期刊
STATISTICS IN MEDICINE
卷 32, 期 5, 页码 864-883出版社
WILEY
DOI: 10.1002/sim.5549
关键词
climate; dengue; early warning systems; random effects; spatio-temporal modelling
类别
资金
- EUROBRISA network project [F/00 144/AT]
- Leverhulme Trust
- European Commission [282 378]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据