New approaches to modelling malaria transmission and insecticide resistance: using realistic mosquito biology and behaviour, and network methods.

模拟疟疾传播和杀虫剂耐药性的新方法：使用现实的蚊子生物学和行为以及网络方法。

• 批准号: 2271213
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271213
• 关键词: New; approaches; modelling; malaria; transmission

The proposed project aims to adapt standard mosquito-borne modelling frameworks by including additional details about mosquito biology and behaviour in the presence of insecticides. There will be a strong focus on time-series dynamics and spatial dynamics, both of which could have implications for real-world disease control programmes. Experimental data on mosquito behavior has been provided by the external partner to inform model development; it was collected during field experiments on the malaria vector, Anopheles gambiae s.l. The aim is to calibrate the adapted model to mosquito surveillance data, and ideally match to human malaria data as well, with the intention to focus on specific locations, depending on data availability, and the external partner's interests, for example Côte d'Ivoire and other African countries.The literature on malaria does not routinely focus on network models of mosquito-borne transmission with detailed vector biology included and so it would be interesting to develop theoretical approaches on networks having mosquito and human biology and behaviour in them. Such approaches, particularly those including the use of bednets and insecticide, would help understand malaria transmission dynamics, and could also be generalised for other mosquito- or vector-borne diseases such as dengue, or Rift Valley fever.The basic research questions are:(a) How does malaria spread and persist over time?(b) What is the impact of insecticide resistance on the spread?Mosquito and host movement during mosquito feeding could potentially give an insight to these questions, along with exploring the impact of insecticide exposure on mosquitoes and their ability to transmit malaria.The context of the research - This research relates to Mathematical Epidemiology, i.e. mathematical modelling of infectious diseases. Specifically, modelling of malaria, a serious mosquito-borne disease, which is the cause of death of thousands of people every year, especially in African countries. Emerging insecticide resistance among mosquitoes is a key concern and worthy of further exploration. The lack of research on networks regarding vector-borne diseases and detailed vector biology gives an opportunity to produce novel methodologies.The aims and objectives of the research - This project will adapt standard mosquito-borne modelling frameworks by taking into consideration details about mosquito biology and behaviour, and further understand the impact of insecticide resistance on malaria transmission. The aim is to calibrate the adapted model to real-life mosquito data collected in Côte d'Ivoire, and explore different scenarios regarding insecticides. An additional aim is to develop theoretical approaches on network models in order to better understand malaria transmission dynamics.The novelty of the research methodology - This project will apply adapted models on data collected from specific regions in Africa to compare the different model frameworks. Additionally, construct network models aiming to develop the limited existing research in network models regarding vector-borne disease and insecticide.The potential impact, applications, and benefits - Malaria affects millions of people each year. Insecticides (bednets or spraying) are one main way to control this disease due to the current lack of vaccine. This project could support understanding of appropriate disease control strategies in African countries by looking at malaria from using a novel network approach.How the research relates to the remit - This project falls into the category of Mathematical Biology and also Complexity Science. It aims to develop and apply mathematical techniques in order to investigate biological systems at a population level.Research area; Mathematical Sciences External Partner - Liverpool School of Tropical Medicine (LSTM)

拟议的项目旨在通过包括蚊子在杀虫剂存在下的生物学和行为的更多细节来调整标准的蚊媒建模框架。将重点关注时间序列动态和空间动态，这两者都可能对现实世界的疾病控制方案产生影响。外部合作伙伴提供了关于蚊子行为的实验数据，为模型开发提供信息；它是在疟疾媒介冈比亚按蚊（Anopheles gambiae s.l.）的实地实验期间收集的。目的是使调整后的模型适应蚊子监测数据，并在理想情况下与人类疟疾数据相匹配，目的是根据数据的可用性和外部合作伙伴的兴趣（例如Côte科特迪瓦和其他非洲国家）将重点放在特定地点。关于疟疾的文献通常不关注包含详细媒介生物学的蚊媒传播网络模型，因此开发具有蚊子和人类生物学及其行为的网络的理论方法将是有趣的。这种方法，特别是那些包括使用蚊帐和杀虫剂的方法，将有助于了解疟疾的传播动态，并且也可以推广到其他蚊子或媒介传播的疾病，如登革热或裂谷热。基本研究问题是：(a)疟疾是如何长期传播和持续的？(b)杀虫剂抗药性对蔓延有何影响？蚊子摄食过程中蚊子和宿主的运动，以及探索杀虫剂暴露对蚊子的影响及其传播疟疾的能力，可能会让我们对这些问题有所了解。研究背景-本研究涉及数学流行病学，即传染病的数学建模。疟疾是一种严重的蚊媒疾病，每年造成成千上万人死亡，特别是在非洲国家。蚊子中出现的杀虫剂抗性是一个关键问题，值得进一步探索。缺乏关于媒介传播疾病的网络研究和详细的媒介生物学为产生新的方法提供了机会。该研究的目的和目标——该项目将通过考虑蚊子生物学和行为的细节来调整标准的蚊媒建模框架，并进一步了解杀虫剂耐药性对疟疾传播的影响。其目的是将调整后的模型与在Côte科特迪瓦收集的真实蚊子数据进行校准，并探索有关杀虫剂的不同情景。另一个目标是发展网络模型的理论方法，以便更好地了解疟疾传播动态。研究方法的新颖性——该项目将对从非洲特定地区收集的数据应用适应性模型，以比较不同的模型框架。此外，构建网络模型，以拓展现有媒介传播疾病和杀虫剂网络模型研究的局限性。潜在的影响、应用和益处——疟疾每年影响数百万人。由于目前缺乏疫苗，杀虫剂（蚊帐或喷雾剂）是控制该病的一种主要方法。该项目可以通过使用一种新的网络方法来研究疟疾，从而支持了解非洲国家的适当疾病控制战略。研究与研究范围的关系：该项目属于数学生物学和复杂性科学的范畴。它旨在发展和应用数学技术，以便在人口水平上调查生物系统。研究领域;利物浦热带医学院（LSTM）