During the first wave of COVID-19, China demonstrated a strong commitment to epidemic prevention and control. This case study focuses on Z University, which adopted closed management when the epidemic was serious, and examines the influence of COVID-19 on students\' psychology and behavior through interviews with 10 students. The research reveals that while students perceive closed management during the epidemic as enhancing safety and promoting learning engagement to some extent, the epidemic also has adverse effects on their physical health, psychology, and social life. These impacts included deteriorating physical health, feelings of rebellion and depression regarding college life, alongside concerns and aspirations regarding future job stability. In the discussion, we suggest that higher education institutions can utilize this information to shape policies and procedures, particularly concerning mental health and risk communication, not only during the current pandemic but also in future emergency or disaster scenarios.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted various aspects of healthcare services, including emergency care services. Healthcare staff face mental issues and physical exertion when caring for patients potentially infected with COVID-19. Understanding the experiences and perspectives of emergency department (ED) healthcare staff during the COVID-19 pandemic is essential to inform evidence-based interventions and strategies to mitigate the impact on emergency care services. This study aims to investigate the experiences of ED healthcare staff regarding emergency care services during the COVID-19 pandemic, thus providing valuable insights into the challenges faced.
METHODS: This study utilized a cross-sectional study design. Data were collected from 256 ED healthcare staff working in nine different hospitals located in Turkey between November 15, 2021, and December 30, 2021. Data were analyzed using descriptive statistics.
RESULTS: A total of 256 participants were included in the study. Of the participants, 58.6% were nurses, 19.5% were ED doctors, and 21.9% were emergency medical technicians. In addition, 67.2% of the participants were infected with COVID-19, and almost all of them (94.1%) were psychologically affected by the pandemic process. It was found that 85.2% of ED healthcare staff felt excluded by society due to being healthcare staff and 71.9% had to be separated from their families. Nurses were separated from their families at the highest rate (78%) during this period.
CONCLUSIONS: More than half of the ED healthcare staff had problems accessing protective equipment and were separated from their families during the pandemic due to the risk of COVID-19 transmission. Although the number of ED visits decreased because of restrictions at the beginning of the pandemic, ED visits increased again with the abolition of restrictions.

UNASSIGNED: Syndromic surveillance represents a potentially inexpensive supplement to test-based COVID-19 surveillance. By strengthening surveillance of COVID-19-like illness (CLI), targeted and rapid interventions can be facilitated that prevent COVID-19 outbreaks without primary reliance on testing.
UNASSIGNED: This study aims to assess the temporal relationship between confirmed SARS-CoV-2 infections and self-reported and health care provider-reported CLI in university and county settings, respectively.
UNASSIGNED: We collected aggregated COVID-19 testing and symptom reporting surveillance data from Cornell University (2020-2021) and Tompkins County Health Department (2020-2022). We used negative binomial and linear regression models to correlate confirmed COVID-19 case counts and positive test rates with CLI rate time series, lagged COVID-19 cases or rates, and day of the week as independent variables. Optimal lag periods were identified using Granger causality and likelihood ratio tests.
UNASSIGNED: In modeling undergraduate student cases, the CLI rate (P=.003) and rate of exposure to CLI (P<.001) were significantly correlated with the COVID-19 test positivity rate with no lag in the linear models. At the county level, the health care provider-reported CLI rate was significantly correlated with SARS-CoV-2 test positivity with a 3-day lag in both the linear (P<.001) and negative binomial model (P=.005).
UNASSIGNED: The real-time correlation between syndromic surveillance and COVID-19 cases on a university campus suggests symptom reporting is a viable alternative or supplement to COVID-19 surveillance testing. At the county level, syndromic surveillance is also a leading indicator of COVID-19 cases, enabling quick action to reduce transmission. Further research should investigate COVID-19 risk using syndromic surveillance in other settings, such as low-resource settings like low- and middle-income countries.

OBJECTIVE: Children are generally considered main drivers of transmission for respiratory viruses, but the emergence of SARS-CoV-2 challenged this paradigm. Human rhinovirus (RV) continued to co-circulate throughout the pandemic, allowing for direct comparison of age-specific infectivity and susceptibility within households between these viruses during a time of low SARS-CoV-2 population immunity.
METHODS: Households with children were prospectively monitored for ≥23 weeks between August 2020 and July 2021. Upon onset of respiratory symptoms in a household, an outbreak study was initiated, including questionnaires and repeated nasal self-sampling in all household members. Swabs were tested by PCR. Age-stratified within-household secondary attack rates (SARs) were compared between SARS-CoV-2 and RV.
RESULTS: 307 households participated including 582 children and 627 adults. Overall SAR was lower for SARS-CoV-2 than for RV (aOR 0.55) and age-distributions differed between both viruses (p<0.001). Following household exposure, children were significantly less likely to become infected with SARS-CoV-2 compared to RV (aOR 0.16), whereas this was opposite in adults (aOR 1.71).
CONCLUSIONS: In households, age-specific susceptibility to SARS-CoV-2 and RV differs and drives differences in household transmission between these pathogens. This highlights the importance of characterizing age-specific transmission risks, particularly for emerging infections, to guide appropriate infection control interventions.

UNASSIGNED: The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies\' impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited.
UNASSIGNED: To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia.
UNASSIGNED: Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020-2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods.
UNASSIGNED: The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies.
UNASSIGNED: This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures\' intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.
Main findings: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.Added knowledge: By providing a detailed analysis of testing disparities and quarantine policies’ effectiveness within a specific Latin American context, our research fills a critical gap in understanding their impacts on health system responses and disease control.Global health impact for policy and action: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.

In a retrospective view, this review examines the impact of mucormycosis on health workers and researchers during the COVID era. The diagnostic and treatment challenges arising from unestablished underlying pathology and limited case studies add strain to healthcare systems. Mucormycosis, caused by environmental molds, poses a significant threat to COVID-19 patients, particularly those with comorbidities and compromised immune systems. Due to a variety of infectious Mucorales causes and regionally related risk factors, the disease\'s incidence is rising globally. Data on mucormycosis remains scarce in many countries, highlighting the urgent need for more extensive research on its epidemiology and prevalence. This review explores the associations between COVID-19 disease and mucormycosis pathology, shedding light on potential future diagnostic techniques based on the fungal agent\'s biochemical components. Medications used in ICUs and for life support in ventilated patients have been reported, revealing the challenge of managing this dual onslaught. To develop more effective treatment strategies, it is crucial to identify novel pharmacological targets through \"pragmatic\" multicenter trials and registries. In the absence of positive mycology culture data, early clinical detection, prompt treatment, and tissue biopsy are essential to confirm the specific morphologic features of the fungal agent. This review delves into the history, pathogens, and pathogenesis of mucormycosis, its opportunistic nature in COVID or immunocompromised individuals, and the latest advancements in therapeutics. Additionally, it offers a forward-looking perspective on potential pharmacological targets for future drug development.

OBJECTIVE: An unprecedented rise in eating disorder presentations has been documented in several countries during the COVID-19 pandemic. We explored this phenomenon by analyzing nationwide psychiatric admissions over 5 years, controlling for demographic variables.
METHODS: We retrospectively analyzed all hospitalizations in New Zealand with a primary psychiatric diagnosis from 2017 to 2021, using Poisson regression to calculate admission rates by diagnosis, before and during the pandemic. Using Fisher\'s exact test and Poisson modeling, national data were validated against a manually collected sample of eating disorder admissions.
RESULTS: Eating disorder admissions rose significantly during the pandemic (RR 1.48, p < 0.0001), while other diagnoses remained unchanged or decreased slightly. Anorexia nervosa in 10 to 19-year-old females drove increases, with persistent elevations noted in the 10-14 age group. Pandemic-associated increases were more striking for Māori (RR 2.55), the indigenous Polynesian population, compared with non-Māori (RR 1.43).
CONCLUSIONS: Eating disorder hospital presentations increased during the COVID-19 pandemic, while other psychiatric presentations to hospital remained relatively unchanged. Possible drivers include disrupted routines, barriers to healthcare access, altered social networks, and increased social media use. Clinical services require additional resources to manage the increased disease burden, especially in vulnerable pediatric and indigenous populations. Ongoing monitoring will be required to establish the time-course of pandemic-related clinical demand.

OBJECTIVE: Guidelines recommend non-invasive positive pressure ventilation (NPPV) for patients with acute decompensated heart failure (ADHF) with an inadequate response to initial oxygen therapy. During Japan\'s coronavirus disease 2019 pandemic, NPPV use in emergency departments (EDs) was limited due to aerosol-spreading concerns. This study compared the respiratory management and clinical outcomes of patients with ADHF in EDs before and during the pandemic.
RESULTS: This retrospective cohort study was conducted at a single centre in Japan using hospital data from September to November 2019 (before the pandemic) and September to November 2020 (during the pandemic). Patients diagnosed with ADHF were included. Patients not responding to standard oxygen therapy were intubated or started on NPPV therapy. The primary outcome measure was discharge after death. The secondary outcomes were length of hospital stay and medical expenses. The study included 37 patients before the pandemic and 36 during the pandemic. No significant differences were found in vital signs or laboratory data between the groups. NPPV utilization decreased significantly from 26 (70.3%) to 7 (19.4%) (P < 0.01). Two patients required intubation during both periods, with no significant differences (P = 0.98). No significant intergroup disparities were observed in discharge after death (1/36 [2.7%] vs. 1/37 [2.7%]; P = 0.19), length of hospital stay (17.5 vs. 19.0 days; P = 0.65), and medical expenses (57 590 vs. 57 600 yen; P = 0.65).
CONCLUSIONS: Despite a large decrease in NPPV use before and during the pandemic, there were no significant differences in discharge after death, hospital stay, or medical expenses.

The impact of COVID-19 on paracoccidioidomycosis (PCM) in Argentina and the consequences generated by the pandemic are discussed. From 2018 to 3 years after the pandemic declaration, 285 proven PCM patients were registered. No association between both diseases was documented. PCM frequency decreased to extremely low levels in 2020. Mandatory social isolation and the emotional and psychological effects generated under pandemic circumstances led to delays in diagnosis, severe disseminated cases, and other challenges for diagnosis in subsequent years. Probable underdiagnosis should be considered due to the overlap of clinical manifestations, the low index of suspicion and the lack of sensitive diagnostic tools.

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.
METHODS: Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).
RESULTS: The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.
CONCLUSIONS: Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients\' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.