On parameter estimation approaches for predicting disease transmission through optimization, deep learning and statistical inference methods

  • Maziar Raissi Brown University
  • Niloofar Ramezani George Mason University
  • Padmanabhan Seshaiyer George Mason University
Keywords: Disease dynamics, compartmental models, deep learning, statistical inference methods

Abstract

In this paper, we consider compartmental disease transmission models and discuss the importance of determining model parameters that provide an insight into disease transmission and prevalence. After a brief review and comparison of the performance of some heuristic approaches, the paper introduces three approaches including an optimization approach, a physics informed deep learning and a statistical inference method to estimate parameters and analyse disease transmission. The deep learning framework utilizes the hidden physics of infectious diseases and infer the latent quantities of interest in the model by approximating them using deep neural networks. The performance of the deep learning method is validated against representative small and big data sets corresponding to a well-known benchmark example and the results indicate that deep learning is a viable candidate to determine model parameters. The paper also presents the need for researchers to understand different types of dependencies exhibited in a typical data set and discovering the most appropriate approaches for statistical inference. Specifically, in this work we apply a time-series inferential method with a variety of statistical models. Our results indicate the efficiency and importance of statistical inference methods for researchers to understand and analyse time-series data to make confident predictions.

Published
2019-01-01
How to Cite
Raissi, Maziar, Niloofar Ramezani, and Padmanabhan Seshaiyer. 2019. “On Parameter Estimation Approaches for Predicting Disease Transmission through Optimization, Deep Learning and Statistical Inference Methods”. Letters in Biomathematics 6 (2), 1-26. https://doi.org/10.1080/23737867.2019.1676172.
Section
Research