Using phenomenological models for forecasting the 2015 Ebola challengeBruce
Using phenomenological models for forecasting the 2015 Ebola challengeBruce
复制标题
DOI：
10.1016/j.epidem.2016.11.002
复制
发表时间：
2018-03-01
期刊：
影响因子：
3.8
通讯作者：
Chowell, Gerardo
中科院分区：
文献类型：
--
作者：
Pell, Bruce;Kuang, Yang;Chowell, Gerardo
背景：威胁人类的新型病原体数量不断增加，促使人们应用数学模型来预测流行病的轨迹和规模。材料和方法：我们总结了2015年埃博拉挑战期间logistic方程的实时预测结果，重点是预测基于埃博拉传播动态和控制的详细个人模型的合成数据。我们还对两个简单的现象学模型进行了挑战后的比较。特别是，我们系统地比较了逻辑增长模型和最近引入的广义理查兹模型（GRM），该模型捕获了从亚指数到指数增长的一系列早期流行病增长曲线。具体而言，我们评估了每个模型的性能，以估计再现数，生成流行病轨迹的短期预测，并预测最终的流行病规模。结果：在挑战过程中，logistic方程始终低估了最终流行规模、高峰时间和高峰时间的病例数，平均绝对百分比误差（MAPE）分别为0.49、0.36和0.40。挑战后，随着获得更多的发病率数据，具有重现从早期亚指数到指数增长动态的一系列流行病增长曲线的灵活性的GRM在确定最终流行病规模方面优于logistic增长模型，而logistic模型即使在不断演变的流行病数据量增加的情况下也低估了最终流行病。广义Richards模型提供的发病率预测在所有情景和时间点上都优于logistic增长模型，平均RMS从78.00 （logistic）下降到60.80 （GRM）。两种模型都提供了对有效繁殖数的合理预测，但GRM的MAPE在所有情景和时间点上的平均值为0.08，略优于logistic增长模型，MAPE为0.10。结论：我们的研究结果进一步支持在预测工具包中考虑将灵活的早期流行病增长概况纳入传播模型的考虑。这种模型对于仅使用传染病暴发早期阶段的病例发病率时间序列快速评估正在发生的传染病暴发特别有用。(c) 2016年作者。Elsevier B.V.出版
Background: The rising number of novel pathogens threatening the human population has motivated the application of mathematical modeling for forecasting the trajectory and size of epidemics.Materials and methods: We summarize the real-time forecasting results of the logistic equation during the 2015 Ebola challenge focused on predicting synthetic data derived from a detailed individual-based model of Ebola transmission dynamics and control. We also carry out a post-challenge comparison of two simple phenomenological models. In particular, we systematically compare the logistic growth model and a recently introduced generalized Richards model (GRM) that captures a range of early epidemic growth profiles ranging from sub-exponential to exponential growth. Specifically, we assess the performance of each model for estimating the reproduction number, generate short-term forecasts of the epidemic trajectory, and predict the final epidemic size.Results: During the challenge the logistic equation consistently underestimated the final epidemic size, peak timing and the number of cases at peak timing with an average mean absolute percentage error(MAPE) of 0.49, 0.36 and 0.40, respectively. Post-challenge, the GRM which has the flexibility to reproduce a range of epidemic growth profiles ranging from early sub-exponential to exponential growth dynamics outperformed the logistic growth model in ascertaining the final epidemic size as more incidence data was made available, while the logistic model underestimated the final epidemic even with an increasing amount of data of the evolving epidemic. Incidence forecasts provided by the generalized Richards model performed better across all scenarios and time points than the logistic growth model with mean RMS decreasing from 78.00 (logistic) to 60.80 (GRM). Both models provided reasonable predictions of the effective reproduction number, but the GRM slightly outperformed the logistic growth model with a MAPE of 0.08 compared to 0.10, averaged across all scenarios and time points.Conclusions: Our findings further support the consideration of transmission models that incorporate flexible early epidemic growth profiles in the forecasting toolkit. Such models are particularly useful for quickly evaluating a developing infectious disease outbreak using only case incidence time series of the early phase of an infectious disease outbreak. (c) 2016 The Authors. Published by Elsevier B.V.