We study a novel multi-strain SIR epidemic model with selective immunity by vaccination. A newer strain is made to emerge in the population when a preexisting strain has reached equilbrium. We assume that this newer strain does not exhibit cross-immunity with the original strain, hence those who are vaccinated and recovered from the original strain become susceptible to the newer strain. Recent events involving the COVID-19 virus shows that it is possible for a viral strain to emerge from a population at a time when the influenza virus, a well-known virus with a vaccine readily available, is active in a population. We solved for four different equilibrium points and investigated the conditions for existence and local stability. The reproduction number was also determined for the epidemiological model and found to be consistent with the local stability condition for the disease-free equilibrium.

Background: The Philippines’ primary care is delivered via local health centers called barangay health centers (BHCs). Barangays are the most local government units in the Philippines. Designed to promote and prevent disease via basic health care, these BHCs are staffed mainly by barangay health workers (BHWs). However, there has been limited research on the social and environmental factors affecting underserved communities’ access to health care in underserved areas of the Philippines. Given the importance of BHCs in disease prevention and health promotion, it is necessary to identify obstacles to providing their services and initiatives. Objective: This study aimed to explore multilevel barriers to accessing and providing basic health care in BHCs. Methods: We used a qualitative approach and the socioecological model as a framework to investigate the multilevel barriers affecting basic health care provision. A total of 18 BHWs from 6 BHCs nationwide participated in focus group interviews. Traditional thematic content analysis was used to analyze the focus group data. After that, we conducted individual semistructured interviews with 4 public health nurses who supervised the BHWs to confirm findings from focus groups as a data source triangulation. The final stage of thematic analysis was conducted using the socioecological model as the framework. Results: Findings revealed various barriers at the individual (lack of staff motivation and misperceptions of health care needs), interpersonal (lack of training, unprofessional behaviors, and lack of communication), institutional (lack of human resources for health, lack of accountability of staff, unrealistic expectations, and lack of physical space or supplies), community (lack of community support, lack of availability of appropriate resources, and belief in traditional healers), and policy (lack of uniformity in policies and resources and lack of a functional infrastructure) levels. Conclusions: Examining individual-, interpersonal-, institutional-, community-, and policy-level determinants that affect BHCs can inform community-based health promotion interventions for the country’s underserved communities. Given the multidimensional barriers identified, a comprehensive program must be developed and implemented in collaboration with health care providers, community leaders, local and regional health care department representatives, and policy makers.

Gambling among college students can start as a pastime activity. However, this pastime can lead to problem gambling and pathological gambling. This cross-sectional study aimed to identify and explain the initiation and sustenance of quitting gambling among university students who had participated in gambling during the past month and those who had not using a novel fourth-generation multi-theory model (MTM) of health behavior change. Data were collected from a sample of 1474 university students at a large southwestern university in the U.S. between January 2023 and February 2023, utilizing a validated 39-item survey. The statistical analyses employed in this study encompassed descriptive statistics, independent samples t-tests, and hierarchical regression modeling. Among students who had engaged in gambling in the past month, the constructs of participatory dialogue (β = 0.052; p < 0.05), behavioral confidence (β = 0.073; p < 0.0001), changes in the physical environment (β = 0.040; p = 0.0137), and demographic variables accounted for 27.7% of the variance in the likelihood of initiating the behavior change. Furthermore, the constructs of emotional transformation (β = 0.104; p = 0.0003) and practice for change (β = 0.060; p = 0.0368), and demographic variables accounted for 22.6% of the variance in the likelihood of sustaining quitting gambling behavior. The Multi-Theory Model (MTM) can be employed to design interventions aimed at reducing problem gambling among college students.

We study a novel multi-strain SIR epidemic model with selective immunity by vaccination. A newer strain is made to emerge in the population when a preexisting strain has reached equilbrium. We assume that this newer strain does not exhibit cross-immunity with the original strain, hence those who are vaccinated and recovered from the original strain become susceptible to the newer strain. Recent events involving the COVID-19 virus demonstrates that it is possible for a viral strain to emerge from a population at a time when the influenza virus, a well-known virus with a vaccine readily available for some of its strains, is active in a population. We solved for four different equilibrium points and investigated the conditions for existence and local stability. The reproduction number was also determined for the epidemiological model and found to be consistent with the local stability condition for the disease-free equilibrium.

Background/Objective: Relative proportion of cases in a multi-strain pandemic like the COVID-19 pandemic provides insight on how fast a newly emergent variant dominates the infected population. However, the behavior of relative proportion of emerging variants is an understudied field. We investigated the emerging behavior of dominant COVID-19 variants using nonlinear statistical methods and calculated the time to dominance of each variant. Method: We used a phenomenological approach to model national- and regional-level variant share data from the national genomic surveillance system provided by the Centers for Disease Control and Prevention to determine the best model to describe the emergence of two recent dominant variants of the SARS-CoV-2 virus: XBB.1.5 and JN.1. The proportions were modeled using logistic, Weibull, and generalized additive models. Model performance was evaluated using the Akaike Information Criteria (AIC) and the root mean square error (RMSE). Findings: The Weibull model performed the worst out of all three approaches. The generalized additive model approach slightly outperformed the logistic model based on fit statistics, but lacked in interpretability compared to the logistic model. These models were then used to estimate the time elapsed from emergence to dominance in the infected population, denoted by the time to dominance (TTD). All three models yielded similar TTD estimates. The XBB.1.5 variant was found to dominate the population faster compared to the JN.1 variant, especially in HHS Region 2 (New York) where the XBB.1.5 was believed to emerge. This research expounds on how emerging viral strains transition to dominance, informing public health interventions against future emergent COVID-19 variants and other infectious diseases.