Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm
Improving epidemic size prediction through stable reconstruction of disease parameters by reduced iteratively regularized Gauss–Newton algorithm
复制标题
通过减少迭代正则化高斯-牛顿算法稳定重建疾病参数来改进流行病规模预测
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Micah J. Sheppard
中科院分区：
文献类型：
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作者：
A. Smirnova;Gerardo Chowell;L. DeCamp;S. Moghadas;Micah J. Sheppard
摘要传染病的经典房室流行病模型跟踪个体在不同流行病状态或危险群体之间的动态转移。这些模型中各种传播途径的可靠量化对于优化资源分配和成功设计公共卫生干预计划至关重要。然而，由于在新出现的疾病情况下可用的流行病学数据有限，基于较少参数的简单现象学模型可以在我们寻求对可能的疫情情景进行前瞻性预测方面发挥重要作用。本文采用广义理查兹模型，利用流行病增长早期的病例发病率数据，对流行病规模（定义为整个流行病期间的感染总数）及其转折点进行了稳定的数值估计。最小化是通过我们称之为简化迭代正则化高斯-牛顿（RIRGN）算法进行的，这是一种面向问题的数值方案，充分利用了手头非线性算子的特定结构。对RIRGN方法进行了收敛性分析，并对2014-15年西非埃博拉疫情的真实的病例发病率数据进行了数值模拟。我们表明，建议的RIRGN提供了一个稳定的算法，使用简单的现象学模型，有限的数据早期估计的转折点。
Abstract Classical compartmental epidemic models of infectious diseases track the dynamic transition of individuals between different epidemiological states or risk groups. Reliable quantification of various transmission pathways in these models is paramount for optimal resource allocation and successful design of public health intervention programs. However, with limited epidemiological data available in the case of an emerging disease, simple phenomenological models based on a smaller number of parameters can play an important role in our quest to make forward projections of possible outbreak scenarios. In this paper, we employ the generalized Richards model for stable numerical estimation of the epidemic size (defined as the total number of infections throughout the epidemic) and its turning point using case incidence data of the early epidemic growth phase. The minimization is carried out by what we call the Reduced Iteratively Regularized Gauss–Newton (RIRGN) algorithm, a problem-oriented numerical scheme that takes full advantage of the specific structure of the non-linear operator at hand. The convergence analysis of the RIRGN method is suggested and numerical simulations are conducted with real case incidence data for the 2014–15 Ebola epidemic in West Africa. We show that the proposed RIRGN provides a stable algorithm for early estimation of turning points using simple phenomenological models with limited data.
影响因子：
2.9
作者：
Bauer, Frank;Hohage, Thorsten;Munk, Axel
通讯作者：
Munk, Axel