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A novel stochastic epidemic model with application to COVID-19

A novel stochastic epidemic model with application to COVID-19
A novel stochastic epidemic model with application to COVID-19
In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated.
q-bio.QM, cs.SY, eess.SY, 9010, 93E20
2331-8422
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Alexandre, Rodrigo e Alvim
c439f66a-0079-4d8a-9be8-dd461b9fd547
Fragoso, Marcelo D.
3e916bff-0055-487b-9cb9-9c275a740bfd
do Val, João B. R.
86f7e4f1-1c1b-416b-b038-e2c0e542ebc3
Thomas, Sinnu S.
8cf7e34f-5892-4578-bb48-0376efa7c39c
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Alexandre, Rodrigo e Alvim
c439f66a-0079-4d8a-9be8-dd461b9fd547
Fragoso, Marcelo D.
3e916bff-0055-487b-9cb9-9c275a740bfd
do Val, João B. R.
86f7e4f1-1c1b-416b-b038-e2c0e542ebc3
Thomas, Sinnu S.
8cf7e34f-5892-4578-bb48-0376efa7c39c

Arruda, Edilson F., Alexandre, Rodrigo e Alvim, Fragoso, Marcelo D., do Val, João B. R. and Thomas, Sinnu S. (2021) A novel stochastic epidemic model with application to COVID-19. arXiv.

Record type: Article

Abstract

In this paper we propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a set up under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate are the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly simpler and more tractable than semi-Markov models with a similar level of generality. Based on the notion of stochastic stability, we derive a sufficient condition for a shrinking epidemic in terms of the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of recent data on the COVID-19 epidemic and assess the effect of different stabilising strategies. The results suggest that it is possible to curb the epidemic with various occupation rate levels, as long as the mitigation is not excessively procrastinated.

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2102.08213v1 - Author's Original
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More information

Accepted/In Press date: 16 February 2021
Published date: 16 February 2021
Keywords: q-bio.QM, cs.SY, eess.SY, 9010, 93E20

Identifiers

Local EPrints ID: 447535
URI: http://eprints.soton.ac.uk/id/eprint/447535
ISSN: 2331-8422
PURE UUID: 2b023511-f937-46a7-ad3e-8dc67916016b
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 15 Mar 2021 17:39
Last modified: 13 Dec 2021 03:40

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Contributors

Author: Rodrigo e Alvim Alexandre
Author: Marcelo D. Fragoso
Author: João B. R. do Val
Author: Sinnu S. Thomas

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