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1.
Mobile Networks and Applications ; 27(2):822-835, 2022.
Article in English | ProQuest Central | ID: covidwho-1899245

ABSTRACT

Given the complexity and uncertainty of the current COVID-19 risks, the elderly people in long-term care facilities are at the highest risk for infection. In order to study the prevention and control strategies of COVID-19 risks in long-term care facilities, this paper uses the prospect theory to construct the decision-making model of COVID-19 risk behavior of long-term care facilities, analyses the risk behavior strategies of the caregivers and managers, and reveals the impact of risk management cost, risk loss and external supervision on the risk behavior decision-making of the caregivers and managers. Furthermore, from the perspective of long-term care facilities, this paper analyzes the constraints that enable it to achieve optimal risk management strategy. Combined with the simulation analysis, it is found that the decision of risk behavior of the caregivers and managers is positively related to the risk behavior choice, risk loss, and supervision. Then, only when the incentives set by the supervision are within a reasonable range can the caregivers and managers be motivated to take proactive risk management strategies. The study has important theoretical and practical significance for the management of COVID-19 risks in long-term care facilities.

2.
BioMed research international ; 2022, 2022.
Article in English | EuropePMC | ID: covidwho-1897669

ABSTRACT

COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19.

3.
Ann Transl Med ; 10(10): 574, 2022 May.
Article in English | MEDLINE | ID: covidwho-1887396

ABSTRACT

Background: Little is known about the change in characteristics of fever-clinic visits during the coronavirus disease 2019 (COVID-19) pandemic. We sought to examine the changes in the volume, characteristics, and outcomes of patients presenting at a fever clinic duringclinic during the first-level response to COVID-19. Methods: We conducted a single tertiary-center retrospective case-control study. We included consecutive patients aged 14 years or older who visited the fever clinic of a tertiary hospital during the period of the first-level response to the COVID-19 pandemic in Fuzhou, China (from 24 January to 26 February 2020). We also analyzed the data of patients in the same period of 2019 as a control. We compared a number of outcome measures, including the fever clinic volumes, consultation length, proportion of patients with pneumonia, hospital admission rate, and in-hospital mortality, using the fever-clinic visit data during the two periods. Results: We included 1,013 participants [median age: 35; interquartile range (IQR): 27-50, 48.7% male] in this retrospective study, including 707 in 2020 and 306 in 2019. The median daily number of participants who presented at the fever clinic in 2020 was significantly higher than that in 2019 [18 (IQR: 15-22) vs. 13 (IQR: 8-17), P=0.001]. Participants in 2020 had a longer consultation length than those in 2019 [127 (IQR: 51-204) vs. 20 (IQR: 1-60) min, P<0.001]. Participants in 2020 were also more likely to be diagnosed with acute pneumonia than those in 2019 [168 (23.8%) vs. 40 (13.1%), P<0.001]. The hospital admission rate in 2020 was higher than in 2019 [73 (10.3%) vs. 13 (4.2%), P=0.001]. No significant difference was found in terms of the in-hospital mortality of participants in 2020 and 2019 [8 (1.1%) vs. 0, P=0.114]. Conclusions: Our findings suggest a higher visits volume, proportion of acute pneumonia, and hospital admission rate among patients presenting at fever clinic during the COVID-19 pandemic. Improved measures need to be implemented.

4.
Front Cell Infect Microbiol ; 12: 862656, 2022.
Article in English | MEDLINE | ID: covidwho-1875399

ABSTRACT

Objectives: To assess humoral and cellular immune responses against SARS-CoV-2 variants in COVID-19 convalescent and confirmed patients, to explore the correlation between disease severity, humoral immunity, and cytokines/chemokines in confirmed patients, and to evaluate the ADE risk of SARS-CoV-2. Methods: Anti-RBD IgG were quantified using an ELISA. Neutralization potency was measured using pseudovirus and real virus. Cellular immunity was measured using ELISpot. Cytokine/chemokine levels were detected using multiplex immunoassays. In vitro ADE assays were performed using Raji cells. Results: One-month alpha convalescents exhibited spike-specific antibodies and T cells for alpha and delta variants. Notably, the RBD-specific IgG towards the delta variant decreased by 2.5-fold compared to the alpha variant. Besides, serum from individuals recently experienced COVID-19 showed suboptimal neutralizing activity against the delta and omicron variants. Humoral immune response, IL-6, IP-10 and MCP-1 levels were greater in patients with severe disease. Moreover, neither SARS-CoV-1 nor SARS-CoV-2 convalescent sera significantly enhanced SARS-CoV-2 pseudovirus infection. Conclusions: Significant resistance of the delta and omicron variants to the humoral immune response generated by individuals who recently experienced COVID-19. Furthermore, there was a significant correlation among disease severity, humoral immune response, and specific cytokines/chemokine levels. No evident ADE was observed for SARS-CoV-2.


Subject(s)
COVID-19 , Cytokines , Immunity, Cellular , Immunity, Humoral , SARS-CoV-2 , COVID-19/immunology , Cytokines/immunology , Humans , Immunoglobulin G , Severity of Illness Index
5.
Appl Soft Comput ; 123: 108973, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1859328

ABSTRACT

COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. The spread and the death rates of the virus displayed dramatic differences among countries globally, showing that there are several factors affecting its spread and mortality. By utilizing the cumulative number of cases from John Hopkins University, the recovery rate, death rate, and the number of active, recovered, and death cases were simulated to analyse the trends and patterns within the chosen countries. 10 countries from 3 different case severity categories (high cases, medium cases, and low cases) and 5 continents (Asia, North America, South America, Europe, and Oceania) were studied. A generalized SEIR model which considers control measures such as isolation, and preventive measures such as vaccination is applied in this study. This model is able to capture not only the dynamics between the states, but also the time evolution of the states by using the fourth-order-Runge-Kutta process. This study found no significant patterns in the countries under the same case severity category, suggesting that there are other factors contributing to the pattern in these countries. One of the factors influencing the pattern in each country is the population's age. COVID-19 related deaths were found to be notably higher among older people, indicating that countries comprising of a larger proportion of older age groups have an increased risk of experiencing higher death rates. Tighter governmental control measures led to fewer infections and eventually reduced the number of death cases, while increasing the recovery rate, and early implementations were found to be far more effective in controlling the spread of the virus and produced better outcomes.

6.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-336878

ABSTRACT

The global emergence of SARS-CoV-2 variants has led to increasing breakthrough infections in vaccinated populations, calling for an urgent need to develop more effective and broad-spectrum vaccines to combat COVID-19. Here we report the preclinical development of RQ3013, an mRNA vaccine candidate intended to bring broad protection against SARS-CoV-2 variants of concern (VOCs). RQ3013, which contains pseudouridine-modified mRNAs formulated in lipid nanoparticles, encodes the spike(S) protein harboring a combination of mutations responsible for immune evasion of VOCs. Here we characterized the expressed S immunogen and evaluated the immunogenicity, efficacy, and safety of RQ3013 in various animal models. RQ3013 elicited robust immune responses in mice, hamsters, and nonhuman primates (NHP). It can induce high titers of antibodies with broad cross-neutralizing ability against the Wild-type, B.1.1.7, B.1.351, B.1.617.2, and the omicron B.1.1.529 variants. In mice and NHP, two doses of RQ3013 protected the upper and lower respiratory tract against infection by SARS-CoV-2 and its variants. We also proved the safety of RQ3013 in NHP models. Our results provided key support for the evaluation of RQ3013 in clinical trials.

7.
Applied soft computing ; 2022.
Article in English | EuropePMC | ID: covidwho-1837936

ABSTRACT

COVID-19 is a highly contagious disease that has infected over 136 million people worldwide with over 2.9 million deaths as of 11 April 2021. In March 2020, the WHO declared COVID-19 as a pandemic and countries began to implement measures to control the spread of the virus. The spread and the death rates of the virus displayed dramatic differences among countries globally, showing that there are several factors affecting its spread and mortality. By utilizing the cumulative number of cases from John Hopkins University, the recovery rate, death rate, and the number of active, recovered, and death cases were simulated to analyze the trends and patterns within the chosen countries. 10 countries from 3 different case severity categories (high cases, medium cases, and low cases) and 5 continents (Asia, North America, South America, Europe, and Oceania) were studied. A generalized SEIR model which considers control measures such as isolation, and preventive measures such as vaccination is applied in this study. This model is able to capture not only the dynamics between the states, but also the time evolution of the states by using the fourth-order-Runge–Kutta process. This study found no significant patterns in the countries under the same case severity category, suggesting that there are other factors contributing to the pattern in these countries. One of the factors influencing the pattern in each country is the population’s age. COVID-19 related deaths were found to be notably higher among older people, indicating that countries comprising of a larger proportion of older age groups have an increased risk of experiencing higher death rates. Tighter governmental control measures led to fewer infections and eventually reduced the number of death cases, while increasing the recovery rate, and early implementations were found to be far more effective in controlling the spread of the virus and produced better outcomes.

8.
Biosens Bioelectron ; 212: 114340, 2022 09 15.
Article in English | MEDLINE | ID: covidwho-1819434
9.
Preprint in English | medRxiv | ID: ppmedrxiv-22273759

ABSTRACT

The COVID-19 pandemic has impacted communities far and wide and put tremendous pressure on healthcare systems of countries across the globe. Understanding and monitoring the major influences on COVID-19 prevalence is essential to inform policy making and device appropriate packages of non-pharmaceutical interventions (NPIs). This study evaluates community level influences on COVID-19 incidence in England and their variations over time with specific focus on understanding the impact of working in so called high-risk industries such as care homes and warehouses. Analysis at community level allows accounting for interrelations between socioeconomic and demographic profile, land use, and mobility patterns including residents self-selection and spatial sorting (where residents choose their residential locations based on their travel attitudes and preferences or social structure and inequality); this also helps understand the impact of policy interventions on distinct communities and areas given potential variations in their mobility, vaccination rates, behavioural responses, and health inequalities. Moreover, community level analysis can feed into more detailed epidemiological and individual models through tailoring and directing policy questions for further investigation. We have assembled a large set of static (socioeconomic and demographic profile and land use characteristics) and dynamic (mobility indicators, COVID-19 cases and COVID-19 vaccination uptake in real time) data for small area statistical geographies (Lower Layer Super Output Areas, LSOA) in England making the dataset, arguably, the most comprehensive set assembled in the UK for community level analysis of COVID-19 infection. The data are integrated from a wider range of sources including telecommunications companies, test and trace data, national travel survey, Census and Mid-Year estimates. To tackle methodological challenges specifically accounting for highly interrelated influences, we have augmented different statistical and machine learning techniques. We have adopted a two-stage modelling framework: a) Latent Cluster Analysis (LCA) to classify the country into distinct land use and travel patterns, and b) multivariate linear regression to evaluate influences at each distinct travel cluster. We have also segmented our data into different time periods based on changes in policies and evolvement in the course of pandemic (such as the emergence of a new variant of the virus). By segmenting and comparing influences across spaces and time, we examine more homogeneous behaviour and uniform distribution of infection risks which in turn increase the potential to make causal inferences and help understand variations across communities and over time. Our findings suggest that there exist significant spatial variations in risk influences with some being more consistent and persistent over time. Specifically, the analysis of industrial sectors shows that communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries tend to carry a higher risk of infection across all spatial clusters and over the whole period we modelled in this study. This demonstrates the key role that workplace risk has to play in COVID-19 risk of outbreak after accounting for the characteristics of workers residential area (including socioeconomic and demographic profile and land use features), vaccination rate, and mobility patterns.

11.
Iran J Public Health ; 50(7): 1483-1485, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1761435
12.
PLoS ONE Vol 16(5), 2021, ArtID e0250770 ; 16(5), 2021.
Article in English | APA PsycInfo | ID: covidwho-1756155

ABSTRACT

Background & aim: The coronavirus disease 2019 (COVID-19) pandemic has affected the life and work of people worldwide. The present study aimed to evaluate the rhythm disruptions of life, work, and entertainment, and their associations with the psychological impacts during the initial phase of the COVID-19 pandemic. Method: A cross-sectional study was conducted from the 10th to 17th March 2020 in China. A structured e-questionnaire containing general information, the Chinese version of Brief Social Rhythm Scale, and Zung's self-rating scales of depression and anxiety (SDS and SAS) was posted and collected online through a public media (i.e. EQxiu online questionnaire platform). Scores in sleeping, getting up, and socializing (SGS) rhythm and eating, physical practice, and entertainment (EPE) rhythm were compared among and between participants with different sociodemographic backgrounds including gender, age, education, current occupation, annual income, health status, and chronic disease status. Correlations of SDS and SAS with SGS-scale and EPE-scale were also analyzed. Results: Overall, 5854 participants were included. There were significant differences in the scores of SGS-scale and EPE-scale among people with different sociodemographic backgrounds. The scores were significantly higher in the groups with female gender, low education level, lower or higher than average income, poor health status, ages of 26-30 years or older than 61 years, nurses and subjects with divorce or widow status. There were also significant differences in SAS and SDS scores among people with different sociodemographic backgrounds (all P< 0.05). The overall prevalence of depression and anxiety was 24.3% and 12.6%, respectively, with nurses having the highest rates of depression (32.94%) and anxiety (18.98%) among the different occupational groups. SGS-scale was moderately correlated with SDS and SAS, and disruption of SGS rhythm was an independent risk factor for depression and anxiety. Conclusion: Social rhythm disruption was independently associated with depression and anxiety. Interventions should be applied to people vulnerable to the rhythm disruption during the COVID-19 pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

13.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329972

ABSTRACT

Background: The classic prescription Chaihu Shugan Powder (CHSGP) has been widely used in clinical Chinese medicine treatment and has clear clinical effects in the treatment of emotional diseases. Based on the increasing incidence of emotional diseases such as insomnia and depression in the population during the COVID-19 pandemic, we will explore the mechanism of CHSGP in the treatment of insomnia and depression with “Same Treatment for Different Diseases”. Methods: : Using a bioinformatics and network pharmacology platform, protein database and STRING database, we collected CHSGP chemical composition and related target data and constructed a "component-target" action network through Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis. Molecular docking technology was used to verify key active ingredients and core targets. Results: : A total of 119 active compounds of CHSGP were screened, such as quercetin, kaempferol, and β-sitosterol, and 113 common related targets overlapped with insomnia and depression. GO enrichment and KEGG pathway analysis mainly involved immune, inflammation, cell proliferation, apoptosis, endocrine and other related targets and signaling pathways. Molecular docking showed that small molecular compounds (kaempferol, luteolin, quercetin, 7-methoxy-2-methyl isoflavone and beta-sitosterol) had good binding effects with five target proteins (AKT1, IL1B, IL-6, FOS, GSK3B) to play a role in regulating immunity, the inflammatory response, cell proliferation, apoptosis, and endocrine signaling. Conclusions: : Under the context of the COVID-19 pandemic, it revealed the complex mechanism of multicomponent, multitarget, and multipathway of the classic CHSGP for insomnia and depression, laying a theoretical foundation for its clinical application of its "same treatment for different diseases".

14.
J Clin Med ; 11(5)2022 Mar 05.
Article in English | MEDLINE | ID: covidwho-1732086

ABSTRACT

During the coronavirus disease (COVID-19) pandemic, we admitted suspected or confirmed COVID-19 patients to our isolation wards between 2 March 2020 and 4 May 2020, following a well-designed and efficient assessment protocol. We included 217 patients suspected of COVID-19, of which 27 had confirmed COVID-19. The clinical characteristics of these patients were used to train artificial intelligence (AI) models such as support vector machine (SVM), decision tree, random forest, and artificial neural network for diagnosing COVID-19. When analyzing the performance of the models, SVM showed the highest sensitivity (SVM vs. decision tree vs. random forest vs. artificial neural network: 100% vs. 42.86% vs. 28.57% vs. 71.43%), while decision tree and random forest had the highest specificity (SVM vs. decision tree vs. random forest vs. artificial neural network: 88.37% vs. 100% vs. 100% vs. 94.74%) in the diagnosis of COVID-19. With the aid of AI models, physicians may identify COVID-19 patients earlier, even with few baseline data available, and segregate infected patients earlier to avoid hospital cluster infections and to ensure the safety of medical professionals and ordinary patients in the hospital.

15.
Sustainability ; 14(4):2134, 2022.
Article in English | ProQuest Central | ID: covidwho-1715686

ABSTRACT

According to the United Nations, the epidemic has exacerbated poverty and weakened our ability to respond to long-term sustainability challenges. [...]SiC sludge qualifies as a potentially useful ingredient in the production of geopolymers containing metakaolin. [...]the hybrid material passed the burning test and demonstrated outstanding flame-retardant properties. [...]because of its thermal performance, BOFS offers a wide range of potential benefits in pavements, particularly for the purpose of achieving the goal of urban heat island mitigation by radiation cooling.

16.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-329211

ABSTRACT

Numerous mutations in the spike protein of SARS-CoV-2 B.1.1.529 Omicron variant pose a crisis for antibody-based immunotherapies. The efficacy of emergency use authorized (EUA) antibodies that developed in early SARS-CoV-2 pandemic seems to be in flounder. In this work, we examined the Omicron variant neutralization using an early B cell antibody repertoire as well as several EUA antibodies in pseudovirus and authentic virus systems. More than half of the antibodies in the repertoire that showed good activity against WA1/2020 previously had completely lost neutralizing activity against Omicron, while antibody 8G3 from our early B cell repertoire displayed non-regressive activity. EUA antibodies Etesevimab, Casirivimab, Imdevimab and Bamlanivimab neutralized authentic WA1/2020 virus with low half maximal inhibitory concentration (IC50) values, but were entirely desensitized by Omicron. Only Sotrovimab targeting the non-ACE2 overlap epitope showed activity but with a dramatic decrease. Interestingly, antibody 8G3 efficiently neutralized Omicron in pseudovirus and authentic virus systems. 8G3 also showed excellent activity against other variants of concern (VOCs). Collectively, our results suggest that neutralizing antibodies with breadth remains broad neutralizing activity in tackling SARS-CoV-2 infection despite the universal evasion from EUA antibodies by Omicron variant.

17.
Applied Intelligence ; : 1-18, 2022.
Article in English | EuropePMC | ID: covidwho-1695769

ABSTRACT

Objective: The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis. Methods: Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the “BiLSTM+Dilated Convolution+3D Attention+CRF” deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular “Bert+BiLSTM+CRF” approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases. Results: The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy. Conclusion: Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.

18.
Frontiers in cellular and infection microbiology ; 12, 2022.
Article in English | EuropePMC | ID: covidwho-1695322

ABSTRACT

Coronaviruses are viruses whose particles look like crowns. SARS-CoV-2 is the seventh member of the human coronavirus family to cause COVID-19 which is regarded as a once-in-a-century pandemic worldwide. It holds has the characteristics of a pandemic, which has broy -55ught many serious negative impacts to human beings. It may take time for humans to fight the pandemic. In addition to humans, SARS-CoV-2 also infects animals such as cats. This review introduces the origins, structures, pathogenic mechanisms, characteristics of transmission, detection and diagnosis, evolution and variation of SARS-CoV-2. We summarized the clinical characteristics, the strategies for treatment and prevention of COVID-19, and analyzed the problems and challenges we face.

19.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-328833

ABSTRACT

Background: The classic prescription Chaihu Shugan Powder (CHSGP) has been widely used in clinical Chinese medicine treatment and has clear clinical effects in the treatment of emotional diseases. Based on the increasing incidence of emotional diseases such as insomnia and depression in the population during the COVID-19 pandemic, we will explore the mechanism of CHSGP in the treatment of insomnia and depression with “Same Treatment for Different Diseases”. Methods: : Using a bioinformatics and network pharmacology platform, protein database and STRING database, we collected CHSGP chemical composition and related target data and constructed a "component-target" action network through Gene Ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analysis. Molecular docking technology was used to verify key active ingredients and core targets. Results: : A total of 119 active compounds of CHSGP were screened, such as quercetin, kaempferol, and β-sitosterol, and 113 common related targets overlapped with insomnia and depression. GO enrichment and KEGG pathway analysis mainly involved immune, inflammation, cell proliferation, apoptosis, endocrine and other related targets and signaling pathways. Molecular docking showed that small molecular compounds (kaempferol, luteolin, quercetin, 7-methoxy-2-methyl isoflavone and beta-sitosterol) had good binding effects with five target proteins (AKT1, IL1B, IL-6, FOS, GSK3B) to play a role in regulating immunity, the inflammatory response, cell proliferation, apoptosis, and endocrine signaling. Conclusions: : Under the context of the COVID-19 pandemic, it revealed the complex mechanism of multicomponent, multitarget, and multipathway of the classic CHSGP for insomnia and depression, laying a theoretical foundation for its clinical application of its "same treatment for different diseases".

20.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325121

ABSTRACT

Early diagnosis and isolation of cases are particularly crucial for coronavirus disease 2019 (COVID-19) in global pandemic. The aim of this study is to determine the diagnostic performance of chest computed tomography (CT) and imaging features for diagnosing COVID-19. Diagnostic accuracy studies of CT and RT-PCR in patients with clinically suspected COVID-19, which were published up to April 25th, 2020 from MEDLINE, EMBASE, and the Cochrane Library. Twelve studies (n=2,204) were included. The pooled sensitivity, specificity, likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of chest CT for detecting COVID-19 were 94.5% (95% confidence interval (CI) 89.5 to 97.2%) and 41.8% (95% CI 24.2 to 61.6%), 1.6 (95% CI: 1.6-2.3), 0.13 (95% CI: 0.06-0.31), and 12.4 (95% CI: 4.0-38.5), respectively. Initial RT-PCR revealed a better diagnostic performance. Peripheral lesions, bilateral involvement, multiple lesions, and ground-glass opacities (GGO), revealed to be with better diagnostic value than other CT manifestations. Using chest CT for COVID-19 diagnosis has a high sensitivity and a relatively low specificity. Bilateral multiple peripheral lesions and GGO revealed to be with better diagnostic value. For areas with high prevalence, chest CT could be a good screening test to preliminary screen patients with COVID-19 quickly.

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