Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics.
Can Machines Learn Respiratory Virus Epidemiology?: A Comparative Study of Likelihood-Free Methods for the Estimation of Epidemiological Dynamics.
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DOI：
10.3389/fmicb.2018.00343
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发表时间：
2018
影响因子：
5.2
通讯作者：
Omori R
中科院分区：
文献类型：
--
作者：
Tessmer HL;Ito K;Omori R
为了估计和预测呼吸道病毒的传播动力学，基本繁殖数R0的估计是必不可少的。最近，近似贝叶斯计算方法已被用作估计流行病学模型参数，特别是R0的似然自由方法。在本文中，我们探索了各种机器学习方法，多层感知器，卷积神经网络和长短期记忆，学习和估计参数。此外，我们还比较了机器学习和近似贝叶斯计算方法对甲型H1N1流感pdm09、腮腺炎和麻疹爆发的模拟和真实流行病学数据的估计和时间要求的准确性。我们发现，机器学习方法可以比近似贝叶斯计算方法更快地进行验证和测试，但近似贝叶斯计算方法在不同的数据集上更具鲁棒性。
To estimate and predict the transmission dynamics of respiratory viruses, the estimation of the basic reproduction number, R0, is essential. Recently, approximate Bayesian computation methods have been used as likelihood free methods to estimate epidemiological model parameters, particularly R0. In this paper, we explore various machine learning approaches, the multi-layer perceptron, convolutional neural network, and long-short term memory, to learn and estimate the parameters. Further, we compare the accuracy of the estimates and time requirements for machine learning and the approximate Bayesian computation methods on both simulated and real-world epidemiological data from outbreaks of influenza A(H1N1)pdm09, mumps, and measles. We find that the machine learning approaches can be verified and tested faster than the approximate Bayesian computation method, but that the approximate Bayesian computation method is more robust across different datasets.