In the political decision process and control of COVID-19 (and other epidemic diseases), mathematical models play an important role. It is crucial to understand and quantify the uncertainty in models and their predictions in order to take the right decisions and trustfully communicate results and limitations. We propose to do uncertainty quantification in SIR-type models using the efficient framework of generalized Polynomial Chaos. Through two particular case studies based on Danish data for the spread of Covid-19 we demonstrate the applicability of the technique. The test cases are related to peak time estimation and superspeading and illustrate how very few model evaluations can provide insightful statistics.
翻译:在政治决策过程和控制COVID-19(和其他流行病)的过程中,数学模型发挥着重要作用,了解和量化模型及其预测的不确定性至关重要,以便做出正确决定,并信任地通报结果和局限性。我们提议利用普遍多民族混乱的有效框架,在SIR型模型中进行不确定性量化。通过根据丹麦数据进行的关于Covid-19传播情况的两个具体案例研究,我们展示了该技术的适用性。测试案例与高峰期估计和超大访问有关,并说明了有多少模型评估能够提供有洞察力的统计数据。