Mathematical modelling of the emergence and spread of antibiotic resistant bacteria in healthcare settings: a stochastic approach

医疗机构中抗生素耐药细菌的出现和传播的数学模型：随机方法

• 批准号: MR/N014855/1
• 负责人: Martin Lopez-Garcia
• 金额: $ 36.05万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FN014855%2F1
• 关键词: Mathematical; modelling; emergence; spread; antibiotic

See Case for Support for abbreviations and references.Antibiotic resistance of pathogenic bacteria has historically arisen in parallel with the development of new antibiotics; this race posing a major health problem worldwide where bacteria seem to be winning [33]. A paradigmatic example is methicillin-resistant Staphylococcus aureus (MRSA), which can cause severe infections in the bloodstream and the lung and that, after developing resistance against penicillin, has become resistant also against a second antibiotic, methicillin. Development of resistance against antibiotic can occur due to antibiotic pressure, where non adequate prescription policies play a fundamental role. This is one of the reasons for DRB being a particular challenging problem in healthcare facilities, together with other reasons such as the presence of aged individuals with weaken immune systems. The problem of the presence of DRB in HCSs has taken the next step by their spread in the community (non-healthcare environments). This has led to the appearance of new strains which are able to cause severe infections in healthy individuals. Moreover, the infiltration of these new community-related strains in HCSs has become an additional challenge.In order to avoid the emergence and spread of DRB in HCSs, different strategies are usually followed: appropriate antibiotic prescription policies, management of staffing levels, isolation of infected patients, compliance of hygiene procedures, etc. However, most of HCSs usually follow a combination of these procedures, and the individual efficacy of each of them is hard to measure. This quantification is important not only due to the scarcity of resources in these clinical environments, but also because some of these policies entail moral and ethical problems. Mathematical models have proven to be a robust tool for addressing the efficacy of these individual strategies, as well as for identifying the factors involved in the emergence and spread of DRB in HCSs. The aim of this fellowship is to contribute to the mathematical modelling in the area, in order to answer a number of open questions. Particular questions that will be addressed within this fellowship are: which is the importance of some factors, such as the contamination of the healthcare setting environment (for example, equipment), in the spread of resistant bacteria in healthcare settings? How does this spread occur in different healthcare settings (for example, in hospitals versus nursing homes)? What is the impact caused by the existing heterogeneities among individuals within the HCS (healthy individuals, such as HCWs, versus moderate or severe ill patients; adults versus children; patients under antibiotic treatment, ...)?. Additional questions to be addressed within this fellowship are related to the use of clinical data for refining the mathematical models, and the consideration of new mathematical models that can explain the process by which DRB arises within a particular individual.The emergence and spread of DRB is a major problem worldwide. However, due to financial reasons (for example, some antibiotics newly developed are only effective for a few years, with the subsequent development of new DRB strains) the number of pharmaceutical companies working in new antibiotics development is scarce, and governmental financial incentives are usually required [24]. Moreover, it is worth noting that, in Europe, it has been estimated that infections with MDRB cause around 25000 deaths per year [25], with an estimated cost of 16 million additional bed-days (translating into 7 billion Euros in direct medical costs) [19]. Thus, it is necessary to combine the development of new antibiotics with control intervention measures to avoid the emergence and the spread of DRB among HCSs, which is at the same time crucial to implement intervention strategies based in quantitative knowledge.

参见支持缩写和参考文献的案例。病原菌的抗药性历来与新抗生素的开发同时出现；这场竞赛构成了一个重大的全球健康问题，细菌似乎正在获胜[33]。一个典型的例子是耐甲氧西林金黄色葡萄球菌(MRSA)，它会导致血液和肺部的严重感染，在对青霉素产生抗药性后，对第二种抗生素甲氧西林也产生抗药性。抗生素压力可能会导致对抗生素产生耐药性，在这种情况下，不适当的处方政策起着重要作用。这就是为什么DRB在医疗机构中是一个特别具有挑战性的问题的原因之一，以及其他原因，如免疫系统较弱的老年人的存在。HCSS中存在DRB的问题已经通过在社区(非医疗保健环境)中传播而采取了下一步行动。这导致了新菌株的出现，这些菌株能够对健康的人造成严重感染。此外，这些新的社区相关菌株在HCSS中的渗透已经成为另一项挑战。为了避免DRB在HCSS中的出现和传播，通常采取不同的策略：适当的抗生素处方政策、人员编制管理、感染患者的隔离、遵守卫生程序等。然而，大多数HCSS通常遵循这些程序的组合，并且很难衡量每一种程序的单独效果。这种量化是重要的，不仅因为这些临床环境中的资源稀缺，而且因为其中一些政策涉及道德和伦理问题。数学模型已被证明是一种强有力的工具，可以用来解决这些个别战略的有效性，以及确定在母婴传播系统中出现和传播DRB所涉及的因素。该奖学金的目的是为该领域的数学建模做出贡献，以回答一些悬而未决的问题。在这项研究中将解决的具体问题是：某些因素，如医疗环境(例如，设备)的污染，在医疗环境中耐药细菌的传播中的重要性是什么？这种传播是如何在不同的医疗环境中发生的(例如，在医院和疗养院之间)？健康中心内个体之间现有的异质性造成的影响是什么(健康个体，如卫生工作者与中等或严重疾病患者；成人与儿童；正在接受抗生素治疗的患者，...)？在这项研究中要解决的其他问题涉及使用临床数据来完善数学模型，以及考虑新的数学模型来解释DRB在特定个体内发生的过程。DRB的出现和传播是世界范围内的一个主要问题。然而，由于经济原因(例如，一些新开发的抗生素只在几年内有效，随后会开发新的DRB菌株)，从事新抗生素开发的制药公司数量很少，通常需要政府的财政激励[24]。此外，值得注意的是，据估计，在欧洲，感染耐多药结核病每年造成约25000人死亡[25]，估计额外花费1,600万个卧床日(相当于70亿欧元的直接医疗费用)[19]。因此，有必要将新抗生素的开发与控制干预措施结合起来，以避免DRB在HCSS中的出现和传播，同时这也是实施基于量化知识的干预策略的关键。

项目成果

Modelling the risk of SARS-CoV-2 infection through PPE doffing in a hospital environment

通过在医院环境中脱下个人防护装备来模拟 SARS-CoV-2 感染的风险

• DOI: 10.1101/2020.09.20.20197368
• 发表时间: 2020
• 作者: King M

First passage events in biological systems with non-exponential inter-event times.

具有非指数事件间时间的生物系统中的首次通过事件。

• DOI: 10.1038/s41598-018-32961-7
• 发表时间: 2018-10-10
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Castro M;López-García M;Lythe G;Molina-París C
• 通讯作者: Molina-París C

A Novel Stochastic Multi-Scale Model of Francisella tularensis Infection to Predict Risk of Infection in a Laboratory.

• DOI: 10.3389/fmicb.2018.01165
• 发表时间: 2018
• 期刊: Frontiers in microbiology
• 影响因子: 5.2
• 作者: Carruthers J;López-García M;Gillard JJ;Laws TR;Lythe G;Molina-París C
• 通讯作者: Molina-París C

Modeling fomite-mediated SARS-CoV-2 exposure through personal protective equipment doffing in a hospital environment.

• DOI: 10.1111/ina.12938
• 发表时间: 2022-01
• 期刊: Indoor air
• 影响因子: 5.8
• 作者: King MF;Wilson AM;Weir MH;López-García M;Proctor J;Hiwar W;Khan A;Fletcher LA;Sleigh PA;Clifton I;Dancer SJ;Wilcox M;Reynolds KA;Noakes CJ
• 通讯作者: Noakes CJ

Multi-Scale Modelling of Bacterial Infections

细菌感染的多尺度建模

• 发表时间: 2019
• 期刊: Mathematics Today
• 作者: Carruthers J