http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Use of Internet Search Queries to Enhance Surveillance of Foodborne Illness
Bahk, Gyung Jin,Kim, Yong Soo,Park, Myoung Su U.S. Department of Health and Human Services * Cen 2015 Emerging Infectious Diseases Vol.21 No.11
<▼1><P>“Food poisoning” queries were correlated with the number of foodborne illness–related hospital stays.</P></▼1><▼2><P>As a supplement to or extension of methods used to determine trends in foodborne illness over time, we propose the use of Internet search metrics. We compared Internet query data for foodborne illness syndrome–related search terms from the most popular 5 Korean search engines using Health Insurance Review and Assessment Service inpatient stay data for 26 International Classification of Diseases, Tenth Revision, codes for foodborne illness in South Korea during 2010–2012. We used time-series analysis with Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Internet search queries for “food poisoning” correlated most strongly with foodborne illness data (<I>r</I> = 0.70, p<0.001); furthermore, “food poisoning” queries correlated most strongly with the total number of inpatient stays related to foodborne illness during the next month (β = 0.069, SE 0.017, p<0.001). This approach, using the SARIMA model, could be used to effectively measure trends over time to enhance surveillance of foodborne illness in South Korea.</P></▼2>
Predictive Modeling for Microbial Risk Assessment (MRA) from the Literature Experimental Data
Gyung-Jin Bahk 한국식품과학회 2009 Food Science and Biotechnology Vol.18 No.1
One of the most important aspects of conducting this microbial risk assessment (MRA) is determining the model in microbial behaviors in food systems. However, to fully these modeling, large expenditures or newly laboratory experiments will be spent to do it. To overcome these problems, it has to be considered to develop the new strategies that can be used data in the published literatures. This study is to show whether or not the data set from the published experimental data has more value for modeling for MRA. To illustrate this suggestion, as example of data set, 4 published Salmonella survival in Cheddar cheese reports were used. Finally, using the GInaFiT tool, survival was modeled by nonlinear polynomial regression model describing the effect of temperature on Weibull model parameters. This model used data in the literatures is useful in describing behavior of Salmonella during different time and temperature conditions of cheese ripening.
식중독 발생 위해인자로서 가정용 냉장고의 온도에 대한 확률분포 분석
박경진(Gyung-Jin Bahk) 한국식품과학회 2010 한국식품과학회지 Vol.42 No.3
본 연구는 국내에서의 가정내 냉장고 온도에 대한 조사를 수행하여, 현 시점에서의 냉장고에서의 식품보관 온도분포를 추정하였고, 이를 MRA(미생물 위해평가: Microbial risk assessment)의 입력변수로 활용할 수 있도록 적정 확률분포 모델을 제시하였다. 일반적으로 가정내 냉장고에서의 식품 보관온도는 식중독 발생 등에서 있어 중요한 위해인자로 작용하는 것으로 알려져 있다. 조사대상 가구는 총 139가구이었으며, 조사기간은 2009년 5월에서부터 9월까지 data logger를 이용하여 측정하였다. 조사된 냉장고 온도의 평균은 3.53±2.96℃로, 5℃ 이상은 23.6%로 나타났다. 수집된 온도자료는 @RISK를 이용, 적합성 검정(GOF: K-S와 AD test)을 수행하여 적정 확률분포모델에 대해 추정하였고, 이중 LogLogistic(?10.407, 13.616, 8.6107)분포 모델이 가장 적절한 국내에서의 가정내 냉장고 식품보관 온도분포 모델로 나타났다. 이 확률분포 모델은 MRA적용에 있어 노출평가에서 입력변수로서 직접적 활용이 가능하다고 할 수 있겠다. The objective of this study was to present the proper probability distribution model based on the data obtained from surveys on domestic refrigerator food storage temperatures in home. Domestic refrigerator temperatures were determined as risk factors in foodborne disease outbreaks for microbial risk assessment (MRA). The temperature was measured by directly visiting 139 homes using a data logger from May to September of 2009. The overall mean temperature for all the refrigerators in the survey was 3.53±2.96℃, with 23.6% of the refrigerators measuring above 5℃. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF, i.e., the Kolmogorov-Smirnov (KS) or Anderson-Darling (AD) test) to determine the proper probability distribution model. This result showed that the LogLogistic (-10.407, 13.616, 8.6107) distribution was found to be the most appropriate for the MRA model. The results of this study might be directly used as input variables in exposure evaluation for conducting MRA.