Novel approaches to investigation and control of important viral diseases in swine populations

调查和控制猪群中重要病毒性疾病的新方法

• 批准号: RGPIN-2016-04500
• 负责人: Poljak, Zvonimir
• 金额: $ 2.26万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=684004
• 关键词: Novel; approaches; investigation; control; important

Investigation of transmission of infectious agents and their successful control are important goals in the health management of swine populations. Infectious agents such as porcine reproductive and respiratory syndrome virus (PRRSV) continue to be the most important cause of mortality and production loss in swine. Although PRRSV and other pathogens circulate endemically, investigating their spread is not straightforward because many variants of the PRRSV exist and the virus continuous to evolve. Thus, epidemic spread of a newly emerging PRRSV could occur even if populations were previously exposed to another variant. Previous work indicates high level of connectivity among individual sites; either through animal movement or specialized service providers. Nonetheless, application of novel approaches such as estimation of the most likely transmission chain based on nucleotide sequence (NSeq) data could be used to assist in outbreak investigations. NSeq data should be augmented by epidemiological data such as animal movement, or other contacts among herds. The interconnectedness of herds also has consequences on the design of control strategies, both at the level of a production system and within a region. This is because interventions applied in one production phase could have implications on infection in other phases of production. Direct evidence for efficacy of such interventions applied at the level of entire production systems with multiple sites is scarce. Thus, additional approaches need to be considered to help understand dynamics of pathogens under field conditions, to investigate critical points that should be targeted for successful intervention, to conduct simulations based on relevant production systems and questions, and to evaluate results of simulations against the observed data. The overarching goal of this proposal is to integrate existing swine health data with observational and simulation studies for the purposes of efficient disease investigations, real-time surveillance of trends in disease frequency, and design of control strategies for important viral infections in swine populations. Specific objectives of this program are to: (i) determine mechanisms of spread of PRRSV among swine herds using molecular and classical epidemiological methods, (ii) establish mechanisms of ongoing processing, analysis, visualization, and interpretation of swine health data from multiple sources, (iii) further develop herd- and regional-level control models for viral infections, and (iv) characterize flow of information regarding disease control options among swine practitioners in Canada. Infectious agents that will be addressed in this proposal include PRRSV, influenza A virus, and newly emerged PEDV. Sources of data will be existing disease control programs; the data analysis will be conducted using statistical and mathematical models and network analysis.**

传染病传播的调查和成功控制是猪群健康管理的重要目标。猪繁殖与呼吸综合征病毒(PRRSV)等感染性病原体仍然是造成猪死亡和生产损失的最重要原因。虽然PRRSV和其他病原体在地方性传播，但调查它们的传播并不容易，因为PRRSV存在许多变种，并且病毒不断进化。因此，即使人群以前接触过另一种变种，也可能发生新出现的PRRSV的流行病传播。以前的工作表明，各个地点之间的连接水平很高；无论是通过动物运动还是通过专门的服务提供商。尽管如此，应用新的方法，如根据核苷酸序列(NSeq)数据估计最可能的传播链，可用于协助暴发调查。NSEQ数据应补充流行病学数据，如动物活动或牛群间的其他接触。牛群之间的相互联系也对控制策略的设计产生影响，无论是在生产系统一级还是在区域内。这是因为在一个生产阶段应用的干预措施可能会对其他生产阶段的感染产生影响。在拥有多个地点的整个生产系统一级应用这种干预措施的有效性的直接证据很少。因此，需要考虑更多的方法，以帮助了解病原体在田间条件下的动态，调查成功干预应针对的临界点，根据相关生产系统和问题进行模拟，并根据观测数据评估模拟结果。这项建议的总体目标是将现有的猪健康数据与观察和模拟研究相结合，目的是有效地进行疾病调查，实时监测疾病频率的趋势，并为猪群中重要的病毒感染设计控制策略。该计划的具体目标是：(I)使用分子和经典流行病学方法确定PRRSV在猪群中传播的机制；(Ii)建立对来自多个来源的猪健康数据进行持续处理、分析、可视化和解释的机制；(Iii)进一步开发猪群和地区层面的病毒感染控制模型；以及(Iv)描述加拿大养猪业从业者之间关于疾病控制选择的信息流。这项提案将涉及的感染源包括PRRSV、A型流感病毒和新出现的PEDV。数据来源将是现有的疾病控制方案；数据分析将使用统计和数学模型以及网络分析。