A Mass-Conservation Model for Stability Analysis and Finite-Time Estimation of Spread of COVID-19

Hossein Rastgoftar, Ella Atkins

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The COVID-19 global pandemic has significantly impacted people throughout the United States and the World. While it was initially believed the virus was transmitted from animal to human, person-to-person transmission is now recognized as the main source of community spread. This article integrates data into physics-based models to analyze stability of the rapid COVID-19 growth and to obtain a data-driven model for spread dynamics among the human population. The proposed mass-conservation model is used to learn the parameters of pandemic growth and to predict the growth of total cases, deaths, and recoveries over a finite future time horizon. The proposed finite-time prediction model is validated by finite-time estimation of the total numbers of infected cases, deaths, and recoveries in the United States from March 12, 2020 to December 9, 2020.

Original languageEnglish (US)
Article number9344853
Pages (from-to)968-975
Number of pages8
JournalIEEE Transactions on Computational Social Systems
Volume8
Issue number4
DOIs
StatePublished - Aug 2021
Externally publishedYes

Keywords

  • Finite-time estimation
  • Finite-time modding
  • Pandemic growth stability

ASJC Scopus subject areas

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

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