Using past pandemics to guide COVID-19 predictions

利用过去的流行病来指导 COVID-19 预测

• 批准号: 554986-2020
• 负责人: Smith, Robert
• 金额: $ 3.64万
• 依托单位: University of Ottawa
• 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=705789
• 关键词: Using; past; pandemics; guide; COVID

With COVID-19 upon us and mathematical modelling being used by both provincial and federal governments to guide decision-making, the reliability of such models becomes paramount. We propose to analyse the modelling predictions done in three recent pandemics - SARS in 2003 (also a coronavirus), the H1N1 "swine flu" outbreak of 2009 and MERS, starting in 2012 (another coronavirus) - in order to assess how accurate these models were in the long term. The proposal will also explore the lessons learned and the predictive limits of random events such as superspreaders (individuals who are vastly more likely to transmit the disease than most people) or the onset of a second (or third) wave. The outbreaks on which the models are based happened during the era of "big data", and various predictions were made at the time using mathematical models. It makes them ideal candidates for this research. While it would be optimal to wait for further data in order to validate current models, in the early stages of a fast-moving pandemic, we do not have the luxury of time. However, the nature of modelling in past pandemics can serve as a guide for the current one. Using the data and past predictions, we will develop and analyse parallel COVID-19 models based on best practices from the most successful past models. These COVID-19 models will allow decision-makers to gain early warning of further waves, as well as other future pandemics, with the knowledge of which models are likely to be reliable and under what circumstances.

随着 COVID-19 的出现以及省级和联邦政府使用数学模型来指导决策，此类模型的可靠性变得至关重要。我们建议分析最近三种大流行病——2003 年的 SARS（也是一种冠状病毒）、2009 年 H1N1“猪流感”爆发和 2012 年开始的 MERS（另一种冠状病毒）——所做的模型预测，以评估这些模型的准确性从长远来看。该提案还将探讨随机事件的经验教训和预测限制，例如超级传播者（比大多数人更有可能传播疾病的个人）或第二波（或第三波）爆发。模型所依据的疫情爆发发生在“大数据”时代，当时利用数学模型做出了各种预测。这使他们成为这项研究的理想人选。虽然等待更多数据来验证当前模型是最佳选择，但在快速蔓延的流行病的早期阶段，我们没有足够的时间。然而，过去流行病模型的性质可以为当前流行病提供指导。利用这些数据和过去的预测，我们将根据过去最成功模型的最佳实践来开发和分析并行的 COVID-19 模型。这些 COVID-19 模型将使决策者能够获得进一步浪潮以及其他未来流行病的早期预警，并了解哪些模型可能可靠以及在什么情况下可靠。