Dynamics and Persistence of a Generalized Multi-strain SIS Model.

Scott Greenhalgh, Tabitha Henriquez, Michael Frutschy, Rebecah Leonard

Abstract read

In one paragraph

Article in Bulletin of mathematical biology, 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.

Scott GreenhalghDepartment of Mathematics, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA. sgreenhalgh@siena.edu.ORCID http://orcid.org/0000-0003-2484-4147

Tabitha HenriquezDepartment of Mathematics, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA.

Michael FrutschyData Science Program, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA.

Rebecah LeonardDepartment of Mathematics, Siena University, 515 Loudon Road, Loudonville, NY, 12211, USA.

Funding

Directorate for Mathematical and Physical Sciences DMS-2052592

6 · The paper itself

Abstract

Autonomous differential equation compartmental models hold broad utility in epidemiology and public health. However, these models typically cannot account explicitly for myriad factors that affect the trajectory of infectious diseases, with seasonal variations in host behavior and environmental conditions as noteworthy examples. Fortunately, using non-autonomous differential equation compartmental models can mitigate some of these deficiencies, as the inclusion of time-varying parameters can account for temporally varying factors. The inclusion of these temporally varying factors does come at a cost though, as many analysis techniques, such as the use of Poincaré maps and Floquet theory, on non-autonomous differential equation compartmental models are typically only tractable numerically. Here, we illustrate a rare

Indexed as

Communicable DiseasesEpidemiological ModelsModels, BiologicalComputer SimulationDisease SusceptibilityHumansMathematical ConceptsAkaike information criteriaFloquet theoryForecast skill scoreOrdinary differential equationsStability analysisSyphilis

Identifiers

PMID40928560

DOI10.1007/s11538-025-01516-z

PMCPMC12423140

What OpenQuestion holds

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LicenceCC BY

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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.