Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model.
Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model.
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DOI：
10.1016/j.epidem.2016.11.003
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发表时间：
2018-03
期刊：
影响因子：
3.8
通讯作者：
Edmunds WJ
中科院分区：
文献类型：
--
作者：
Funk S;Camacho A;Kucharski AJ;Eggo RM;Edmunds WJ
将贝叶斯半机制模型应用于埃博拉预测挑战。采用粒子马尔可夫链蒙特卡罗方法对模拟数据进行拟合。模型参数的后验样本用于生成预测轨迹。使用随后发布的模拟点评估预测结果。传染病的实时预报有助于公共卫生规划，特别是在疫情暴发期间。如果预报是由机械模型产生的，它们可以进一步用于确定资源目标或比较可能的干预措施的影响。然而，当有关行为变化、干预措施和传播途径的信息不容易获得时，对这种模型进行实时参数化往往很困难。在这里，我们提出了一个半机制的传染病动力学模型，该模型在2013-2016年西非埃博拉疫情期间实时使用，并显示适合2015年底进行的埃博拉预测挑战，模拟数据模拟真实的疫情。我们评估了模型在不同情况下的表现，并确定了我们方法的优点和缺点。像这里介绍的模型这样的模型结合了机制模型的力量和灵活性，可以将精确的爆发动态的不确定性包括在内，这可能是防治未来爆发的重要工具。
A Bayesian semi-mechanistic model was applied to the Ebola Forecasting Challenge. Model fits to simulated data were obtained from particle Markov-chain Monte Carlo. Posterior samples of model parameters were used to generate forecast trajectories. The forecasts were assessed using subsequently released simulation points. Real-time forecasts of infectious diseases can help public health planning, especially during outbreaks. If forecasts are generated from mechanistic models, they can be further used to target resources or to compare the impact of possible interventions. However, paremeterising such models is often difficult in real time, when information on behavioural changes, interventions and routes of transmission are not readily available. Here, we present a semi-mechanistic model of infectious disease dynamics that was used in real time during the 2013–2016 West African Ebola epidemic, and show fits to a Ebola Forecasting Challenge conducted in late 2015 with simulated data mimicking the true epidemic. We assess the performance of the model in different situations and identify strengths and shortcomings of our approach. Models such as the one presented here which combine the power of mechanistic models with the flexibility to include uncertainty about the precise outbreak dynamics may be an important tool in combating future outbreaks.