• 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.

• Tuberculosis and Respiratory Diseases
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• v.44 no.2
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• pp.360-378
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• 1997

Background : Severe acute lung injury(ALI), also known as the adult respiratory distress syndrome(ARDS), is a heterogenous nature of dynamic and explosive clinical synrome that exacts a mortality of approximately 50%. Endotoxin(ETX) is an abundant component of the outer membrane of gram-negative bacteria capable of inducing severe lung injury in gram-negative sepsis and gram-negative bacterial pneumonia, which are among the most common predisposing causes of ARDS. The influx of PMNs into airway tissue is a pathological hallmark of LPS-induced lung injury. And there is a substantial evidence suggesting that cytokines are important mediators of lung injury in gram-negative sepsis. However, the kinetics of phagocytes and cytokines by an exact time sequence and their respective pathogenic importance remain to be elucidated. This study was performed to investigate the role of phagocytes and proinflammatory cytokines in ETX-induced ALI through a time course of changes in the concentration of protein, $TNF{\alpha}$ and IL-6, and counts of total and its differential cells in BALF. The consecutive histologic findings were also evaluated. Method : The experimental animals, healthy male Sprague-Dawley, weighted $200{\pm}50g$, were divided into control- and ALI- group. ALI was induced by an intravenous administration of ETX, 5mg/kg. Above mentioned all parameters were examined at 0(control), 3, 6, 24, 72 h after administration of ETX. $TNF{\alpha}$ and IL-6 cone. in BALF were measured by a bioassay. Results : The protein concentration and total leukocyte count(TC) in BALF was significantly increased at 3h compared to controls(p < 0.05). The protein conc. was significantly elavated during observation period, but TC was significantly decreased at 72h(p < 0.05 vs. 24h). There was a close relationship between TC and protein cone. in BALF(r = 0.65, p < 0.001). The PMN and monocyte count was well correlated with TC in BALF, and the correlation of PMN(r = 0.97, p < 0.001) appeared to be more meaningful than that of monocyte(r = 0.61, p < 0.001). There was also a significant correlation between protein cone. and PMN or monocyte count in BALF(PMN vs. monocyte : r = 0.55, p < 0.005 vs. r = 0.64, p < 0.001). The count of monocyte was significantly elavated during observation period though a meaningful reduction of PMN count in BALF at 72h, this observation suggested that monocyte may, at least, partipate in the process of lung injury steadly. In this study, there was no relationship between IL-6 and $TNF{\alpha}$ cone., and $TNF{\alpha}$ but not IL-6 was correlated with TC(r = 0.61, p < 0.05) and monocyte(r = 0.67, p < 0.05) in BALF only at 3, 6h after ETX introduced. In particular, the IL-6 cone. increased earlier and rapidly peaked than $TNF{\alpha}$ cone. in BALF. In histologic findings, the cell counts of lung slices were increased from 3 to 72h(p < 0.001 vs. NC). Alveolar wall-thickness was increased from 6 to 24h(p < 0.001 vs. NC). There was a significant correlation between the cell counts of lung slices and alveolar wall-thickness(r= 0.61, p < 0.001). This result suggested that the cellular infiltrations might be followed by the alterations of interstitium, and the edematous change of alveolar wall might be most rapidly recovered to its normal condition in the process of repair. Conclusion : We concluded that although the role of PMN is partly certain in ETX-induced ALI, it is somewhat inadequate to its known major impact on ALL Alveolar macrophage and/or non-immune cells such as pulmonary endothelial or epithelial cells, may be more importantly contributed to the initiation and perpetual progression of ETX-induced ALI. The IL-6 in ETX-induced ALI was independent to $TNF{\alpha}$, measured by a bioassay in BALF. The early rise in IL-6 in BALF implies multiple origins of the IL-6.

• Tuberculosis and Respiratory Diseases
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• v.42 no.4
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• pp.535-547
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• 1995

Background: Intensive care units(ICUs) probably represent the single largest identifiable source of infection within the hospital. Although there are several studies on ICU infections in respect to their bacteriology or mortality rate for individual types of ICU, few studies have compared ICU infections between different types of ICU. The aim of this study was to identify clinical differences in bacteremia between medical ICU(MICU) and surgical ICU(SICU) patients. Methods: 256 patients with bacteremia were retrospectively evaluated. Medical records were reviewed to obtain the clinical and bacteriologic informations. Results: 1) The mean age of the patients with bacteremia of MICU($58.6{\pm}17.2\;yr$) was greater than that of all MICU patients($54.3{\pm}17.1\;yr$)(p<0.01), but there was no significant difference in SICU patients(patients with bacteremia of SICU: $56.3{\pm}18.6\;yr$, all SICU patients: $62.0{\pm}16.8$)(p>0.05). ICU stay was longer(MICU patients: $23.4{\pm}40.8$ day, SICU patients: $30.3{\pm}26.8$ day) than the mean stay of all patients($6.8{\pm}15.5$ day)(p<0.05, respectively). Bacteremia of both ICU patients developed past the average day of ICU stay(all MICU patients: 7.9 day, all SICU patients: 6.0 day, MICU bacteremia: 19th day, SICU bacteremia: 17th day of ICU stay)(p<0.05, respectively). 2) There were no significant differences in mean age, sex, and length of stay of both ICU patients with bacteremia. 3) Use of antibiotics or steroid, use of percutaneous devices and invasive procedures before development of bacteremia were more frequent in SICU patients than in MICU patients(prior antibiotics use: MICU 45%, SICU 63%, p<0.05; steroid use: MICU 14%, SICU 36%, p<0.01; use of percutaneous devices: MICU 19%, SICU 39%, p<0.01; invasive procedures: MICU 19%, SICU 61 %, p<0.01). 4) The prevalence of community acquired infections was significantly higher in MICU patients than in SICU patients(MICU 42%, SICU 9%)(p<0.01), whereas SICU patients showed higher prevalence of ICU-acquired infection than MICU patients(MICU 48%, SICU 78%)(p<0.01). 5) There were no differences in causative organisms, primary sites of infection and time interval to bacteremia between both ICUs. 6) There were no significant differences in outcome according to pathogenic organisms or primary sites of infection. 7) The mortality rate was higher in patients with bacteremia than without bacteremia(MICU mortality rate: patients with bacteremia 72.5%, patients without bacteremia 36.0%, p<0.01; SICU mortality rate: patients with bacteremia 40.3%, patients without bacteremia 8.5%, p<0.05), and the mortality rate of MICU bacteremia was significantly higher compared with that of SICU bacteremia(MICU 72.5%, SICU 40.3%)(p<0.01). Conclusion: ICU patients with bacteremia stayed longer before the development of bacteremia, and showed higher mortality than the overall ICU population. The incidence of bacteremia was higher in MICU patients than SICU patients. MICU patients with bacteremia showed higher prevalence of liver diseases and acute respiratory failure, community-acquired bacteremia and greater mortality rate than SICU patients with bacteremia. SICU patients with bacteremia, on the other hand, showed higher prevalence of trauma, prior use of immunosuppressive agents, invasive procedures, and ICU-acquired bacteremia, and lower mortality rate than MICU patients with bacteremia.