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Front. Microbiol. | doi: 10.3389/fmicb.2018.00343

Can machines learn respiratory virus epidemiology?: A comparative study of likelihood-free methods for the estimation of epidemiological dynamics

  • 1Research Center for Zoonosis Control, Hokkaido University, Japan
  • 2PRESTO, Japan Science and Technology Agency, Japan

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.

Keywords: Basic Reproduction Number, respiratory virus, infectious disease epidemiology, machine learning, Approximate Bayesian Computation, mathematical model

Received: 11 Dec 2017; Accepted: 12 Feb 2018.

Edited by:

Qiwei Zhang, Southern Medical University, China

Reviewed by:

Frederick R. Adler, University of Utah, United States
Hetron M. Munang'Andu, NMBU, Norway
Weifeng Shi, Taishan Medical University, China  

Copyright: © 2018 Tessmer, Ito and Omori. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Ryosuke Omori, Hokkaido University, Research Center for Zoonosis Control, Sapporo, 001-0020, Hokkaido, Japan, omori@czc.hokudai.ac.jp