Estimating the distribution of time to extinction of infectious diseases in mean-field approaches.
Estimating the distribution of time to extinction of infectious diseases in mean-field approaches.
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DOI：
10.1098/rsif.2020.0540
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Keeling MJ
中科院分区：
文献类型：
--
作者：
Aliee M;Rock KS;Keeling MJ
许多传染病面临的一个关键挑战是预测在特定干预措施下的灭绝时间。一般来说，这个问题需要使用随机模型，它认识到动态内在的基于个体的、机会驱动的性质；然而，随机模型固有的计算成本很高，特别是当参数的不确定性也需要考虑在内的时候。确定性模型经常用于预测，因为它们更容易处理；然而，它们无法准确地达到零感染，使得预测灭绝时间成为问题。在这里，我们通过对感染和康复过程的有效的‘生灭’描述，研究确定性模型中的灭绝问题。我们提出了一种实用的方法来估计灭绝时间的分布，从而通过计算它们在生灭框架内的不同时刻来估计稳健的平均值和预测区间。我们通过分析简化的易感-易感(SIS)动力学，以及研究考虑非洲昏睡病(布氏锥虫冈比亚锥虫)感染和控制的更复杂和现实的动力学实例，表明这些预测与随机模型的结果非常吻合。
A key challenge for many infectious diseases is to predict the time to extinction under specific interventions. In general, this question requires the use of stochastic models which recognize the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when parameter uncertainty also needs to be incorporated. Deterministic models are often used for prediction as they are more tractable; however, their inability to precisely reach zero infections makes forecasting extinction times problematic. Here, we study the extinction problem in deterministic models with the help of an effective ‘birth–death’ description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth–death framework. We show that these predictions agree very well with the results of stochastic models by analysing the simplified susceptible–infected–susceptible (SIS) dynamics as well as studying an example of more complex and realistic dynamics accounting for the infection and control of African sleeping sickness (Trypanosoma brucei gambiense).
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影响因子：
3.2
作者：
Hollingsworth TD;Adams ER;Anderson RM;Atkins K;Bartsch S;Basáñez MG;Behrend M;Blok DJ;Chapman LA;Coffeng L;Courtenay O;Crump RE;de Vlas SJ;Dobson A;Dyson L;Farkas H;Galvani AP;Gambhir M;Gurarie D;Irvine MA;Jervis S;Keeling MJ;Kelly-Hope L;King C;Lee BY;Le Rutte EA;Lietman TM;Ndeffo-Mbah M;Medley GF;Michael E;Pandey A;Peterson JK;Pinsent A;Porco TC;Richardus JH;Reimer L;Rock KS;Singh BK;Stolk W;Swaminathan S;Torr SJ;Townsend J;Truscott J;Walker M;Zoueva A;NTD Modelling Consortium
通讯作者：
NTD Modelling Consortium
影响因子：
1.9
作者：
Andersson, H;Britton, T
通讯作者：
Britton, T
影响因子：
10.2
作者：
Forgoston, Eric;Moore, Richard O.
通讯作者：
Moore, Richard O.
影响因子：
2.4
作者：
Assaf, Michael;Meerson, Baruch
通讯作者：
Meerson, Baruch
DOI：
10.1098/rspb.2001.1898
发表时间：
2002-02-22
影响因子：
4.7
作者：
Keeling, MJ;Grenfell, BT
通讯作者：
Grenfell, BT