLINE | ID: covidwho-1041500

ABSTRACT

BACKGROUND: The COVID-19 outbreak has increased challenges associated with health management, especially cancer management. In an effort to provide continuous pharmaceutical care to cancer patients, Sun Yat-sen University Cancer Center (SYSUCC) implemented a remote pharmacy service platform based on its already existing web-based hospital app known as Cloud SYSUCC. OBJECTIVE: The aim of this study was to investigate the characteristics, acceptance, and initial impact of the Cloud SYSUCC app during a COVID-19 outbreak in a tertiary cancer hospital in China. METHODS: The total number of online prescriptions and detailed information on the service were obtained during the first 6 months after the remote service platform was successfully set up. The patients' gender, age, residence, primary diagnosis, drug classification, weekly number of prescriptions, and prescribed drugs were analyzed. In addition, a follow-up telephonic survey was conducted to evaluate patients' satisfaction in using the remote prescription service. RESULTS: A total of 1718 prescriptions, including 2022 drugs for 1212 patients, were delivered to 24 provinces and municipalities directly under the Central Government of China between February 12, 2020, and August 11, 2020. The majority of patients were female (841/1212, 69.39%), and 90.18% (1093/1212) of them were aged 31-70 years old. The top 3 primary diagnoses for which remote medical prescriptions were made included breast cancer (599/1212, 49.42%), liver cancer (249/1212, 20.54%), and thyroid cancer (125/1212, 10.31%). Of the 1718 prescriptions delivered, 1435 (83.5%) were sent to Guangdong Province and 283 (16.5%) were sent to other provinces in China. Of the 2022 drugs delivered, 1012 (50.05%) were hormonal drugs. The general trend in the use of the remote prescription service declined since the 10th week. A follow-up telephonic survey found that 88% (88/100) of the patients were very satisfied, and 12% (12/100) of the patients were somewhat satisfied with the remote pharmacy service platform. CONCLUSIONS: The remote pharmacy platform Cloud SYSUCC is efficient and convenient for providing continuous pharmaceutical care to patients with cancer during the COVID-19 crisis. The widespread use of this platform can help to reduce person-to-person transmission as well as infection risk for these patients. Further efforts are needed to improve the quality and acceptance of the Cloud SYSUCC platform, as well as to regulate and standardize the management of this novel service.


Subject(s)
COVID-19/epidemiology , Neoplasms/drug therapy , Patient Satisfaction , Pharmacy Service, Hospital/statistics & numerical data , SARS-CoV-2 , Telemedicine/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Child , China/epidemiology , Female , Humans , Middle Aged , Pandemics , Surveys and Questionnaires , Tertiary Care Centers , Young Adult
8.
Epidemiol Infect ; 148: e125, 2020 06 25.
Article in English | MEDLINE | ID: covidwho-615326

ABSTRACT

The clinical characteristics of patients with COVID-19 were analysed to determine the factors influencing the prognosis and virus shedding time to facilitate early detection of disease progression. Logistic regression analysis was used to explore the relationships among prognosis, clinical characteristics and laboratory indexes. The predictive value of this model was assessed with receiver operating characteristic curve analysis, calibration and internal validation. The viral shedding duration was calculated using the Kaplan-Meier method, and the prognostic factors were analysed by univariate log-rank analysis and the Cox proportional hazards model. A retrospective study was carried out with patients with COVID-19 in Tianjin, China. A total of 185 patients were included, 27 (14.59%) of whom were severely ill at the time of discharge and three (1.6%) of whom died. Our findings demonstrate that patients with an advanced age, diabetes, a low PaO2/FiO2 value and delayed treatment should be carefully monitored for disease progression to reduce the incidence of severe disease. Hypoproteinaemia and the fever duration warrant special attention. Timely interventions in symptomatic patients and a time from symptom onset to treatment <4 days can shorten the duration of viral shedding.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Virus Shedding/physiology , Adult , Analysis of Variance , COVID-19 , China , Coronavirus Infections/mortality , Coronavirus Infections/therapy , Coronavirus Infections/virology , Disease Progression , Female , Humans , Hypoproteinemia , Kaplan-Meier Estimate , Logistic Models , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Prognosis , Proportional Hazards Models , ROC Curve , Retrospective Studies , Risk Factors , SARS-CoV-2 , Time Factors
9.
Genes (Basel) ; 11(6)2020 06 09.
Article in English | MEDLINE | ID: covidwho-591861

ABSTRACT

The severe respiratory disease COVID-19 was initially reported in Wuhan, China, in December 2019, and spread into many provinces from Wuhan. The corresponding pathogen was soon identified as a novel coronavirus named SARS-CoV-2 (formerly, 2019-nCoV). As of 2 May, 2020, over 3 million COVID-19 cases had been confirmed, and 235,290 deaths had been reported globally, and the numbers are still increasing. It is important to understand the phylogenetic relationship between SARS-CoV-2 and known coronaviruses, and to identify its hosts for preventing the next round of emergency outbreak. In this study, we employ an effective alignment-free approach, the Natural Vector method, to analyze the phylogeny and classify the coronaviruses based on genomic and protein data. Our results show that SARS-CoV-2 is closely related to, but distinct from the SARS-CoV branch. By analyzing the genetic distances from the SARS-CoV-2 strain to the coronaviruses residing in animal hosts, we establish that the most possible transmission path originates from bats to pangolins to humans.


Subject(s)
Betacoronavirus/genetics , Coronavirus Infections/transmission , Coronavirus/genetics , Models, Biological , Pneumonia, Viral/transmission , Animals , Betacoronavirus/classification , COVID-19 , Chiroptera/virology , Coronavirus/classification , Coronavirus 3C Proteases , Coronavirus Infections/virology , Cysteine Endopeptidases/chemistry , Cysteine Endopeptidases/genetics , Disease Outbreaks , Disease Reservoirs , Humans , Mammals/classification , Mammals/virology , Pandemics , Phylogeny , Pneumonia, Viral/virology , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/genetics
10.
Int J Mol Sci ; 21(11)2020 May 29.
Article in English | MEDLINE | ID: covidwho-436912

ABSTRACT

Advances in sequencing technology have made large amounts of biological data available. Evolutionary analysis of data such as DNA sequences is highly important in biological studies. As alignment methods are ineffective for analyzing large-scale data due to their inherently high costs, alignment-free methods have recently attracted attention in the field of bioinformatics. In this paper, we introduce a new positional correlation natural vector (PCNV) method that involves converting a DNA sequence into an 18-dimensional numerical feature vector. Using frequency and position correlation to represent the nucleotide distribution, it is possible to obtain a PCNV for a DNA sequence. This new numerical vector design uses six suitable features to characterize the correlation among nucleotide positions in sequences. PCNV is also very easy to compute and can be used for rapid genome comparison. To test our novel method, we performed phylogenetic analysis with several viral and bacterial genome datasets with PCNV. For comparison, an alignment-based method, Bayesian inference, and two alignment-free methods, feature frequency profile and natural vector, were performed using the same datasets. We found that the PCNV technique is fast and accurate when used for phylogenetic analysis and classification of viruses and bacteria.


Subject(s)
Phylogeny , Sequence Analysis, DNA/methods , Sequence Homology, Nucleic Acid , Algorithms , Genome, Bacterial , Genome, Viral , Sequence Alignment
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