Sharmin Sifat, Rayhan Israt
Institute of Statistical Research and Training, University of Dhaka, Ramna, Dhaka 1000, Bangladesh.
J Health Popul Nutr. 2011 Dec;29(6):567-73. doi: 10.3329/jhpn.v29i6.9893.
The detection of unusual patterns in the occurrence of diseases is an important challenge to health workers interested in early identification of epidemics. The objective of this study was to provide an early signal of infectious disease epidemics by analyzing the disease dynamics. A two-stage monitoring system was applied, which consists of univariate Box-Jenkins model or autoregressive integrated moving average model and subsequent tracking signals from several statistical process-control charts. The analyses were illustrated on January 2000-August 2009 national measles data reported monthly to the Expanded Programme on Immunization (EPI) in Bangladesh. The results of this empirical study revealed that the most adequate model for the occurrences of measles in Bangladesh was the seasonal autoregressive integrated moving average (3, 1, 0) (0, 1, 1)12 model, and the statistical process-control charts detected no measles epidemics during September 2007-August 2009. The two-stage monitoring system performed well to capture the measles dynamics in Bangladesh without detection of an epidemic because of high measles-vaccination coverage.
对于关注传染病早期识别的卫生工作者而言，检测疾病发生中的异常模式是一项重大挑战。本研究的目的是通过分析疾病动态为传染病流行提供早期信号。应用了一个两阶段监测系统，该系统由单变量Box-Jenkins模型或自回归积分移动平均模型以及随后来自多个统计过程控制图的跟踪信号组成。分析以2000年1月至2009年8月每月向孟加拉国扩大免疫规划（EPI）报告的全国麻疹数据为例进行说明。这项实证研究的结果表明，孟加拉国麻疹发病情况的最合适模型是季节性自回归积分移动平均(3, 1, 0)(0, 1, 1)12模型，并且统计过程控制图在2007年9月至2009年8月期间未检测到麻疹流行。由于麻疹疫苗接种覆盖率高，该两阶段监测系统在捕获孟加拉国麻疹动态方面表现良好，未检测到疫情。
