Modeling, Analysis and Physics Informed Neural Network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction Journal Article uri icon



  • Abstract; In this work, the dynamics of the spread of COVID-19 is considered in the presence of both human-to-human transmission as well as environment-to-human transmission. Specifically, we expand and modify traditional epidemiological model for COVID-19 by incorporating a compartment to study the dynamics of pathogen concentration in the environmental reservoir, for instance concentration of droplets in closed spaces. We perform a mathematical analysis for the model proposed including an endemic equilibrium analysis as well as a next-generation approach both of which help to derive the basic reproduction number. We also study the e˚cacy of wearing a facemask through this model. Another important contribution of this work is the introduction to physics informed deep learning methods (PINNs) to study the dynamics. We propose this as an alternative to traditional numerical methods for solving system of differential equations used to describe dynamics of infectious diseases. Our results show that the proposed PINNs approach is a reliable candidate for both solving such systems and for helping identify important parameters that control the disease dynamics.

publication date

  • January 1, 2022

has restriction

  • gold

Date in CU Experts

  • February 11, 2023 1:27 AM

Full Author List

  • Nguyen L; Raissi M; Seshaiyer P

author count

  • 3

Other Profiles

Electronic International Standard Serial Number (EISSN)

  • 2544-7297

Additional Document Info

start page

  • 1

end page

  • 17


  • 10


  • 1