Evidence map›Paper›PMID 38881537›Full record

ArticleInfectious Disease Modelling2024

Parameter identifiability of a within-host SARS-CoV-2 epidemic model.

Junyuan Yang, Sijin Wu, Xuezhi Li, Xiaoyan Wang, Xue-Song Zhang, Lu Hou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Infectious Disease Modelling, 2024. 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

6 authors.

Junyuan YangComplex Systems Research Center, Shanxi University, Taiyuan, 030006, China.
Sijin WuShanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Shanxi University, Taiyuan, 030006, China.
Xuezhi LiSchool of Mathematics and Science, Henan Normal University, Xinxiang, 453000, China.
Xiaoyan WangSchool of Information, Shanxi University of Finance and Economics, Taiyuan, 030006, China.
Xue-Song ZhangAgriculture and Animal Husbandry Technology Promotion Center of Xingan League, Xingan League, 137400, China.
Lu HouAgriculture and Animal Husbandry Technology Promotion Center of Xingan League, Xingan League, 137400, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models. In this investigation, we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model, taking into account an array of observable datasets. Furthermore, Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters. Lastly, sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.

Indexed as

Practical identifiabilitySensitivity analysisStructural identifiabilityThe basic reproduction number

Identifiers

PMID38881537
PMCPMC11180336

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.