Evidence map›Paper›PMID 40213022›Full record

ArticleInfectious Disease Modelling2025

A refractory density approach to a multi-scale SEIRS epidemic model.

Anton Chizhov, Laurent Pujo-Menjouet, Tilo Schwalger, Mattia Sensi

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anton ChizhovInstitute for Theoretical Physics, University of Bremen, Bibliothekstr. 1, Bremen, 28359, Germany.
Laurent Pujo-MenjouetUniversite Claude Bernard Lyon 1, CNRS, Centrale Lyon, INSA Lyon, Université Jean Monnet, ICJ, UMR5208, Inria, Villeurbanne, 69622, France.
Tilo SchwalgerBernstein Center for Computational Neuroscience, Philippstr. 13, Haus 6, Berlin, 10115, Germany.
Mattia SensiCentre Inria d'Université Côte d'Azur, 2004 Rte des Lucioles, Biot, 06410, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations, taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.

Indexed as

Age-structured modelFinite-size fluctuationsPartial differential equationsRefractory densityTime since last infection

Identifiers

PMID40213022
PMCPMC11982974

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.