Kwak Jaewon, Kim Soojun, Kim Gilho, Singh Vijay P, Hong Seungjin, Kim Hung Soo
Forecast and Control Division, Nakdong River Flood Control Office, Busan 604-851, Korea.
Columbia Water Center, Columbia University, New York, NY 10027, USA.
Int J Environ Res Public Health. 2015 Jun 29;12(7):7254-73. doi: 10.3390/ijerph120707254.
Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.
自1986年再次出现以来,恙虫病每年都会发生,被认为是韩国最流行的疾病之一。恙虫病是一种国家三级法定报告传染病,自2000年以来在韩国大幅增加。本研究的目的是构建一个疾病发病率模型,用于预测和量化恙虫病的发病率。利用2001年至2010年的数据,基于格兰杰因果关系和频谱分析,构建了考虑恙虫病与最低温度、降水量和平均风速之间时间滞后的发病率人工神经网络(ANN)模型,并对2011年至2012年进行了测试。结果表明,所选预测因子对恙虫病发病率的模拟可靠,并表明应考虑气象数据的季节性。