The Impact of Deniers on Epidemics: A Temporal Network Model

Lorenzo Zino, Alessandro Rizzo, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


We propose a novel network epidemic model to elucidate the impact of deniers on the spread of epidemic diseases. Specifically, we study the spread of a recurrent epidemic disease, whose progression is captured by a susceptible-infected-susceptible model, in a population partitioned into two groups: cautious individuals and deniers. Cautious individuals may adopt self-protective behaviors, possibly incentivized by information campaigns implemented by public authorities; on the contrary, deniers reject their adoption. Through a mean-field approach, we analytically derive the epidemic threshold for large-scale homogeneous networks, shedding light onto the role of deniers in shaping the course of an epidemic outbreak. Specifically, our analytical insight suggests that even a small minority of deniers may jeopardize the effort of public health authorities when the population is highly polarized. Numerical results extend our analytical findings to heterogeneous networks.

Original languageEnglish (US)
Pages (from-to)685-690
Number of pages6
JournalIEEE Control Systems Letters
StatePublished - 2023


  • control of networks
  • Network analysis and control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization


Dive into the research topics of 'The Impact of Deniers on Epidemics: A Temporal Network Model'. Together they form a unique fingerprint.

Cite this