• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.295-304
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• 1994

This paper describes some preliminary attempts to formulate simple mathematical models of the transmission dynamics of HIV infection in homosexual communities. In conjunction with a survey of the available epidemiological data on HIV infection and the incidence of AIDS, the model is used to assess how various processes influence the course of the initial epidemic following the introduction of the virus. Models of the early stages of viral spread provide crude methods for estimating the basic reproductive rate of the virus, given a knowledge of the incubation period of AIDS and the initial doubling time of the epidemic. More complex models are formulated to assess the influence of heterogeneity in sexual activity. This latter factor is shown to have a major effect on the predicted pattern of the epidemic.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• Nonlinear Functional Analysis and Applications
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• v.28 no.2
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• pp.473-495
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• 2023

The use of mathematical modelling in the study of epidemiological disorders continues to grow substantially. In order to better support global policy initiatives and explain the possible consequence of an outbreak, mathematical models were constructed to forecast how epidemic illnesses spread. In this paper, fractional derivatives and (${\varpi}$ - F_𝓒)-contractions are used to explore the existence and uniqueness solutions of the novel coronavirus-19 model.

• 재활복지
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• v.21 no.1
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• pp.47-70
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• 2017

The aim of this study was to explore the effects of the epidemiological factor (depression), the behavioral factor (activities of daily living), the predisposing factor (self-efficacy), reinforcing factors (family support, professional support), and enabling factor (resource availability, accessibility) on quality of life in home-dwelling disabled people in rural areas. The conceptual model for this study was established on the basis of the PRECEDE model which was developed by Green and Kreuter. Data were drawn from the "Preliminary Investigation for Community-centered rehabilitation" conducted by a public health center located in the O province in 2011 and 186 of 190 disabled people who participated in the survey were included in the final analysis. Data were analyzed using Direct effects on quality of life arose from latent variables depicting the epidemiological factor (depression)and reinforcing factor (family support, professional support), while indirect effects arose from the behavioral factor (activities of daily living), the predisposing factor (self-efficacy), and enabling support (resource availability, accessibility). This model explained 85.5% of the variance in quality of life among rural disabled individuals. These findings may have shed some light on the necessity of including strategies to reduce depression and to strengthen supports from family and healthcare professionals when performing rehabilitation programs to improve quality of life in home-dwelling disabled people in rural areas. Furthermore, it suggested that it would be useful to develop specific strategies and tactics which might increase self-efficacy and to expand linkages between public health centers and other professional institutions such as hospitals for community-centered rehabilitation services for individuals with disabilities.

• Journal of applied mathematics & informatics
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• v.31 no.5_6
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• pp.747-767
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• 2013

In this paper we have constructed a mathematical model of alcohol abuse which consists of four compartments corresponding to four population classes, namely, moderate and occasional drinkers, heavy drinkers, drinkers in treatment and temporarily recovered class. Basic reproduction number $R_0$ has been determined and sensitivity analysis of $R_0$ indicates that ${\beta}1$ (the transmission coefficient from moderate and occasional drinker to heavy drinker) is the most useful parameter for preventing drinking habit. Stability analysis of the model is made using the basic reproduction number. The model is locally asymptotically stable at disease free or problem free equilibrium (DFE) $E_0$ when $R_0<1$. It is found that, when $R_0=1$, a backward bifurcation can occur and when $R_0>1$, the endemic equilibrium $E^*$ becomes stable. Further analysis gives the global asymptotic stability of DFE under some conditions. Our important analytical findings are illustrated through computer simulation. Epidemiological implications of our analytical findings are addressed critically.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.8
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• pp.4897-4902
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• 2013

Here we assessed the effectiveness of cervical cancer screening using data from the Hiroshima Prefecture Cancer Registry regarding patient age at the start of screening and differences in screening intervals. A screening model was created to calculate the health status in relation to prognosis following cervical cancer screening and its influence on life expectancy. Epidemiological data on the mortality rate of cervical cancer by age groups and mortality rates from the Hiroshima Prefecture Cancer Registry were used for the model projections. Our results showed that life expectancy when screening rate was 100% compared with 0% was extended by approximately 1 month. Furthermore, when the incidence of cervical cancer was 0% compared with the screening rate was 100%, life expectancy was extended by a maximum of 3 months. Moreover, among individuals affected by cervical c ancer, a difference of 13 years in life expectancy was calculated between screened and unscreened groups.

• Safety and Health at Work
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• v.12 no.4
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• pp.505-510
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• 2021

Background: Factors related to root causes can cause commonly occurring accidents such as falls, slips, and jammed injuries. An important means of reducing the frequency of occupational accidents in small- to medium-sized enterprises (SMSEs) of South Korea is to perform intensity analysis of the root cause factors for accident prevention in the cause and effect model like decision models, epidemiological models, system models, human factors models, LCU (life change unit) models, and the domino theory. Especially intensity analysis in a robot system and smart technology as Industry 4.0 is very important in order to minimize the occupational accidents and fatal accident because of the complexity of accident factors. Methods: We have developed the modern cause and effect model that includes factors of root cause through statistical testing to minimize commonly occurring accidents and fatal accidents in SMSEs of South Korea and systematically proposed educational policies for accident prevention. Results: As a result, the consciousness factors among factors of root cause such as unconsciousness, disregard, ignorance, recklessness, and misjudgment had strong relationships with occupational accidents in South Korean SMSEs. Conclusion: We conclude that the educational policies necessary for minimizing these consciousness factors include continuous training procedures followed by periodic hands-on experience, along with perceptual and cognitive education related to occupational health and safety.

• Journal of Climate Change Research
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• v.9 no.2
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.242-248
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• 2024

Contemporary societal and environmental transformations coincide with the emergence of novel mental health challenges. anxiety disorder, a chronic and highly debilitating illness, presents with diverse clinical manifestations. Epidemiological investigations indicate a global prevalence of 5%, with an additional 10% exhibiting subclinical symptoms. Notably, 9% of adolescents demonstrate clinical features. Untreated, anxiety disorder exerts profound detrimental effects on individuals, families, and the broader community. Therefore, it is very meaningful to predict anxiety disorder through machine learning algorithm analysis model. The main research content of this paper is the analysis of the prediction model of anxiety disorder by machine learning algorithms. The research purpose of machine learning algorithms is to use computers to simulate human learning activities. It is a method to locate existing knowledge, acquire new knowledge, continuously improve performance, and achieve self-improvement by learning computers. This article analyzes the relevant theories and characteristics of machine learning algorithms and integrates them into anxiety disorder prediction analysis. The final results of the study show that the AUC of the artificial neural network model is the largest, reaching 0.8255, indicating that it is better than the other two models in prediction accuracy. In terms of running time, the time of the three models is less than 1 second, which is within the acceptable range.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.12
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• pp.5029-5034
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• 2014

This study aimed to summarize published epidemiological evidence for the relationship between pancreatitis and subsequent risk of pancreatic cancer (PC). We searched Medline and Embase for epidemiological studies published by February $5^{th}$, 2014 examining the risk of PC in pancreatitis patients using highly inclusive algorithms. Information about first author, year of publication, country of study, recruitment period, type of pancreatitis, study design, sample size, source of controls and attained age of subjects were extracted by two researchers and Stata 11.0 was used to perform the statistical analyses and examine publication bias. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated with the random effects model. A total of 17 articles documenting 3 cohort and 14 case-control studies containing 14,667 PC cases and 17,587 pancreatitis cases were included in this study. The pooled OR between pancreatitis and PC risk was 7.05 (95%CI: 6.42-7.75). Howeever, the pooled ORs of case-control and cohort studies were 4.62 (95%CI: 4.08-5.22) and 16.3 (95%CI: 14.3-18.6) respectively. The risk of PC was the highest in patients with chronic pancreatitis (pooled OR=10.35; 95%CI: 9.13-11.75), followed by unspecified type of pancreatitis (pooled OR=6.41; 95%CI: 4.93-8.34), both acute and chronic pancreatitis (pooled OR=6.13; 95%CI: 5.00-7.52), and acute pancreatitis (pooled OR=2.12; 95%CI: 1.59-2.83). The pooled OR of PC in pancreatitis cases diagnosed within 1 year was the highest (pooled OR=23.3; 95%CI: 14.0-38.9); and the risk in subjects diagnosed with pancreatitis for no less than 2, 5 and 10 years were 3.03 (95%CI: 2.41-3.81), 2.82 (95%CI: 2.12-3.76) and 2.25 (95%CI: 1.59-3.19) respectively. Pancreatitis, especially chronic pancreatitis, was associated with a significantly increased risk of PC; and the risk decreased with increasing duration since diagnosis of pancreatitis.