Advanced Mathematical Technologies for Respiratory Infection Risk Assessment and Pharmaceutical Intervention Scenario Analysis

呼吸道感染风险评估和药物干预场景分析的先进数学技术

• 批准号: 576914-2022
• 负责人: Wu, JianhongJ
• 金额: $ 10.26万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758869
• 关键词: Advanced; Mathematical; Technologies; Respiratory; Infection

The program will establish and sustain a dedicated cadre of academic researchers and train highly qualified individuals, in collaboration with Sanofi scientists, to provide evidence-based and data-driven strategic direction and enhanced research capacity around respiratory infectious diseases relevant to vaccine product pipeline and the optimal use of vaccinations in Canada and globally. The program will support mathematical modelling, neural network-based data analytics and health economics for rapid response to outbreaks, and for proactive, real-time, and both prospective and retrospective evaluation of control and prevention programs using multi-source data. The initial focus will be on Sanofi's vaccines at different stages of development and registration against prototypical respiratory infections such as influenza, Covid-19 and Respiratory Syncytial Virus, but the methodologies and technologies, as well as the datasets collected and analyzed will be as generic as possible. The established capacity will serve as a vaccine R&D platform and can be activated for rapid mobilization in response to emerging public health threats and industrial production needs. We develop models and modelling technologies for scenario planning as a central component of risk management efforts to meet the critical need in compressing timelines for vaccines to be discovered, produced and made accessible at scale. Our research supports scenario planning over three distinct periods. During a short-term period, our Nowcasting and Nearcasting Techniques support decisions on interventions and the required manufacturing capacity for development and deployment, and provide decision support on clinical trials for new vaccines. During a mid- to long-term period, our Transmission Dynamics Models and Scenario Optimization Technologies support decision making on where/when to test new vaccine candidates and what scale of the production capacity is needed. During a long-term period, when the disease may be endemic and can cause recurrent outbreaks, our Bifurcation and Optimization Techniques support planing for further vaccine manufacturing capacity needs, and inform optimal vaccine boosting scenarios and intervals.

该计划将建立和维持一支专门的学术研究人员队伍，并与赛诺菲科学家合作培训高素质的个人，以提供基于证据和数据驱动的战略方向，并增强与疫苗产品管道相关的呼吸道传染病的研究能力，以及加拿大和全球疫苗的最佳使用。该计划将支持数学建模，基于神经网络的数据分析和卫生经济学，以快速应对疫情，并使用多源数据对控制和预防计划进行前瞻性，实时，前瞻性和回顾性评估。最初的重点将是赛诺菲针对典型呼吸道感染（如流感、新冠肺炎和呼吸道合胞病毒）的不同开发和注册阶段的疫苗，但方法和技术以及收集和分析的数据集将尽可能通用。已建立的能力将作为疫苗研发平台，并可启动以迅速动员，应对新出现的公共卫生威胁和工业生产需求。 我们开发用于情景规划的模型和建模技术，作为风险管理工作的核心组成部分，以满足压缩疫苗发现，生产和大规模提供的时间表的关键需求。我们的研究支持三个不同时期的情景规划。在短期内，我们的近播和近播技术支持有关干预措施的决策以及开发和部署所需的制造能力，并为新疫苗的临床试验提供决策支持。在中长期内，我们的传播动力学模型和情景优化技术支持在何处/何时测试新候选疫苗以及需要多大规模的生产能力的决策。在长期内，当疾病可能是地方性的并可能导致反复爆发时，我们的分叉和优化技术支持规划进一步的疫苗生产能力需求，并为最佳的疫苗接种方案和间隔提供信息。