In the event of a disease outbreak emergency, such as COVID-19, the ability to construct detailed stochastic models of infection spread is key to determining crucial policy-relevant metrics such as the reproduction number, true prevalence of infection, and the contribution of population characteristics to transmission. In particular, the interaction between space and human mobility is key to prioritising outbreak control resources to appropriate areas of the country. Model-based epidemiological intelligence must therefore be provided in a timely fashion so that resources can be adapted to a changing disease landscape quickly. The utility of these models is reliant on fast and accurate parameter inference, with the ability to account for large amount of censored data to ensure estimation is unbiased. Yet methods to fit detailed spatial epidemic models to national-level population sizes currently do not exist due to the difficulty of marginalising over the censored data. In this paper we develop a Bayesian data-augmentation method which operates on a stochastic spatial metapopulation SEIR state-transition model, using model-constrained Metropolis-Hastings samplers to improve the efficiency of an MCMC algorithm. Coupling this method with state-of-the-art GPU acceleration enabled us to provide nightly analyses of the UK COVID-19 outbreak, with timely information made available for disease nowcasting and forecasting purposes.

翻译：在COVID-19等疫情紧急事件中，构建详细的感染传播随机模型是确定关键政策相关指标（如基本再生数、真实感染流行率以及人口特征对传播的贡献）的关键。特别是，空间与人口流动之间的相互作用对于将疫情控制资源优先分配至国家适当区域至关重要。因此，基于模型的流行病学情报必须及时提供，以便资源能够迅速适应不断变化的疫情格局。这些模型的实用性依赖于快速且准确的参数推断，同时需要能够处理大量删失数据以确保估计无偏。然而，由于对删失数据进行边缘化的困难，目前尚不存在能够将详细的空间流行病模型拟合至国家级人口规模的方法。在本文中，我们开发了一种贝叶斯数据增广方法，该方法基于随机空间集合种群SEIR状态转移模型，利用模型约束的Metropolis-Hastings采样器来提高MCMC算法的效率。将该方法与最先进的GPU加速相结合，使我们能够对英国COVID-19疫情进行夜间分析，并为疾病临近预测和远期预测提供及时信息。