Predictability in a highly stochastic system: final size of measles epidemics in small populations.
Predictability in a highly stochastic system: final size of measles epidemics in small populations.
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DOI：
10.1098/rsif.2014.1125
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发表时间：
2015-01-06
期刊：
影响因子：
--
通讯作者：
Grenfell BT
中科院分区：
文献类型：
--
作者：
Caudron Q;Mahmud AS;Metcalf CJ;Gottfreðsson M;Viboud C;Cliff AD;Grenfell BT
流行病动力学建模的一个标准假设是，感兴趣的人群是很好的混合，不存在集合人群的集群。众所周知的和经常使用的SIR模型，可以说是理论流行病学中最重要的房室模型，除了这些人口混合假设外，还假设所建模的疾病具有强免疫性，直接传播，并且具有明确的感染期。儿童感染，如麻疹，是符合类似SIR机制的疾病的主要例子。这些感染已被很好地研究了许多系统的大，混合人口与地方性感染。在这里，我们考虑一个设置，人口少，孤立。感染的动态是由随机的免疫-免疫事件驱动的，在由于缺乏易感宿主而迅速消亡之前，会产生大规模、突然和短暂的流行病。使用TSIR模型，我们适合接种前麻疹发病率和人口数据在博恩霍尔姆，法罗群岛和冰岛的四个地区，1901年和1965年之间。每个国家的数据集都有不同程度的数据异质性和稀疏性。我们探索了这个模型的预测潜力：给定历史发病率数据和最新的人口统计信息，并且知道新的流行病刚刚开始，我们能预测它会有多大吗？我们表明，尽管麻疹病例的发病率缺乏显着的季节性，并在人口水平上潜在的严重异质性，我们能够估计即将到来的流行病的规模，条件是第一个时间步骤，在合理的信心。我们的研究结果对小人群中新流行病早期阶段的可能控制措施具有潜在影响。
A standard assumption in the modelling of epidemic dynamics is that the population of interest is well mixed, and that no clusters of metapopulations exist. The well-known and oft-used SIR model, arguably the most important compartmental model in theoretical epidemiology, assumes that the disease being modelled is strongly immunizing, directly transmitted and has a well-defined period of infection, in addition to these population mixing assumptions. Childhood infections, such as measles, are prime examples of diseases that fit the SIR-like mechanism. These infections have been well studied for many systems with large, well-mixed populations with endemic infection. Here, we consider a setting where populations are small and isolated. The dynamics of infection are driven by stochastic extinction–recolonization events, producing large, sudden and short-lived epidemics before rapidly dying out from a lack of susceptible hosts. Using a TSIR model, we fit prevaccination measles incidence and demographic data in Bornholm, the Faroe Islands and four districts of Iceland, between 1901 and 1965. The datasets for each of these countries suffer from different levels of data heterogeneity and sparsity. We explore the potential for prediction of this model: given historical incidence data and up-to-date demographic information, and knowing that a new epidemic has just begun, can we predict how large it will be? We show that, despite a lack of significant seasonality in the incidence of measles cases, and potentially severe heterogeneity at the population level, we are able to estimate the size of upcoming epidemics, conditioned on the first time step, to within reasonable confidence. Our results have potential implications for possible control measures for the early stages of new epidemics in small populations.
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影响因子：
6.1
作者：
de Roos, AM;Leonardsson, K;Mittelbach, GG
通讯作者：
Mittelbach, GG
DOI：
10.1098/rstb.1996.0150
发表时间：
1996-12-29
影响因子：
6.3
作者：
Rhodes, CJ;Anderson, RM
通讯作者：
Anderson, RM
影响因子：
2
作者：
BLACK, FL
通讯作者：
BLACK, FL
影响因子：
64.8
作者：
Rhodes, CJ;Anderson, RM
通讯作者：
Anderson, RM
影响因子：
3.9
作者：
Metcalf, C. J. E.;Munayco, C. V.;Bjornstad, O. N.
通讯作者：
Bjornstad, O. N.