[YY-EEID US-UK XXXX] Predictive phylogenetics for evolutionary and transmission dynamics of newly emerging avian influenza viruses

[YY-EEID US-UK XXXX] 新兴禽流感病毒进化和传播动力学的预测系统发育学

• 批准号: BB/V011286/1
• 负责人: Paul Digard
• 金额: $ 159.4万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FV011286%2F1
• 关键词: YY; EEID; US; UK; XXXX

Influenza virus is a global problem, causing widespread harm to human health and the food production system because it also infects chickens and pigs. Vaccination is difficult because of the variety and changeability of flu strains found in nature - primarily in wild birds, where often they cause little harm. However, when these strains of virus spill over into domestic poultry or humans, they can cause massive economic losses and fatal disease respectively. In the last twenty years, this has been graphically illustrated by the H5N1 and H7N9 outbreaks. Global surveillance programmes track the virus' movement and as part of this, characterise the sequence of the viral genome. Some aspects of virus behaviour can be accurately predicted from these sequences. However, many other important aspects of virus biology, such as whether it will travel across continents, which species it will infect and whether it will cause serious harm, are much harder to forecast. Our premise is that the volume of sequencing data now available, along with recent advances in computational methods of using such data, will make it possible for the first time to generate virtual models of how the virus will evolve under specific circumstances and how these viral variants will behave. Such models have the potential to produce risk estimates of new strains as they arise that can be used to inform policy and direct strategies to head off impending threats. To achieve this goal, we have brought together a team of international experts with interdisciplinary expertise in mathematical modelling, influenza surveillance and biology, and the infectious disease-public and animal health interface. Importantly, this includes colleagues from China, the likely epicentre of the virus. Together, we will create the computer models that can understand and forecast virus evolution; models that will be made accurate and then tested through a series of focussed laboratory experiments designed to produce the needed data, and whose types of output will be tailored to the needs of end users through a series of workshops that include the primary stake holders so they can inform the scientists on what information they need.

流感病毒是一个全球性问题，对人类健康和食品生产系统造成广泛危害，因为它也感染鸡和猪。接种疫苗是困难的，因为在自然界中发现的流感病毒株的多样性和可变性-主要是在野生鸟类，在那里他们往往造成很小的伤害。然而，当这些病毒株溢出到家禽或人类中时，它们可以分别造成巨大的经济损失和致命的疾病。在过去的二十年里，H5 N1和H7N9的爆发生动地说明了这一点。全球监测计划跟踪病毒的移动，并作为其中的一部分，对病毒基因组的序列进行测序。从这些序列中可以准确地预测病毒行为的某些方面。但是，病毒生物学的许多其他重要方面，例如它是否会跨越大陆，它会感染哪些物种以及它是否会造成严重危害，都很难预测。我们的前提是，现有的大量测序数据，沿着使用这些数据的计算方法的最新进展，将首次有可能生成病毒在特定环境下如何进化以及这些病毒变体将如何表现的虚拟模型。这些模型有可能在新菌株出现时产生风险估计，可用于为政策提供信息，并指导战略以阻止即将发生的威胁。为了实现这一目标，我们汇集了一个国际专家团队，他们在数学建模、流感监测和生物学以及传染病-公共和动物卫生界面方面具有跨学科专业知识。重要的是，这包括来自中国的同事，中国可能是病毒的震中。我们将共同创建能够理解和预测病毒进化的计算机模型;这些模型将变得准确，然后通过一系列旨在产生所需数据的集中实验室实验进行测试，其输出类型将通过一系列研讨会根据最终用户的需求量身定制，其中包括主要利益相关者，以便他们可以告知科学家他们需要什么信息。

项目成果

Highly Pathogenic Avian Influenza and its Complex Patterns of Reassortment

高致病性禽流感及其复杂的重配模式

• DOI: 10.2139/ssrn.4655071
• 发表时间: 2023
• 期刊: SSRN Electronic Journal
• 作者: Harvey W

Novel Avian Influenza Virus (H5N1) Clade 2.3.4.4b Reassortants in Migratory Birds, China.

• DOI: 10.3201/eid2906.221723
• 发表时间: 2023-06
• 期刊: EMERGING INFECTIOUS DISEASES
• 影响因子: 11.8
• 作者: Yang, Jing;Zhang, Chunge;Yuan, Yue;Sun, Ju;Lu, Lu;Sun, Honglei;Sun, Heting;Chu, Dong;Qin, Siyuan;Chen, Jianjun;Zhang, Chengbo;Hao, Xiyan;Shi, Weifeng;Liu, Wenjun;Gao, George F.;Digard, Paul;Lycett, Samantha;Bi, Yuhai
• 通讯作者: Bi, Yuhai

Spatiotemporal genotype replacement of H5N8 avian influenza viruses contributed to H5N1 emergence in 2021/2022 panzootic

• DOI: 10.1128/jvi.01401-23
• 发表时间: 2024-02-15
• 期刊: JOURNAL OF VIROLOGY
• 影响因子: 5.4
• 作者: Zeng，Jinfeng;Du，Fanshu;Pu，Juan
• 通讯作者: Pu，Juan