• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.153-161
• /
• 2017

Branching processes which is used for epidemic dispersion as stochastic process model have advantages to estimate parameters by real data. We have to estimate both mean and dispersion parameter in order to use the negative binomial distribution as an offspring distribution on branching processes. In existing studies on biology and epidemiology, it is estimated using maximum-likelihood methods. However, for most of epidemic data, it is hard to get the best precision of maximum-likelihood estimator. We suggest a Bayesian inference that have good properties of statistics for small-sample. After estimating dispersion parameter we modelled the posterior distribution for 2015 Korea MERS cases. As the result, we found that the estimated dispersion parameter is relatively stable no matter how we assume prior distribution. We also computed extinction probabilities on branching processes using estimated dispersion parameters.

• Convergence Security Journal
• /
• v.8 no.4
• /
• pp.173-181
• /
• 2008

Recently, Worm epidemic models have been developed in response to the cyber threats posed by worms in order to analyze their propagation and predict their spread. Some of the most important ones involve mathematical model techniques such as Epidemic(SI), KM (Kermack-MeKendrick), Two-Factor and AAWP(Analytical Active Worm Propagation). However, most models have several inherent limitations. For instance, they target worms that employ random scanning in the network such as CodeRed worm and it was able to be applied to the specified threats. Therefore, we propose the probabilistic of worm propagation based on the Markov Chain, which can be applied to cyber threats such as Mass SQL Injection worm. Using the proposed method in this paper, we can predict the occurrence probability and occurrence frequency for each threats in the entire system.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.1
• /
• pp.149-158
• /
• 2009

Tsutsugamushi fever occupies more than 80% of total fall epidemic diseases and has an incubation period of 1 or 2 weeks as well. We have assumed that the incubation period distribution is gamma and therefore, reach an agreement that the infected distribution is normal with ${\hat{\mu}}=309.92$, ${\hat{\sigma}}=14.154$ by back calculation method. The infection cases are found severely large around the month of October. The infection case distribution demonstrates the incidence number increasing rapidly and progresses fast during the month of November. In this study, we have calculated the future prevalence number of maximum 1,200 people by inferred infection probability and incubation period distribution with some sort of limitation that the trend of increasing incidence number is not taking into an account. We considered the SIRS model which is also known as epidemic model, familiar to interaction between epidemiological classes. Our estimated parameters converged well with the initial parameter values.

• 전기의세계
• /
• v.26 no.2
• /
• pp.66-71
• /
• 1977

A mathematical model, which can be used for the study of an influenza epidemic, was derived. The model of influenza takes into full consideration the incubation period and inapparent infection. That was analysed by means of digital computer under the conditions of changing the infection rate, .betha., from 4 to 5, for three types of communities (First type: the initial distribution of population, x$_{1}$(0)=89% susceptibles, x$_{2}$(0)=3% incubatives, x$_{3}$(0)=0.5% carriers, x$_{4}$(0)=7.5% immunes; Second type: x$_{1}$(0)=79%, x$_{2}$(0)=3%, x$_{3}$(0)=0.5%, x$_{4}$(0)=17.5%; Third type: x$_{1}$(0)=69%, x$_{2}$(0)=3%, x$_{3}$(0)=0.5%, x$_{4}$(0)=27.5%, considering the rate of population increase, in Seoul. In conclusion, the outcomes of this study are summarized as follow. 1) The new model is quite reasonable in representing many phenomena connected with influenza spread. 2) The more influenza does prevail, the smaller the valve of attack rate becomes, while the contagious period becomes slightly longer. 3) The average infection rate, .betha., of influenza is approximately 5 per week time and X$_{4}$(0) is about 27.5 percent of the total population in Seoul spring 1961. 4) The number of carriers of influenza in Seoul spring 1961 becomes maximum within approximately 2.4 weeks after the attack of diseases. 5) About 68 percent of all cases in the contagious period is infected with influenza from 5 to 15 days after the attack of diseases. The auther believes that the method to study the influenza models in this paper will be helpful to study the characteristics of other epidemics. It will also contribute to public healthe management and the preventive policy decision against epidemics.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.12
• /
• pp.597-603
• /
• 2001

In the mathematical model for the transmission dynamics of HIV infection described in previous papers, the population under consideration is assumed to be homogeneous community of homosexual males for which the parameter x represents the constant rate at which individual members of the population acquire new sexual partners. This is a gross oversimplification since it is well known that individuals vary widely in their levels of sexual activity and in this papers the heterogeneous model is modified to allow for this variation. The pattern on the epidemic character of HIV, the causative agent of AIDS, was analysed by heterogeneous-mixing model. The computer simulation was performed using real date.

• Journal of Korea Multimedia Society
• /
• v.22 no.5
• /
• pp.588-600
• /
• 2019

Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.

• Journal of Distribution Science
• /
• v.21 no.8
• /
• pp.47-55
• /
• 2023

Purpose: Online meeting is chosen instead of face-to-face conferences as a solution that ensures both effectiveness and legality during times of strong epidemic outbreaks. In the current period, managers can have different types of meeting options for information distribution. This study has examined the effect of past behavior on the managers' intention of organizing online meetings. Research design, data and methodology: Data were collected from a survey with 475 managers and put into SmartPLS 4.0 for analysis. Partial least squares structural equation modeling (PLS-SEM) was employed to test relationships in the research model. Results: The findings indicated that past behavior plays the most critical role in explaining the organizing online meeting intention of managers, followed by attitude and subjective norms. Meanwhile, the perceived behavioral control factor has absolutely no effect on intention in the context of this study. Notably, attitude and subjective norms also remarkably mediated the impact of past behavior on managers' intention. Conclusions: This study has added to the understanding of the meeting organization behavior of managers. Even if the epidemic is under control, the administrators should still organize some meetings in the form of online because it will affect the social perceptions of future behavior and behavioral intention.

• ETRI Journal
• /
• v.30 no.1
• /
• pp.81-88
• /
• 2008

The security threat posed by worms has steadily increased in recent years. This paper discusses the application of the optimal and sub-optimal Internet worm control via Pontryagin's maximum principle. To this end, a control variable representing the optimal treatment strategy for infectious hosts is introduced into the two-factor worm model. The numerical optimal control laws are implemented by the multiple shooting method and the sub-optimal solution is computed using genetic algorithms. Simulation results demonstrate the effectiveness of the proposed optimal and sub-optimal strategies. It also provides a theoretical interpretation of the practical experience that the maximum implementation of treatment in the early stage is critically important in controlling outbreaks of Internet worms. Furthermore, our results show that the proposed sub-optimal control can lead to performance close to the optimal control, but with much simpler strategies for long periods of time in practical use.

• Journal of Biomedical Engineering Research
• /
• v.14 no.4
• /
• pp.365-370
• /
• 1993

Of the assumptions commonly used in continuous infection model, the least likely to be even approximately true in large population, is that of homogeneous mixing. In this paper, We investigate a model for the spread of infection amongst a population which is divided into classes, such that the individuals of each class mix homogeneously amongst themselves, but mix to a lesser degree with individuals of other class. The stochastic model in this form is intractable and approximations are made, yielding results in reasonable agreement with simulation trials.

• ETRI Journal
• /
• v.28 no.5
• /
• pp.692-695
• /
• 2006

Recently, there has been a constant barrage of worms over the Internet. Besides threatening network security, these worms create an enormous economic burden in terms of loss of productivity not only for the victim hosts, but also for other hosts, as these worms create unnecessary network traffic. Further, measures taken to filter these worms at the router level incur additional network delays because of the extra burden placed on the routers. To develop appropriate tools for thwarting the quick spread of worms, researchers are trying to understand the behavior of worm propagation with the aid of epidemiological models. In this study, we present an optimization model that takes into account infection and treatment costs. Using this model we can determine the level of treatment to be applied for a given rate of infection spread.