• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.2
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• pp.111-115
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• 2022

Objectives: The purpose of this study is to verify whether droplet-induced propagation, the main route of infectious diseases such as COVID-19, can occur in semiconductor FAB (Fabrication), based on research results on general droplet propagation. Methods: Through data surveys droplet propagation was modeled through simulation and experimental case analysis according to general (without mask) and mask-wearing conditions, and the risk of droplet propagation was inferred by reflecting semiconductor FAB operation conditions (air current, air conditioning system, humidity, filter conditions). Results: Based on the results investigated to predict the possibility of spreading infectious diseases in semiconductor FAB, the total amount of droplet propagation (concentration), propagation distance, and virus life in FAB were inferred by reflecting the management parameter of semiconductor FAB. Conclusions: The total amount(concentration) of droplet propagation in the semiconductor fab is most affected by the presence or absence of wearing a mask and the line air dilution rate has some influence. when worn it spreads within 0.35~1m, and since the humidity is constant the virus can survive in the air for up to 3 hours. as a result the semiconductor fab is judged to be and effective space to block virus propagation due to the special environmental condition of a clean room.

• Journal of Korean Society on Water Environment
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• v.39 no.3
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• pp.231-239
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• 2023

Public sewage treatment facilities are a necessary infrastructure for public health that treat sewage generated in cities and basin living areas and discharge it into rivers or seas. Recently, the role of public sewage treatment is receiving attention as a place of use of wastewater-based epidemiology (WBE), which analyzes human specific metabolic emissions or biomarkers present in sewage to investigate the environment to which the population is exposed in the water drain. WBE is mainly applied to investigate legal and water-law drug use or to predict and analyze the lifestyle of local residents. WBE has also been applied to predict and analyze the degree of infectious diseases that are prevalent worldwide, such as COVID-19. Since sewage flowing into public sewage treatment facilities includes living information of the population living in the drainage area, it is easy to collect basic data to predict the confirmation and spread of infectious diseases. Therefore, it is necessary to establish a new role of public sewage treatment facilities as an infrastructure necessary for WBE that can obtain information on the confirmation and spread of infectious diseases other than the traditional role of public sewage treatment. In South Korea, the sewerage supply rate is about 95.5% and the number of public sewage treatment facility is 4,209. This means that the infrastructure of sewerage is fully established. However, to successfully drive for WBE , research on monitoring and big-data analysis is needed.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Parasites, Hosts and Diseases
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• v.47 no.1
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• pp.1-5
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• 2009

A mathematical model for transmission of schistosomes is useful to predict effects of various control measures on suppression of these parasites. This review focuses on epidemiological and environmental factors in Schistosoma japonicum and Schistosoma mekongi infections and recent advances in mathematical models of Schistosoma transmission.

• Journal of agricultural medicine and community health
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• v.44 no.1
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• pp.39-42
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• 2019

Zoonoses are the diseases that are transmitted to human being from vertebrate animals either from livestock animals or from wildlife. Recently, zoonoses are increasingly common as a result of incremental human-animal contact. Propagative infections in wild animals and livestock are transmitted to human beings who are encountered with them. In general, wild animals can transmit infectious agents to livestock, and then livestock further transmit them to human being is a simple model of on how zoonotic diseases get transmitted to human being. This model emphasizes the importance of early detection of zoonoses by surveillance at its incipient stage. Cooperation between the respective ministries plays an important role in the identification of zoonoses and planning for the formulation of better preventive and control policy and strategy. We will be able to predict the occurrence of zoonotic diseases in human on the basis of disease trends in wildlife and livestock once when we obtain the surveillance data and data generated by respective ministries through sound cooperation and collaboration.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Korean Journal of Veterinary Research
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• v.42 no.3
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• pp.371-376
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• 2002

Swine respiratory diseases have induced severe economic lasses in swine industry worldwide. Therefore, several methods have been made and applied to prevent and control the diseases. However, these methods still have a problem and also induce side effects. Recently, the use of egg yolk antibody was introduced to control and prevent the diseases as one of new trials. As a study of using egg yolk antibody, antibody titers against several different antigens of major pathogens in swine respiratory diseases were compared in egg yolk and serum of hens immunized with those antigens. The titers were measured by ELISA using the antigens as coating antigens. The relationship in antibody titers between egg yolk and serum were identified by analysis of variance for linear regression. Almost of antigens used in this study showed the high relationship in antibody titers between egg yolk and serum (r = 0.87 ~ 0.93) even though the relationship in antibody titers against P. multocida A:3 IROMP was slightly low (r = 0.74)(P<0.01). These results indicated that antibody titer in egg yolk could be useful to predict the titer in serum of chicken.

• Genomics & Informatics
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• v.19 no.1
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• pp.11.1-11.8
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• 2021

For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020-December 31, 2020 and January 20, 2020-January 31, 2021) and testing data (January 1, 2021-February 28, 2021 and February 1, 2021-February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.

• Korean Journal of Occupational Health Nursing
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• v.29 no.4
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• pp.228-234
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• 2020

Purpose: This study aimed to identify the past and present status of occupational safety and health education in Korea and to explore future plans for these fields. Methods: We summarized past empirical or theoretical literature. Results: Occupational safety and health education strive to protect workers' health and create healthy workplaces by solving various problems such as workers' occupational diseases and mental health in the rapidly changing occupational environment. For occupational safety and health education to be effectively utilized in occupational sites, a live education that can be applied to the field should be provided. The need for education to explore and develop the ability to prepare for new hazards, including infectious diseases such as COVID-19, has increased. Conclusion: It is believed that the occupational health education element of the new era will be occupational health education. This focus will develop the ability to closely assess and predict the collective, organizational, and personal responses of affected workplaces and the impact of occupational health sciences.