Activity-driven network modeling and control of the spread of two concurrent epidemic strains

Daniel Alberto Burbano Lombana, Lorenzo Zino, Sachit Butail, Emanuele Caroppo, Zhong Ping Jiang, Alessandro Rizzo, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain—phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible–exposed–infectious–removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

Original languageEnglish (US)
Article number66
JournalApplied Network Science
Issue number1
StatePublished - Dec 2022


  • Bi-virus
  • Complex networks
  • Control
  • Epidemics
  • Temporal network

ASJC Scopus subject areas

  • General
  • Computer Networks and Communications
  • Computational Mathematics


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