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Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease caused by the SFTS virus, posing significant public health challenges in East Asia. This study aimed to evaluate the seroprevalence of SFTS on Jeju Island, Korea, and to identify the demographic and geographic factors influencing exposure to the virus. A total of 1001 serum samples collected from healthy individuals between 2009 and 2016 were analyzed using a double-antigen enzyme-linked immunosorbent assay. The overall seroprevalence was 1.7%, with slightly higher rates observed in females (2.06%) than in males (1.29%); however, this difference was not statistically significant. Seroprevalence increased with age, peaking at 2.50% in individuals over aged 60 and over. Regional analysis revealed elevated seroprevalence in the eastern coastal areas (4.41%), which was attributed to population density and environmental factors favoring human–tick interactions. These findings suggest that population distribution and land use patterns, rather than altitude alone, significantly affect the exposure of SFTS on Jeju Island. Targeted tick control strategies and public health interventions that focus on high-risk regions and demographics could mitigate SFTS transmission. This study provides valuable insights into the epidemiological characteristics of SFTS and emphasizes the importance of tailored preventive measures in endemic regions.

This study examines the interplay of air connectivity, sports events, infrastructures, and fiscal support during the period 2017 and 2022 in a designated area called Special Economic Zone in Mandalika, Lombok Island, West Nusa Tenggara to boost tourism development in Indonesia by utilizing big data cognitive analytics. We examine the tourism development impacted by the MotoGP event in 2022 and air connectivity. Further, this paper discusses the network connectivity of flights at Zainuddin Abdul Madjid International Airport during the COVID-19 Pandemic and the new normal. We found that the combination of an international airport, globally recognized sports events, and government support has directly and positively improved the tourism industry's performance in the country and especially within Lombok Island. We suggest policy recommendations to support economic activities in Mandalika's Special Economic Zone and its hinterland to maintain business sustainability and utilize the existing infrastructures at the optimum level. Lessons learned from the Indonesian experience could help other developing countries that are devising policies and strategies to develop the tourism industry by employing proper instruments such as infrastructure, events, and fiscal policies.

To determine the compliance rate with testosterone replacement therapy (TRT) in patients with testosterone deficiency syndrome (TDS), we evaluated the treatment continuation rate and the reasons for discontinuation of initial treatment according to each formulations and patient characteristics. Among men over 40 years of age who were diagnosed with TDS and then underwent TRT, their medical records were retrospectively analyzed for those who were followed up for more than 10 years. A total of 640 patients were included in the analysis. It was found that 75.9% of patients continued treatment for 1 year after starting. Patients treated with 1,000 mg of testosterone undecanoate injection had the highest treatment rate. Inconvenience of medication was the most common reason for discontinuing treatment, followed by cost, concern about side effects, lack of efficacy, and symptom recovery. The reasons for discontinuing treatment differed according to the type of formulations, and the longest continuous treatment period in all patients was 15.4±7.6 months on average. The treatment continuation rate tended to be high in patients with low serum total testosterone before starting treatment, in patients with severe erectile dysfunction, and in patients using phosphodiesterase-5 (PDE5) inhibitors. Among the various formulations of TDS, testosterone undecanoate injection (1,000 mg) had the highest compliance rate. In addition, it was found that the reasons for discontinuation of treatment varied according to the characteristics of each formulation.

Background This study aimed to examine whether the socioeconomic context of urban areas affects differences in adult mortality from injuries in the districts of all seven South Korean metropolitan cities, after adjusting for individual demographic and socioeconomic indicators. Methods Two different sets of data were used in this study: (1) the National Death Registration data from 2003 to 2008; and (2) the National Census in 2005. Variables for individual characteristics were gender, age, residential area and educational level. A geographic deprivation index was calculated based on the Carstairs Index. Multilevel Poisson regression models were used to analyse the relationship between area deprivation levels and injury mortality. Results Greater mortality risks of traffic accidents, falls, suicide and all injuries were found in the elderly, the less educated and men, compared with their counterparts. The most deprived districts were at greater risks of death due to traffic accidents (risk ratio (RR)=1.34; 95% CI 1.05 to 1.73), falls (RR=1.63; 95% CI 1.20 to 2.20), suicide (RR=1.09; 95% CI 1.01 to 1.17) and all injuries (RR=1.14; 95% CI 1.07 to 1.22) compared with the least deprived districts, even after individual level socioeconomic variables were controlled for. However, area level deprivation did not show cross level interactions with the individual level education in estimating fatal injury risks. Conclusions Both contextual and compositional effects of socioeconomic status on injury mortality among urban areas in South Korea should be considered in allocating resources for injury prevention.

Objectives A neighborhood-level analysis of mortality from suicide would be informative in developing targeted approaches to reducing suicide. This study aims to examine the association of community characteristics with suicide in the 424 neighborhoods of Seoul, South Korea. Methods Neighborhood-level mortality and population data (2005–2011) were obtained to calculate age-standardized suicide rates. Eight community characteristics and their associated deprivation index were employed as determinants of suicide rates. The Bayesian hierarchical model with mixed effects for neighborhoods was used to fit age-standardized suicide rates and other covariates with consideration of spatial correlations. Results Suicide rates for 424 neighborhoods were between 7.32 and 71.09 per 100,000. Ninety-nine percent of 424 neighborhoods recorded greater suicide rates than the Organization for Economic Cooperation and Development member countries’ average. A stepwise relationship between area deprivation and suicide was found. Neighborhood-level indicators for lack of social support (residents living alone and the divorced or separated) and socioeconomic disadvantages (low educational attainment) were positively associated with suicide mortality after controlling for other covariates. Conclusions Finding from this study could be used to identify priority areas and to develop community-based programs for preventing suicide in Seoul, South Korea.

Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model effects, including Hierarchical Generalized Linear Models (HGLMs), Double Hierarchical Generalized Linear Models (DHGLMs), Multivariate Double Hierarchical Generalized Linear Models (MDHGLMs), Survival Analysis, Frailty Models, Support Vector Machines (SVMs), and Hierarchical Likelihood Structural Equation Models (HSEMs). We provide 94 types of dataset examples.

We analyze a real data set pertaining to reindeer fecal pellet‐group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi‐Poisson hierarchical generalized linear model (HGLM), zero‐inflated Poisson (ZIP), and hurdle models. The quasi‐Poisson HGLM allows for both under‐ and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi‐Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi‐Poisson HGLM with spatial random effects. In analyzing the real data set on reindeer pellet group counts, where the observed data contain over 70% of zeros and show evidence of spatial correlation, we find that the quasi‐hierarchical generalized linear models (HGLMs) can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial zero‐inflated Poisson, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. The above results lead us to conclude that the count responses with extremely high proportion of zeros, and underdispersion, can be successfully modeled using HGLM with spatial random effects.

Frailty models with a non-parametric baseline hazard are widely used for the analysis of survival data. However, their maximum likelihood estimators can be substantially biased in finite samples, because the number of nuisance parameters associated with the baseline hazard increases with the sample size. The penalized partial likelihood based on a first-order Laplace approximation still has non-negligible bias. However, the second-order Laplace approximation to a modified marginal likelihood for a bias reduction is infeasible because of the presence of too many complicated terms. In this article, we find adequate modifications of these likelihood-based methods by using the hierarchical likelihood.

Background: In this paper, we examine how social media influencers can influence visit intention, especially in the case of Raffi Ahmad and Nagita Slavina, a top influencer who by 2 September 2021 had reached 21.3 M subscribers on YouTube and 54.9 m followers on Instagram with an engagement rate of 0.42%. The focus of this study is Generation Y or Millennials (born 1981–1996) and Generation Z (born 1997–2012). Design/methodology/approach: Snowball sampling was performed to arrive at a representative group of Millennials. Data analysis was performed using hierarchical likelihood via structural equation modeling. Findings: The study results are helpful for a comprehensive understanding of factors affecting visit intention. Effects of the study results summary, tourists from Generations Y and Z are thriving within the internet of things and the digital age, an era in which information can be accessed via various forms of technology across multiple platforms. Practical implications: We discuss and identify the relative importance of each factor through the use of logistics with variational approximation and structural equation models using hierarchical likelihood. Originality: The technique we use is an integrated and extended version of the structural equation model with hierarchical likelihood estimation and features selection using logistics variational approximation.

Design: Health issues throughout the sustainable development goals have also been integrated into one ultimate goal, which helps to ensure a healthy lifestyle as well as enhances well-being for any and all human beings of all social level. Meanwhile, regarding the clime change, we may take urgent action to its impacts. Purpose: Nowadays, climate change makes it much more difficult to control the pattern of diseases transmitted and sometimes hard to prevent. In line with this, Centres for Disease Control (CDC) Taiwan grouped the spread of disease through its source in the first six main groups. Those are food or waterborne, airborne or droplet, vector-borne, sexually transmitted or blood-borne, contact transmission, and miscellaneous. According to this, academics, government, and the private sector should work together and collaborate to maintain the health issue. This article examines and connects the climate and communicable aspects towards Penta-Helix in Taiwan. Finding: In summary, we have been addressing the knowledge center on the number of private companies throughout the health care sector, the number of healthcare facilities, and the education institutions widely recognized as Penta Helix. In addition, we used hierarchical likelihood structural equation modeling (HSEMs). All the relationship variables among climate, communicable disease, and Penta Helix can be interpreted through the latent variables with GoF 79.24%.