Using past pandemics to guide COVID-19 predictions

随着新冠肺炎的支持，以及省和联邦政府都使用数学建模来指导决策，此类模型的可靠性变得至关重要。我们建议分析最近三次大流行--2003年SARS(也是一种冠状病毒)、2009年H1N1“猪流感”暴发和2012年开始的MERS(另一种冠状病毒)--的建模预测，以评估这些模型的长期准确性。该提案还将探讨吸取的教训和随机事件的预测限度，例如超级传播者(比大多数人更有可能传播疾病的人)或第二(或第三)浪潮的开始。这些模型所依据的疫情爆发发生在“大数据”时代，当时人们使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。虽然在一场快速蔓延的大流行的早期阶段，等待进一步的数据以验证当前的模型是最好的，但我们没有充裕的时间。然而，过去大流行的建模性质可以作为当前大流行的指导。利用这些数据和过去的预测，我们将基于最成功的过去模型的最佳实践，开发和分析并行的新冠肺炎模型。这些新冠肺炎模型将允许决策者在知道哪些模型可能可靠以及在什么情况下可能可靠的情况下，获得有关进一步浪潮以及其他未来大流行的早期预警。

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.