Wave Of COVID-19 Research Articles

Balancing act: How do local governments use social media in crises?

Governments can benefit from social media usage in crisis management, but the amplified public scrutiny facilitated by social media may also impede crisis management and damage reputations. How governments balance such benefits and risks when using social media for crisis communication remains an open question. With a data set of epidemic statistics and social media posts of local governments across China during the first wave of COVID-19, we quantitatively investigate how the local crisis situations and coping resources influenced government usage of social media. Our analysis reveals that the first confirmed case in a city leads to a significant increase in government social media use. This effect is stronger in cities with more medical resources. In addition, the number of cumulative cases has an inverted U-shaped influence on government social media use. These findings suggest that governments value the importance of social media for crisis handling and reputation protection, adapting their communication strategies based on the crisis severity and their own capacities.

Risks of SARS-CoV-2 JN.1 infection and COVID-19 associated emergency-department (ED) visits/hospitalizations following updated boosters and prior infection: a population-based cohort study.

Data on protection afforded by updated COVID-19 vaccines (bivalent/XBB 1.5 monovalent) against the emergent JN.1 variant remains limited. We conducted a retrospective population-based cohort study amongst all boosted Singaporeans aged ≥18 years during a COVID-19 wave predominantly driven by JN.1, from 26th November 2023 to 13th January 2024. Multivariable Cox regression was utilised to assess risk of SARS-CoV-2 infection and COVID-19 associated emergency-department (ED) visits/hospitalizations, stratified by vaccination status/prior infection; with individuals last boosted ≥1 year utilized as the reference category. Vaccination and infection status were classified using national registries. 3,086,562 boosted adult Singaporeans were included in the study population, accounting for 146,863,476 person-days of observation. During the JN.1 outbreak, 28,160 SARS-CoV-2 infections were recorded, with 2,926 hospitalizations and 3,747 ED-visits. Compared with individuals last boosted ≥1 year prior with ancestral monovalent vaccines, receipt of an updated XBB.1.5 booster 8-120 days prior was associated with lower risk of JN.1 infection (adjusted-hazard-ratio, aHR = 0.59[0.52-0.66]), COVID-19 associated ED-visits (aHR = 0.50[0.34-0.73]) and hospitalizations(aHR = 0.58[0.37-0.91]), while receipt of a bivalent booster 121-365 days prior was associated with lower risk of JN.1 infection (aHR = 0.92[0.88-0.95]) and ED-visits (aHR = 0.80[0.70-0.90]). Lower risk of COVID-19 hospitalization during the JN.1 outbreak (aHR = 0.57[0.33-0.97]) was still observed following receipt of an updated XBB.1.5 booster 8-120 days prior, even when analysis was restricted to previously infected individuals. Recent receipt of updated boosters conferred protection against SARS-CoV-2 infection and ED-visits/hospitalization during a JN.1 variant wave, in both previously infected and uninfected individuals. Annual booster doses confer protection during COVID-19 endemicity.

The impact of COVID-19 post-infection on the cognition of adults from Peru.

The COVID-19 pandemic, with over 83 million confirmed cases and 1.8 million deaths, has raised concerns about long-term cognitive issues, especially in populations facing disparities. Despite a few years since Peru's first COVID-19 wave, the cognitive effects on adults remain unclear. This study is the first in Peru to explore COVID-19's impact on general cognition and executive function. A retrospective cross-sectional study compared individuals with COVID-19 history to controls, assessing general cognition, verbal fluency, attention, and executive function. Among 240 assessed, 154 met the study inclusion criteria, with about 60% female and an average age of 38.89 ± 16.001 years. Groups included controls (n = 42), acute phase (AP, n = 74) (1-14 days of symptoms), and hyperinflammatory phase (HP, n = 38) (>14 days of symptoms). Significant cognitive differences were observed. The HP group exhibited lower general cognitive performance (p = 0.02), working memory (p = 0.01), and executive function (planning; p < 0.001; flexibility; p = 0.03) than controls. Those with <14 days of illness (AP vs. HP) had deficits in general cognitive performance (p = 0.02), working memory (p = 0.02), and planning (p < 0.001), mainly during the hyperinflammatory phase, showing differences in working memory (p = 0.003) and planning (p = 0.01). Gender differences emerged, with males in the HP phase having poorer working memory (p = 0.003) and planning (p = 0.01). This study underscores COVID-19's negative impact on cognitive function, even in mild cases, with potential heightened effects in men during acute or hyperinflammatory phases. The findings provide Peru's first evidence, highlighting the vulnerability of populations facing socioeconomic disparities.

Ambulatory antibiotic prescription rates for acute respiratory infection rebound two years after the start of the COVID-19 pandemic.

During the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain lowered. We used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend. At baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period. The decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates.

Stock Market Resilience: Evaluating Islamic and Conventional Indices through COVID-19 Waves

The study examined the impact of the five COVID-19 waves on Pakistan's conventional (KSE-100) and Islamic (KMI-30) stock indices from March 3, 2020, to August 30, 2022. The DCC GARCH (1, 1) model found weak, predominantly negative connections between COVID-19 cases each day and stock returns. Visual data indicated a slight decrease in both indices as cases increased, reflecting previous findings on pandemics' adverse effects on stock markets. A thorough examination of the linkages between COVID-19 cases and financial indicators using Wavelet Coherence analysis highlighted intricate connections that are important to policymakers and financiers. Pakistan's KSE100 and KMI-30 indices showed a constant significant positive dynamic conditional association that persisted throughout the investigation. This challenges the perception that the Islamic index serves as a safe haven during financial crises. According to the research, Islamic stock markets, as reflected by the KMI-30, were not as protective or as good at hedging during crises like the COVID-19 pandemic. In fact, they were more susceptible to economic downturns. When navigating the effects of global health crises on financial markets, investors and policymakers must have a thorough understanding of these dynamics.

Determinants of Perceived Stress among Indian Adults during Second Wave of COVID-19

The present study was conducted from June–July, 2021 in online mode and a questionnaire was designed at the Department of Food Science and Nutrition, College of Community Science, G. B. Pant University of Agriculture and Technology, Pantnagar. The study aims to find an association of perceived stress with socio-demographic factors, health profile and physical activity among Indian adults during the second wave of COVID-19. Data on perceived stress was collected through an online cross-sectional survey of 1933 Indian adults based on the Perceived Stress Scale (PSS). Multivariate ordinal regression was conducted to identify the predictors of perceived stress. Indian adults were moderately stressed, with a mean PSS score of 19.7±5.5 during the crisis time of COVID-19. The study population remained moderately active (59.3%) during the lockdown period. Perceived stress was negatively associated with age, education, income, and physical activity and positively associated with random blood sugar levels and sitting time. Odds of perceived stress increased with female gender, separated marital status, having one child or adolescent in the family, graduate or postgraduate as the educational status of the family’s head, and increased sitting time. High educational status, retirement, regular employment, middle or lower-class social class, and high physical activity level were negative predictors of perceived stress. The current investigation highlights the role of multiple factors in the perception of stress and the need for tailored interventions for vulnerable groups focusing on education and physical activity.

Seroprevalence of SARS-CoV-2 and risk factors for infection among children in Uganda: a serial cross-sectional study.

Understanding COVID-19's impact on children is vital for public health policy, yet age-specific data is scarce, especially in Uganda. This study examines SARS-CoV-2 seroprevalence and risk factors among Ugandan children at two timepoints, along with COVID-19-related knowledge and practices in households, including adult vaccination status. Baseline surveys were conducted in 12 communities from April to May 2021 (post-Alpha wave) and follow-up surveys in 32 communities from November 2021 to March 2022 (Omicron wave). Household questionnaires and blood samples were collected to test for malaria by microscopy and for SARS-CoV-2 using a Luminex assay. Seroprevalence was estimated at both the survey and community level. Mixed-effects logistic regression models assessed the association between individual and household factors and SARS-CoV-2 seropositivity in children, adjusting for household clustering. More households reported disruptions in daily life at baseline compared to follow-up, though economic impacts lingered. By the follow-up survey, 52.7% of adults had received at least one COVID-19 vaccine dose. Overall seroprevalence in children was higher at follow-up compared to baseline (71.6% versus 19.2%, p < 0.001). Seroprevalence in children ranged across communities from 6-37% at baseline and 50-90% at follow-up. At baseline, children from the poorest households were more likely to be infected. Increasing age remained the only consistent risk factor for SARS-CoV-2 seroconversion at both timepoints. Results indicate that a larger number of children were infected by the Delta and Omicron waves of COVID-19 compared to the Alpha wave. This study is the largest seroprevalence survey in children in Uganda, providing evidence that most children were infected with SARS-CoV-2 before the vaccine was widely available to pediatric populations. Pediatric infections were vastly underreported by case counts, highlighting the importance of seroprevalence surveys in assessing disease burden when testing and reporting rates are limited and many cases are mild or asymptomatic.

SARS-CoV-2 viral titer measurements in Ontario, Canada wastewaters throughout the COVID-19 pandemic

During the COVID-19 pandemic, the Province of Ontario, Canada, launched a wastewater surveillance program to monitor SARS-CoV-2, inspired by the early work and successful forecasts of COVID-19 waves in the city of Ottawa, Ontario. This manuscript presents a dataset from January 1, 2021, to March 31, 2023, with RT-qPCR results for SARS-CoV-2 genes and PMMoV from 107 sites across all 34 public health units in Ontario, covering 72% of the province’s and 26.2% of Canada’s population. Sampling occurred 2–7 times weekly, including geographical coordinates, serviced populations, physico-chemical water characteristics, and flowrates. In doing so, this manuscript ensures data availability and metadata preservation to support future research and epidemic preparedness through detailed analyses and modeling. The dataset has been crucial for public health in tracking disease locally, especially with the rise of the Omicron variant and the decline in clinical testing, highlighting wastewater-based surveillance’s role in estimating disease incidence in Ontario.

Identifying waves of COVID-19 mortality using skew normal curves

We propose a model for multiple waves of an epidemic that decomposes the health outcome of interest into the sum of scaled skew normal curves. When applied to daily COVID-19 mortality in six regions (Japan, Italy, Belgium, Ontario, Texas, and Peru), this model provides three notable results. First, when fit to data from early 2020 to May 31, 2022, the estimated skew normal curves substantially overlap with the dates of COVID-19 waves in Ontario and Belgium, as determined by their respective health authorities. Second, the asymmetry of the skew normal curves changes over time – they progress from increasing more quickly to decreasing more quickly, indicating changes in the relative speed that daily COVID-19 mortality rises and falls over time. Third, most regions have day-of-the-week effects, which suggests that day-of-the-week effects should be included when modeling daily COVID-19 mortality. We conclude by discussing limitations and possible extensions of this model and its results, including commenting on its applicability to potential future COVID-19 waves.

Influence of the first wave of COVID-19 on Chinese students' psychology and behavior: a case study approach.

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.

Automated surveillance of antimicrobial consumption in intensive care, northern Sweden: an observational case study

BackgroundThe digitalization of information systems allows automatic measurement of antimicrobial consumption (AMC), helping address antibiotic resistance from inappropriate drug use without compromising patient safety.ObjectivesDescribe and characterize a new automated AMC surveillance service for intensive care units (ICUs), with data stratified by referral clinic and linked with individual patient risk factors, disease severity, and mortality.MethodsAn automated service collecting data from the electronic medical record was developed, implemented, and validated in a healthcare region in northern Sweden. We performed an observational study from January 1, 2018, to December 31, 2021, encompassing general ICU care for all ≥18-years-olds in a catchment population of 270000 in secondary care and 900000 in tertiary care. We used descriptive analyses to associate ICU population characteristics with AMC outcomes over time, including days of therapy (DOT), length of therapy, defined daily doses, and mortality.ResultsThere were 5608 admissions among 5190 patients with a median age of 65 (IQR 48-75) years, 41.2% females. The 30-day mortality was 18.3%. Total AMC was 1177 DOTs in secondary and 1261 DOTs per 1000 patient days and tertiary care. AMC varied significantly among referral clinics, with the highest total among 810 general surgery admissions in tertiary care at 1486 DOTs per 1000 patient days. Case-mix effects on the AMC were apparent during COVID-19 waves highlighting the need to account for case-mix. Patients exposed to more than three antimicrobial drug classes (N = 242) had a 30-day mortality rate of 40.6%, with significant variability in their expected rates based on admission scores.ConclusionWe introduce a new service and instructions for automating local ICU-AMC data collection. The versatile long-term ICU-AMC metrics presented, covering patient factors, referral clinics and mortality outcomes, are expected to be beneficial in refining antimicrobial drug use.

University Students' Vaccination Intention after the Fifth Wave of the COVID-19 Outbreak in Hong Kong: Inspiration from a Health Belief Model.

The fifth wave of COVID-19, driven by the Omicron variant, started to surge in Hong Kong in December 2021. Previous studies have shown that younger adults, compared to older adults, are vulnerable to increased risks of side effects after vaccination. However, little is known about the COVID-19 vaccination behavior among younger adults, especially university students, in Hong Kong. Therefore, the present online survey study aimed to investigate the predictors of COVID-19 vaccination intention among university students in Hong Kong using the Health Belief Model (HBM) as a framework. Two other potential predictors, the previous influenza vaccine uptake frequency and the Hong Kong SAR government vaccination lottery program, were also examined. The intention to receive another dose of the COVID-19 vaccine was low (36.4%). Multivariate binomial logistic regression analysis showed that, after controlling for demographic and baseline characteristics, the perceived susceptibility (OR = 2.98, CI = 1.18-7.53) and previous influenza vaccine uptake frequency (OR = 1.54, CI = 1.08-2.19) significantly and positively predicted the COVID-19 vaccination intention. However, the government vaccination lottery program (i.e., wining prizes for being vaccinated) (OR = 0.87, CI = 0.34-2.26) was not a significant motivator of COVID-19 vaccination. Future public health campaigns should focus on the individual susceptibility to COVID-19 and past influenza vaccination history to promote increased vaccination uptake among university students.

Credit risk and Tunisian bank stability in the Covid-19 wave

&lt;p style="text-align:justify"&gt;&lt;span style="font-size:16px"&gt;&lt;span style="font-family:Times New Roman,Times,serif"&gt;&lt;span dir="ltr" lang="EN-US"&gt;&lt;span style="color:black"&gt;Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

Complexity and Diversity of the Neurological Spectrum of SARS-CoV-2 over Three Waves of COVID-19.

Background/Objectives: SARS-CoV-2 continually mutates, with five identified variants. Many neurological manifestations were observed during the COVID-19 pandemic, with differences between virus variants. The aim of this study is to assess the frequency and characteristics of neurological manifestations during COVID-19 in hospitalized patients over three waves in Poland with comparison and analysis correlation with the course of infection. Methods: This retrospective single-center study included 600 consecutive adults with confirmed COVID-19, hospitalized during 3 waves (pre-Delta, Delta and Omicron) in Poland. Demographic and clinical information and neurological manifestations were collected and compared across three periods. Results: The median age of the study group was 68, lower during the Delta wave. In the Omicron period, the disease severity at admission and inflammatory markers concentration were the lowest. Neurological manifestations were observed in 49%. The most common were altered mentation, headache, myalgia, mood disorder, ischemic stroke and encephalopathy. Smell and taste disturbances (STDs) were less frequent in the Omicron period. Neurological complications were predominant in the pre-Delta and Omicron periods. Ischemic stroke was observed more often in pre-Delta period. Altered mentation was related to higher severity at admission, worse lab test results, higher admission to ICU and mortality, while headache reduced mortality. Pre-existing dementia was related to higher mortality. Conclusions: Neurological manifestations of COVID-19 are frequent, with a lower rate of STDs in the Omicron period and more often cerebrovascular diseases in the pre-Delta period. Headache improves the course of COVID-19, while altered mentation, stroke and neurological comorbidities increase severity and mortality.

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study.

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

Police-referred psychiatric emergency presentations during the first and second wave of COVID-19 in Berlin, Germany: a retrospective chart review

BackgroundLiterature on psychiatric emergency services (PES) presentations during the COVID-19 pandemic showed heterogeneous results regarding patients brought in by police (BIBP). This is the first study primarily focusing on patients BIBP in a PES during the COVID-19-period.MethodsCase documentation records during the first and second wave of the COVID-19 pandemic in a PES in Berlin, Germany were analyzed using descriptive data analysis and binomial logistic regression analysis to detect factors that predict presentations BIBP.Results5440 PES presentations: 20.4% BIBP during the first wave vs. 16.3% during its control period; second wave: 17.6% BIBP vs. 14.9% during its control period. In both waves, absolute increases in presentations BIBP were seen compared to control (p = .029, p = .028, respectively). COVID-19-period was a predictor for presentations BIBP during the first and the second wave. The following factors also predicted presentations BIBP: younger age, male gender, aggressive behavior, suicide attempt prior to presentation and diagnosis of psychotic or substance use disorders; depressive disorders were negatively associated.ConclusionsDuring the two first waves of the COVID-19 pandemic, there was an increase in presentations BIBP in a PES in Berlin. Regression analysis shows that the pandemic itself was a predictor of presentations BIBP. The underlying factors of this association need to be further elucidated in future research. Additionally, general factors predicting PES presentations BIBP are reported that replenish the present literature.

Sectoral performance of Indian stock market during 1st and 2nd wave of COVID-19 pandemic: an event study analysis

PurposeCapital markets are the backbone of an economy. COVID-19 has created an unacceptable and unpredictable economic environment worldwide, resulting in a significant setback for securities exchanges. India also experienced two waves of this pandemic, which led to a significant downturn in the capital market.Design/Methodology/ApproachResearchers have endeavored to study the impact of the first and second waves of COVID-19 on the performance of various sectoral stocks in India. The performance of selected sectoral indices of the Bombay Stock Exchange was compared with the market performance of the S&P BSE 100. An event study was conducted to analyze the normal return, abnormal return (A.R.), and t-statistics of A.R. for various sectoral stocks. In addition, the abnormal returns of sectoral stocks between the first and second waves of COVID-19 in India were comparedFindingsThe results of the tests showed heterogeneous A.R. between different sectors in both the first and second waves of COVID-19 in India. Positive investor outlook and government financial support programs for various sectors helped them recover from the second wave of COVID-19.Research limitations/implicationsThe study analyzed the impact of the peak of the first and second waves of COVID-19 on selected sectoral indices. There may be several reasons for the performance of this particular stock index. However, we have tried to analyze the best possible reasons for this turbulence in the performance of stocks of selected sectoral indices. The study can be further analyzed to examine the long-term impact of such a pandemic on other sectors.Originality/valueThe study is based on the panic behavior of investors during such a pandemic. No one was prepared for this and expected this pandemic to last this long. This pandemic has taught so many lessons to everyone involved. Investors need to be prepared and cautious for such unforeseen disasters before making any investment decision. They need to analyze which industry can survive under such circumstances, and then they should invest there. Industries and enterprises must adapt and improve by honestly looking at their weaknesses and trying to meet investors' expectations.

The Impact of Cytokines on Coagulation Profile in COVID-19 Patients: Controlled for Socio-Demographic, Clinical, and Laboratory Parameters.

Background: Severe coagulation abnormalities are common in patients with COVID-19 infection. We aimed to investigate the relationship between pro-inflammatory cytokines and coagulation parameters concerning socio-demographic, clinical, and laboratory characteristics. Methods: Our study included patients hospitalized during the second wave of COVID-19 in the Republic of Serbia. We collected socio-demographic, clinical, and blood-sample data for all patients. Cytokine levels were measured using flow cytometry. Results: We analyzed data from 113 COVID-19 patients with an average age of 58.15 years, of whom 79 (69.9%) were male. Longer duration of COVID-19 symptoms before hospitalization (B = 69.672; p = 0.002) and use of meropenem (B = 1237.220; p = 0.014) were predictive of higher D-dimer values. Among cytokines, higher IL-5 values significantly predicted higher INR values (B = 0.152; p = 0.040) and longer prothrombin times (B = 0.412; p = 0.043), and higher IL-6 (B = 0.137; p = 0.003) predicted longer prothrombin times. Lower IL-17F concentrations at admission (B = 0.024; p = 0.050) were predictive of higher INR values, and lower IFN-γ values (B = -0.306; p = 0.017) were predictive of higher aPTT values. Conclusions: Our findings indicate a significant correlation between pro-inflammatory cytokines and coagulation-related parameters. Factors such as the patient's level of education, gender, oxygen-therapy use, symptom duration before hospitalization, meropenem use, and serum concentrations of IL-5, IL-6, IL-17F, and IFN-γ were associated with worse coagulation-related parameters.

Analysis of the Epidemic Curve of the Waves of COVID-19 Using Integration of Functions and Neural Networks in Peru

The coronavirus (COVID-19) pandemic continues to claim victims. According to the World Health Organization, in the 28 days leading up to 25 February 2024 alone, the number of deaths from COVID-19 was 7141. In this work, we aimed to model the waves of COVID-19 through artificial neural networks (ANNs) and the sigmoidal–Boltzmann model. The study variable was the global cumulative number of deaths according to days, based on the Peru dataset. Additionally, the variables were adapted to determine the correlation between social isolation measures and death rates, which constitutes a novel contribution. A quantitative methodology was used that implemented a non-experimental, longitudinal, and correlational design. The study was retrospective. The results show that the sigmoidal and ANN models were reasonably representative and could help to predict the spread of COVID-19 over the course of multiple waves. Furthermore, the results were precise, with a Pearson correlation coefficient greater than 0.999. The computational sigmoidal–Boltzmann model was also time-efficient. Moreover, the Spearman correlation between social isolation measures and death rates was 0.77, which is acceptable considering that the social isolation variable is qualitative. Finally, we concluded that social isolation measures had a significant effect on reducing deaths from COVID-19.

Nurses Lived Experiences, Burdens and Coping Strategies During COVID-19 Pandemic

This is a qualitative phenomenological study that was designed to navigate through nurses’ lived experiences, burdens, and coping strategies while working with COVID-19 patients. The sample included 20 nurses who had worked with COVID-19 patients for more than or equal to 6 months. The interviews were conducted between October 1, 2021, and April 15, 2022. At that time, the third COVID wave had elapsed, and we were peaking on a fourth pandemic wave, so included participants had lived through a minimum of 2 to 3 peaks. Six themes emerged, which were: nurses coping with COVID-19 crisis, professional relationship burden, personal burden, environmental burden, physical symptom burden, and emotional burden of the crisis. Nurses’ lived experiences during the pandemic were deep, intense, and moderately to highly affecting their ways of thinking, feeling, and behaving. That experience opened nurses’ eyes on countless number of challenges that require special attention, care, and preparation on many levels. The minimal preparatory levels are personal, departmental, organizational, and strategic.