A Time-Stepper Neural Network Model for the Maximum Likelihood Estimation of Epidemic Parameters

Wai Meng Kwok*, George Streftaris, Sarat Chandra Dass

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inference for dynamical systems and their relevant physical parameters provide us with important insights that aid decision-making, especially in the case of epidemics. In recent years, there has been growing interest in utilizing artificial neural network models to approximate or solve dynamical system trajectories, due to their universal approximation capabilities and availability of various architectures that provide a large range of flexibility during training. This article explores time-stepper neural network models which approximate the evolution of epidemic compartments (susceptibles, infectious and removed) between subsequent time points, within a specified range of parameter settings. The resulting approximation is then applied to a maximum likelihood estimation algorithm to estimate epidemic parameters such as the contact rates and infectious rates. Crucially, the analytical tractability of the neural network model allows convenient uncertainty quantifications of such parameter estimates. We examine the accuracy of this model in comparison to high-accuracy numerical integration methods as benchmark, and discuss its advantages and limitations.
Original languageEnglish
Title of host publication2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA)
PublisherIEEE
ISBN (Electronic)9798350382310, 9798350370751
ISBN (Print)9798350382327
DOIs
Publication statusPublished - 14 May 2024
Event20th IEEE International Colloquium on Signal Processing and Its Applications 2024 - Langkawi, Malaysia
Duration: 1 Mar 20242 Mar 2024
Conference number: 20
https://www.aconf.org/conf_194153.html

Conference

Conference20th IEEE International Colloquium on Signal Processing and Its Applications 2024
Abbreviated titleCSPA 2024
Country/TerritoryMalaysia
CityLangkawi
Period1/03/242/03/24
Internet address

Keywords

  • Artificial Neural Networks
  • Dynamical Systems
  • Epidemics
  • Machine Learning
  • Parameter Estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Media Technology
  • Control and Optimization
  • Modelling and Simulation

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