Zoonotic diseases: Modelling Lassa fever in Nigeria

人畜共患疾病：尼日利亚拉沙热建模

• 批准号: 2271319
• 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: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271319
• 关键词: Zoonotic; diseases; Modelling; Lassa; fever

Throughout my PhD, I aim to build upon the foundations established during my MSc dissertation on Epidemiological Modelling of Lassa fever (LF) in Nigeria. I will develop a suite of mathematical models, primarily systems of ordinary differential equations, to robustly describe the spatio-temporal dynamics of LF throughout Nigeria. I will be fitting various models to data using an Abstract Bayesian Computation scheme, specifically fitting the transmission rates and other intangible and difficult to directly measure quantities of infection transmission. These models will be fitted to case reports from 2016 onwards for LF, which have been and will continue to be supplied by the NCDC. With these tools, I will gradually build understanding of the epidemiology of Lassa fever within the country and identify areas in which additional data collection may be beneficial. This will in turn highlight the ecological factors involved in driving the seasonal dynamics of Lassa fever and ultimately help guide policy to control LF in Nigeria.The context of the research - LF, a haemorrhagic disease endemic in West Africa, is spread primarily through contact with the urine or faeces of the common rodent, Mastomys natalensis with spillover to human populations primarily happening during the months of January through to March. Over 2019 there were more than 1,000 confirmed cases in Nigeria with a case fatality rate close to 20%. Reported cases tend to have a strong seasonal signature providing an interesting problem as the drivers behind the dynamics have not been conclusively determined.The aims and objectives of the research - The research aims to parameterise and quantify transmission rates and seasonally driven aspects of LF in Nigeria and the ecology of Mastomys natalensis - including breeding rates, contact rates and prevalence of the disease within populations by state - fully described within a robust mathematical framework.The novelty of the research methodology - I will be fitting various models to data using an Abstract Bayesian Computation scheme. Published papers report fitting LF models to data are either likelihood based or lack detail in their methods for Nigeria. Moreover, I will be trying to illuminate the details of the dynamics within the reservoir of Mastomys natalensis to represent the strongly seasonal case reporting. I believe there is much that can be discerned about the spillover dynamics within such a system and the NCDC has provided data more detailed than that which is available in their situation reports on the NCDC website. The situation reports appear to be the sole source of data for some recent modelling papers, so improvements can certainly be made.The potential impact, applications, and benefits - Furthering the understanding of disease propagation will enable better control policies. It is possible that insights gained from these studies are applicable to other countries and other diseases, such as other vector-transmitted diseases. In the event of not being able to make an appropriately fitting model to the data, the work will potentially identify gaps in our understanding of LF dynamics and thus spur further research in other fields, such as ecology.How the research relates to the remit - The research fulfils the criteria for research in Mathematical sciences, particularly under the ESPRC theme of mathematical biology as a mathematical model of LF outbreaks would be appropriately classed as a "tool for the mathematical treatment of biological processes operating at any spatial or temporal scale particularly at the population level".Research area - mathematical SciencesExternal Partner - Nigerian Centre for Disease Control (NCDC)

在我的博士学位，我的目标是建立在我的硕士论文在尼日利亚拉沙热（LF）的流行病学建模的基础上。我将开发一套数学模型，主要是系统的常微分方程，强大的描述LF整个尼日利亚的时空动态。我将使用抽象贝叶斯计算方案将各种模型与数据进行拟合，特别是拟合传播率和其他无形且难以直接测量的感染传播量。从2016年起，这些模型将适用于LF病例报告，这些病例报告已经并将继续由NCDC提供。有了这些工具，我将逐步了解拉沙热在该国的流行病学，并确定在哪些领域收集更多的数据可能是有益的。这反过来将突出参与驱动拉沙热季节性动态的生态因素，并最终帮助指导政策，以控制尼日利亚的LF。研究的背景- LF，一种西非流行的出血性疾病，主要通过接触普通啮齿动物的尿液或粪便传播，Mastomys natalensis，主要发生在1月至3月期间，对人群的溢出。2019年，尼日利亚确诊病例超过1,000例，病死率接近20%。报告的病例往往具有强烈的季节性特征，这提供了一个有趣的问题，因为动态背后的驱动因素尚未最终确定。研究的目的和目标-研究旨在参数化和量化尼日利亚LF的传播率和季节性驱动方面以及Mastomys natalensis的生态-包括繁殖率，接触率和流行的疾病在人群中的状态-充分描述了一个强大的数学框架。研究方法的新奇-我将拟合各种模型的数据使用抽象贝叶斯计算方案。已发表的论文报告，拟合LF模型的数据是基于可能性或缺乏细节的方法，为尼日利亚。此外，我将试图阐明水库内的动态Mastomys natalensis代表强烈的季节性病例报告的细节。我认为，在这样一个系统内，可以看出许多溢出动态，全国疾病控制和预防委员会提供的数据比全国疾病控制和预防委员会网站上的情况报告中提供的数据更详细。情况报告似乎是最近一些建模论文的唯一数据来源，因此肯定可以进行改进。潜在的影响，应用和好处-进一步了解疾病传播将使更好的控制政策成为可能。从这些研究中获得的见解可能适用于其他国家和其他疾病，如其他病媒传播疾病。如果无法对数据进行适当的拟合模型，这项工作将有可能确定我们对LF动力学的理解中的差距，从而推动其他领域的进一步研究，例如生态学。研究如何与职权范围相关-这项研究符合数学科学研究的标准，特别是在ESPRC的数学生物学主题下，LF爆发的数学模型将被适当地归类为“研究领域-数学科学外部合作伙伴-尼日利亚疾病控制中心（NCDC）