Evidence map›Paper›PMID 36937240›Full record

ArticleApplied mathematics and optimization2023

Functional Law of Large Numbers and PDEs for Epidemic Models with Infection-Age Dependent Infectivity.

Guodong Pang, Étienne Pardoux

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Article in Applied mathematics and optimization, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

2 citing papers in PubMed, 8 citations in OpenAlex.

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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

2 authors at 2 institutions in 2 countries.

Guodong PangDepartment of Computational Applied Mathematics and Operations Research, George R. Brown College of Engineering, Rice University, Houston, TX 77005 USA.ORCID 0000-0001-6468-0429
Étienne PardouxAix-Marseille Univ, CNRS, I2M, Marseille, France.
Centre National de la Recherche Scientifique · FRRice University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We study epidemic models where the infectivity of each individual is a random function of the infection age (the elapsed time since infection). To describe the epidemic evolution dynamics, we use a stochastic process that tracks the number of individuals at each time that have been infected for less than or equal to a certain amount of time, together with the aggregate infectivity process. We establish the functional law of large numbers (FLLN) for the stochastic processes that describe the epidemic dynamics. The limits are described by a set of deterministic Volterra-type integral equations, which has a further characterization using PDEs under some regularity conditions. The solutions are characterized with boundary conditions that are given by a system of Volterra equations. We also characterize the equilibrium points for the PDEs in the SIS model with infection-age dependent infectivity. To establish the FLLNs, we employ a useful criterion for weak convergence for the two-parameter processes together with useful representations for the relevant processes via Poisson random measures.

Indexed as

Deterministic Volterra integral equationsEquilibrium in the SIS modelFunctional law of large numbersInfection-age dependent (varying) infectivityNon-Markovian epidemic modelsPDEsPoisson random measureSIRSIS

Identifiers

PMID36937240
PMCPMC10009871
OpenAlexW4327896714

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.