A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models.
A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models.
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DOI：
10.1371/journal.pcbi.1005257
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发表时间：
2017-01
影响因子：
4.3
通讯作者：
Cohen T
中科院分区：
文献类型：
--
作者：
Zimmer C;Yaesoubi R;Cohen T
随机传播动力学模型对于研究新病原体的早期出现特别有用，因为当感染个体的数量很少时，偶然事件非常重要。然而，这些类型的随机模型的参数估计和预测的方法仍然有限。在这篇手稿中，我们描述了一个校准和预测框架的流行病的随机房室传播模型。所提出的方法，多重射击随机系统（MSS），适用于线性噪声近似描述的波动的大小，并使用每个新的监测观察更新的信念关于真正的流行病状态。使用一种新的病毒病原体的模拟爆发，我们评估MSS的实时参数估计和预测流行病期间的准确性。我们假设每周新诊断病例数是可用的，并作为发病率的不完美代理。我们表明，MSS产生的关键流行病参数（即平均持续时间的传染性，R 0，和参考）的准确估计，并可以提供一个准确的估计，在流行病的过程中未观察到的传染性个体的数量。MSS还可以准确预测未来住院的数量和时间以及总体发病率。我们比较MSS的性能，以三个国家的最先进的基准方法：1）一个独立的泊松观测假设的似然近似; 2）粒子滤波方法;和3）集合卡尔曼滤波方法。我们发现，在测试的大多数流行病场景中，MSS的性能显着优于这三种基准方法中的每一种。总之，MSS是一种很有前途的方法，可以改善目前的方法校准和预测使用随机模型的流行病。新型人类病原体的零星出现和传播对全球公共卫生构成持续威胁。需要对流行病行为进行早期和准确的预测，以便为有效的公共卫生政策决策提供信息，这些决策在很大程度上平衡了重大疫情的风险和干预的巨大成本。然而，控制流行病行为的关键参数不能直接观察到，因此需要在不完善的监测数据的基础上进行参数估计和预测的计算技术。在本文中，我们开发了一种方法（随机系统的多重射击，MSS），利用积累的流行病数据来实时估计（1）关键的流行病参数，包括继发病例的平均数量和平均传染持续时间，（2）未来的病例数，（3）未观察到的感染人数。采用全面的模拟实验，我们证明，MSS优于现有的国家的最先进的校准和预测技术在大多数模拟场景。因此，管理支助系统可使决策者在面对新出现的流行病威胁时作出更有效的反应，更有效地利用资源。
Stochastic transmission dynamic models are especially useful for studying the early emergence of novel pathogens given the importance of chance events when the number of infectious individuals is small. However, methods for parameter estimation and prediction for these types of stochastic models remain limited. In this manuscript, we describe a calibration and prediction framework for stochastic compartmental transmission models of epidemics. The proposed method, Multiple Shooting for Stochastic systems (MSS), applies a linear noise approximation to describe the size of the fluctuations, and uses each new surveillance observation to update the belief about the true epidemic state. Using simulated outbreaks of a novel viral pathogen, we evaluate the accuracy of MSS for real-time parameter estimation and prediction during epidemics. We assume that weekly counts for the number of new diagnosed cases are available and serve as an imperfect proxy of incidence. We show that MSS produces accurate estimates of key epidemic parameters (i.e. mean duration of infectiousness, R0, and Reff) and can provide an accurate estimate of the unobserved number of infectious individuals during the course of an epidemic. MSS also allows for accurate prediction of the number and timing of future hospitalizations and the overall attack rate. We compare the performance of MSS to three state-of-the-art benchmark methods: 1) a likelihood approximation with an assumption of independent Poisson observations; 2) a particle filtering method; and 3) an ensemble Kalman filter method. We find that MSS significantly outperforms each of these three benchmark methods in the majority of epidemic scenarios tested. In summary, MSS is a promising method that may improve on current approaches for calibration and prediction using stochastic models of epidemics. The sporadic emergence and spread of novel human pathogens poses a continuing threat to global public health. Early and accurate prediction of epidemic behavior is needed to inform effective public health policy decisions which much balance the risk of major outbreaks with the substantial costs of interventions. Key parameters governing the behavior of epidemics, however, cannot be directly observed and hence computational techniques are required for parameter estimation and prediction on the basis of imperfect surveillance data. In this paper, we develop a method (Multiple Shooting for Stochastic Systems, MSS) that utilizes accumulating epidemic data to estimate in real-time (1) key epidemic parameters including the average number of secondary cases and the mean duration of infectiousness, (2) the future number of cases, and (3) the unobserved number of infected individuals in the population. Employing comprehensive simulation experiments, we demonstrate that MSS outperforms the existing state-of-the-art calibration and prediction techniques in the majority of simulated scenarios. MSS may thus allow policy makers to respond more effectively and use resources more efficiently in the face of emerging epidemic threats.