Predicting the effects of coinfection on TB dynamics, pathology and treatment

预测合并感染对结核病动态、病理和治疗的影响

• 批准号: 2887662
• 金额: --
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2887662
• 关键词: Predicting; effects; coinfection; TB; dynamics

Most animals, including humans in the developing world, are simultaneously infected by multiple pathogen species,termed coinfection. Coinfecting pathogens often interact with one another and this can affect: i) the number of pathogenswithin a host, ii) pathogen transmission between hosts, iii) disease severity and iv) the effectiveness of pathogen controlmeasures (e.g. vaccines). Despite their importance, we know little about the consequences of the vast majority ofcoinfections, as there are so many possible pathogen combinations that could occur in any given host. Our research teamhas developed a way to deal with this complexity, creating a theoretical framework by which the consequences of a widerange of coinfections might be predicted [1,2]. We have already tested this framework in one scenario but for theframework to be useful in a practical context we need to develop it further and test it for a range of pathogencoinfections.

大多数动物，包括发展中国家的人类，同时被多种病原体感染，称为混合感染。共感染的病原体经常相互相互作用，这可能会影响：i) 宿主内病原体的数量，ii) 宿主之间病原体的传播，iii) 疾病的严重程度，以及 iv) 病原体控制措施（例如疫苗）的有效性。尽管它们很重要，但我们对绝大多数混合感染的后果知之甚少，因为任何特定宿主中都可能出现多种可能的病原体组合。我们的研究团队已经开发出一种方法来处理这种复杂性，创建了一个理论框架，通过该框架可以预测各种合并感染的后果[1,2]。我们已经在一个场景中测试了这个框架，但为了使该框架在实际环境中有用，我们需要进一步开发它并测试它是否适用于一系列病原体感染。