Evidence map›Paper›PMID 39809805›Full record

ArticleScientific reports2025

Compartmental Models Driven by Renewal Processes: Survival Analysis and Applications to SVIS Epidemic Models.

Divine Wanduku, Md Mahmud Hasan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Divine WandukuDepartment of Mathematical Sciences, Georgia Southern University, 65 Georgia Ave, Room 3309, Statesboro, Georgia, 30460, USA. dwanduku@georgiasouthern.edu.
Md Mahmud HasanDepartment of Biostatistics, Data Science and Epidemiology, School of Public Health, Augusta University, 1120, 15th Street, Augusta, GA, 30912, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compartmental models with exponentially distributed lifetime stages assume a constant hazard rate, limiting their scope. This study develops a theoretical framework for systems with general lifetime distributions, modeled as transition rates in a renewal process. Applications are provided for the SVIS (Susceptible-Vaccinated-Infected-Susceptible) disease epidemic model to investigate the impacts of hazard rate functions (HRFs) on disease control. The novel SVIS model is formulated as a non-autonomous nonlinear system (NANLS) of ordinary differential equations (ODEs), with coefficients defined by HRFs. Key statistical properties and the basic reproduction number ([Formula: see text]) are derived, and conditions for the system's asymptotic autonomy are established for specific lifetime distributions. Four HRF behaviors-monotonic, bathtub, reverse bathtub, and constant-are analyzed to determine conditions for disease eradication and the asymptotic population under these scenarios. Sensitivity analysis examines how HRF behaviors shape system trajectories. Numerical simulations illustrate the influence of diverse lifetime models on vaccine efficacy and immunity, offering insights for effective disease management.

Indexed as

COVID-19EpidemicsEpidemiological ModelsModels, BiologicalBasic Reproduction NumberComputer SimulationDisease SusceptibilityHumansSurvival AnalysisVaccinationAsymptotically autonomous systemHazard rate functionJump ProcessNon-autonomous ODERandom interjump timesRenewal processSVIS model

Identifiers

PMID39809805
PMCPMC11733156

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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.