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首页> 外文期刊>Bulletin of the World Health Organization >Estimating the measles effective reproduction number in Australia from routine notification data
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Estimating the measles effective reproduction number in Australia from routine notification data

机译:根据常规通报数据估算澳大​​利亚的麻疹有效繁殖数

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Objective To estimate the measles effective reproduction number (R) in Australia by modelling routinely collected notification data.Methods R was estimated for 2009–2011 by means of three methods, using data from Australia’s National Notifiable Disease Surveillance System. Method 1 estimated R as 1 ? P, where P equals the proportion of cases that were imported, as determined from data on place of acquisition. The other methods estimated R by fitting a subcritical branching process that modelled the spread of an infection with a given R to the observed distributions of outbreak sizes (method 2) and generations of spread (method 3). Stata version 12 was used for method 2 and Matlab version R2012 was used for method 3. For all methods, calculation of 95% confidence intervals (CIs) was performed using a normal approximation based on estimated standard errors.Findings During 2009–2011, 367 notifiable measles cases occurred in Australia (mean annual rate: 5.5 cases per million population). Data were 100% complete for importation status but 77% complete for outbreak reference number. R was estimated as < 1 for all years and data types, with values of 0.65 (95% CI: 0.60–0.70) obtained by method 1, 0.64 (95% CI: 0.56–0.72) by method 2 and 0.47 (95% CI: 0.38–0.57) by method 3.Conclusion The fact that consistent estimates of R were obtained from all three methods enhances confidence in the validity of these methods for determining R.
机译:目的通过对常规收集的通报数据进行建模,估算澳大利亚的麻疹有效繁殖数(R)。使用澳大利亚国家法定传染病监测系统的数据,通过三种方法对2009-2011年的方法R进行估算。方法1估计R为1? P,其中P等于从获取地点获得的数据确定的导入案例的比例。其他方法通过对亚临界分支过程进行拟合来估计R,该亚临界分支过程将给定R的感染传播建模为观察到的爆发大小分布(方法2)和传播世代(方法3)。方法2使用Stata版本12,方法3使用Matlab版本R2012。对于所有方法,均使用基于估计的标准误差的正态近似值来计算95%置信区间(CI)。2009-2011年间的发现为367在澳大利亚发生了应通报的麻疹病例(年平均发病率:每百万人口5.5例)。数据输入状态的数据完整率为100%,而疫情参考编号的数据完整为77%。对于所有年份和数据类型,R均估计为<1,方法1获得的值为0.65(95%CI:0.60–0.70),方法2获得的值为0.64(95%CI:0.56-0.72)和0.47(95%CI) :0.38–0.57)的方法3。结论从所有三种方法获得的R的估计值一致,这一事实增强了人们对这些方法确定R的有效性的信心。

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