SIR-Type Epidemic Models as Block-Structured Markov Processes

Claude Lefèvre () and Matthieu Simon ()
Additional contact information
Claude Lefèvre: Université Libre de Bruxelles
Matthieu Simon: University of Melbourne

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 2, 433-453

Abstract: Abstract This paper proposes a block-structured Markov process to describe the spread of epidemics of Susceptible-Infected-Removed (SIR) type. Our purpose is to determine the distribution of the final state of the process and of some other interesting measures of the dimension of the epidemic. The followed modeling approach proves to be simple and systematic. Its flexibility is underlined by the presentation of several specific models that extend the classical general epidemic. Finally, two numerical examples illustrate some of the results obtained.

Keywords: SIR epidemic models; Block-structured Markov processes; Matrix-analytic methods; 60J28; 60K10; 60K37 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-019-09710-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:22:y:2020:i:2:d:10.1007_s11009-019-09710-y

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-019-09710-y

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().