Network Reconstruction and Prediction of Epidemic Outbreaks for General Group-Based Compartmental Epidemic Models

Bastian Prasse, Piet Van Mieghem

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)

Abstract

The underlying core of most epidemic models is the graph that specifies the contacts between healthy and infected individuals. However, in the majority of applications, the contact network is unknown. To understand and predict an epidemic outbreak nonetheless, network reconstruction methods aim to estimate the contact network from viral state observations. This work considers general compartmental epidemic models (GEMF) in discrete time, which describe the viral spread between groups of individuals. The reconstruction of the network translates into a set of linear equations that is severely ill-conditioned. Counterintuitively, we show that the contact network cannot be reconstructed from one epidemic outbreak with any finite machine precision, although an accurate prediction of the epidemic outbreak is possible.

Original languageEnglish
Article number9069319
Pages (from-to)2755-2764
Number of pages10
JournalIEEE Transactions on Network Science and Engineering
Volume7
Issue number4
DOIs
Publication statusPublished - 2020

Keywords

  • Epidemic models
  • network reconstruction
  • prediction of epidemic outbreaks

Fingerprint

Dive into the research topics of 'Network Reconstruction and Prediction of Epidemic Outbreaks for General Group-Based Compartmental Epidemic Models'. Together they form a unique fingerprint.

Cite this