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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709338
• 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.

传染性病原体传播的调查及其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）等传染性病原体仍然是猪死亡和产量损失的最重要原因。尽管PRRSV和其他病原体在地方性传播，但调查它们的传播并不简单，因为存在许多PRRSV变体，并且病毒不断进化。因此，即使人群先前暴露于另一种变体，也可能发生新出现的PRRSV的流行性传播。以前的工作表明，通过动物运动或专业服务提供商，各个网站之间的连接水平很高。尽管如此，新方法的应用，如基于核苷酸序列（NSeq）数据估计最可能的传播链，可用于协助疫情调查。NSeq数据应通过流行病学数据（如动物运动或畜群之间的其他接触）加以补充。畜群的相互关联性也对生产系统和区域内控制战略的设计产生影响。这是因为在一个生产阶段采取的干预措施可能对其他生产阶段的感染产生影响。在多个地点的整个生产系统一级采取这种干预措施的效果的直接证据很少。因此，需要考虑其他方法，以帮助了解病原体在田间条件下的动态，调查成功干预的关键点，根据相关生产系统和问题进行模拟，并根据观察到的数据评估模拟结果。该提案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，以进行有效的疾病调查，实时监测疾病频率趋势，并设计猪群中重要病毒感染的控制策略。该方案的具体目标是：（i）使用分子和经典流行病学方法确定猪群中PRRSV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）进一步开发用于病毒感染的群体和区域水平的控制模型，和（iv）描述加拿大养猪从业者之间疾病控制选择的信息流。本提案中将涉及的传染性病原体包括PRRSV、甲型流感病毒和新出现的PEDV。数据来源将是现有的疾病控制计划;数据分析将使用统计和数学模型以及网络分析进行。