A mathematically-driven framework for pandemic planning and management

COVID-19使人们关注到缺乏明确和强大的工具来管理广泛流行的流行病，尽管COVID-19是过去20年来第四次主要的全球大流行病（2012- 2013年的中东呼吸综合征，2009- 2010年的H1N1, 2002-04年的SARS）。该研究计划通过设计最佳缓解策略和疫苗优先政策，解决加拿大公共卫生机构和医疗保健机构在管理COVID和未来流行病方面的需求。为了获得这些最优策略，本研究利用我们完成的基于COVID代理的仿真模型（ABM），在可推广到任何人群和疾病的框架中进行深入的数学驱动分析。公共卫生机构主要依靠高级疾病传播预测模型，称为区室模型（通常是传统易感-感染-康复（SIR）模型的延伸），以每日病例数、住院人数和死亡人数来估计大流行将如何传播。这些模型的好处是，它们只需要有关疾病的高级信息，例如，繁殖数（R0）（单个病例引起的新感染的平均数量）和人口，例如，大小和接触率；对最小数据的需求意味着这些模型可以在大流行的早期阶段迅速开发出来。缺点是只有高层次的信息进入，因此只有高层次的信息出来，使得这些模型不适合详细的政策评估。通过包括详细的人口统计、医疗和经济信息，个体及其独特特征被单独代表的ABMs可以更精确地模拟疾病传播，并测试和优化细微的缓解策略。特别是，我们将个人健康状况（例如，合并症）纳入基于区域流行率的研究，允许对人口结果进行新的调查，并公平地解释人口多样性。例如，农村和较贫穷的社会经济地区通常健康状况较差，合并症发病率较高，获得医疗保健的机会较少；这些地区将更容易受到流行病的打击，并且无法在分区模型中得到体现。公共卫生政策制定者根据经验、直觉、有限的数据分析（由于大流行病的新颖性）以及与建模专家协商来制定应对新出现的大流行病的缓解战略，以回答有关潜在缓解战略的“如果”问题，这些战略几乎肯定不会包含最佳战略。本研究通过在ABM及其接触网络上结合优化和机器学习方法，直接优化策略，而不是以一种临时的假设方式，超越了这种被动方法。此外，这项工作为公共卫生官员可以使用的政策“杠杆”的有效性提供了人类可解释的评估。

COVID-19 has brought into focus the lack of well-defined and robust tools to manage widespread pandemics, despite the fact that COVID is the fourth major global pandemic in the past 20 years (MERS in 2012-13, H1N1 in 2009-10, SARS in 2002-04). This research program addresses the needs of Canada's public health agencies and healthcare institutions in managing COVID and future pandemics by designing optimal mitigation strategies and vaccine prioritization policies. To obtain these optimal policies, this research leverages our completed COVID agent-based simulation model (ABM) for deep, mathematically-driven analysis in a framework generalizable to any population and disease. Public health agencies predominantly rely on high-level disease spread prediction models, called compartmental models (typically extensions of the traditional susceptible-infectious-recovered (SIR) model), to estimate how the pandemic will spread in terms of daily number of cases, hospitalizations, and deaths. The benefit of these models is that they only require high-level information about the disease, e.g., reproduction number (R0) (the average number of new infections caused by a single case), and population, e.g., size and contact rate; the need for minimal data means these models can be rapidly developed in the early stages of a pandemic. The drawback is that only high-level information goes in, so only high-level information comes out, making these models ill-suited for detailed policy assessments. By including detailed population demographic, medical, and economic information, ABMs, where individuals and their unique characteristics are individually represented, can more precisely simulate disease spread and test and optimize nuanced mitigation strategies. In particular, our incorporation of individual health status (e.g., comorbidities) based on regional prevalence allows for novel investigation of population outcomes, as well as equitably accounts for population diversity. For example, rural and poorer socio-economic areas usually have worse health status and higher prevalence of comorbidities, as well as reduced access to healthcare; these areas will be hit harder by pandemics, and are not represented in compartmental models. Mitigation strategies to respond to an emerging pandemic are generated by public health policymakers based on experience, intuition, limited data analysis (due to novelty of the pandemic), and consultation with modelling experts to answer "what if" questions regarding potential mitigation strategies, which almost certainly will not contain the optimal strategy. This research goes beyond this reactive approach by optimizing policies directly, rather than in an ad hoc what-if fashion, through a combination of optimization and machine learning approaches on the ABM and its contact networks. Additionally, this work provides human-interpretable assessments of the effectiveness of policy "levers" that can be pulled by public health officials.