An Analysis of COVID-19 Mortality During the Dominancy of Alpha, Delta, and Omicron in the USA.

BACKGROUND: The objective of the study was to measure the risk of death due to COVID-19 in relation to individuals' characteristics, and severity of their disease during the dominant periods of Alpha, Delta, and Omicron variants have influenced mortality rates. METHODS: This study was conducted using COVID-19 Centers for Disease Control and Prevention (CDC) Case Surveillance Public Data Taskforce for 57 states, and United States territories between January 1, 2020 and March 20, 2022. Multivariable binary Hyperbolastic regression of type I was used to analyzes the data. RESULTS: Seniors and ICU-admitted patients had the highest risk of death. For each additional percent increase in fully vaccinated individuals, the odds of death deceased by 1%. The odds of death prior to vaccine availability, compared to post vaccine availability, was 1.27. When comparing the time periods each variant was dominant, the odds of death was 3.45-fold higher during Delta compared to Alpha. All predictor variables had P-values ≤.001. CONCLUSION: There was a noticeable difference in the odds of death among subcategories of age, race/ethnicity, sex, PMCs, hospitalization, ICU, vaccine availability, variant, and percent of fully vaccinated individuals.

Stochasticity among Victims of COVID-19 Pandemic

This article provides a thorough explanation of methods and theoretical concepts to detect infectivity of COVID-19. The concept of heterogeneity is discussed and its impacts on COVID-19 pandemics are explored. Observable heterogeneity is distinguished from non-observable heterogeneity. The data support the concepts of heterogeneity and the methods to extract and interpret the data evidence for the conclusions in this paper. Heterogeneity among the vulnerable to COVID-19 is a significant factor in the contagion of COVID-19, as demonstrated with incidence rates using data of a Diamond Princess cruise ship. Given the nature of the pandemic, its heterogeneity with different social norms, pre- and post-voyage quick testing procedures ought to become the new standard for cruise ship passengers and crew. With quick testing, identification of those infected and thus, not allowing to embark on a cruise or quarantine those disembarking, and other mitigation strategies, the popular cruise adventure could become norm for safe voyage. The novel method used in this article adds valuable insight in the modeling of disease and specifically, the COVID-19 virus.

Generational conflict, alcohol use and brotherhood: an exploratory survey of a rural career fire department in the Midwest

PurposeThe purpose of this article was to identify factors impacting burnout, resilience and quality of life in rural career firefighters. In addition, sources of stress and the impact of generational differences were explored.Design/methodology/approachAn exploratory cross-sectional survey was conducted at a rural career fire department.FindingsThe findings of the project indicate that the firefighters had high levels of compassion satisfaction (CS) and relatively low levels of secondary traumatic stress and burnout;displayed moderate to high psychological resilience and the majority felt moderate to high organizational support, but there was a noticeable minority who did not feel supported by the department. Findings indicate that organizational support is significantly related to both burnout and resilience. The majority of the men (88.3%) reported moderate to high risk for alcohol-related problems and over three-quarters (78.6%) reported binge drinking behavior in the past year. Qualitative findings highlight generational differences and chain of command challenges as primary stressors.Originality/valueThis is a unique study in that it focuses on a rural career department. What was found were issues similar to those facing urban career fire departments.

Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.

We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model's requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model's ability to estimate the community's health system memory, as future policies might reduce COVID's spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.

Structural Zero Data of COVID-19 Discovers Exodus Probabilities.

BACKGROUND: Challenges to manage, mitigate, or prevent the COVID-19's pandemics are felt by medical, healthcare professionals and governing agencies. Health researchers conduct survey among the citizens to capture their opinion on COVID-19. In such surveys like in Hanafiah and Wan (2020), structural-zero (different from sampling zero) category occurs as they question about perception, knowledge, and communication regarding COVID-19. MATERIALS: The data were collected in a survey conducted among Malaysians by Hanafiah and Wan regarding COVID-19. The survey focused on people's response about the public communication, knowledge, and perception. METHODS: One of the four question categories in the survey is mutually exclusive with the other three questions. Consequently, there will be no entry in that category. Such group is called structurally zero category in the literature. The literature never probed the migrative split to other categories of the unknown proportion belonging to the structural zero category. In this article, the probability-based new and innovative method configures what proportion in that mutually exclusive category and it is the essence of our method. RESULTS: The mutually exclusive nature of subquestions manufactured structural zero in their data. A careful analysis of the data has created so far unknown probability concepts in the literature, which we named as "Exodus probabilities" in this article. Its discovery and utility are illustrated and elaborated with application in COVID-19. This methodology is also useful in applications in engineering, epidemiology, marketing, communication networking, etc. CONCLUSION: What is quite novel about the discovery of the exodus probability in this article is the evolution of the concepts from the structural-zero category. In such situation, when a category is eliminated, the proportions of the sample might have uncommunicatively transited to other viable categories and our research question is all about configuring their proportions. This is an innovative approach.

Influences of reopening businesses and social venues: COVID-19 incidence rate in East Texas county.

As the on-going severe acute respiratory syndrome coronavirus 2 pandemic, we aimed to understand whether economic reopening (EROP) significantly influenced coronavirus disease 2019 (COVID-19) incidence. COVID-19 data from Texas Health and Human Services between March and August 2020 were analysed. COVID-19 incidence rate (cases per 100 000 population) was compared to statewide for selected urban and rural counties. We used joinpoint regression analysis to identify changes in trends of COVID-19 incidence and interrupted time-series analyses for potential impact of state EROP orders on COVID-19 incidence. We found that the incidence rate increased to 145.1% (95% CI 8.4-454.5%) through 4th April, decreased by 15.5% (95% CI -24.4 -5.9%) between 5th April and 30th May, increased by 93.1% (95% CI 60.9-131.8%) between 31st May and 11th July and decreased by 13.2% (95% CI -22.2 -3.2%) after 12 July 2020. The study demonstrates the EROP policies significantly impacted trends in COVID-19 incidence rates and accounted for increases of 129.9 and 164.6 cases per 100 000 populations for the 24- or 17-week model, respectively, along with other county and state reopening ordinances. The incidence rate decreased sharply after 12th July considering the emphasis on a facemask or covering requirement in business and social settings.