Public Health Interventions in the COVID-19 Endgame: Insights from Percolation Theory

COVID-19 终局之战中的公共卫生干预措施：渗透理论的见解

• 批准号: 554380-2020
• 负责人: Bauch, Christopher
• 金额: $ 3.64万
• 依托单位: University of Waterloo
• 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=705403
• 关键词: Public; Health; Interventions; COVID; 19

The COVID-19 pandemic calls for innovation in a broad range of scientific methods, including mathematical models. Thus far, most mathematical models developed during the pandemic have studied epidemics in a single, well-mixed population. However, on the far side of a flattened epidemic curve, disease spread will be characterized by sporadic, geographically dispersed outbreaks. It will be necessary to understand how this phase of the epidemic curve will change depending on what infectious disease interventions we apply, as we begin to re-open our economies. Percolation theory-the study of how fluids move through a material-is a physics-based theory that is much better suited to study epidemic dynamics in the later stages of the pandemic than many conventional mathematical modelling approaches. We propose to develop a percolation-based approach to understanding late-stage COVID-19 dynamics and how it responds to different testing, isolation, vaccination, and contact tracing approaches. Then, we will integrate the results of this percolation model into an agent-based network simulation of COVID-19 spread between population centres across Canadian provinces, including the impacts of physical distancing and school/workplace closure on infection spread. We will use realistic demographic and COVID-19 disease data as model inputs. The model will project cases, hospitalizations, and deaths under different approaches to re-opening the economy, such as on a county-by-county basis according to stipulated "trigger" conditions on the number of local cases. This will enable us to assess combined strategies of testing, physical distancing, and school/workplace closure. Most crucially, it will provide a tool that provincial decision-makers can use to determine how to re-open provincial economics without risking a resurgence of COVID-19 cases.

COVID-19大流行需要在广泛的科学方法上进行创新，包括数学模型。 到目前为止，在大流行期间开发的大多数数学模型都是在单一的、混合良好的人群中研究流行病。 然而，在平坦的流行病曲线的远端，疾病传播的特点是零星的、地理上分散的爆发。 在我们开始重新开放我们的经济时，有必要了解流行病曲线的这一阶段将如何变化，这取决于我们采取何种传染病干预措施。 逾渗理论研究流体如何在物质中流动是一种基于物理学的理论，它比许多传统的数学建模方法更适合于研究大流行后期的流行动力学。 我们建议开发一种基于预防的方法，以了解COVID-19后期的动态，以及它如何应对不同的检测、隔离、疫苗接种和接触者追踪方法。 然后，我们将把这个渗透模型的结果整合到一个基于代理的网络模拟中，模拟COVID-19在加拿大各省人口中心之间的传播，包括物理距离和学校/工作场所关闭对感染传播的影响。 我们将使用真实的人口统计数据和COVID-19疾病数据作为模型输入。 该模型将根据不同的重新开放经济的方法预测病例、住院和死亡情况，例如根据当地病例数量的规定“触发”条件逐县预测。 这将使我们能够评估测试，物理距离和学校/工作场所关闭的综合策略。 最重要的是，它将为省级决策者提供一个工具，以确定如何在不冒COVID-19病例死灰复燃风险的情况下重新开放省级经济。