• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

艰难梭菌传播流行病学模型中疫苗接种率的最优控制

Optimal control of vaccination rate in an epidemiological model of Clostridium difficile transmission.

作者信息

Stephenson Brittany, Lanzas Cristina, Lenhart Suzanne, Day Judy

机构信息

Department of Mathematics, University of Tennessee, Knoxville, TN, USA.

Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC, USA.

出版信息

J Math Biol. 2017 Dec;75(6-7):1693-1713. doi: 10.1007/s00285-017-1133-6. Epub 2017 May 8.

DOI:10.1007/s00285-017-1133-6
PMID:28484801
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5643219/
Abstract

The spore-forming, gram-negative bacteria Clostridium difficile can cause severe intestinal illness. A striking increase in the number of cases of C. difficile infection (CDI) among hospitals has highlighted the need to better understand how to prevent the spread of CDI. In our paper, we modify and update a compartmental model of nosocomial C. difficile transmission to include vaccination. We then apply optimal control theory to determine the time-varying optimal vaccination rate that minimizes a combination of disease prevalence and spread in the hospital population as well as cost, in terms of time and money, associated with vaccination. Various hospital scenarios are considered, such as times of increased antibiotic prescription rate and times of outbreak, to see how such scenarios modify the optimal vaccination rate. By comparing the values of the objective functional with constant vaccination rates to those with time-varying optimal vaccination rates, we illustrate the benefits of time-varying controls.

摘要

形成孢子的革兰氏阴性细菌艰难梭菌可导致严重的肠道疾病。医院中艰难梭菌感染（CDI）病例数的显著增加凸显了更好地了解如何预防CDI传播的必要性。在我们的论文中，我们修改并更新了一个医院内艰难梭菌传播的分区模型，以纳入疫苗接种。然后，我们应用最优控制理论来确定随时间变化的最优疫苗接种率，该接种率能将疾病在医院人群中的流行率和传播以及与疫苗接种相关的时间和金钱成本的组合降至最低。我们考虑了各种医院场景，例如抗生素处方率增加的时期和爆发时期，以了解这些场景如何改变最优疫苗接种率。通过将恒定疫苗接种率下的目标函数值与随时间变化的最优疫苗接种率下的目标函数值进行比较，我们说明了随时间变化控制的益处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c54140d07e29/nihms874730f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/7722c8a90edc/nihms874730f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/0138cd32c01c/nihms874730f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c29edbd991b9/nihms874730f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/3c9cf86e877f/nihms874730f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/bdb5f1180d6a/nihms874730f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/1e2c9c093642/nihms874730f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c8ed143d7901/nihms874730f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/7204c932f8f1/nihms874730f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/6dd3749db894/nihms874730f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/4f57e33104fd/nihms874730f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c54140d07e29/nihms874730f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/7722c8a90edc/nihms874730f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/0138cd32c01c/nihms874730f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c29edbd991b9/nihms874730f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/3c9cf86e877f/nihms874730f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/bdb5f1180d6a/nihms874730f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/1e2c9c093642/nihms874730f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c8ed143d7901/nihms874730f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/7204c932f8f1/nihms874730f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/6dd3749db894/nihms874730f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/4f57e33104fd/nihms874730f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e42/5643219/c54140d07e29/nihms874730f11.jpg

相似文献

1
Optimal control of vaccination rate in an epidemiological model of Clostridium difficile transmission.艰难梭菌传播流行病学模型中疫苗接种率的最优控制
J Math Biol. 2017 Dec;75(6-7):1693-1713. doi: 10.1007/s00285-017-1133-6. Epub 2017 May 8.
2
An Economic Analysis of Strategies to Control Clostridium Difficile Transmission and Infection Using an Agent-Based Simulation Model.使用基于主体的模拟模型对控制艰难梭菌传播和感染策略的经济分析
PLoS One. 2016 Mar 31;11(3):e0152248. doi: 10.1371/journal.pone.0152248. eCollection 2016.
3
An agent-based simulation model for Clostridium difficile infection control.一种基于主体的艰难梭菌感染控制模拟模型。
Med Decis Making. 2015 Feb;35(2):211-29. doi: 10.1177/0272989X14545788. Epub 2014 Aug 11.
4
Antimicrobial Stewardship and Environmental Decontamination for the Control of Clostridium difficile Transmission in Healthcare Settings.医疗机构中艰难梭菌传播控制的抗菌药物管理与环境去污
Bull Math Biol. 2017 Jan;79(1):36-62. doi: 10.1007/s11538-016-0224-7. Epub 2016 Nov 8.
5
Clostridium difficile infection in the intensive care unit.重症监护病房中的艰难梭菌感染
Infect Dis Clin North Am. 2009 Sep;23(3):727-43. doi: 10.1016/j.idc.2009.04.011.
6
Characterisation of Clostridium difficile hospital ward-based transmission using extensive epidemiological data and molecular typing.利用广泛的流行病学数据和分子分型技术对艰难梭菌医院病房传播进行特征描述。
PLoS Med. 2012 Feb;9(2):e1001172. doi: 10.1371/journal.pmed.1001172. Epub 2012 Feb 7.
7
Multipronged intervention strategy to control an outbreak of Clostridium difficile infection (CDI) and its impact on the rates of CDI from 2002 to 2007.2002年至2007年期间，采用多管齐下的干预策略控制艰难梭菌感染（CDI）疫情及其对CDI发病率的影响。
Infect Control Hosp Epidemiol. 2009 Feb;30(2):156-62. doi: 10.1086/593955.
8
A mathematical model to evaluate the routine use of fecal microbiota transplantation to prevent incident and recurrent Clostridium difficile infection.评估常规使用粪便微生物群移植预防艰难梭菌感染（CDI）的发生和复发的数学模型。
Infect Control Hosp Epidemiol. 2014 Jan;35(1):18-27. doi: 10.1086/674394. Epub 2013 Dec 2.
9
Analysis of an outbreak of Clostridium difficile infection controlled with enhanced infection control measures.一起艰难梭菌感染暴发事件的分析：通过强化感染控制措施得以控制
Am J Infect Control. 2009 Aug;37(6):458-64. doi: 10.1016/j.ajic.2008.11.010. Epub 2009 Feb 25.
10
Severe Clostridium difficile infection: incidence and risk factors at a tertiary care university hospital in Vienna, Austria.严重艰难梭菌感染：奥地利维也纳一家三级护理大学医院的发病率及危险因素
Wien Klin Wochenschr. 2014 Jul;126(13-14):427-30. doi: 10.1007/s00508-014-0549-x. Epub 2014 Jun 6.

引用本文的文献

1
Optimal control prevents itself from eradicating stochastic disease epidemics.最优控制无法根除随机疾病流行。
PLoS Comput Biol. 2025 Feb 18;21(2):e1012781. doi: 10.1371/journal.pcbi.1012781. eCollection 2025 Feb.
2
Mathematically modeling the effect of touch frequency on the environmental transmission of Clostridioides difficile in healthcare settings.数学模型研究触摸频率对医疗机构中艰难梭菌环境传播的影响。
Math Biosci. 2021 Oct;340:108666. doi: 10.1016/j.mbs.2021.108666. Epub 2021 Jul 24.
3
Comparing intervention strategies for reducing Clostridioides difficile transmission in acute healthcare settings: an agent-based modeling study.

本文引用的文献

1
Immune response to infection by Leishmania: A mathematical model.对利什曼原虫感染的免疫反应：一个数学模型。
Math Biosci. 2016 Jun;276:28-43. doi: 10.1016/j.mbs.2016.02.015. Epub 2016 Mar 14.
2
The impact of spatial arrangements on epidemic disease dynamics and intervention strategies.空间布局对传染病动态和干预策略的影响。
J Biol Dyn. 2016;10:222-49. doi: 10.1080/17513758.2016.1156172.
3
Optimal control applied to community-acquired methicillin-resistant Staphylococcus aureus in hospitals.应用于医院社区获得性耐甲氧西林金黄色葡萄球菌的最优控制
比较急性医疗机构中降低艰难梭菌传播的干预策略：基于代理的建模研究。
BMC Infect Dis. 2020 Oct 28;20(1):799. doi: 10.1186/s12879-020-05501-w.
4
Applying optimal control theory to a spatial simulation model of sudden oak death: ongoing surveillance protects tanoak while conserving biodiversity.将最优控制理论应用于橡树猝死病的空间模拟模型：持续监测在保护生物多样性的同时保护了加州黑栎。
J R Soc Interface. 2020 Apr;17(165):20190671. doi: 10.1098/rsif.2019.0671. Epub 2020 Apr 1.
5
Simulation models for transmission of health care-associated infection: A systematic review.用于传播与医疗保健相关的感染的仿真模型：系统评价。
Am J Infect Control. 2020 Jul;48(7):810-821. doi: 10.1016/j.ajic.2019.11.005. Epub 2019 Dec 18.
6
Bone metastasis treatment modeling via optimal control.通过最优控制进行骨转移治疗建模
J Math Biol. 2019 Jan;78(1-2):497-526. doi: 10.1007/s00285-018-1281-3. Epub 2018 Aug 21.
7
Applying optimization algorithms to tuberculosis antibiotic treatment regimens.将优化算法应用于结核病抗生素治疗方案。
Cell Mol Bioeng. 2017 Dec;10(6):523-535. doi: 10.1007/s12195-017-0507-6. Epub 2017 Aug 30.
J Biol Dyn. 2017 Mar;11(sup1):65-78. doi: 10.1080/17513758.2016.1151564. Epub 2016 Feb 26.
4
Clostridium difficile infection.艰难梭菌感染
N Engl J Med. 2015 Apr 16;372(16):1539-48. doi: 10.1056/NEJMra1403772.
5
Burden of Clostridium difficile infection in the United States.美国艰难梭菌感染的负担
N Engl J Med. 2015 Feb 26;372(9):825-34. doi: 10.1056/NEJMoa1408913.
6
Strategies to prevent Clostridium difficile infections in acute care hospitals: 2014 Update.急性护理医院预防艰难梭菌感染的策略：2014年更新版
Infect Control Hosp Epidemiol. 2014 Jun;35(6):628-45. doi: 10.1086/676023.
7
Prevalence and risk factors for asymptomatic Clostridium difficile carriage.无症状艰难梭菌携带的患病率及危险因素
Clin Infect Dis. 2014 Jul 15;59(2):216-22. doi: 10.1093/cid/ciu258. Epub 2014 Apr 21.
8
Vaccines against Clostridium difficile.抗艰难梭菌疫苗
Hum Vaccin Immunother. 2014;10(6):1466-77. doi: 10.4161/hv.28428. Epub 2014 Mar 17.
9
Optimal control of vancomycin-resistant enterococci using preventive care and treatment of infections.通过预防保健和感染治疗实现对耐万古霉素肠球菌的优化控制。
Math Biosci. 2014 Mar;249:8-17. doi: 10.1016/j.mbs.2014.01.004. Epub 2014 Jan 27.
10
Epidemiological model for Clostridium difficile transmission in healthcare settings.医疗机构中艰难梭菌传播的流行病学模型。
Infect Control Hosp Epidemiol. 2011 Jun;32(6):553-61. doi: 10.1086/660013.