Estimating the time-dependent contact rate of sir and seir models in mathematical epidemiology using physics-informed neural networks

Viktor Grimm, Alexander Heinlein, Axel Klawonn, Martin Lanser, Janine Weber

Research output: Contribution to journalArticleScientificpeer-review

Abstract

The course of an epidemic can often be successfully described mathematically using compartment models. These models result in a system of ordinary differential equations. Two well-known examples are the SIR and the SEIR models. The transition rates between the different compartments are defined by certain parameters that are specific for the respective virus. Often, these parameters are known from the literature or can be determined using statistics. However, the contact rate or the related effective reproduction number are in general not constant in time and thus cannot easily be determined. Here, a new machine learning approach based on physics-informed neural networks is presented that can learn the contact rate from given data for the dynamical systems given by the SIR and SEIR models. The new method generalizes an already known approach for the identification of constant parameters to the variable or time-dependent case. After introducing the new method, it is tested for synthetic data generated by the numerical solution of SIR and SEIR models. The case of exact and perturbed data is considered. In all cases, the contact rate can be learned very satisfactorily. Finally, the SEIR model in combination with physics-informed neural networks is used to learn the contact rate for COVID-19 data given by the course of the epidemic in Germany. The simulation of the number of infected individuals over the course of the epidemic, using the learned contact rate, shows a very promising accordance with the data.

Original languageEnglish
Pages (from-to)1-27
Number of pages27
JournalElectronic Transactions on Numerical Analysis
Volume56
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • COVID-19
  • Epidemic modeling
  • Machine learning
  • Parameter estimation
  • Physics-informed neural networks
  • SARS-CoV-2
  • Scientific machine learning
  • SEIR model
  • SIR model

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