Evidence map›Paper›PMID 38710802›Full record

ArticleScientific reports2024

A stochastic approach for co-evolution process of virus and human immune system.

Qura Tul Ain, Jiahao Shen, Peng Xu, Xiaoli Qiang, Zheng Kou

Abstract read
In one paragraph

Article in Scientific reports, 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

5 authors.

Qura Tul AinSchool of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China.
Jiahao ShenInstitute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.
Peng XuInstitute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.
Xiaoli QiangSchool of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China.
Zheng KouInstitute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China. kouzheng@gzhu.edu.cn.

Funding

This work was supported by the Natural Science Foundation of Guangdong Province of China (2022A1515011468) and the Fundings by Science and Technology Projects in Guangzhou202201020237, SL2022A03J01035). 202201020237
6 · The paper itself

Abstract

Infectious diseases have long been a shaping force in human history, necessitating a comprehensive understanding of their dynamics. This study introduces a co-evolution model that integrates both epidemiological and evolutionary dynamics. Utilizing a system of differential equations, the model represents the interactions among susceptible, infected, and recovered populations for both ancestral and evolved viral strains. Methodologically rigorous, the model's existence and uniqueness have been verified, and it accommodates both deterministic and stochastic cases. A myriad of graphical techniques have been employed to elucidate the model's dynamics. Beyond its theoretical contributions, this model serves as a critical instrument for public health strategy, particularly predicting future outbreaks in scenarios where viral mutations compromise existing interventions.

Indexed as

Stochastic ProcessesBiological EvolutionEvolution, MolecularHumansImmune SystemVirusesEquilibriumExtinctionImmune systemStability analysis

Identifiers

PMID38710802
PMCPMC11074307

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.