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Attendance absences have a substantial impact on student's future physical and mental health as well as academic progress. Numerous personal, familial, and social issues are among the causes of student absences. Any kind of absence from school should be minimized. Extremely high rates of student absences may indicate the abrupt commencement of a serious school health crisis or public health crisis, such as the spread of tuberculosis or COVID-19, which provides school health professionals with an early warning. We take the extreme values in absence data as the object and attempt to apply the extreme value theory (EVT) to describe the distribution of extreme values. This study aims to predict extreme instances of student absences. School health professionals can take preventative measures to reduce future excessive absences, according to the predicted results. Five statistical distributions were applied to individually characterize the extreme values. Our findings suggest that EVT is a useful tool for predicting extreme student absences, thereby aiding preventative measures in public health., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Aims: To explore the association between cognitive emotion regulation strategies and anxiety and depression among nurses during the COVID-19 outbreak., Background: Nurses play a vital role in responding to the COVID-19 outbreak, but many of them suffer from psychological problems due to the excessive workload and stress. Understanding the correlation between cognitive emotion regulation strategies and anxiety and depression will promote targeted psychosocial interventions for these affected nurses., Methods: This cross-sectional study of 586 nurses was conducted in Eastern China. Participants completed online questionnaires that investigated anxiety, depression and cognitive emotion regulation strategies., Results: The prevalence of nurses' anxiety and depression was 27.6% and 32.8%, respectively. Lower self-blame, rumination and catastrophizing, as well as greater acceptance and positive refocusing, were related to fewer symptoms of anxiety or depression., Conclusion: The cognitive emotion regulation strategies of acceptance and positive refocusing contribute to reducing anxiety or depression. These strategies should be considered when implementing psychotherapeutic interventions to improve nurses' adverse emotional symptoms., Implications for Nursing Management: This study highlights the need to assess cognitive emotion regulation strategies use in screening for anxiety and depression. Nurse managers should develop psychosocial interventions including appropriate strategies to help nurses with adverse emotions during a pandemic., (© 2021 John Wiley & Sons Ltd.)

Background: The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood., Methods: Using the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal-Wallis tests were used in our statistical analysis., Results: A total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties., Conclusions: The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19.

Introduction: The secondary attack rate (SAR) measures the transmissibility of an infectious agent. The reported SAR of COVID-19 varied in a broad range, and between different contact settings., Methodology: We conducted a meta-analysis on the SAR of COVID-19 with adherence to the PRISMA guideline. We searched published literatures and preprints in international databases of PubMed and medRxiv, and in five major Chinese databases as of 20 April 2020, using the following search terms: ("COVID-19" and "secondary attack rate") or ("COVID-19" and "close contact"). The random effect model was chosen for pooled analyses, using R (version 3.6.3)., Results: A total of 1,136 references were retrieved and 18 of them remained after screening. The pooled SAR of COVID-19 was 0.07 (95%: 0.03-0.12) in general. It differed significantly between contact settings, peaking in households (0.20, 95%: 0.15-0.28), followed by in social gatherings (0.06, 95%: 0.03-0.10). The point estimates of the pooled SARs in health facilities, transports, and work/study settings were all as low as 0.01. Among all the secondary cases, the proportion of asymptomatic infections was estimated to be 0.17 (95% CI: 0.09 - 0.34). The proportion was higher in households (0.26, 95% CI: 0.12-0.56), than in other contact settings., Conclusions: The transmission risk of SARS-CoV-2 is much higher in households than in other scenarios. Identification of asymptomatic secondary infections should be enhanced in households., Competing Interests: No Conflict of Interest is declared, (Copyright (c) 2020 Ting Tian, Xiang Huo.)

Currently, coronavirus disease 2019 (COVID-19) is spreading rapidly around the world. This study aimed to investigate whether the presence of acute kidney injury (AKI) might increase the risk of severe infection and fatality in COVID-19 patients. We searched the PubMed, Web of Science, ScienceDirect, MedRxiv and COVID-19 academic research communication platforms for studies reporting severe infection rates and case-fatality rates in COVID-19 patients with and without AKI up to June 20, 2020. The main outcomes were the comparisons of the severe infection rates and fatality rates in COVID-19 patients with and without AKI and the estimation of the odds ratio (OR) and its 95 % confidence interval (CI) for severe infection and mortality. Statistical analyses were performed with R statistical software. A total of 40 studies involving 24,527 patients with COVID-19 were included in our meta-analysis. The incidence of AKI was 10 % (95 % CI 8%-13 %) in COVID-19 patients. The patients had higher severe infection and fatality rates (55.6 % vs. 17.7 % and 63.1 % vs. 12.9 %, respectively, all P < 0.01) with COVID-19. AKI was a predictor of fatality (OR = 14.63, 95 % CI: 9.94-21.51, P < 0.00001) and severe infection (OR = 8.11, 95 % CI: 5.01-13.13, P < 0.00001) in patients with COVID-19. Higher levels of serum creatinine (Scr) and blood urea nitrogen (BUN) were associated with a significant increase in fatality [Scr: mean difference (MD): 20.19 μmol/L, 95 % CI: 14.96-25.42, P < 0.001; BUN: MD: 4.07 mmol/L, 95 % CI: 3.33-4.81, P < 0.001] and severe infection (Scr: MD: 7.78 μmol/L, 95 % CI: 4.43-11.14, P < 0.00001, BUN: MD: 2.12 mmol/L, 95 % CI: 1.74-2.50, P < 0.00001) in COVID-19 patients. In conclusion, AKI is associated with severe infection and higher fatality rates in patients with COVID-19. Clinicians should pay more attention to the monitoring and treatment of COVID-19 patients with AKI., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Heterologous immunity, when the memory T cell response elicited by one pathogen recognizes another pathogen, has been offered as a contributing factor for the high variability in coronavirus disease 2019 (COVID-19) severity outcomes. Here we demonstrate that sensitization with bacterial peptides can induce heterologous immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) derived peptides and that vaccination with the SARS-CoV-2 spike protein can induce heterologous immunity to bacterial peptides. Using in silico prediction methods, we identified 6 bacterial peptides with sequence homology to either the spike protein or non-structural protein 3 (NSP3) of SARS-CoV-2. Notwithstanding the effects of bystander activation, in vitro co-cultures showed that all individuals tested (n=18) developed heterologous immunity to SARS-CoV-2 peptides when sensitized with the identified bacterial peptides. T cell recall responses measured included cytokine production (IFN-γ, TNF, IL-2), activation (CD69) and proliferation (CellTrace). As an extension of the principle of heterologous immunity between bacterial pathogens and COVID-19, we tracked donor responses before and after SARS-CoV-2 vaccination and measured the cross-reactive T cell responses to bacterial peptides with similar sequence homology to the spike protein. We found that SARS-CoV-2 vaccination could induce heterologous immunity to bacterial peptides. These findings provide a mechanism for heterologous T cell immunity between common bacterial pathogens and SARS-CoV-2, which may explain the high variance in COVID-19 outcomes from asymptomatic to severe. We also demonstrate proof-of-concept that SARS-CoV-2 vaccination can induce heterologous immunity to pathogenic bacteria derived peptides.

Since February 2020, COVID-19 has spread rapidly to more than 200 countries in the world. During the pandemic, local governments in China have implemented different interventions to efficiently control the spread of the epidemic. Characterizing transmission of COVID-19 under some typical interventions is essential to help countries develop appropriate interventions. Based on the pre-symptomatic transmission patterns of COVID-19, we established a novel compartmental model: Susceptible-Infectious-Confirmed-Removed (SICR) model, which allowed the effective reproduction number to change over time, thus the effects of policies could be reasonably estimated. Using the epidemic data of Wuhan, Wenzhou, and Shenzhen, we migrated the corresponding estimated policy modes to South Korea, Italy, and the United States and simulated the potential outcomes for these countries when they adopted similar policy strategies to China. We found that the mild interventions implemented in Shenzhen were effective in controlling the epidemic in the early stage, while more stringent policies which were implemented in Wuhan and Wenzhou were necessary if the epidemic became severe and needed to be controlled in a short time. [ABSTRACT FROM AUTHOR]

The pandemic of COVID-19 has caused severe public health consequences around the world. Many interventions of COVID-19 have been implemented. It is of great public health and social importance to evaluate the effects of interventions in the pandemic of COVID-19. With the help of a synthetic control method, the regression discontinuity, and a state-space compartmental model, we evaluated the treatment and stagewise effects of the intervention policies. We found statistically significant treatment effects of broad stringent interventions in Wenzhou and mild interventions in Shanghai to subdue the epidemic's spread. If those reduction effects were not activated, the expected number of positive individuals would increase by 2.18 times on February 5, 2020, for Wenzhou and 7.69 times on February 4, 2020, for Shanghai, respectively. Alternatively, regression discontinuity elegantly identified the stringent (p-value: <0.001) and mild interventions (p-value: 0.024) lowered the severity of the epidemic. Under the compartmental modeling for different interventions, we understood the importance of implementing the interventions. The highest level alert to COVID-19 was practical and crucial at the early stage of the epidemic. Furthermore, the physical/social distancing policy was necessary once the spread of COVID-19 continued. If appropriate control measures were implemented, then epidemic would be under control effectively and early. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]