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

最近出现的2019冠状病毒病（COVID-19）全球大流行就是对管理系统提出挑战的重大公共卫生威胁的一个例子。COVID-19在全球大部分地区的快速传播尚不清楚。由于复杂的疾病病因过程，传播模式跨越多个尺度，并且来自多个管辖区和不同采样方案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疾病（例如COVID-19）中也很常见，可能难以在定量框架中纳入，并妨碍使用数据和建模产品准确监测病毒和识别弱势群体的能力。我们将通过推进数学和统计建模技术，解决与COVID-19大流行相关的若干实际问题，引领疾病建模创新。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)准确预测冠状病毒在省和国家层面的传播，以提高我们对其在人群层面的行为的理解；2)确定因COVID-19风险最大的弱势人群；3)研究省级和国家级感染人群的地理变异，更好地了解病毒在环境中的持久性。通过实现本研究计划中提出的目标，我们将提供新的统计技术，以解决COVID-19大流行带来的普遍问题。这项研究提案的一个直接成果是帮助我们的伙伴组织（PHAC和马尼托巴卫生部）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。利用我们提出的模型，可以更好地反映真实的传染病动态和不完善的数据，省和国家层面的决策者将有更好的模型来理解疾病病因并为人口健康管理提供建议。

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.