Modeling, estimation, and analysis of epidemics over networks: An overview

Philip E. Paré, Carolyn L. Beck, Tamer Başar

Research output: Contribution to journalReview articlepeer-review


We present and discuss a variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes. We first present traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs. Then we consider models driven by similar principles, but involving non-trivial networks where spreading occurs between connected nodes. We present stability analysis results for selected models from both classes, as well as simple least squares approaches for estimating the spreading parameters of the virus from data for each basic networked model structure. We also provide some simulation models. The paper should serve as a succinct, accessible guide for systems and control research efforts toward understanding and combating COVID-19 and future pandemics.

Original languageEnglish (US)
Pages (from-to)345-360
Number of pages16
JournalAnnual Reviews in Control
StatePublished - 2020
Externally publishedYes


  • COVID-19
  • Epidemic processes
  • Network-dependent spread
  • Networked control systems
  • Nonlinear systems
  • Parameter estimation
  • Stability analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering


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