Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model
IntroductionSince 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 inte...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1542759/full |
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| author | Zuiyuan Guo Yuheng Chen Hongbo Liu Guangquan Xiao Di Yu Zhaojia Zhang Yimin Yang Zhongwei Yin Huibin Zhang |
| author_facet | Zuiyuan Guo Yuheng Chen Hongbo Liu Guangquan Xiao Di Yu Zhaojia Zhang Yimin Yang Zhongwei Yin Huibin Zhang |
| author_sort | Zuiyuan Guo |
| collection | DOAJ |
| description | IntroductionSince 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.MethodsTo 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.ResultsOur 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.DiscussionPresently, 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. |
| format | Article |
| id | doaj-art-c77619e8b81d499fa9e17fa73a1dea40 |
| institution | DOAJ |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-c77619e8b81d499fa9e17fa73a1dea402025-08-20T02:56:09ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-05-011310.3389/fpubh.2025.15427591542759Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic modelZuiyuan Guo0Yuheng Chen1Hongbo Liu2Guangquan Xiao3Di Yu4Zhaojia Zhang5Yimin Yang6Zhongwei Yin7Huibin Zhang8The First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaDepartment of Information, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaThe First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaThe First Department of Infectious Disease Prevention and Control, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaDepartment of Comprehensive Planning, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaDepartment of Critical Care Medicine, The First Hospital of Jilin University, Changchun, ChinaDepartment of Medical Protection and Military Operational Medicine, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaDepartment of Medical Protection, Center for Disease Control and Prevention in Northern Theater Command, Shenyang, ChinaIntroductionSince 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.MethodsTo 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.ResultsOur 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.DiscussionPresently, 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.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1542759/fullSARS-CoV-2mutationvirus evolutionsocial distancegenetic algorithmdynamic model |
| spellingShingle | Zuiyuan Guo Yuheng Chen Hongbo Liu Guangquan Xiao Di Yu Zhaojia Zhang Yimin Yang Zhongwei Yin Huibin Zhang Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model Frontiers in Public Health SARS-CoV-2 mutation virus evolution social distance genetic algorithm dynamic model |
| title | Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| title_full | Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| title_fullStr | Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| title_full_unstemmed | Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| title_short | Quantitatively analyzing the relationship between non-pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| title_sort | quantitatively analyzing the relationship between non pharmaceutical interventions and the direction of virus evolution using a dynamic model |
| topic | SARS-CoV-2 mutation virus evolution social distance genetic algorithm dynamic model |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1542759/full |
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