Abstract

We model an epidemic where the per-person infectiousness in a network of geographic localities changes with the total number of active cases. This would happen as people adopt more stringent non-pharmaceutical precautions when the population has a larger number of active cases. We show that there exists a sharp threshold such that when the curing rate for the infection is above this threshold, the expected time for the epidemic to die out is logarithmic in the initial infection size, whereas when the curing rate is below this threshold, the expected time for epidemic extinction is infinite. We also show that when the per-person infectiousness goes to zero asymptotically as a function of the number of active cases, the expected extinction times all have the same asymptote independent of network structure. We make no mean-field assumption while deriving these results. Simulations on real-world network topologies bear out these results, while also demonstrating that if the per-person infectiousness is large when the epidemic size is small (i.e., the precautions are lax when the epidemic is small and only get stringent after the epidemic has become large), it might take a very long time for the epidemic to die out. We also provide some analytical insight into these observations.

Original languageEnglish (US)
Pages (from-to)1104-1116
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Volume9
Issue number3
DOIs
StatePublished - May 2022

Keywords

  • Epidemic modeling
  • network analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

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