A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.
A multi-state spatio-temporal Markov model for categorized incidence of meningitis in sub-Saharan Africa.
复制标题
用于撒哈拉以南非洲脑膜炎分类发病率的多状态时空马尔可夫模型。
DOI：
10.1017/s0950268812001926
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发表时间：
2013
影响因子：
4.2
通讯作者：
Agier L
中科院分区：
文献类型：
--
作者：
Agier L
脑膜炎球菌性脑膜炎是非洲地带的一个主要公共卫生问题。尽管流行病具有明显的季节性，但对驱动流行病的因素仍然知之甚少。在这里，我们提供了第一次尝试预测流行病的时空尺度所需的一年内的反应，使用一个纯粹的经验方法。根据预先规定的流行病学阈值，将尼日尔（1986-2007年）区级每周发病率离散化为潜伏、警戒和流行状态。我们模拟了状态之间的转换概率，考虑了季节性和时空依赖性。提前一周预测进入流行状态的特异性和阴性预测值> 99%，灵敏度和阳性预测值> 72%。在年度尺度上，我们预测一个地区首次进入流行状态的敏感性为65.0%，阳性预测值为49.0%，平均时间为4.6周。这些结果可为关于筹备行动的决定提供信息。
Meningococcal meningitis is a major public health problem in the African Belt. Despite the obvious seasonality of epidemics, the factors driving them are still poorly understood. Here, we provide a first attempt to predict epidemics at the spatio-temporal scale required for in-year response, using a purely empirical approach. District-level weekly incidence rates for Niger (1986–2007) were discretized into latent, alert and epidemic states according to pre-specified epidemiological thresholds. We modelled the probabilities of transition between states, accounting for seasonality and spatio-temporal dependence. One-week-ahead predictions for entering the epidemic state were generated with specificity and negative predictive value >99%, sensitivity and positive predictive value >72%. On the annual scale, we predict the first entry of a district into the epidemic state with sensitivity 65·0%, positive predictive value 49·0%, and an average time gained of 4·6 weeks. These results could inform decisions on preparatory actions.