Epidemiological characteristics of four times of community transmission of COVID-19 in Hebei

Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.

The Pandemic in Our Country, the Pandemic in Their Countries: News Values and Media Representation of the COVID-19 Pandemic

The article investigates how the COVID-19 pandemic around the world is represented by the New York Times (NYT) and China Daily (CD), newspapers from two countries with long-standing political tension. The data consists of 2572 reports that are classified into three groups: reports on COVID-19 in the US, reports on COVID-19 in China, and reports on COVID-19 in other countries/regions. The reports are analyzed in terms of their discursive news values. Analysis results show that NYT represents the pandemic in the US, and CD represents the pandemic in China as the least negative, the least impactful and with the most personal accounts. NYT represents the pandemic in China, and CD represents the pandemic in the US as the most negative, the most impactful, the most severe, and the least positive and proximate. The selection and representation of other countries/regions reflect a differentiated coverage of the "Others" by both NYT and CD. The study lends support to a previous hypothesis that domestic crises tend to be presented as less negative than those in other countries, and invites contemplation on the influence of nationalism and political antagonism on the news media in times of global health crises.

Modeling and Reviewing Analysis of the COVID-19 Epidemic in Algeria with Diagnostic Shadow

In this paper, we formulate a special epidemic dynamic model to describe the transmission of COVID-19 in Algeria. We derive the threshold parameter con-trol reproduction number (R0c), and present the effective control reproduction number (Rc(t)) as a real-time index for evaluating the epidemic under different control strate-gies. Due to the limitation of the reported data, we redefine the number of accumu-lative confirmed cases with diagnostic shadow and then use the processed data to do the optimal numerical simulations. According to the control measures, we divide the whole research period into six stages. And then the corresponding medical resource estimations and the average effective control reproduction numbers for each stage are given. Meanwhile, we use the parameter values which are obtained from the optimal numerical simulations to forecast the whole epidemic tendency under different control strategies.

Epidemiological characteristics of four times of community transmission of COVID-19 in Hebei

Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81∶1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers′ markets, medical workers and other key areas and groups, and ensure early detection and timely response. © 2022 China Tropical Medicine. All rights reserved.

[Chest imaging-based artificial intelligence in the diagnosis of coronavirus disease 2019 and prospects for future research].

Artificial intelligence (AI) has been applied increasingly in the medical field during the past 5 years. Within respiratory medicine, chest imaging AI is one of the relevant hotspots, commonly trained to identify pulmonary nodules/lung tumors, tuberculosis, pneumonia, interstitial lung disease, chronic obstructive pulmonary disease, pulmonary embolism and other pathologies. Due to the non-specific clinical manifestations and the low detection rate of pathogens, precise diagnosis and treatment of pneumonia remain challengeable. Since the outbreak of coronavirus disease 2019 (COVID-19), chest imaging AI has demonstrated its clinical value in accurate diagnosis and quantitative measurements of COVID-19. Moreover, an AI system can assist the clinicians to identify the high-risk COVID-19 patients who warrant close monitoring and timely intervention. However, there are still some limitations in the existing studies, such as small sample size, lack of multi-modal assessment of the AI model, and rough classification of pneumonia. Therefore, some suggestions for future research were put forward in this paper. Most of all, more attention should be paid to the collection of high-quality datasets, standardization of image annotation, technology innovation, algorithm optimization and model verification. Besides, the application of imaging AI on other types of pneumonia including viral pneumonia, bacterial pneumonia and pneumomycosis deserves further study. In conclusion, chest imaging AI is expected to play a vital role in decision-making for pneumonia in the future.

The Virtual Fashion in the Global Apparel Industry in the Post-Epidemic Era

The global economy has experienced a downturn due to the COVID-19 epidemic, and the entire apparel industry has been hit hard. However, in the post-epidemic era, the digital trend of the world has become more pronounced. The virtual consumer market and virtual fashion took this opportunity to overgrow. More and more people are willing to spend money on virtual products. In a way, virtual fashion is the acceleration and continuation of future fashion trends. The most crucial difference between virtual and physical fashion is that the latter does not undergo physical production. Instead, virtual clothing can be purchased and “worn” immediately. More and more fashion brands have paused traditional fashion shows in favor of visuals such as video game collaborations, mobile apps, virtual icons, virtual showrooms, and virtual clothing brands. This paper compares and analyzes ten international brands associated with virtual fashion through data collection and case studies. It shows that with the help of modern cutting-edge technology, the traditional apparel industry has ushered in a new transformation point. New virtual fashion will gradually enter our daily life from the experimental field, driving the transformation of the apparel industry. Copyright © 2022 Textile Bioengineering and Informatics Society.

Sonographic Phenotyping of the Upper Airway in OSA With Backscattered Imaging Analysis by Machine Learning

Introduction: Anatomic assessment of the upper airway remains important in directing and monitoring of care for patients with obstructive sleep apnea (OSA). Nasopharyngoscopy is routine in clinical practice, but it can be invasive and potentially less attractive in the post-COVID-19 care setting. It also only allows subjective assessment. Ultrasound imaging of the upper airway with backscattered imaging analyzed via machine learning algorithm is investigated as a potential alternative. Method(s): Sixty-three subjects (14 female) with a mean age of 39.4 (12.6) years, body mass index (BMI) of 26.4 (4.6) kg/m2, and apnea-hypopnea index (AHI) of 19.0 (16.1) were consented from Stanford sleep surgery (July 2020 to May 2021). A standardized ultrasound protocol was used to image the soft palate, oropharynx, tongue base, and epiglottis. Via ultrasound device cleared by US Food and Drug Administation, backscattered ultrasound imaging (BUI) of the upper airway was performed and analyzed with machinelearning algorithms. Combined with B-mode measurements of airway muscular cross-sections, a logistic regression model was built to correlate with OSA severity. Result(s): The BUI of subjects with mild OSA was different from moderate-severe (AHI>=15) OSA at the soft palate (P=.0007). The axial-to-lateral ratio of upper airway length was reduced in the lower soft palate of the moderate-severe group (P=.0207). The logistic regression model with BUI, axial-to-lateral ratio at the soft palate, and BMI showed an area under the receiver-operating characteristic curve of 0.84 (95% CI, 0.726-0.920) in moderate-severe OSA. Conclusion(s): A noninvasive yet replicable technique to visualize and phenotype the upper airway is critical in the management of patients with sleep-disordered breathing. Sonographic BUI combined with B-mode airway measurements analyzed by machine learning show promise in characterizing the upper airway in patients with moderate-severe OSA.

Analysis of public information demand during the COVID-19 pandemic based on four-stage crisis model

At the end of 2019, the outbreak of the Corona Virus Disease 2019 (COVID-19) became a grave global public health emergency. At that time, there was a lack of information about this virus. Nowadays, social media has become the main source for the public to obtain information, especially during the COVID-19 pandemic. Therefore, in order to know about the public of information demand after the outbreak, the research collects the data of hot search on Sina-microblog from 1 January 2020 to 30 December 2020, and then conducts data mining by combining text processing with topic models. Then we show the topics mined in the knowledge map. The results show that with the outbreak of the COVID-19, people's attention to the topics related to the epidemic reaches the maximum in a short time, and then decreases with fluctuation, but does not disappear immediately. Some topics fluctuate violently due to the emergence of special events. The results conformed to the four-stage crisis model in the emergency management. We analyze the role of social media in four stages for this. The findings of this study could help the government and emergency agencies to better understand the main aspects, which the public's concern about COVID-19, and accelerate public opinion guidance and emotional reassurance.

Prevalence of alcohol-tolerant and antibiotic-resistant bacterial pathogens on public hand sanitizer dispensers.

BACKGROUND: Since the advent of the COVID-19 pandemic, alcohol-based hand sanitizer dispensers (HSDs) have been installed in most public and clinical settings for hygiene purposes and convenient application. AIM: To determine whether sanitizer-tolerant bacterial pathogens can colonize HSDs, spreading diseases and antibiotic resistance. METHODS: Sampling was conducted from operational automatic HSDs, specifically the dispensing nozzle in direct contact with sanitizer. Culture-dependent cultivation of bacteria and MALDI-TOF were employed to assess microbiological contamination. Bacterial isolates were selected for rapid killing and biofilm eradication assays with alcohol treatment. Antibiotic minimum inhibitory concentration assays were performed according to the Clinical and Laboratory Standards Institute guidelines. Virulence potential of bacterial isolates was evaluated in the Caenorhadbitis elegans infection model. FINDINGS: Nearly 50% of HSDs from 52 locations, including clinical settings, food industry, and public spaces, contain microbial contamination at 103-106 bacteria/mL. Bacterial identification revealed Bacillus cereus as the most frequent pathogen (29%), while Enterobacter cloacae was the only Gram-negative bacterial pathogen (2%). Selecting B. cereus and E. cloacae isolates for further evaluation, these isolates and associated biofilms were found to be tolerant to alcohol with survival up to 70%. They possessed resistance to various antibiotic classes, with higher virulence than laboratory strains in the C. elegans infection model. CONCLUSION: HSDs serve as potential breeding grounds for dissemination of pathogens and antibiotic resistance across unaware users. Proper HSD maintenance will ensure protection of public health and sustainable use of sanitizing alcohols, to prevent emergence of alcohol-resistant pathogens.

Construction of SARS-CoV-2 pseudovirus and its application in measuring the neutralization activity of serum antibodies

Objective To construct SARS-CoV-2 pseudovirus, optimize its preparation protocol, and apply it to the evaluation of antibody neutralization activity. Methods The optimized sequence of spike (S) gene of SARS-CoV-2 was synthesized, the pseudovirus titers were measured, and the expressed S protein was then detected by Western blot. Finally, quantitative ELISA was used to measure the serum IgG antibody titers in recipients who had received either one or two doses of COVID-19 inactivated vaccine. Meanwhile, the sera were tested for their reactivity with the pseudovirus using neutralization tests. Results S gene was confirmed to have been successfully cloned into the vector, generating the pcDNA3.1-S plasmid. Co-transfection of pNL4-3.Luc.R-E- and pcDNA3.1-S at a ratio of 2∶1 could lead to higher packing efficacy and pseudovirus titers. Expression of the S protein was verified by Western blot. Moreover, this SARS-CoV-2 pseudovirus showed a broad host infectivity in Vero, Huh7.5, A549-hACE2 and 293T-hACE2 cells, with the highest relative luciferase unit (RLU) in 293T-hACE2. Comparing the IgG levels measured by ELISA in sera collected from COVID-19 vaccine recipients, we observed a higher titer in those who received two doses of inactivated vaccine (S/CO=10.27±3.33), measured one week after the second shot. However, the IgG level significantly dropped（S/CO=2.36±2.25）half year post-vaccination. Amongst the serum samples tested, one with an S/CO of 10.32 could successfully inhibit the infection of SARS-CoV-2 pseudovirus in 293T-hACE2 cells at a dilution of 1/1 066. Conclusion We have established a method for preparing the SARS-CoV-2 pseudovirus, which can be used for detection of the neutralizing antibodies and the evaluation of humoral immune response post-vaccination. © 2022 Editorial Office of Chinese Journal of Schistosomiasis Control. All Rights Reserved.

Next-Generation Nonviral Vectors for mRNA Vaccine Delivery

Vaccination is one of the most promising approaches to protect individuals from serious illness and complications of vaccine-preventable diseases. Currently, several mRNA therapeutic pipelines have been established to prevent and treat various diseases, including infectious diseases, heart disease, fibrosis, etc.;note that BNT162b2 and mRNA-1273 are now in clinical application against coronavirus disease 2019 (COVID-19), which marks an unprecedented development for mRNA application. However, some drawbacks of mRNA vaccination such as restricted transfection efficiency, potential adverse effects, and fast degradation severely limit their clinical application. Therefore, additional in-depth research is required to optimize the delivery systems for the improvement of safety, effectiveness, and stability of mRNA vaccines. In this perspective, the design principles of biomaterials for mRNA targeted delivery are summarized and proposed, and an outlook for the tendency and opportunities of mRNA vaccines is given.

The development of virtual fashion in the global clothing industry

Affected by the COVID-19 epidemic, the global economy has declined. However, the overall clothing industry has been hit hard. Virtual fashion has taken this opportunity to develop rapidly. Due to the pandemic, consumers behavior has changed to become more susceptible to the influence of social media and paying more attention to the social responsibility of brands. Virtual fashion represented by game collaboration, mobile apps, virtual avatars, and virtual clothing has gained traction in the post-epidemic era. In this paper, ten international brands related to virtual fashion were compared and analyzed by data collection. It indicates that the traditional clothing industry has ushered in a new transformation point with the help of modern cutting-edge technology, and the new virtual fashion will also enter our daily life from the experimental field. © 2019 Textile Bioengineering and Informatics Symposium Proceedings 2021 - 14th Textile Bioengineering and Informatics Symposium, TBIS 2021. All rights reserved.

Design and engineering application of medical oxygen supply system in novel coronavirus pneumonia treatment hospital

Respiratory supporting, as an important medical treatment for new coronavirus pneumonia patients, must be effectively guaranteed by medical oxygen supply. However, the medical oxygen system designed and configured by the existing hospitals according to the current specifications cannot meet the oxygen needs for patients with new coronavirus pneumonia. This paper aimed to study the design of medical oxygen system in new coronavirus pneumonia emergency hospital. By investigating the oxygen treatment plan for the novel coronavirus pneumonia patients in the health emergency hospital, the oxygen treatment characteristics of different patients were studied. The oxygen characteristics of different respiratory support terminals were explored to study the oxygen demands of new coronavirus pneumonia emergency hospitals. Through calculating flow rates of medical gas system air source referring to 'technical code for medical gases engineering', the proportion coefficient of severe patients converted into respiratory distress patients was introduced, and the model of calculating flow rates of medical oxygen system air source in emergency hospital was proposed. The cases were verified in a typical health emergency hospital that the developed calculation flow model of medical oxygen source met the demands of hospital oxygen. The outcomes provide a reference for the design and construction of medical oxygen in such health emergency hospitals.

[Expressions of MPV, P-LCR and NLR in patients with novel coronavirus disease 2019].

To provide new ideas for clinical diagnosis and treatment of coronavirus disease 2019 (COVID-19), this study explore the expression level and prognostic value of platelet parameters in mild, moderate and severe COVID-19. This is a retrospective analysis. From January to May 2020, a total of 69 patients who were diagnosed with COVID-19 in the Third Central Hospital and the Jinnan Hospital (both situated in Tianjin) were enrolled in the disease group. According to the severity, these patients were divided into mild group (15 cases), moderate group (46 cases), and severe group (8 cases). In the same period, 70 non-infected patients were enrolled in control group. The level of white blood cell count (WBC), absolute neutrophil count (NEU#), absolute lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large contrast ratio (P-LCR) before and after treatment were analyzed. Binary logistic regression analysis is used to establish a mathematical model of the relationship between these indexes and the outcome of severe COVID-19 patients. The receiver operating characteristic(ROC) curve is used to further explore the prognosis value of MPV, P-LCR, NLR separately and jointly in COVID-19 patients. Compare to the control group, WBC and NE# increase (Z=-5.63, P<0.01;Z=-9.19,P<0.01) and LY# decrease (Z=-9.34, P<0.01) in the severe group; NLR increase with the aggravation of the disease, there is significant difference between groups (Z=17.61, P<0.01); PLT, PDW, MPV and P-LCR decrease with the aggravation of the disease, there is significant difference between groups (Z=9.47, P<0.01; Z=11.41, P<0.01; Z =16.76, P<0.01; Z=13.97, P<0.01). Binary logistic regression analysis shows MPV, P-LCR and NLR have predictive value for severe COVID-19 patients. There is a negative correlation between MPV, P-LCR and severe COVID-19 patients (OR=1.004, P=0.034; OR=1.097, P=0.046). There is a positive correlation between NLR and severe COVID-19 patients (OR=1.052, P=0.016). MPV and P-LCR of patients with good prognosis after treatment were significantly higher than those before treatment (Z=-6.47, P<0.01; Z=-5.36, P<0.01). NLR was significantly lower than that before treatment (Z=-8.13, P<0.01). MPV and P-LCR in poor prognosis group were significantly lower than those before treatment (Z=-9.46, P<0.01; Z=-6.81, P<0.01). NLR was significantly higher than that before treatment (Z=-3.24, P<0.01). There were significant differences between good and poor prognosis groups before and after treatment in MPV, P-LCR and NLR (P<0.01). Combination of these three indexes, ROC shows the AUC is 0.931, the sensitivity is 91.5%, the specificity is 94.1%, the positive predictive value is 88.9%, and the negative predictive value is 87.4%, which is better than any of these indexes separately. Changes in these parameters are closely related to clinical stage of COVID-19 patients. MPV, P-LCR and NLR are of great value in the prediction and prognosis of severe COVID-19 patients.

Investigating students' dynamic learning emotions and interactions in online discussion

Affected by COVID-19, college students had to study at home in China. Therefore, a great significance is to investigate learners' emotions and interactions in online discussion. Many researchers have studied the emotions and interactions among students. However, most of them have overlooked how students' emotions and interactions vary over time, which is a dynamic process in online asynchronous discussion. Using emotion analysis and temporal network analysis, this paper investigates learners' positive, negative and confused states during learning, and uses a methodological approach to build, visualize, and quantitatively analyze temporal network in each week of the course. Results revealed that the ratio of students' positive states was extremely large at the beginning of the semester, and then decreases while the confused or negative ratios increased. The interactions in each week varied with time and could be divided into three patterns according the characteristics of temporal networks. Finally, the relationships among emotions, interactions, and academic performances were analyzed based on temporal networks, and the data revealed that middle-achieving learners were the major contributors while learning emotional states have a slight effect on learning outcomes. This study might assist teachers to provide timely and effective assistance during early warning, so to achieve the ultimate goal of improving the effect of online learning.

[Spatial and temporal distribution and predictive value of chest CT scoring in patients with COVID-19].

Objective: To explore a modified CT scoring system, its feasibility for disease severity evaluation and its predictive value in coronavirus disease 2019 (COVID-19) patients. Methods: This study was a multi-center retrospective cohort study. Patients confirmed with COVID-19 were recruited in three medical centers located in Beijing, Wuhan and Nanchang from January 27, 2020 to March 8, 2020. Demographics, clinical data, and CT images were collected. CT were analyzed by two emergency physicians of more than ten years' work experience independently through a modified scoring system. Final score was determined by average score from the two reviewers if consensus was not reached. The lung was divided into 6 zones (upper, middle, and lower on both sides) by the level of trachea carina and the level of lower pulmonary veins. The target lesion types included ground-glass opacity (GGO), consolidation, overall lung involvement, and crazy-paving pattern. Bronchiectasis, cavity, pleural effusion, etc., were not included in CT reading and analysis because of low incidence. The reviewers evaluated the extent of the targeted patterns (GGO, consolidation) and overall affected lung parenchyma for each zone, using Likert scale, ranging from 0-4 (0=absent; 1=1%-25%; 2=26%-50%; 3=51%-75%; 4=76%-100%). Thus, GGO score, consolidation score, and overall lung involvement score were sum of 6 zones ranging from 0-24. For crazy-paving pattern, it was only coded as absent or present (0 or 1) for each zone and therefore ranging from 0-6. Results: A total of 197 patients from 3 medical centers and 522 CT scans entered final analysis. The median age of the patients was 64 years, and 54.8% were male. There were 76(38.8%) patients had hypertension and 30(15.3%) patients had diabetes mellitus. There were 75 of the patients classified as moderate cases, as well as 95 severe cases and 27 critical cases. As initial symptom, dry cough occurred in 170 patients, 134 patients had fever, and 125 patients had dyspnea. Reparatory rate, oxygen saturation, lymphocyte count and CURB 65 score on admission day varied among patients with different disease severity scale. There were 50 of the patients suffered from deterioration during hospital stay. The median time consumed for each CT by clinicians was 86.5 seconds. Cronbach's alpha for GGO, consolidation, crazy-paving pattern, and overall lung involvement between two clinicians were 0.809, 0.712, 0.678, and 0.906, respectively, showing good or excellent inter-rater correlation. There were 193 (98.0%) patients had GGO, 147 (74.6%) had consolidation, and 126(64.0%) had crazy-paving pattern throughout clinical course. Bilateral lung involvement was observed in 183(92.9%) patients. Median time of interval for CT scan in our study was 7 days so that the whole clinical course was divided into stages by week for further analysis. From the second week on, the CT scores of various types of lesions in severe or critically patients were higher than those of moderate cases. After the fifth week, the course of disease entered the recovery period. The CT score of the upper lung zones was lower than that of other zones in moderate and severe cases. Similar distribution was not observed in critical patients. For moderate cases, the ground glass opacity score at the second week had predictive value for the escalation of the severity classification during hospitalization. The area under the receiver operating characteristic curve was 0.849, the best cut-off value was 5 points, with sensitivity of 84.2% and specificity of 75.0%. Conclusions: It is feasible for clinicians to use the modified semi-quantitative CT scoring system to evaluate patients with COVID-19. Severe/critical patients had higher scores for ground glass opacity, consolidation, crazy-paving pattern, and overall lung involvement than moderate cases. The ground glass opacity score in the second week had an optimal predictive value for escalation of disease severity during hospitalization in moderate patients on admission. The frequency of CT scan should be reduced after entering the recovery stage.

Spatio-temporal propagation of COVID-19 pandemics (vol 131, 58003, 2020)

Original article: EPL, 131 (2020) 58003.