Evidence map›Paper›PMID 40416663›Full record

ArticleFrontiers in public health2025

Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model.

Zuiyuan Guo, Yuheng Chen, Hongbo Liu, Guangquan Xiao, Di Yu, Zhaojia Zhang, Yimin Yang, Zhongwei Yin, Huibin Zhang

Abstract read
In one paragraph

Article in Frontiers in public health, 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
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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

9 authors.

Zuiyuan Guo *The First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Yuheng Chen *College of Communication Engineering, Jilin University, Changchun, China.
Hongbo LiuDepartment of Information, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Guangquan XiaoThe First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Di YuThe First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Zhaojia ZhangDepartment of Comprehensive Planning, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Yimin Yang *Department of Critical Care Medicine, The First Hospital of Jilin University, Changchun, China.
Zhongwei Yin *Department of Medical Protection and Military Operational Medicine, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.
Huibin Zhang *Department of Medical Protection, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Since the emergence of COVID-19 in 2019, SARS-CoV-2 has persisted in mutating, giving rise to multiple variants of concern that have triggered several pandemics globally. The evolutionary trajectory of the virus is shaped by a combination of stochastic factors and non-pharmaceutical interventions (NPIs). Investigating the direction of virus evolution and its underlying determinants is crucial for forecasting epidemic trends and formulating scientific responses to emerging infectious diseases. Methods: To delve into the intricate relationship between NPIs and the virus's transmissibility, virulence, and immune evasion capabilities, as well as to explore the sociological mechanisms driving virus evolution, we developed a genetic algorithm grounded in a population dynamics model. This model simulates the processes of virus mutation and epidemic dissemination, enabling us to analyze the correlation between intervention strategies and the evolutionary path of the virus. Results: Our study reveals that, under the influence of NPIs, dominant strains capable of widespread transmission within the population exhibit substantially elevated immune evasion capabilities and heightened infectivity. Notably, the evolution of virulence did not display a discernible trend, aligning with the observed epidemic characteristics of COVID-19. It was found that the stricter the implementation of NPIs, the more favorable the conditions for rapidly and thoroughly containing virus transmission and mutation. Conversely, the relaxation of these measures may pose a risk of recurring epidemics fueled by continuous viral mutations. Discussion: Presently, the potential emergence and widespread transmission of SARS-CoV-2 variants with increased virulence cannot be discounted. Therefore, it is imperative to continuously monitor the dynamic shifts in the epidemic landscape and the antigenic variations of new variants. Simultaneously, it is necessary to devise and prepare prevention and control strategies to effectively manage outbreaks caused by highly pathogenic variants.

Indexed as

COVID-19SARS-CoV-2HumansImmune EvasionMutationPandemicsVirulencedynamic modelgenetic algorithmmutationSARS-CoV-2social distancevirus evolution

Identifiers

PMID40416663
PMCPMC12098584

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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.