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An Age of Infection Kernel, an R Formula, and Further Results for Arino–Brauer A, B Matrix Epidemic Models with Varying Populations, Waning Immunity, and Disease and Vaccination Fatalities

Florin Avram (avramf3@gmail.com), Rim Adenane, Lasko Basnarkov, Gianluca Bianchin, Dan Goreac and Andrei Halanay
Additional contact information
Florin Avram: Laboratoire de Mathématiques Appliquées, Université de Pau, 64012 Pau, France
Rim Adenane: Département des Mathématiques, Université Ibn-Tofail, Kenitra 14000, Morocco
Lasko Basnarkov: Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia
Gianluca Bianchin: ICTEAM & Department Mathematical Engineering, University of Louvain, 1348 Louvain-la-Neuve, Belgium
Dan Goreac: School of Mathematics and Statistics, Shandong University, Weihai 264209, China
Andrei Halanay: Department of Mathematics and Informatics, Polytechnic University of Bucharest, 060042 Bucharest, Romania

Mathematics, 2023, vol. 11, issue 6, 1-21

Abstract: In this work, we first introduce a class of deterministic epidemic models with varying populations inspired by Arino et al. (2007), the parameterization of two matrices, demography, the waning of immunity, and vaccination parameters. Similar models have been focused on by Julien Arino, Fred Brauer, Odo Diekmann, and their coauthors, but mostly in the case of “closed populations” (models with varying populations have been studied in the past only in particular cases, due to the difficulty of this endeavor). Our Arino–Brauer models contain SIR–PH models of Riano (2020), which are characterized by the phase-type distribution ( α → , A ) , modeling transitions in “disease/infectious compartments”. The A matrix is simply the Metzler/sub-generator matrix intervening in the linear system obtained by making all new infectious terms 0. The simplest way to define the probability row vector α → is to restrict it to the case where there is only one susceptible class s , and when matrix B (given by the part of the new infection matrix, with respect to s ) is of rank one, with B = b α → . For this case, the first result we obtained was an explicit formula (12) for the replacement number (not surprisingly, accounting for varying demography, waning immunity and vaccinations led to several nontrivial modifications of the Arino et al. (2007) formula). The analysis of ( A , B ) Arino–Brauer models is very challenging. As obtaining further general results seems very hard, we propose studying them at three levels: (A) the exact model, where only a few results are available—see Proposition 2; and (B) a “first approximation” (FA) of our model, which is related to the usually closed population model often studied in the literature. Notably, for this approximation, an associated renewal function is obtained in (7); this is related to the previous works of Breda, Diekmann, Graaf, Pugliese, Vermiglio, Champredon, Dushoff, and Earn. (C) Finally, we propose studying a second heuristic “intermediate approximation” (IA). Perhaps our main contribution is to draw attention to the importance of ( A , B ) Arino–Brauer models and that the FA approximation is not the only way to tackle them. As for the practical importance of our results, this is evident, once we observe that the ( A , B ) Arino–Brauer models include a large number of epidemic models (COVID, ILI, influenza, illnesses, etc.).

Keywords: epidemic models; varying population models; stability; next-generation matrix approach; basic replacement number; vaccination; waning immunity; endemic equilibria (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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