The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil
The development of an early warning system for climate-sensitive disease risk with a focus on dengue epidemics in Southeast Brazil
复制标题
DOI：
10.1002/sim.5549
复制
发表时间：
2013-02-28
影响因子：
2
通讯作者：
Carvalho, Marilia Sa
中科院分区：
文献类型：
--
作者：
Lowe, Rachel;Bailey, Trevor C.;Carvalho, Marilia Sa
以前的研究表明，疾病与气候变化之间存在统计学上显著的关联，这突出了开发基于气候的流行病早期预警系统的潜力。然而，局限性包括没有考虑到非气候混杂因素，有限的地理/时间分辨率，或缺乏预测有效性评估。在这里，我们使用一个时空广义线性混合模型来考虑巴西东南部登革热的这些问题，该模型的参数在贝叶斯框架中估计，允许在时间和空间上推导后验预测分布。本文以Lowe等人的初步研究为基础，但使用了扩展的、较新的数据和改进的模型公式，其中除其他调整外，还纳入了过去的登革热风险，以改进模型预测。这是第一次使用样本外预测对模型性能进行全面评估和验证，并证明了比反映当前监测实践的模型有相当大的改进。利用该模型，我们可以对预定义的预警阈值发布概率登革热预警。如果采用每10万居民病例超过300例的概率大于50%的标准，那么在2008年2月至4月经历登革热流行发病率的54个地区中，有81%的地区可以成功发出流行病警报，相应的误报率为25%。我们提出了一种新的可视化技术来映射登革热风险的三元概率预测。这一技术使决策者能够确定模型确定预测特定登革热风险类别的地区，从而有效地将有限的资源用于特定季节风险最大的地区。版权所有：John Wiley & Sons， Ltd。
Previous studies demonstrate statistically significant associations between disease and climate variations, highlighting the potential for developing climate-based epidemic early warning systems. However, limitations include failure to allow for non-climatic confounding factors, limited geographical/temporal resolution, or lack of evaluation of predictive validity. Here, we consider such issues for dengue in Southeast Brazil using a spatio-temporal generalised linear mixed model with parameters estimated in a Bayesian framework, allowing posterior predictive distributions to be derived in time and space. This paper builds upon a preliminary study by Lowe et al. but uses extended, more recent data and a refined model formulation, which, amongst other adjustments, incorporates past dengue risk to improve model predictions. For the first time, a thorough evaluation and validation of model performance is conducted using out-of-sample predictions and demonstrates considerable improvement over a model that mirrors current surveillance practice. Using the model, we can issue probabilistic dengue early warnings for pre-defined alert' thresholds. With the use of the criterion greater than a 50% chance of exceeding 300 cases per 100,000 inhabitants', there would have been successful epidemic alerts issued for 81% of the 54 regions that experienced epidemic dengue incidence rates in FebruaryApril 2008, with a corresponding false alarm rate of 25%. We propose a novel visualisation technique to map ternary probabilistic forecasts of dengue risk. This technique allows decision makers to identify areas where the model predicts with certainty a particular dengue risk category, to effectively target limited resources to those districts most at risk for a given season. Copyright (c) 2012 John Wiley & Sons, Ltd.