Vulnerability, Infection, and Anti-discrimination Among Asian Americans and COVID-19: Explorations into Race and Health Intersections in California

The purpose of this chapter is to report and analyze the double victimization among Asians/Asian Americans during COVID-19, including their vulnerability to infection and anti-Asian racism. We first test the validity of the CDC's SVI (Social Vulnerability Index) in analyzing COVID-19 infections, then construct an Asian-specific Social Vulnerability Index (ASVI) to compare with the CDC SVI, mapping them out nationally to visualize the differential geographical patterns. We then conduct an empirical study of the state of California with correlation analysis, analysis of variance, and GIS mapping to explore the association of ASVI with Asian COVID-19 infection incidence rate, and anti-Asian discrimination incidents. We conclude that the method of constructing ASVI may be applied to other vulnerable groups. The findings contribute to our knowledge of the unequal social outcomes of pandemics across people and place. The chapter ends with summarizing findings and contributions, revealing data limitations, providing policy suggestions and suggesting future research directions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Design and Construction of the New Web Learning System of Tsinghua University

Online learning platforms play an important role in supporting colleges and universities in integrating online and offline learning. The project team designed and developed the Web Learning System of Tsinghua University, which has become a basic supporting platform for teachers and students to carry out teaching and learning activities. The Web Learning System possesses functionalities such as course announcement management, courseware management, assignment management, discussion, Q & A, and online timetables, providing supporting services for course teaching, teacher-student interaction, and especially online and offline integrated learning during the COVID-19 pandemic. © 2022 IEEE.

Outpatient Versus Inpatient Robotic Assisted Radical Prostatectomy: Complications, Time Driven Activities Based Costing and Patient Satisfaction

INTRODUCTION AND OBJECTIVE: Radical prostatectomy lengths of stay decreased with ketorolac analgesia and adoption of robotic assisted radical prostatectomy (RARP). During the COVID-19 pandemic, the transition to outpatient RARP freed up critically needed hospital beds. The healthcare cost reduction afforded by the shift to outpatient RARP and its effect on patient satisfaction has yet to be explored. We compared healthcare costs, patient satisfaction and complications for outpatient vs. inpatient RARP. METHOD(S): We identified and compared a series of consecutive RARP performed as outpatient vs. inpatient and determined the capacity cost rate for every resource, including personnel, equipment, and space. After the lifting of hospital restrictions, men were given the option of inpatient vs. outpatient RARP. We also administered a validated Patient Satisfaction Outcome Questionnaire (PSOQ) postoperatively and compared median scores in perceived outcomes and satisfaction. A time-driven activity-based costing (TDABC) analysis was applied to compare the total costs of care for RARP performed. Finally, we captured complications within 30 days of surgery using the Clavien-Dindo classification. We used multivariable regression to adjust for age, race, BMI, and ASA classification to assess the impact of outpatient vs. inpatient RARP on complications. RESULT(S): There were no significant differences in patient characteristics for outpatient (n=145) vs. inpatient (n=80) RARP. When given the choice, 86.6% of men elected for outpatient vs. inpatient RARP. Outpatient RARP netted a $1387 (13.5%) cost reduction compared to inpatient RARP. There were no significant differences in outpatient vs. inpatient median satisfaction survey scores or complications within 30 days (11.0% vs. 11.3%, p=0.961). CONCLUSION(S): Outpatient RARP can be safely performed, with similar outcomes and compared to inpatient RARP. Outpatient RARP has significantly lower costs compared to inpatient RARP while maintaining similar patient satisfaction outcomes.

Investigation and analysis on the infection control and radiation safety of radiodiagnostic workplace for COVID-19.

Objective: To survey and supervise the risk of infection control and radiation safety in the radiological diagnostic workplace for COVID-19, and provide data support for the safety protection of radiographers and related staff. Method(s): 4 emergency hospitals for COVID-19 including 2 makeshift hospitals, module hospital and brick pattern hospital in Hubei province were performed for testing and evaluation of imaging performance and radiological protection for the 8 new installed CT scanners and places according to the national standards of WS 519-2019 and GBZ 130-2013. The infection control safety factors such as the layout of the equipment room were monitored and investigated. Two COVID-19 designated hospitals including general hospital and infectious disease specialized hospital were selected to carry out field investigation and sampling of environmental biological samples for 4 CT rooms. Then the samples were detected for the nucleic acid of novel coronavirus. The results of radiodiagnostic workplace overall arrangement, infection prevention and the nucleic acid testing were analyzed, and the biological safety reliability and risk point were evaluated. Result(s): The indicators of imaging performance and radiation protection for 8 CT scanners in emergency hospitals could meet the requirements of national standards.Each of 2 makeshift hospitals had 3 CT rooms with the area of 38.8 m2 and 4 mm Pb equivalent thickness of protective shielding. The CT rooms in module hospital and brick pattern hospital were 20.0 m2, and 35.8 m2 in areas, with 4 mm Pb equivalent and 3 mm Pb equivalent thickness of protection shielding, respectively. The 8 radiological diagnostic workplaces of the emergency hospitals were designed and constructed based on " three zones with two passage ways". The result of the nucleic acid test indicated that the positive samples were found at the multiple sites such as scanning bed, internal of gantry and ground touched by patients in CT scanning room. The areas such as console panel and ground were risked of pollution by the virus infected hands and feet of radiographers. In addition, the similar positive samples were found in the areas in scanning room with no touch of patients, such as observation window and air outlet. Conclusion(s): 8 CT scanners and rooms in 4 emergency hospitals basically meet the requirements of imaging performance and radiation protection. The disinfection of COVID-19 radiodiagnostic workplace should be standardized.Copyright © 2020 by the Chinese Medical Association.

Deep Dual Attention Network for Precise Diagnosis of COVID-19 From Chest CT Images

Automatic diagnosis of COVID-19 using chest CT images is of great significance for preventing its spread. However, it is difficult to precisely identify COVID-19 due to the following problems: 1) the location and size of lesions can vary greatly in CT images;2) its unique characteristics are often imperceptible in imaging findings. To solve these problems, a Deep Dual Attention Network (<inline-formula><tex-math notation="LaTeX">$\textrm {D}

[Progress in research of 2019-nCoV Omicron variant].

2019-nCoV Omicron (B.1.1.529) variant, which has brought new challenges to the prevention and control of COVID-19 pandemic, has the characteristics of stronger transmissibility and more rapid transmission and more significant immune evasion. It took only two months to become a predominant strain worldwide after its identification in South Africa in November 2021. Local epidemics caused by Omicron variant have been reported in several provinces in China. However, the epidemiological characteristics of highly mutated Omicron variant remain unclear. This article summarizes the progress in the research of functional mutations, transmissibility, virulence, immune evasion and cross-reactive immune responses of Omicron variant, to provide references for the effective prevention and control of COVID-19 pandemic caused by Omicron variant.

Comparison of models' performance for predicting depression under COVID-19

The prediction on depression with respect to the effect of safety behaviour during COVID-19 has been seldom investigated previously. Furthermore, the effect of balance of data based on regenerating methods is hardly ever discussed. In this paper, the performance of prediction is investigated with data collected across 26 countries across the world in consideration of the effect on the variables of potential affecting factors. Specifically, the data was retrieved from the open-source dataset conducted by IGHI, at imperial college London, containing 384,250 valid individuals with measurement of age, gender, country, covid status, employment status and behaviour score. Five machine-learning methods, namely logistic regression, MLR, RF, SVM and k-NN, were used for comparison of the performance metric by different statistical measurements. Based on the six chosen latent factors, RF is evaluated as an optimal model with the highest F1 score (0.787) and G-mean (0.503) without using re-sampling methods. Linear SVM, on the other hand, has the highest specificity (0.998) with original data. Furthermore, although there is an increase in sensitivity, using oversampling and undersampling procedure reduce the prediction accuracy to a nearly random value (0.5). Overall, RF without re-sampling method is considered to be the comparatively best model for its highest sensitivity, precision, F1-score and G-mean among all five data analysis algorithms;especially for minimizing the false positive rate such that all patients with depression are successfully identified. These results shed light on the choice of models when applied on prediction of depression status under different scenario. © COPYRIGHT SPIE.

Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization

This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models. Specifically, by studying a variational equation for the dynamics of the infected, we derive, using contractivity arguments, a condition that can be used to guarantee that the effective reproduction number is less than unity. This condition (i) is easily computable, (ii) is interpretable, being directly related to the model parameters, and (iii) can be used to enforce a scalability condition that prohibits the amplification of disturbances within the network system. We then include satisfaction of such a condition as a constraint in a Model Predictive Control problem so as to mitigate (or suppress) the spread of the epidemic while minimizing the economic impact of the interventions. A data-driven model of Italy as a network of three macro-regions (North, Center, and South), whose parameters are identified from real data, is used to illustrate and evaluate the effectiveness of the proposed control strategy.

[Progress in research of 2019-nCoV Omicron variant]

2019-nCoV Omicron (B.1.1.529) variant, which has brought new challenges to the prevention and control of COVID-19 pandemic, has the characteristics of stronger transmissibility and more rapid transmission and more significant immune evasion. It took only two months to become a predominant strain worldwide after its identification in South Africa in November 2021. Local epidemics caused by Omicron variant have been reported in several provinces in China. However, the epidemiological characteristics of highly mutated Omicron variant remain unclear. This article summarizes the progress in the research of functional mutations, transmissibility, virulence, immune evasion and cross-reactive immune responses of Omicron variant, to provide references for the effective prevention and control of COVID-19 pandemic caused by Omicron variant.

Adult asthma and copd healthcare utilization during the COVID-19 pandemic

Rationale: The Coronavirus Disease 2019 (COVID-19) pandemic has greatly impacted health care delivery patterns, including for patients with asthma and chronic obstructive pulmonary disease (COPD). Patients who experience both asthma and COPD symptoms, referred to as asthma-COPD overlap (ACO), have increased morbidity and healthcare utilization than patients with either disease alone. Methods: We obtained de-identified encounter-level electronic health record (EHR) data for adult asthma and COPD encounters within Penn Medicine occurring between January 1, 2015 and October 31, 2020. Variables extracted included codified demographic information, ICD-10 encounter codes and encounter type (i.e., office visit, hospital encounter, telemedicine or other). Patients were included if they were aged at least 18 years at first encounter and had at least two encounters over a period of at least a year. We classified patients as having asthma, COPD or ACO based on ICD-10 diagnoses (J45∗ for asthma and J41-44∗ for COPD) and medication prescriptions (short-acting β2-agonist for asthma and either shortacting β2-agonist or short-acting muscarinic antagonist for COPD), where ACO patients were defined as those meeting asthma and COPD criteria. We compared asthma and COPD healthcare encounters before (pre-pandemic) and after (pandemic) March 17, 2020 in terms of encounter volume and type. We also tested for differences in the demographic distributions of telemedicine vs. in-person visits using t- and χ2-tests. Results: A total 66,191 patients met our inclusion criteria, of which 46,257 (70%) had asthma, 12,580 (19%) had COPD, and 7,354 (11%) had ACO. Average monthly volume of office visits and hospital encounters for asthma/COPD decreased considerably during the pandemic (>50% decrease in office visits and >25% decrease in hospital encounters across all patient groups), while the volume of telemedicine visits increased dramatically (from 0-1 to 481-1,343 average monthly telemedicine visits across all patient groups) [Figure 1]. During the pandemic, non-Hispanic Black patients were more likely to be encountered via telemedicine than in-person encounters across all patient groups (39% vs. 34% of asthma patients encountered via telemedicine and in-person visits, respectively, were non-Hispanic Black;33% vs. 26% of COPD patients, and 50% vs. 44% among ACO patients;p<0.01 for all comparisons). Telemedicine and in-person visits for asthma/COPD during the pandemic did not differ significantly by mean age or sex among any of the patient groups. Conclusions: Telemedicine visits for asthma and COPD increased dramatically during the COVID-19 pandemic, while office visits and hospital encounters decreased. Further study.

Associations between neighborhood-level characteristics and COVID-19 incidence and mortality in southeastern pennsylvania

Rationale: The COVID-19 pandemic has disproportionately impacted racial/ethnic minority and socioeconomically disadvantaged groups in the United States, including at-risk populations within Southeastern Pennsylvania. We sought to determine whether neighborhood-level health, demographic, and socioeconomic characteristics in Southeastern Pennsylvania were associated with COVID-19 incidence and mortality at the zip code and municipality level, thereby establishing whether neighborhood-level disparities mirror individual-level ones. Methods: Cumulative zip code- and municipality-level data on COVID-19 cases and deaths were obtained from the public data hubs of 5 counties in Southeast Pennsylvania, and those of individual long-term care facilities (LTCFs) were obtained from the Pennsylvania Department of Health. For corresponding geographic areas, demographic and socioeconomic status variables were obtained from the American Community Survey, and data on the health status and behaviors of local residents were obtained from the Southeastern Pennsylvania Household Health Survey. COVID-19 cases and deaths reported by LTCFs were excluded from area-aggregated counts. Multivariable quasi-Poisson models with offsets for population counts were created to determine whether neighborhood-level variables were associated with COVID-19 incidence and mortality. Before adjusted incidence rate ratios were calculated, such models included individual predictors that were significantly associated (p<0.05) with COVID-19 outcomes and excluded highly collinear terms as determined by having variance inflation factors greater than 3. Results: Among 208 zip codes and municipalities that had complete data, the COVID-19 cumulative incidence through July 24, 2020 ranged from 0 to 331.9 per 10,000 residents, and the COVID-19 mortality rate ranged from 0 to 1.0 per 10,000 residents. Among 45 neighborhood-level variables considered, 5 were independently associated with COVID-19 incidence (p<0.01): 1) the proportion of residents aged 65 years or older (incidence rate ratio [IRR] = 1.341, 95%-CI: 1.147-1.567 for a 10% increase), 2) population density (IRR = 1.002, 95%-CI: 1.001-1.003 for a 100 people/square kilometer increase), 3) the proportion of individuals eating 3 or more servings of fruits/vegetables daily (IRR = 0.891, 95%-CI: 0.836-0.950 for a 10% increase), 4) average median house value (IRR = 0.989, 95%-CI: 0.980-0.994 for a $10,000 USD increase), and 5) the proportion of 2-person households (IRR = 0.997, 95%-CI: 0.995-0.999 for a 10% increase). The proportion of individuals aged 65 years or older was the only factor independently associated with COVID-19 mortality (IRR = 2.59, 95%-CI: 1.55-4.41 for a 10% increase). Conclusions: Neighborhood-level data can help identify specific needs of vulnerable populations and inform policies to address health disparities related to COVID-19.

Multi-disease prediction based on deep learning: A survey

In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research. © 2021 Tech Science Press. All rights reserved.

Effect of serum LDH and CK levels on condition and prognosis of COVID-19 patients

Objective: To explore the factors affecting the condition and prognosis of COVID-19. Methods: The clinical data of 228 patients with COVID-19 diagnosed in Renmin Hospital of Wuhan University from January 15 to March 1, 2020 were collected. The relationship between the levels of serum lactate dehydrogenase (LDH)/creatine kinase(CK) and the condition/prognosis of these patients were retrospectively analyzed. Results: The levels of serum LDH/CK in male patients with COVID-19 were significantly higher than those in female patients, and increased with age;more importantly, the levels of serum LDH and CK in severe group were significantly higher than those in non-severe group (P< 0.05);LDH and CK levels were related to the overall survival (OS) of COVID-19 patients. The OS of LDH increasing group was lower than that of normal LDH+CK group, and OS of LDH+CK increasing group was lower than that of LDH increasing group. Conclusion: The increase of LDH and CK can reduce the overall survival of COVID-19 patients, and serum LDH and CK levels can be used as risk factors to predict the progression of the disease. © 2021, Editorial Board of Medical Journal of Wuhan University. All right reserved.

[Epidemiological characteristics of asymptomatic infection cases of COVID-19 in Guangdong province].

Objective: To analyze the epidemiological characteristics of the cases firstly reported as "asymptomatic infection of COVID-19" in Guangdong province. Methods: The follow-up observation method was used to continuously track and observe the cases firstly reported as "asymptomatic patients with COVID-19" in Guangdong province from January 14 to March 31, 2020. The epidemiological data of the cases were collected to analyze their epidemiological characteristics, outcome and influencing factors. Results: From January 14 to March 31, 2020, a total of 325 cases were firstly reported as "asymptomatic infections of COVID-19" in Guangdong province. The epidemic curve of asymptomatic infection cases was similar to that of confirmed cases, and it had two peaks. The first peak was from January 27 to February 5, and the second peak was from March 17 to March 26. Of the 325 cases, 184 (56.6%) were subsequently converted to confirmed cases. These cases were defined as incubation period asymptomatic infection cases. The age median of the cases was 40 years, and 93.5% (172/184) of the cases showed symptoms within 3 days after the first positive nucleic acid tests were conducted, and 141 (43.4%) of the 325 cases remained asymptomatic status until they were cured and discharged. They were inapparent infection cases, accounting for 8.6% (141/1 642) of those diagnosed with COVID-19 in Guangdong province during the same period. The age median of inapparent infection cases was 27 years. The median of the interval between the first positive nucleic acid test and discharge was 14 days. Up to 90.8% (138/141) of the inapparent infection cases were discharged for centralized medical observation within 28 days. The longest interval between the first positive nucleic acid test and the last positive nucleic acid test was 73 days. The positive rate of nucleic acid test was 0.3% in close contacts of inapparent infection cases and 2.2% in close contacts of incubation period asymptomatic infection cases. There were significant differences in age distribution and source of infection between incubation period asymptomatic infection cases and inapparent infection cases (P<0.05). Old age was the risk factor for the conversion of firstly reported asymptomatic infection cases to confirmed cases. Compared with the 0-19-year-old group, The patients aged 40-59 years and 60 years and above were more likely to become confirmed cases. The OR (95%CI) values were 2.730 (1.380-5.402) and 5.302 (2.199-12.783), and P values were 0.004 and 0.000, respectively. People being infected in China were more likely to become confirmed cases (OR=7.121, P=0.000). Conclusions: There were asymptomatic infection cases among patients diagnosed with COVID-19. The infectiousness of incubation period asymptomatic infection cases might be stronger than that of inapparent infection cases. The proportion of younger cases among asymptomatic infection cases was higher than that of the confirmed cases. Old age and domestic infection were the risk factors for the conversion of asymptomatic infection cases to confirmed cases, to which more attention should be paid. Further serological investigations are needed to provide a basis for the development of COVID-19 prevention and control strategies.

Investigation and analysis on the infection control and radiation safety of radiodiagnostic workplace for COVID-19

Objective: To survey and supervise the risk of infection control and radiation safety in the radiological diagnostic workplace for COVID-19, and provide data support for the safety protection of radiographers and related staff. Methods: 4 emergency hospitals for COVID-19 including 2 makeshift hospitals, module hospital and brick pattern hospital in Hubei province were performed for testing and evaluation of imaging performance and radiological protection for the 8 new installed CT scanners and places according to the national standards of WS 519-2019 and GBZ 130-2013. The infection control safety factors such as the layout of the equipment room were monitored and investigated. Two COVID-19 designated hospitals including general hospital and infectious disease specialized hospital were selected to carry out field investigation and sampling of environmental biological samples for 4 CT rooms. Then the samples were detected for the nucleic acid of novel coronavirus. The results of radiodiagnostic workplace overall arrangement, infection prevention and the nucleic acid testing were analyzed, and the biological safety reliability and risk point were evaluated. Results: The indicators of imaging performance and radiation protection for 8 CT scanners in emergency hospitals could meet the requirements of national standards.Each of 2 makeshift hospitals had 3 CT rooms with the area of 38.8 m2 and 4 mm Pb equivalent thickness of protective shielding. The CT rooms in module hospital and brick pattern hospital were 20.0 m2, and 35.8 m2 in areas, with 4 mm Pb equivalent and 3 mm Pb equivalent thickness of protection shielding, respectively. The 8 radiological diagnostic workplaces of the emergency hospitals were designed and constructed based on " three zones with two passage ways". The result of the nucleic acid test indicated that the positive samples were found at the multiple sites such as scanning bed, internal of gantry and ground touched by patients in CT scanning room. The areas such as console panel and ground were risked of pollution by the virus infected hands and feet of radiographers. In addition, the similar positive samples were found in the areas in scanning room with no touch of patients, such as observation window and air outlet. Conclusions: 8 CT scanners and rooms in 4 emergency hospitals basically meet the requirements of imaging performance and radiation protection. The disinfection of COVID-19 radiodiagnostic workplace should be standardized. Copyright © 2020 by the Chinese Medical Association.