Modeling of COVID-19 Pandemic in Canada: Projection and Interventions

加拿大 COVID-19 大流行的建模：预测和干预措施

• 批准号: 554825-2020
• 负责人: Torabi, Mahmoud
• 金额: $ 3.64万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 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=705772
• 关键词: Modeling; COVID; 19; Pandemic; Canada

The recent emergence of COVID-19 as a global pandemic is one example of a critical public health threat that challenged management systems. The rapid spread of COVID-19 across much of the globe is not well understood yet. Patterns of spread span multiple scales due to complex disease etiological processes and biases from surveillance data generated from multi-jurisdictions with varying sampling protocols are real challenges. These issues, which are also common to high priority diseases in Canada (e.g., COVID-19), can be difficult to accommodate in quantitative frameworks, and hamper the ability to use data and modeling products to accurately monitor the virus and identify vulnerable populations. We will spearhead innovation in disease modeling by addressing several practical problems related to the COVID-19 pandemic by advancing mathematical and statistical modeling techniques. Our research goal is to better understand the spread of the coronavirus in Canada using sophisticated modeling approaches to be able to predict the needs of the Canadian healthcare system and plan for interventions. In particular, our study goals are to 1) accurately predict the spread of coronavirus at the provincial and national level to improve our understanding of its behavior at the population level; 2) identify vulnerable populations who are most at risk due to COVID-19; 3) study the geographical variation of infected people at the provincial and national level to better understand viral persistence in the environment. By addressing the objectives proposed in this research proposal, we will provide new statistical techniques that solve prevalent problems due to the COVID-19 pandemic. An immediate outcome of this research proposal is helping our partner organizations (PHAC and Manitoba Health) to implement novel modeling products that will improve current technologies that are used to inform population health. Using our proposed models, which offer a better reflection of the true infectious disease dynamics and imperfect data, policymakers at the provincial and national levels will have improved models for understanding disease etiology and advising population health management.

最近出现的新冠肺炎全球大流行就是一个对管理系统构成挑战的重大公共卫生威胁的例子。新冠肺炎在全球大部分地区的迅速传播还没有得到很好的理解。由于复杂的疾病病因过程，传播模式跨越多个范围，以及来自具有不同抽样方案的多个司法管辖区产生的监测数据的偏差，是真正的挑战。这些问题在加拿大的高优先级疾病(如新冠肺炎)中也很常见，可能难以纳入量化框架，并阻碍了使用数据和建模产品准确监测病毒和识别脆弱人群的能力。我们将通过推进数学和统计建模技术，解决与新冠肺炎大流行相关的几个实际问题，带头创新疾病建模。我们的研究目标是使用复杂的建模方法更好地了解冠状病毒在加拿大的传播，以便能够预测加拿大医疗保健系统的需求并计划干预措施。特别是，我们的研究目标是1)在省和国家层面准确预测冠状病毒的传播，以提高我们在人口水平对其行为的了解；2)识别因新冠肺炎而处于最高风险的易感人群；3)在省和国家层面研究感染者的地理变异，以更好地了解病毒在环境中的持久性。通过解决这项研究提案中提出的目标，我们将提供新的统计技术，以解决由于新冠肺炎大流行而产生的普遍问题。这项研究提案的一个直接结果是帮助我们的合作伙伴组织(PHAC和马尼托巴省健康)实施新的建模产品，这些产品将改进用于告知人口健康的现有技术。使用我们提出的模型，可以更好地反映真实的传染病动态和不完善的数据，省级和国家一级的政策制定者将改进模型，以了解疾病病因并为人口健康管理提供建议。