Infectious diseases co-occurrence and co-infection: effects on epidemic dynamics.

传染病同时发生和共同感染：对流行动态的影响。

• 批准号: 1965135
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1965135
• 关键词: Infectious; diseases; co; occurrence; infection

Nowadays, the co-occurrence of infectious diseases is poorly understood in terms of both geography and time. As many as 30% of infectious diseases may result from co-infections, climbing as high as 80% in certain populations. Several methodological challenges remain when it comes to understanding co-occurrences, in particular, detecting interaction among associations and understanding the complexity of host-pathogen-environment interactions. Existing patterns of infectious disease co-occurrence could thus play a critical role in resolving or anticipating current and future disease threats. Therefore, understanding the ecology and transmission of co-occurring pathogens within populations is crucial to design suitable disease prevention programmes, but methods to gain insights into host-pathogen-environment interactions are currently underdeveloped. By modelling the prevalence of viruses in areas where pathogens co-occur and determining the risk for pathogens to co-occur and co-infect, the project aims to develop statistical frameworks for public health surveillance and control. The aim of this project is to create a platform capable of detecting the potential of infectivity in an area where co-infection of vector populations occurs and to develop a model that can determine co-occurrence attributed risk of infectious diseases. This could then lead to identification of hotspots or areas of high risk attributed to co-occurrence and inform policies.The project will start by working with two distinct types of data. The first is remote sensing data (satellite data) from MODIS (Moderate Resolution Imaging Spectroradiometer) and IRI Maproom (map data). The second is secondary data (mosquitoes sampling) pulled from the OIE animal health information database for Europe, since no (open) human health database has the same level of detail in the geographic and epidemiological characteristics of outbreaks. With spatial and temporal information about 116 animal diseases in the world (available from 2005 and updated in real time), the OIE dataset is one of the largest database freely available to use (containing hundreds of thousands disease locations). However, the methods will be equally applicable to human diseases should suitable data become available.The project will start by reviewing current methods, mainly applied to species co-occurrence, which often focus on bi-co-occurrence, and only implicitly consider interactions between species, and do not incorporate non-independence among hosts, and more importantly do not account for the biology inherent the disease transmission. Therefore, the project aims to develop a new statistical framework to model pathogens co-occurrence for transmission risk mapping by taking into account intra-relationships within pathogens and hosts, between pathogens and hosts and between pathogens, hosts and environment.The initial method will be a bivariate Gaussian process model, which is a geostatistical model, as it will allow us to combine the secondary data which contains surveys on mosquitoes sampled at arbitrary locations and the spatial data. Furthermore, the objective is to predict, over a certain location, the prevalence of viruses in the mosquitoes (presence/absence/abundance). This would help us to find out if an association exists between co-infection in mosquitoes and their potential of disease transmission to humans.

如今，无论从地理还是时间上，人们对传染病的共同发生都知之甚少。多达30%的传染病可能是由混合感染引起的，在某些人群中，这一比例高达80%。当涉及到理解共生现象时，仍然存在一些方法学挑战，特别是在检测关联之间的相互作用和了解宿主-病原体-环境相互作用的复杂性方面。因此，传染病共同发生的现有模式可在解决或预测当前和未来的疾病威胁方面发挥关键作用。因此，了解共生病原体在种群中的生态和传播对于设计适当的疾病预防方案至关重要，但目前了解宿主-病原体-环境相互作用的方法还不够发达。通过对病原体共同出现地区的病毒流行情况进行建模，并确定病原体共同出现和共同感染的风险，该项目旨在为公共卫生监测和控制制定统计框架。该项目的目的是建立一个平台，能够在发生媒介人群混合感染的地区检测传染病的潜在传染性，并开发一种能够确定传染病共同发生的归因风险的模型。这可能导致识别共同发生的热点或高风险区域，并向政策提供信息。该项目将从两种不同类型的数据开始。第一种是MODIS(中分辨率成像光谱仪)和IRI Maproom(地图数据)的遥感数据(卫星数据)。第二个是从世界动物卫生组织欧洲动物健康信息数据库中提取的次要数据(蚊子抽样)，因为没有一个(开放的)人类健康数据库在疫情的地理和流行病学特征方面具有相同程度的详细信息。世界动物卫生组织的数据集拥有关于世界上116种动物疾病的空间和时间信息(从2005年开始提供，并实时更新)，是可免费使用的最大数据库之一(包含数十万个疾病地点)。然而，如果有合适的数据，这些方法将同样适用于人类疾病。该项目将首先回顾目前的方法，主要应用于物种共现，通常侧重于双发生，只隐含地考虑物种之间的相互作用，没有纳入宿主之间的非独立性，更重要的是没有考虑疾病传播所固有的生物学因素。因此，该项目旨在开发一个新的统计框架，通过考虑病原体与宿主之间、病原体与宿主之间以及病原体与宿主与环境之间的内在关系，建立病原体共生模型，以绘制传播风险图。最初的方法将是双变量高斯过程模型，这是一个地统计学模型，因为它将允许我们将包含在任意地点抽样的蚊子调查的次要数据与空间数据相结合。此外，目标是预测某一地点的蚊子中病毒的流行情况(存在/不存在/丰度)。这将有助于我们找出蚊子合并感染与它们向人类传播疾病的可能性之间是否存在关联。