{"title":"具有潜伏期和行为改变影响的流行病的最终和峰值大小。","authors":"Tianyu Cheng, Xingfu Zou","doi":"10.1007/s00285-025-02249-2","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we use the renewal equation approach to explore the impact of behaviour change and/or non-pharmaceutical interventions (NPIs) on the final size and peak size of an infectious disease without demography. To this end, we derive the renewal equations (REs) for the force of infection (both instantaneous and cumulative) that have reflected the NPIs and/or behaviour change by the notion of practically susceptible population. A novelty in these REs is that they contain time-varying kernels arising from the incorporation of effect of behaviour change. We then build the new REs into the Kermack-McKendrick model to obtain a general full model. Following Breda et al. (J Biol Dyn 6(sup2):103-117, 2012) and Diekmann et al. (Proc Natl Acad Sci USA 118(39):e2106332118, 2021), we analyze this new model to derive a general formula for the final size relation, by which we find that the final size relation depends not only on the basic reproduction number [Formula: see text] but also on other associated values that reflect the impact of behaviour change. Specifically, we demonstrate that behaviour change can reduce the infection peak and herd immunity threshold in some specific models.</p>","PeriodicalId":50148,"journal":{"name":"Journal of Mathematical Biology","volume":"91 2","pages":"19"},"PeriodicalIF":2.3000,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On final and peak sizes of an epidemic with latency and effect of behaviour change.\",\"authors\":\"Tianyu Cheng, Xingfu Zou\",\"doi\":\"10.1007/s00285-025-02249-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this paper, we use the renewal equation approach to explore the impact of behaviour change and/or non-pharmaceutical interventions (NPIs) on the final size and peak size of an infectious disease without demography. To this end, we derive the renewal equations (REs) for the force of infection (both instantaneous and cumulative) that have reflected the NPIs and/or behaviour change by the notion of practically susceptible population. A novelty in these REs is that they contain time-varying kernels arising from the incorporation of effect of behaviour change. We then build the new REs into the Kermack-McKendrick model to obtain a general full model. Following Breda et al. (J Biol Dyn 6(sup2):103-117, 2012) and Diekmann et al. (Proc Natl Acad Sci USA 118(39):e2106332118, 2021), we analyze this new model to derive a general formula for the final size relation, by which we find that the final size relation depends not only on the basic reproduction number [Formula: see text] but also on other associated values that reflect the impact of behaviour change. Specifically, we demonstrate that behaviour change can reduce the infection peak and herd immunity threshold in some specific models.</p>\",\"PeriodicalId\":50148,\"journal\":{\"name\":\"Journal of Mathematical Biology\",\"volume\":\"91 2\",\"pages\":\"19\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mathematical Biology\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s00285-025-02249-2\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s00285-025-02249-2","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
在本文中,我们使用更新方程方法来探索行为改变和/或非药物干预(npi)对无人口统计学的传染病的最终规模和峰值规模的影响。为此,我们通过实际易感人群的概念推导了反映npi和/或行为变化的感染力(瞬时和累积)的更新方程(REs)。这些正则表达式的新颖之处在于,它们包含了由行为变化的影响所产生的时变核。然后,我们将新的REs构建到Kermack-McKendrick模型中,以获得一个一般的完整模型。继Breda et al. (J Biol Dyn 6(sup2):103- 117,2012)和Diekmann et al. (Proc Natl Acad Sci USA 118(39): e2106332117,2021)之后,我们对这个新模型进行了分析,得出了最终尺寸关系的一般公式,通过该公式我们发现最终尺寸关系不仅取决于基本繁殖数[公式:见文本],还取决于反映行为变化影响的其他相关值。具体来说,我们证明了行为改变可以降低某些特定模型中的感染峰值和群体免疫阈值。
On final and peak sizes of an epidemic with latency and effect of behaviour change.
In this paper, we use the renewal equation approach to explore the impact of behaviour change and/or non-pharmaceutical interventions (NPIs) on the final size and peak size of an infectious disease without demography. To this end, we derive the renewal equations (REs) for the force of infection (both instantaneous and cumulative) that have reflected the NPIs and/or behaviour change by the notion of practically susceptible population. A novelty in these REs is that they contain time-varying kernels arising from the incorporation of effect of behaviour change. We then build the new REs into the Kermack-McKendrick model to obtain a general full model. Following Breda et al. (J Biol Dyn 6(sup2):103-117, 2012) and Diekmann et al. (Proc Natl Acad Sci USA 118(39):e2106332118, 2021), we analyze this new model to derive a general formula for the final size relation, by which we find that the final size relation depends not only on the basic reproduction number [Formula: see text] but also on other associated values that reflect the impact of behaviour change. Specifically, we demonstrate that behaviour change can reduce the infection peak and herd immunity threshold in some specific models.
期刊介绍:
The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena.
Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.