Mathematical models to characterize early epidemic growth: A review.
Mathematical models to characterize early epidemic growth: A review.
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DOI：
10.1016/j.plrev.2016.07.005
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发表时间：
2016-09
影响因子：
11.7
通讯作者：
Viboud C
中科院分区：
文献类型：
--
作者：
Chowell G;Sattenspiel L;Bansal S;Viboud C
使用数学模型来深入了解传染病的传播动态并评估不同干预策略的潜在影响，这是一个悠久的传统。越来越多地使用数学模型进行流行病预测，这突出了设计能够捕捉特定病原体和社会背景的基线传播特征的可靠模型的重要性。然而，需要更精确的模型，特别是为了解释实际流行病早期增长动态的变化，并更好地了解起作用的机制。在这里，我们回顾了最近在传染病暴发数据建模和表征早期流行病增长模式方面的进展，并调查了最有助于捕获从亚指数到指数增长动态的各种早期流行病增长概况的数学公式类型。具体来说，我们回顾了包含空间细节或现实种群混合结构的数学模型，包括元种群模型、基于个体的网络模型和包含反应性行为变化或非均匀混合影响的简单sir型模型。在此过程中，我们还分析了先前设计和校准的详细的大规模基于主体的模型的模拟数据，以研究现实社会网络和疾病传播特征如何影响早期流行病增长模式，一般传播动态以及2009年A/H1N1流感大流行和2014-15年西非埃博拉疫情等国际疾病紧急情况的控制。
There is a long tradition of using mathematical models to generate insights into the transmission dynamics of infectious diseases and assess the potential impact of different intervention strategies. The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing reliable models that capture the baseline transmission characteristics of specific pathogens and social contexts. More refined models are needed however, in particular to account for variation in the early growth dynamics of real epidemics and to gain a better understanding of the mechanisms at play. Here, we review recent progress on modeling and characterizing early epidemic growth patterns from infectious disease outbreak data, and survey the types of mathematical formulations that are most useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth dynamics. Specifically, we review mathematical models that incorporate spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing. In this process, we also analyze simulation data stemming from detailed large-scale agent-based models previously designed and calibrated to study how realistic social networks and disease transmission characteristics shape early epidemic growth patterns, general transmission dynamics, and control of international disease emergencies such as the 2009 A/H1N1 influenza pandemic and the 2014-15 Ebola epidemic in West Africa.