Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
Optimising Renewal Models for Real-Time Epidemic Prediction and Estimation
复制标题
优化更新模型以实现实时流行病预测和估计
DOI：
10.1101/835181
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
C. Donnelly
中科院分区：
文献类型：
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作者：
K. Parag;C. Donnelly
有效繁殖数RT是预测传染病流行的重要指标。RT的显著变化可以对新的传播发出预警或预测干预的效果。更新模型从发病数据推断RT，并已应用于埃博拉病毒疾病和大流行性流感爆发等。该模型使用长度为k的滑动窗口来估计RT。虽然这有助于实时检测统计上显著的RT波动，但推断是高度k敏感的。K太大或太小的模型可能会忽略有意义的变化，或者过度解释噪音引起的变化。不存在有原则的k-选择方案。我们利用信息论中的累积预测误差(APE)度量开发了一种实用而又严格的方案。我们推导出准确的发病率预测分布，并将这些分布集成到APE框架中，以确定可用数据支持的最佳k。我们发现，这一k优化了短期预测的准确性，并揭示了看似明智的启发式k-选择是多么常见，可能具有误导性。
The effective reproduction number, Rt, is an important prognostic for infectious disease epidemics. Significant changes in Rt can forewarn about new transmissions or predict the efficacy of interventions. The renewal model infers Rt from incidence data and has been applied to Ebola virus disease and pandemic influenza outbreaks, among others. This model estimates Rt using a sliding window of length k. While this facilitates real-time detection of statistically significant Rt fluctuations, inference is highly k -sensitive. Models with too large or small k might ignore meaningful changes or over-interpret noise-induced ones. No principled k -selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory. We derive exact incidence prediction distributions and integrate these within an APE framework to identify the k best supported by available data. We find that this k optimises short-term prediction accuracy and expose how common, heuristic k -choices, which seem sensible, could be misleading.