Uncertainty Quantification for Expensive COVID-19 Simulation Models (UQ4Covid)

准确的传播数学模型对于瞄准成功的干预措施以抗击SARS-Cov2的传播至关重要。在英国，已建立的模型通过大流行性流感科学建模小组（SPI-M）向政府提供实时政策支持。SPI-M的建模人员具有可靠的跟踪记录，并且不断调整模型以应对不断变化的大流行。当使用模型为决策提供信息时，至关重要的是，在校准和预测时，所有不确定性的来源都得到适当的考虑。30年来，英国在发展不确定度量化（UQ）方面一直处于世界领先地位；当使用模型来了解世界时，提供不确定性的正式处理方法，允许有效和稳健的校准和预测。尽管如此，这些技术目前还不能用于COVID-19模拟模型，导致自适应模型开发速度低于必要水平（uq允许快速重新校准），并且在向决策者提供的预测中没有充分反映不确定性。该项目将为英国的COVID-19模型调整和提供昆士兰大学的技术、代码和教程，为SPI-M建模人员提供工具，以便在为应对不断变化的大流行而进行更改时快速重新校准其模型，并更准确地反映其预测中的不确定性。我们将与MetaWards密切合作，这是一个有助于SPI-M的空间元种群传播框架（Danon等人，2009,2020），在我们进入冬季时开发和应用这些工具；能够快速评估应对局部疫情的干预措施、疫苗推广战略的效力、免疫持续时间等。

Accurate mathematical models of transmission are crucial for targeting successful interventions to combat the spread of SARS-Cov2. In the UK, established models are used to provide real time policy support to Government through the Scientific Pandemic Influenza group - Modelling (SPI-M). Modellers in SPI-M have a proven track record, and models are continually adapted to respond to the evolving pandemic. When using models to inform decision making, it is crucial that all sources of uncertainty are properly accounted for when calibrating and predicting. For 30 years the UK has been a world-leader in developing Uncertainty Quantification (UQ); delivering methods for formal treatments of uncertainty when using models to understand the world, allowing efficient and robust calibration and prediction. Despite this, these techniques are not currently in place for COVID-19 simulation models, leading to slower-than-necessary adaptive model development-UQ allows for fast re-calibration-and an under-representation of uncertainty in predictions delivered to policymakers.This project will adapt and deliver UQ techniques, code and tutorials for models of COVID-19 in the UK, providing SPI-M modellers with tools to facilitate rapid re-calibration of their models when changes are made in response to the evolving pandemic, and to more accurately represent uncertainty in their predictions. We will work closely with MetaWards, a spatial meta-population transmission framework (Danon et al. 2009, 2020) that contributes to SPI-M, to develop and apply these tools as we move into the winter; enabling fast evaluation of interventions responding to localised outbreaks, efficacy of vaccine rollout strategies, duration of immunity and more.