A real-world data validation of the value of early-stage SIR modelling to public health.

Performance of Susceptible-Infected-Recovered (SIR) model in the early stage of a novel epidemic may be hindered by data availability. Additionally, the traditional SIR model may oversimplify the disease progress, and knowledge about the virus and transmission is limited early in the epidemic, resulting in a greater uncertainty of such modelling. We aimed to investigate the impact of model inputs on the early-stage SIR projection using COVID-19 as an illustration to evaluate the application of early infection models. We constructed a modified SIR model using discrete-time Markov chain to simulate daily epidemic dynamics and estimate the number of beds needed in Wuhan in the early stage of COVID-19 epidemic. We compared eight scenarios of SIR projection to the real-world data (RWD) and used root mean square error (RMSE) to assess model performance. According to the National Health Commission, the number of beds occupied in isolation wards and ICUs due to COVID-19 in Wuhan peaked at 37,746. In our model, as the epidemic developed, we observed an increasing daily new case rate, and decreasing daily removal rate and ICU rate. This change in rates contributed to the growth in the needs of bed in both isolation wards and ICUs. Assuming a 50% diagnosis rate and 70% public health efficacy, the model based on parameters estimated using data from the day reaching 3200 to the day reaching 6400 cases returned a lowest RMSE. This model predicted 22,613 beds needed in isolation ward and ICU as on the day of RWD peak. Very early SIR model predictions based on early cumulative case data initially underestimated the number of beds needed, but the RMSEs tended to decline as more updated data were used. Very-early-stage SIR model, although simple but convenient and relatively accurate, is a useful tool to provide decisive information for the public health system and predict the trend of an epidemic of novel infectious disease in the very early stage, thus, avoiding the issue of delay-decision and extra deaths.

Omicron BA.1 breakthrough infections in inactivated COVID-19 vaccine recipients induced distinct pattern of antibody and T cell responses to different Omicron sublineages.

The adaptive immunity against SARS-CoV-2 prototype strain and Omicron sublineages induced by BA.1 breakthrough infection in vaccinees of inactivated COVID-19 vaccines have not been well characterized. Here, we report that BA.1 breakthrough infection induced mucosal sIgA and resulted in higher IgG titers against prototype strain and Omicron sublineages in vaccinees than in vaccine naïve-infected individuals. BA.1 breakthrough infection boosted antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis to prototype strain and BA.1, BA.1.1, BA.2, BA.2.12.1, and BA.2.75 but not BA.4/5 and induced neutralization against prototype strain and BA.1, BA.1.1, BA.2, BA.2.12.1, BA.2.75, and BA.4/5 but not BF.7, BQ.1, and XBB. In total, BA.1 breakthrough infection individuals produced less extensive sIgA, plasma IgG and NAb responses against Omicron sublineages compared with those against prototype strain. Further, BA.1 breakthrough infection induced recall B cell response to prototype strain and Omicron variant, primarily targeting memory B cells producing conserved epitopes. Memory T cell responses against Omicron is largely preserved. Individuals with vaccine booster did not induce more beneficial immune responses to Omicron sublineages upon BA.1 breakthrough infection than those with primary vaccine dose only. The breakthrough infection individuals produced stronger adaptive immunity than those of inactivated vaccine-healthy individuals. These data have important implications for understanding the vaccine effectiveness and adaptive immunity to breakthrough infection in individuals fully immunized with inactivated vaccines. Omicron sublineages, especially for those emerged after BA.4/5 strain, evade NAb responses induced by BA.1 breakthrough infection. It is urgent to optimize the vaccine immunogen design and formulations to SARS-CoV-2 variants.

How do temperature, humidity, and air saturation state affect the COVID-19 transmission risk?

Environmental parameters have a significant impact on the spread of respiratory viral diseases (temperature (T), relative humidity (RH), and air saturation state). T and RH are strongly correlated with viral inactivation in the air, whereas supersaturated air can promote droplet deposition in the respiratory tract. This study introduces a new concept, the dynamic virus deposition ratio (α), that reflects the dynamic changes in viral inactivation and droplet deposition under varying ambient environments. A non-steady-state-modified Wells-Riley model is established to predict the infection risk of shared air space and highlight the high-risk environmental conditions. Findings reveal that a rise in T would significantly reduce the transmission of COVID-19 in the cold season, while the effect is not significant in the hot season. The infection risk under low-T and high-RH conditions, such as the frozen seafood market, is substantially underestimated, which should be taken seriously. The study encourages selected containment measures against high-risk environmental conditions and cross-discipline management in the public health crisis based on meteorology, government, and medical research.

Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital.

Aim: COVID-19 patients' security is related to their mental health. However, the classification of this group's sense of security is still unclear. The aim of our research is to clarify the subtypes of security of patients infected with COVID-19, explore the factors affecting profile membership, and examine the relationship between security and psychological capital for the purpose of providing a reference for improving patients' sense of security and mental health. Methods: A total of 650 COVID-19 patients in a mobile cabin hospital were selected for a cross-sectional survey from April to May 2022. They completed online self-report questionnaires that included a demographic questionnaire, security scale, and psychological capital scale. Data analysis included latent profile analysis, variance analysis, the Chi-square test, multiple comparisons, multivariate logistical regression, and hierarchical regression analysis. Results: Three latent profiles were identified-low security (Class 1), moderate security (Class 2), and high security (Class 3)-accounting for 12.00, 49.51, and 38.49% of the total surveyed patients, respectively. In terms of the score of security and its two dimensions, Class 3 was higher than Class 2, and Class 2 was higher than Class 1 (all P < 0.001). Patients with difficulty falling asleep, sleep quality as usual, and lower tenacity were more likely to be grouped into Class 1 rather than Class 3; Patients from families with a per capita monthly household income <3,000 and lower self-efficacy and hope were more likely to be grouped into Classes 1 and 2 than into Class 3. Psychological capital was an important predictor of security, which could independently explain 18.70% of the variation in the patients' security. Conclusions: Security has different classification features among patients with COVID-19 infection in mobile cabin hospitals. The security of over half of the patients surveyed is at the lower or middle level, and psychological capital is an important predictor of the patients' security. Medical staff should actively pay attention to patients with low security and help them to improve their security level and psychological capital.

Safety protocols at the American Board of Pediatric Dentistry Oral Clinical Examination during COVID-19.

BACKGROUND: There are continued concerns about holding large group gatherings with the ongoing COVID-19 pandemic. The American Board of Pediatric Dentistry (ABPD) made the decision to cancel the Oral Clinical Examination (OCE) in 2020 with a goal to host the exam in 2021. This cancellation postponed the board certification of over 400 pediatric dentists. There is limited published data on safety protocols for in-person examinations. AIM: To determine if safety protocols utilized in the administration of the 2021 ABPD Oral Clinical Examination (OCE) were reassuring to candidates and examiners, and effective at addressing prevention of COVID-19 infection. DESIGN: An electronic survey was emailed to candidates and examiners following completion of the board examination process. The candidate survey had 784 and the examiner survey had 108 eligible participants. Eight questions overlapped between the two surveys. Descriptive statistics were utilized. p-values < 0.05 were considered significant. RESULTS: Three hundred ninety-four candidates (response rate = 50.3%) and 97 examiners (response rate = 89.8%) completed their respective surveys. The majority of candidates (86.8%) and examiners (94.8%) felt safe participating the OCE, and found COVID-19 protocols reassuring, 69.3% and 89.7%. CONCLUSIONS: Participants agreed that safety protocols enacted by the ABPD kept everyone safe and led to a successful and safely conducted high-stakes oral clinical examination during the COVID-19 pandemic.

A comparative study of virus nucleic acid re-positive and non-re-positive patients infected with SARS-CoV-2 Delta variant strain in the Ningxia Hui Autonomous Region.

Objective: This study aimed to provide a basis for epidemic prevention and control measures as well as the management of re-positive personnel by analyzing and summarizing the characteristics of re-positive patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant infections discharged from a hospital in the Ningxia Hui Autonomous Region in 2021. Methods: This case-control study included a total of 45 patients with Delta variant infections diagnosed in the Fourth People's Hospital of the Ningxia Hui Autonomous Region between October 17 and November 28, 2021. Based on the nucleic acid test results post-discharge, the patients were dichotomized into re-positive and non-re-positive groups. Based on the time of the first re-positive test, the re-positive group was further divided into <7 and ≥7 days groups to compare their clinical characteristics and explore the possible influencing factors of this re-positivity. Results: Of the 45 total patients, 16 were re-positive (re-positivity rate: 35.6%), including four patients who were re-positive after 2 weeks (re-positivity rate: 8.8%). The median time of the first re-positive after discharge was 7 days (IQR: 14-3). The re-positive group was younger than the non-re-positive group (35 vs. 53, P < 0.05), had a higher proportion of patients who were not receiving antiviral therapy (56.2 vs. 17.2%, P < 0.05). The median CT value of nucleic acid in the re-positive group was considerably greater than that at admission (36.7 vs. 22.6 P < 0.05). The findings demonstrated that neutralizing antibody treatment significantly raised the average IgG antibody level in patients, particularly in those who had not received COVID-19 vaccine (P < 0.05). The median lowest nucleic acid CT value of the ≥7 days group during the re-positive period and the immunoglobulin G (IgG) antibody level at discharge were lower than those in the <7 days group (P < 0.05). When compared to the non-positive group, patients in the ≥7 days group had a higher median virus nucleic acid CT value (27.1 vs. 19.2, P < 0.05) and absolute number of lymphocytes at admission (1,360 vs. 952, P < 0.05), and a lower IgG antibody level at discharge (P < 0.05). Conclusions: In conclusion, this study found that: (1) The re-positivity rate of SARS-CoV-2 Delta variant infection in this group was 35.6%, while the re-positivity rate was the same as that of the original strain 2 weeks after discharge (8.0%). (2) Young people, patients who did not use antiviral therapy or had low IgG antibody levels at discharge were more likely to have re-positive. And the CT value of nucleic acid at the time of initial infection was higher in re-positive group. We speculated that the higher the CT value of nucleic acid at the time of initial infection, the longer the intermittent shedding time of the virus. (3) Re-positive patients were asymptomatic. The median CT value of nucleic acid was > 35 at the re-positive time, and the close contacts were not detected as positive. The overall transmission risk of re-positive patients is low.

Latent profile analysis of security among patients with COVID-19 infection in mobile cabin hospitals and its relationship with psychological capital

Aim COVID-19 patients' security is related to their mental health. However, the classification of this group's sense of security is still unclear. The aim of our research is to clarify the subtypes of security of patients infected with COVID-19, explore the factors affecting profile membership, and examine the relationship between security and psychological capital for the purpose of providing a reference for improving patients' sense of security and mental health. Methods A total of 650 COVID-19 patients in a mobile cabin hospital were selected for a cross-sectional survey from April to May 2022. They completed online self-report questionnaires that included a demographic questionnaire, security scale, and psychological capital scale. Data analysis included latent profile analysis, variance analysis, the Chi-square test, multiple comparisons, multivariate logistical regression, and hierarchical regression analysis. Results Three latent profiles were identified—low security (Class 1), moderate security (Class 2), and high security (Class 3)—accounting for 12.00, 49.51, and 38.49% of the total surveyed patients, respectively. In terms of the score of security and its two dimensions, Class 3 was higher than Class 2, and Class 2 was higher than Class 1 (all P < 0.001). Patients with difficulty falling asleep, sleep quality as usual, and lower tenacity were more likely to be grouped into Class 1 rather than Class 3;Patients from families with a per capita monthly household income <3,000 and lower self-efficacy and hope were more likely to be grouped into Classes 1 and 2 than into Class 3. Psychological capital was an important predictor of security, which could independently explain 18.70% of the variation in the patients' security. Conclusions Security has different classification features among patients with COVID-19 infection in mobile cabin hospitals. The security of over half of the patients surveyed is at the lower or middle level, and psychological capital is an important predictor of the patients' security. Medical staff should actively pay attention to patients with low security and help them to improve their security level and psychological capital.

Effect of an online resourcefulness training in improving psychological well-being of front-line medical staff: a quasi-experimental study.

BACKGROUND: The global COVID-19 pandemic is still not under effective control, and strong workplace supports with comprehensive mental health interventions are urgently needed to help medical staff effectively respond to the pandemic. This study aimed to verify the effect of an online resourcefulness training program on the resourcefulness, and psychological variables of front-line medical staff working in the COVID-19 isolation ward. DESIGN: A pre-test and post-test quasi-experimental design with control group was employed. PARTICIPANTS: A total of 60 participants working in two isolation wards were recruited via convenience sampling. The two isolation wards were randomly assigned to the control group (isolation ward 1, n = 30) and the intervention group (isolation ward 2, n = 30). INTERVENTION: The participants were trained online by video conferences and WeChat. The control group received conventional training (e.g., psychological training, psychological counseling), while the intervention group received a 4-h online resourcefulness training. Both groups learned updated guidelines of COVID-19 simultaneously via video conference. The primary outcomes (resourcefulness, anxiety, depression and coping styles) and the secondary outcome (psychological resilience) were measured before intervention and three time points after intervention. RESULTS: After the intervention and one week after the intervention, the resourcefulness, resilience, and positive response scores of the participants in the intervention group were significantly higher than those of the control group. The anxiety and negative response scores in the intervention group were significantly lower than those of the control group (all p < 0.05). One month after the intervention, the scores of resourcefulness, tenacity, and positive response of the intervention group were higher than those of the control group (all p < 0.05). Repeated measures analysis of variance showed that the two groups of participants had statistically significant changes in the time-based effect and group-based effect in resourcefulness, resilience, anxiety scores and coping styles (p < 0.01). CONCLUSION: The results showed that our online resourcefulness training can significantly improve the resourcefulness, resilience, and positive response scores and effectively reduce anxiety and depression scores of front-line medical staff. This demonstrates that online resourcefulness training would be an effective tool for the psychological adjustment of front-line medical staff in fighting against COVID-19.

Analysis of College Students' Network Moral Behavior by the History of Ideological and Political Education under Deep Learning.

The research on the history of ideological and political education (IPE) is the basis for deepening it, and it is also of great help to higher education. The diversity of network information also easily leads to poor guidance for college students who are not strong in discrimination. This study adopts the method of a questionnaire survey to investigate the common moral anomie among college students in the network space. The survey data are sorted and classified and then input into the recurrent neural network structure for data analysis using deep learning (DL) algorithms. The results are fed back to the investigators intuitively and understandably. The results show that some college students have some problems, such as lack of network moral knowledge, vague values, moral behavior anomia, spatial knowledge and behavior inconsistency, and moral and emotional indifference. DL algorithms are added to the analysis process to make the findings more objective. These conclusions provide reference suggestions for subsequent research on college students' online moral behavior in the context of IPE history.

The impact of public health interventions on critical illness in the pediatric emergency department during the SARS-CoV-2 pandemic.

Study objective: The impact of public health interventions during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on critical illness in children has not been studied. We seek to determine the impact of SARS-CoV-2 related public health interventions on emergency healthcare utilization and frequency of critical illness in children. Methods: This was an interrupted time series analysis conducted at a single tertiary pediatric emergency department (PED). All patients evaluated by a provider from December 31 through May 14 of 6 consecutive years (2015-2020) were included. Total patient visits (ED and urgent care), shock trauma suite (STS) volume, and measures of critical illness were compared between the SARS-CoV-2 period (December 31, 2019 to May 14, 2020) and the same period for the previous 5 years combined. A segmented regression model was used to explore differences in the 3 outcomes between the study and control period. Results: Total visits, STS volume, and volume of critical illness were all significantly lower during the SARS-CoV-2 period. During the height of public health interventions, per day there were 151 fewer total visits and 7 fewer patients evaluated in the STS. The odds of having a 24-hour period without a single critical patient were >5 times higher. Trends appeared to start before the statewide shelter-in-place order and lasted for at least 8 weeks. Conclusions: In a metropolitan area without significant SARS-CoV-2 seeding, the pandemic was associated with a marked reduction in PED visits for critical pediatric illness.

Proadrenomedullin Predicts Severe Disease in Children With Suspected Community-acquired Pneumonia.

BACKGROUND: Proadrenomedullin (proADM), a vasodilatory peptide with antimicrobial and anti-inflammatory properties, predicts severe outcomes in adults with community-acquired pneumonia (CAP) to a greater degree than C-reactive protein and procalcitonin. We evaluated the ability of proADM to predict disease severity across a range of clinical outcomes in children with suspected CAP. METHODS: We performed a prospective cohort study of children 3 months to 18 years with CAP in the emergency department. Disease severity was defined as mild (discharged home), mild-moderate (hospitalized but not moderate-severe or severe), moderate-severe (eg, hospitalized with supplemental oxygen, broadening of antibiotics, complicated pneumonia), and severe (eg, vasoactive infusions, chest drainage, severe sepsis). Outcomes were examined using proportional odds logistic regression within the cohort with suspected CAP and in a subset with radiographic CAP. RESULTS: Among 369 children, median proADM increased with disease severity (mild: median [IQR], 0.53 [0.43-0.73]; mild-moderate: 0.56 [0.45-0.71]; moderate-severe: 0.61 [0.47-0.77]; severe: 0.70 [0.55-1.04] nmol/L) (P = .002). ProADM was significantly associated with increased odds of developing severe outcomes (suspected CAP: OR, 1.68; 95% CI, 1.2-2.36; radiographic CAP: OR, 2.11; 95% CI, 1.36-3.38) adjusted for age, fever duration, antibiotic use, and pathogen. ProADM had an AUC of 0.64 (95% CI, .56-.72) in those with suspected CAP and an AUC of 0.77 (95% CI, .68-.87) in radiographic CAP. CONCLUSIONS: ProADM was associated with severe disease and discriminated moderately well children who developed severe disease from those who did not, particularly in radiographic CAP.

Effect of virtual reality simulation training on the response capability of public health emergency reserve nurses in China: a quasiexperimental study.

OBJECTIVE: To develop a virtual reality simulation training programme, and further verify the effect of the programme on improving the response capacity of emergency reserve nurses confronting public health emergencies. DESIGN: A prospective quasiexperimental design with a control group. PARTICIPANTS: A total of 120 nurses were recruited and randomly divided into the control group and the intervention group. INTERVENTION: Participants underwent a 3-month training. The control group received the conventional training of emergency response (eg, theoretical lectures, technical skills and psychological training), while the intervention group underwent the virtual reality simulation training in combination with skills training. The COVID-19 cases were incorporated into the intervention group training, and the psychological training was identical to both groups. At the end of the training, each group conducted emergency drills twice. Before and after the intervention, the two groups were assessed for the knowledge and technical skills regarding responses to fulminate respiratory infectious diseases, as well as the capacity of emergency care. Furthermore, their pandemic preparedness was assessed with a disaster preparedness questionnaire. RESULTS: After the intervention, the scores of the relevant knowledge, the capacity of emergency care and disaster preparedness in the intervention group significantly increased (p<0.01). The score of technical skills in the control group increased more significantly than that of the intervention group (p<0.01). No significant difference was identified in the scores of postdisaster management in two groups (p>0.05). CONCLUSION: The virtual reality simulation training in combination with technical skills training can improve the response capacity of emergency reserve nurses as compared with the conventional training. The findings of the study provide some evidence for the emergency training of reserve nurses in better response to public health emergencies and suggest this methodology is worthy of further research and popularisation.

A Stepwise Approach to Inbound Passenger Management at a COVID-19 Temporary Medical Observation Site.

OBJECTIVE: To implement epidemic prevention among entry personnel, Shanghai has launched a number of Medical Observation Sites (Observation Site) for which high expectations are set. Ours is one such Observation Site. METHODS: As part of a novel project, we did not have any previous experience to use as reference. Despite some challenges, we achieved satisfactory outcomes by establishing a stepwise approach to inbound passenger management, including the division of the working area of the Observation Site, dynamic management of the rooms, closed-loop management of the isolated personnel. RESULTS: As of May 14, 2020, a total of 42 Observation Sites were operational in Pudong New Area. The following are the detailed descriptions of our Temporary Medical Observation Site set up and work flow. CONCLUSION: Early screening of inbound passengers as well as prompt and dynamic management of information about passengers' close contacts play an active role in preventing an influx of cases. As a pilot program, we have a model that is effective despite some limitations.

Analysis on the risk of respiratory virus transmission by air conditioning system operation based on experimental evidence.

It is important to know whether SARS-CoV-2 is spread through the air conditioning systems. Taking the central air conditioning system as an example, we analyze the mechanism and potential health risk of respiratory virus transmission in air-conditioned rooms and propose a method to study the risk of virus transmission in central air conditioning systems by investigating the data from medical experiments. The virus carrying capacity and the decay characteristics of indoor pathogen droplets are studied in this research. Additionally, the effects of air temperature and relative humidity on the virus survival in the air or on surfaces are investigated. The removal efficiency of infectious droplet nuclei by using an air conditioning filter was then determined. Thus, the transmission risk during the operation of the centralized air conditioning system is evaluated. The results show that the indoor temperature and humidity are controlled in the range of 20-25 °C and 40-70% by central air conditioning during the epidemic period, which not only benefits the health and comfort of residents, but also weakens the vitality of the virus. The larger the droplet size, the longer the viruses survive. Since the filter efficiency of the air conditioning filter increases with the increase in particle size, increasing the number of air changes of the circulating air volume can accelerate the removal of potential pathogen particles. Therefore, scientific operation of centralized air conditioning systems during the epidemic period has more advantages than disadvantages.

AN Analysis of COVID-19 Clinical Research Based on Trials & Publications in 2020

The global pandemic of COVID-19 has brought huge public health challenges to the world. To meet the challenge, researchers worldwide have carried out a series of clinical studies. This article aims to analyze the progress of COVID-19, and explore the development and main research directions in 2020. The clinical trials focus on the design of the trial plan, which can be registered on the platform after the design is completed. The purpose of clinical publications is to publish trial results, focusing on in vitro tests, drug screening and so on. Based on these characteristics, this paper analyzes both clinical publications and clinical trials, and explores the development of global clinical research in 2020 from countries, intervention methods and trial designs. The experimental results show that the United States and China have published the most publications and carried out the most clinical trials. The maximum intervention methods in clinical trials & publications are focused on the drugs.

PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.

AIM: COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. METHODS: In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. RESULTS: The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. CONCLUSION: This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

Fake news detection for epidemic emergencies via deep correlations between text and images.

In recent years, major emergencies have occurred frequently all over the world. When a major global public heath emergency like COVID-19 broke out, an increasing number of fake news in social media networks are exposed to the public. Automatically detecting the veracity of a news article ensures people receive truthful information, which is beneficial to the epidemic prevention and control. However, most of the existing fake news detection methods focus on inferring clues from text-only content, which ignores the semantic correlations across multimodalities. In this work, we propose a novel approach for Fake News Detection by comprehensively mining the Semantic Correlations between Text content and Images attached (FND-SCTI). First, we learn image representations via the pretrained VGG model, and use them to enhance the learning of text representation via hierarchical attention mechanism. Second, a multimodal variational autoencoder is exploited to learn a fused representation of textual and visual content. Third, the image-enhanced text representation and the multimodal fusion eigenvector are combined to train the fake news detector. Experimental results on two real-world fake news datasets, Twitter and Weibo, demonstrate that our model outperforms seven competitive approaches, and is able to capture the semantic correlations among multimodal contents.