Prediction of the Epidemic Trend of Novel Coronavirus Pneumonia Based on SIR Model

This paper collects real-time epidemic data released by the World Health Organization and various Internet authorities, predict the development of the epidemic through the classical model (SIR model) in the field of communication disease, bring historical data into the model, verify the parameters of the model and establish a new model, compare multiple sets of data, obtain the system that is closest to the real data, and speculate on the development direction and turning point of the subsequent NEW CROWN epidemic. The use of scientific and technical means to reason and analyze the overall situation of the new crown epidemic situation provides a solid backing for the prevention and control of the epidemic. © 2022 IEEE.

Improvement of stress adaptation and insulin resistance in women with GDM by WeChat group management during novel coronavirus pneumonia.

Aim: To evaluate the improvement of glycemic control and stress adaptation in patients with GDM by mobile phone WeChat management during novel coronavirus pneumonia. Methods: In this study, 75 women with GDM were included, of whom 35 were included in mobile WeChat group management as the GDM-M group and 40 as the GDM group. Results: After mobile WeChat group management for 4 weeks, E and NE were lower. MDA was lower, and SOD was higher. HOMA-IR was lower. E, NE, and cortisol were related to HOMA-IR positively, MDA was positively related to HOMA-IR, and SOD was negatively related to HOMA-IR. E and cortisol were positively related to MDA but negatively related to SOD. Conclusion: The stress adaptation disorder and insulin resistance in patients with GDM who have completed mobile WeChat group management can be improved during novel coronavirus pneumonia. Mobile WeChat management played a positive role in improving the insulin resistance of women with GDM under special circumstances, which may reduce the risk of maternal and fetal complications.

Mechanism of Cold-Dampness Epidemic Prescription in Treatment of Novel Coronavirus Pneumonia Based on Network Pharmacology

Background:The emergence of novel coronavirus pneumonia has seriously affected people's normal life and health. Cold-dampness epidemic prescription has a good effect in the prevention and treatment of novel coronavirus pneumonia. Methods:TCMSP, PubChem, Swiss Target Prediction, PharmMapper database and related literatures were used to retrieve and predict the main chemical components and corresponding targets of Traditional Chinese Medicine (TCM)TCM. GeneCard, OMIM, NCBI and TTD databases were used to collect disease targets. Uniprot disease database was used to standardize target names. Cytoscape3.8.2 software was used to establish the 'active components-action target' network. Protein interaction (PPI) network was established by using protein interaction database (STRING), and core genes were screened by CytoNCA plug-in of Cytoscape3.8.2 software.GO enrichment analysis and KEGG pathway enrichment analysis were carried out through DAVID network database, and Hiplot network platform was used for visualization. Molecular docking technology was used to verify the docking between core components and targets. Results:After preliminary screening, 102 effective components, 255 potential targets and 2230 COVID-19 disease targets were obtained, and it was speculated that the mechanism might be related to 177 pathways such as TNF signaling pathway, IL-17 signaling pathway and AGE-RAGE signaling pathway in diabetic complications. The absolute values of docking binding energy between active components such as quercetin, luteolin and wogonin and targets such as PTGS2, AR, TP53 and CASP3 were greater than 5.0 Kcal/mol, and the docking results were good. Conclusion:Cold-dampness epidemic prescription has the characteristics of multiple components, multiple targets and multiple pathways in the prevention and treatment of COVID-19, and may play a therapeutic role through anti-inflammatory, antiviral and immune regulation. © 2022 IEEE.

Current status of the application of artificial intelligence technology in the prevention and control of novel infectious coronavirus pneumonia

Artificial intelligence (AI), a new branch of research in computer science, has been applied to a variety of fields in recent years and has received increasing attention from research scholars. Since the outbreak of the new epidemic, AI has played an extremely important role in the diagnosis of the epidemic, the development of drugs, and the mental health of patients. This paper summarises some of the current applications of AI technology during the epidemic, to contribute to the control of the epidemic and the development of AI technology. © 2022 ACM.

Research progress of traditional Chinese medicine in the prevention and treatment of COVID-19

The coronavirus disease 2019 (COVID-19) has become a global public health problem due to its highly contagious nature. This article aims to discuss the current situation of traditional Chinese medicine in the prevention and treatment of COVID-19, and to provide a basis for traditional Chinese medicine research and scientific and standardized treatment of COVID-19. In this article, the etiology, pathogenesis, treatment plan and research progress were summarized, analyzed and concluded by retrieving and reviewing the literature and clinical reports related to the prevention and treatment of COVID-19 with traditional Chinese medicine. Traditional Chinese medicine has obvious effects in the prevention and treatment of COVID-19, improvement of clinical symptoms, and control of disease progression, which had the unique advantages of mild curative efficacy and safety. It has important practical significance in relieving patients' early symptoms and reducing the incidence of progression from mild to severe, and had great potential for development in the treatment of COVID-19. The traditional Chinese medicine intervention and the formulation of diagnosis and treatment plans for the COVID-19 need to be continuously optimized and improved. Scientific and rational application of traditional Chinese medicine to prevent and treat COVID-19, optimization diagnosis and treatment programs, and in-depth exploration of pharmacological mechanisms, especially the provide reference for early intervention of new coronavirus pneumonia by traditional Chinese medicine, the control of disease progression in the middle stage, and improve prognosis in the late stage with Western medicine. © 2022 Editorial Office of Chinese Journal of Schistosomiasis Control. All rights reserved.

Clinical efficacy of combination therapy of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs in the treatment of novel coronavirus pneumonia: A prospective interventional study.

Introduction: The National Administration of Traditional Chinese Medicine of the People's Republic of China (NATCM) and the State Administration of Traditional Chinese medicine (TCM) advocated a combination therapy of TCM and anti-viral drugs for novel coronavirus pneumonia (NCP) to improve the efficacy of clinical treatment. Methods: Forty-six patients diagnosed with NCP were sequentially divided into intent-to-treat population: the experimental group (combination of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs; n = 23) and the control group (anti-viral drugs only) (n = 23). The two groups were compared in terms of duration of fever, cough symptom score, fatigue, appetite, dyspnea, out-of-bed activities, chest computer tomography (CT) recovery, virological clearance, average length of hospital stay, and clinical effective rate of drug. After 6 days of observation, patients from the control group were divided into as-treated population: experimental subgroup (n = 14) to obtain clinical benefit and control subgroup (n = 9). Results: There was a significant improvement in the duration of fever (1.087 ± 0.288 vs 4.304 ± 2.490), cough (0.437 ± 0.589 vs 2.435 ± 0.662; P < 0.05), chest CT evaluation (82.6% vs 43.4%; P < 0.05), and virological clearance (60.8% vs 8.7%; P < 0.05) in patients of the experimental group compared with patients in the control group. Further observation in as-treated population reported that cough (0.742 ± 0.463 vs 1.862 ± 0.347; P < 0.05) and fatigue (78.5% vs 33.3%; P < 0.05) were significantly relieved after adding FuXi-Tiandi-Wuxing Decoction to the existing treatment. Conclusion: An early treatment with combination therapy of FuXi-Tiandi-Wuxing Decoction and anti-viral drugs significantly relieves the clinical symptoms of NCP, shows improvement in chest CT scan, improves virological clearance, shortens average length of hospital stay, and reduces the risk of severe illness. The effect of FuXi-Tiandi-Wuxing Decoction in NCP may be clinically important and require further consideration.

Analysis of Public Emotion and Disseminator-Audience Framework during Outbreak of Public Crisis Events: A case study of Microblog Account Xiake Island

This study crawled the cross-sectional data of the contents and comments from Microblog Account Xiake Island during the outbreak of coronavirus pneumonia as subjects, to examine the deviation and resonance association among affective fluctuations of the Chinese public, media framework, and audiences' cognitive framework. Using SnowNLP to conduct sentiment analysis of text comments, we found that during the outbreak of coronavirus pneumonia, the public spent most of the time in low-intensity negative affectivity, and the average affective propensity in response to individual microblog fluctuated greatly, and the public was easily caught in an emotional frenzy, which reduces the level of trust in government. Through a comparison of public affectivity and related epidemic data, Xiake Island focuses on reporting emotional facts, whose construction of social reality contains obvious emotional trajectories. Clustering analysis of thematic framework by LDA algorithm reveals that in terms of framework, the framework Xiake Island uses resonates to a large degree with the framework users focus on. In terms of the level of concerns over the framework, Xiake Island deviates to a certain extent from the public. This deviation, together with the strategy of focusing on reporting emotional facts, is a discursive strategy adopted by the new mainstream media to seek the reconstruction of cultural leadership. © 2022 Owner/Author.

Regional disparities in Preventive measures of COVID-19 pandemic in China. A study from international students' prior knowledge, perception and vulnerabilities.

The COVID-19 pandemic needs immediate solution before inflicting more devastation. So far, China has successfully controlled transmission of COVID-19 through implementing stringent preventive measures. In this study, we analyze the effectiveness of preventive measures taken in thirteen regions of China based on the feedback provided by 1135 international students studying in China. The study uses factor analysis combined with varimax rotation of variables. It was found that awareness raising and dispersing actionable knowledge regarding trust and adapting measures remained significantly important. Therefore, recognition of information gaps, improvements in the level of alertness, and development of preventive measures in each sector are imperative. The findings of this study revealed that trust, students' health, waste disposal, and the efforts of the Chinese government/international institute of education to prevent this pandemic were significantly and positively associated with preventive measures. The results showed that prior knowledge, global pandemics, and food and grocery purchases were firmly related to the preventive measures of COVID-19. Moreover, anxiety, transportation, and economic status were negatively related to the preventive measures. During this epidemic situation, international students suffered various types of mental stresses and anxiety, especially living in most affected regions of China. The study adopted a mixed (qualitative and quantitative) approach where the findings can act as a set of guidelines for governmental authorities in formulating, assisting in the preparation, instructing, and guiding policies to prevent and control the epidemic COVID-19 at national, local, and divisional levels.

CT Imaging Features of Patients Infected with 2019 Novel Coronavirus

Novel coronavirus pneumonia is an acute, infectious pneumonia caused by a novel coronavirus infection. Computed tomographic (CT) imaging is one of the main methods to screen and diagnose patients with this disease. Here, the importance and clinical value of chest CT examination in the diagnosis of COVID-19 is expounded, and the pulmonary CT findings of COVID-19 patients in different stages are briefly summarized, thus providing a reference document for the CT diagnosis of COVID-19 patients. © 2021 The Authors.

Seroprevalence of severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies among health-care workers prior and after 4-6 weeks of coronavirus disease vaccine administration at tertiary care center of Southwest Bihar, India

AIM: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. To control the spread, a mass vaccination program is initiated. Antibody titer after vaccination can be a better marker to monitor immunological response. MATERIALS AND METHODS: The study was carried out at the Department of Microbiology, Narayan Medical College and Hospital, Jamuhar Sasaram, southwest Bihar, considering the sample size, type, and collection. First, antibody was tested before vaccination and second antibody value after 28 days of the first dose of COVID vaccine among the health-care workers and housekeeping staff. RESULTS: A total of 251 subjects were administered with vaccination (Covishield) to check the immunoglobulin g (IgG) responses. The concentration of the SARS-CoV-2 IgG antibody in female patients tended to be higher than in male patients. CONCLUSION: There is a difference in antibody positivity among males and females. Most of the participants had IgG positivity, because of their profession, vaccination boosted percentage positivity in both males and females. Females have more IgG levels compared to males. Hence, recommend that separate guidelines can be made between males and females for vaccination dosages. [ FROM AUTHOR] Copyright of Indian Journal of Health Sciences & Biomedical Research is the property of Wolters Kluwer India Pvt Ltd 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.)

CT Imaging Features and Clinical Characteristics of 2019 Novel Coronavirus Pneumonia (COVID-19) During Rehabilitation.

This study aims to explore the clinical characteristics of the patients with novel coronavirus pneumonia (COVID-19) during rehabilitation. One hundred and twelve confirmed patients were enrolled, while 72 were females (64.3%) and 40 were males (35.7%). The age of the patients was 51.63 ± 4.07 years old. Those patients were divided into mild group, moderate group and severe group based on lesion volume and proportion of total lesion on CT images. The age, gender, past medical history, finger pulse oxygen (SPO2), heart rate (HR) and body temperature and other clinical characteristics of patients were collected. Lesion volume was measured by CT. Compared with mild group, age, lesion volume and total lesion proportion in moderate group were significantly higher. Age, lesion volume and total lesion proportion in severe group were also higher than those in moderate group. Age and past medical history were the risk factors for the lesion volume of COVID-19. Older the patient has larger CT lesion range (R = 0.232, P = 0.045). Without past medical history or combination of post-medical history, the COVID-19 patients had smaller CT lesion ranges, and the history of previous cardiovascular disease and pulmonary disease was important risk factors for the larger CT lesion ranges. The patients who were older or combined with chronic diseases, especially cardiovascular diseases, respiratory disease and diabetes, tended to have the larger lesions. Age and past medical history of patients with COVID-19 period are significantly related to the lesion volume and total lesion proportion on CT images.

Use of rotational thromboelastometry to predict the outcome of COVID-19 patients: A retrospective observational study.

Background and Aims: The hypercoagulability occurring in COVID-19 patients is detected only by Rotational thromboelastometry (ROTEM). However, the benefit of performing ROTEM in the management of disease and predicting the outcome of COVID-19 patients is yet to be established. Material and Methods: The data of 23 critically ill and 11 stable COVID-19 adult patients were extracted from the hospital information system admitted between July and August 2020 and patient charts and analyzed retrospectively. The critically ill patients were divided as a survivor and non-survivor groups. The Intrinsic pathway part of ROTEM (INTEM) and Fibrinogen part of ROTEM (FIBTEM) were performed on day 0 for both critically ill and stable patients, and on day 10 for critically ill patients. The statistical package for social science (SPSS) version 26 was used for statistical analysis. Results: The median FIBTEM amplitude at 5 min (A5) and maximum clot firmness (MCF) were elevated in both stable and critically ill patients (24 vs 27 mm, P = 0.46 and 27.5 vs 40 mm, P = 0.011) with a significant difference in FIBTEM MCF. But there was no significant difference between number of survivors and non-survivors with FIBTEM MCF >25 at day 0 and day 10. Conclusion: The Hypercoagulability state as detected by ROTEM parameters at day 0 and day 10 had no association with the outcome (mortality) of critically ill COVID-19 patients. Hence it cannot be used as a prognostic test. The increasing age, comorbidities and D-dimer values were associated with a poor prognosis in COVID-19 patients.

Current status and prospect of global development of lung transplantation

During the novel coronavirus pneumonia (COVID-19) pandemic from 2020 to 2021, lung transplantation entered a new stage of development worldwide. Globally, more than 70 000 cases of lung transplantation have been reported to the International Society for Heart and Lung Transplantation (ISHLT). With the development of medical techniques over time, the characteristics of lung transplant donors and recipients and the indications of pediatric lung transplantation recipients have undergone significant changes. Application of lung transplantation in the treatment of COVID-19-related acute respiratory distress syndrome (ARDS) has also captivated worldwide attention. Along with persistent development of lung transplantation, it will be integrated with more novel techniques to make breakthroughs in the fields of artificial lung and xenotransplantation. In this article, research progresses on the characteristics of lung transplant donors and recipients around the world were reviewed and the development trend was predicted, enabling patients with end-stage lung disease to obtain more benefits from the development of lung transplantation technique. © 2022 Organ Transplantation. All rights reserved.

Study on the Influence of Risk Information on Risk Perception and Coping Behavior of College Students in the Context of Epidemic Prevention and Control

Objective: This study aims to explore the influence of risk information on college students'coping behavior during the pandemic and the mediating role of risk perception in between. Methods: 553 college students were surveyed by risk information questionnaire, risk perception questionnaire and coping behavior measurement. Results: (1)In risk information, the cure information and the government prevention and control measures could reduce the level of individual's risk perception, while the disease information and the self-related information could cause college students'high-risk perception;(2) The four kinds of risk information significantly correlated with risk perception and coping behavior, and risk perception and coping behavior were significantly correlated;(3) Risk perception played an mediating role between risk information and coping behavior. Conclusion: Risk information and risk perception during the epidemic significantly affected college students'coping behavior. © 2022 ACM.

Edge Blockchain Construction Efficiency Maximization for COVID-19 Detection in a Body Area Network

The detection of traces of patients with novel coronavirus pneumonia (COVID-19) is a prerequisite for avoiding the rapid spread of the virus. However, too much patient privacy data uploaded to the cloud centre will overwhelm the network and cause user information security to not be guaranteed. In this paper, we propose a personal prediction method for COVID-19 infections by perceiving the information of worn biosensors and monitoring equipment in a body area network (BAN). Edge computing and blockchain technology are introduced to solve the problems of user privacy protection and perceptual data transmission and storage. We first construct an edge body area network (EBAN) and characterize the maximization function of the edge blockchain cost by considering the constraints on the bandwidth, storage space, and energy consumption. Then we build a blockchain without redundant perception information and select effective transmission paths by using the edge blockchain construction efficiency maximization (EBCEM) algorithm. Finally, we use the network simulator (NS-2) to simulate the performance of the EBCEM algorithm and compare it with the excellent assignment game algorithm (AGA) in terms of the effective requester ratio (ERR), effective provider ratio (EPR), edge blockchain construction success ratio (EBCSR), and average storage usage ratio (ASUR) in the EBAN. Author

Novel Coronavirus and Common Pneumonia Detection from CT Scans Using Deep Learning-Based Extracted Features.

COVID-19 which was announced as a pandemic on 11 March 2020, is still infecting millions to date as the vaccines that have been developed do not prevent the disease but rather reduce the severity of the symptoms. Until a vaccine is developed that can prevent COVID-19 infection, the testing of individuals will be a continuous process. Medical personnel monitor and treat all health conditions; hence, the time-consuming process to monitor and test all individuals for COVID-19 becomes an impossible task, especially as COVID-19 shares similar symptoms with the common cold and pneumonia. Some off-the-counter tests have been developed and sold, but they are unreliable and add an additional burden because false-positive cases have to visit hospitals and perform specialized diagnostic tests to confirm the diagnosis. Therefore, the need for systems that can automatically detect and diagnose COVID-19 automatically without human intervention is still an urgent priority and will remain so because the same technology can be used for future pandemics and other health conditions. In this paper, we propose a modified machine learning (ML) process that integrates deep learning (DL) algorithms for feature extraction and well-known classifiers that can accurately detect and diagnose COVID-19 from chest CT scans. Publicly available datasets were made available by the China Consortium for Chest CT Image Investigation (CC-CCII). The highest average accuracy obtained was 99.9% using the modified ML process when 2000 features were extracted using GoogleNet and ResNet18 and using the support vector machine (SVM) classifier. The results obtained using the modified ML process were higher when compared to similar methods reported in the extant literature using the same datasets or different datasets of similar size; thus, this study is considered of added value to the current body of knowledge. Further research in this field is required to develop methods that can be applied in hospitals and can better equip mankind to be prepared for any future pandemics.

Application of Tele-Ultrasound Robot During COVID-19 Pandemic: A Feasibility Study.

OBJECTIVE: To investigate the accuracy of ultrasonic diagnosis using the tele-ultrasound robot in Leishen Shan Hospital. METHOD: Twenty-two patients with novel coronavirus pneumonia from Leishen Shan Hospital voluntarily participated in this study. Their thyroids, neck vessels, hepatobiliaries and kidneys were scanned by both tele-ultrasound robot manufactured by Imabot Co., Ltd, Wuhan and conventional method. The ultrasound diagnosis of each patient was compared, and the ultrasound images obtained by the two methods were mixed together and double-blindly diagnosed by an experienced ultrasound radiologist. RESULTS: There were 44 positive lesions in 110 sites of 22 patients. Of which the two methods, 40 positive lesions were detected by the robotic method with 4 lesions missed (2 small polyps of gallbladder, 1 small hemangioma of liver and 1 small cyst of kidney) and 1 lesion misdiagnosed (normal carotid artery was misdiagnosed as carotid atherosclerotic plaque); 44 positive lesions were detected by conventional method with 1 small cyst of the liver was missed. There was no statistically significant difference in the accuracy rate between the robotic method and the conventional method using the chi-square test of the four-grid data (P>.05). CONCLUSION: The application of tele-ultrasound robot meets the standard of patient care during the pandemic. The method is feasible to provide adequate ultrasound information to diagnose common abdominal, vascular, superficial organ pathologies in patients with COVID-19 with acceptable accuracy compared with a conventional ultrasound scan.

Transmission dynamics model and the coronavirus disease 2019 epidemic: applications and challenges.

Since late 2019, the beginning of coronavirus disease 2019 (COVID-19) pandemic, transmission dynamics models have achieved great development and were widely used in predicting and policy making. Here, we provided an introduction to the history of disease transmission, summarized transmission dynamics models into three main types: compartment extension, parameter extension and population-stratified extension models, highlight the key contribution of transmission dynamics models in COVID-19 pandemic: estimating epidemiological parameters, predicting the future trend, evaluating the effectiveness of control measures and exploring different possibilities/scenarios. Finally, we pointed out the limitations and challenges lie ahead of transmission dynamics models.

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).