Predicting case numbers during infectious disease outbreaks when some cases are undiagnosed
Predicting case numbers during infectious disease outbreaks when some cases are undiagnosed
复制标题
DOI：
10.1002/sim.2523
复制
发表时间：
2007-01-15
影响因子：
2
通讯作者：
Clements, M.
中科院分区：
文献类型：
--
作者：
Glass, K.;Becker, N.;Clements, M.
我们描述了一种方法，用于计算95%的界限，目前的隐藏病例数和未来的诊断病例数在传染病爆发。贝叶斯马尔可夫链蒙特卡罗方法被用来拟合考虑未确诊病例的传染病传播模型。评估这种方法的模拟数据，我们发现，它提供了保守的95%的未确诊病例数和未来的病例数的界限，这些界限是强大的修改产生的模拟数据的假设。此外，该方法提供了一个很好的估计的初始繁殖数量，并在后期阶段的繁殖数量的爆发。将该方法应用于香港、新加坡、台湾和加拿大的SARS数据，发现未来确诊病例的界限是可靠的，隐藏病例的界限表明，在每个地区爆发结束时，几乎没有隐藏病例。我们估计，最初的繁殖数介于1.5和3之间，在爆发的后期阶段的繁殖数介于0.36和0.6之间。版权所有（c）2006约翰威利父子有限公司。
We describe a method for calculating 95 per cent bounds for the current number of hidden cases and the future number of diagnosed cases during an outbreak of an infectious disease. A Bayesian Markov chain Monte Carlo approach is used to fit a model of infectious disease transmission that takes account of undiagnosed cases. Assessing this method on simulated data, we find that it provides conservative 95 per cent bounds for the number of undiagnosed cases and future case numbers, and that these bounds are robust to modifications in the assumptions generating the simulated data. Moreover, the method provides a good estimate of the initial reproduction number, and the reproduction number in the latter stages of the outbreak. Applying the approach to SARS data from Hong Kong, Singapore, Taiwan and Canada, the bounds on future diagnosed cases are found to be reliable, and the bounds on hidden cases suggests that there were few hidden cases remaining at the end of the outbreaks in each region. We estimate that the initial reproduction numbers lay between 1.5 and 3, and the reproduction numbers in the later stages of the outbreak lay between 0.36 and 0.6. Copyright (c) 2006 John Wiley & Sons, Ltd.