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

基本信息

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

项目摘要

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.

有关缩写和参考文献，请参阅支持案例。历史上，病原菌的抗生素耐药性与新抗生素的开发同时出现。这场竞赛在全世界范围内造成了重大健康问题，而细菌似乎获胜了[33]。一个典型的例子是耐甲氧西林金黄色葡萄球菌（MRSA），它可以引起血液和肺部的严重感染，并且在对青霉素产生耐药性后，也对第二种抗生素甲氧西林产生耐药性。由于抗生素压力，可能会出现抗生素耐药性，其中不充分的处方政策发挥着根本作用。这是 DRB 在医疗机构中成为一个特别具有挑战性的问题的原因之一，还有其他原因，例如免疫系统较弱的老年人的存在。 HCS 中 DRB 存在的问题已通过其在社区（非医疗保健环境）中的传播而迈出了下一步。这导致了能够在健康个体中引起严重感染的新菌株的出现。此外，这些新的社区相关菌株在 HCS 中的渗透已成为额外的挑战。为了避免 HCS 中 DRB 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、人员配备水平管理、隔离然而，大多数 HCS 通常遵循这些程序的组合，并且每个程序的单独疗效很难衡量。这种量化很重要，不仅因为这些临床环境中资源稀缺，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个强大的工具，可用于解决这些单独策略的有效性，以及用于识别 HCS 中 DRB 的出现和传播所涉及的因素。该奖学金的目的是为该领域的数学建模做出贡献，以回答许多悬而未决的问题。该奖学金将解决的具体问题是：某些因素（例如医疗机构环境（例如设备）的污染）在医疗机构中耐药细菌传播中的重要性是什么？这种传播如何在不同的医疗机构（例如，医院与疗养院）中发生？ HCS 内个体之间现有的异质性（健康个体，如医护人员，与中度或重病患者；成人与儿童；接受抗生素治疗的患者，...）造成什么影响？该奖学金要解决的其他问题涉及使用临床数据来完善数学模型，以及考虑可以解释特定个体中 DRB 出现过程的新数学模型。 DRB 的出现和传播是世界范围内的一个重大问题。然而，由于财务原因（例如，一些新开发的抗生素的有效期只有几年，随后又开发出新的DRB菌株），从事新抗生素开发的制药公司数量稀少，政府的财政激励措施通常很少需要[24]。此外，值得注意的是，在欧洲，据估计，MDRB 感染每年导致约 25000 人死亡[25]，预计额外增加 1600 万个床位日的费用（相当于 70 亿欧元的直接医疗费用））[19]。因此，有必要将新型抗生素的开发与控制干预措施结合起来，以避免DRB在HCS中的出现和传播，同时这对于实施基于定量知识的干预策略至关重要。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
期刊：
影响因子：
0
作者：
King M
通讯作者：
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

A unified stochastic modelling framework for the spread of nosocomial infections.

DOI：
10.1098/rsif.2018.0060
发表时间：
2018-06
期刊：
Journal of the Royal Society, Interface
影响因子：
0
作者：
López-García M;Kypraios T
通讯作者：
Kypraios T

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