论文标题
通过模型预测的组合，COVID-19的流行病学预测的不确定性定量
Uncertainty quantification for epidemiological forecasts of COVID-19 through combinations of model predictions
论文作者
论文摘要
在整个Covid-19的大流行中，对英国政府的科学建议已得到科学大流行性流感模型组（SPI-M）成员提供的流行病学模型的团结。除其他应用外，模型集合已用于预测日常发病率，死亡和住院。这些模型的方法（例如确定性或基于代理的）以及对疾病和人群的假设有所不同。这些差异在理解疾病动力学和简化模型基础的假设方面的理解中占据了真正的不确定性。尽管当时间框架短时间对多模型集合的分析可能在逻辑上具有挑战性，但考虑结构不确定性可以提高准确性并降低预测过度信心的风险。在这项研究中，我们比较了各种集合方法的性能，以结合大流行反应背景下的短期（14天）Covid-19的预测。我们解决了有关模型预测的可用性的实际问题，并提出一些初步建议，以解决这种挑战性情况下标准方法的缩写。
Scientific advice to the UK government throughout the COVID-19 pandemic has been informed by ensembles of epidemiological models provided by members of the Scientific Pandemic Influenza group on Modelling (SPI-M). Among other applications, the model ensembles have been used to forecast daily incidence, deaths and hospitalizations. The models differ in approach (e.g. deterministic or agent-based) and in assumptions made about the disease and population. These differences capture genuine uncertainty in the understanding of disease dynamics and in the choice of simplifying assumptions underpinning the model. Although analyses of multi-model ensembles can be logistically challenging when time-frames are short, accounting for structural uncertainty can improve accuracy and reduce the risk of over-confidence in predictions. In this study, we compare the performance of various ensemble methods to combine short-term (14 day) COVID-19 forecasts within the context of the pandemic response. We address practical issues around the availability of model predictions and make some initial proposals to address the short-comings of standard methods in this challenging situation.
