Real-time modelling and inference of Covid-19 transmission and control

自2019年12月中国报告首例病例以来，由新型病毒SARS-CoV2引发的新冠肺炎疫情已在全球范围内增长至2.15亿例，450万人死亡。它对人口健康产生了广泛的直接和间接影响，并对国家卫生和社会保健服务造成了重大负担。作为一种重大突发公共卫生事件，了解其传播动态并确定有效的控制策略至关重要。控制的方法包括接种疫苗和非药物干预(NPI)，如社会距离、戴口罩、旅行限制、学校关闭和国家全面封锁。NPIS通常有巨大的社会和经济成本，接种疫苗需要考虑成本和供应。面对这些成本和大流行带来的不确定情况，重要的是只有在严格必要的时候才颁布非营利机构，并战略性地部署疫苗接种，以最大限度地产生影响。然而，大流行的当前状态只反映在病例数量和入院人数上，具有很大的时滞，可能会迅速变化，新措施的影响往往不会立即明朗。实时建模旨在通过提供有关大流行的当前状态(“即时预测”)、其轨迹(预测)和控制策略的效果(情景建模)的信息来缓解这些问题。这在帮助围绕新冠肺炎干预的决策方面一直是关键，在改进实时建模方法方面取得了很大进展。对新冠肺炎进行实时建模的一个关键挑战在于它的新颖性：几乎没有历史数据，关于病原体和疾病的证据相对有限。在传播、病例确定、有效控制策略、人类行为的影响以及新变种的影响方面存在许多不确定性。这使得很难先验地知道哪种建模方法以及哪些数据流最适合建模。解决这一挑战的一个有希望的方法是将多个基本模型集成到一个预测中。在这个项目中，我们将比较和扩展新冠肺炎的实时建模和预测方法，并评估它们在评估干预措施和实时决策方面的有效性。这将使用数学建模和统计分析相结合的方法，应用于国家以下各级的新冠肺炎病例、住院、疫苗接种和来自官方来源的死亡的时间序列，如英国政府新冠肺炎数据仪表板和国家统计局。我们还将探索更广泛的新颖数据流，这些数据流可以被纳入以改进模型，例如谷歌移动数据和COMIX研究数据来估计社会行为的变化，以及来自著名研究的数据，如REPACT和SIREN，它们分别产生了关于血清感染率和再感染风险的有用结果和数据。这个项目将发展学生的量化技能，包括统计学、数学建模、预测性评估和计算技能，如R中的高级编程。学生将学习将这些技能应用于上述各种数据源。学生还将发展多学科交叉技能，例如将计算技能应用于生物医学和流行病学背景；科学与政治决策之间的接口；以及行为科学和经济学等社会科学在应对大流行中的作用。

The COVID-19 pandemic, caused by the novel virus SARS-CoV2, has grown to more than 215 million cases and 4.5 million deaths worldwide since the first case was reported in China in December 2019. It has had wide-ranging direct and indirect effects on population health and created a significant burden on national health and social care services. As a major public health emergency, it is of paramount importance to understand its transmission dynamics and identify effective control strategies to stem transmission.Methods for control include vaccination and non-pharmaceutical interventions (NPIs) such as social distancing, mask wearing, travel restrictions, school closures, and full national lockdowns. NPIs often have significant social and economic costs, and vaccination comes with considerations of costs and supply. In the face of these costs and the uncertain situation posed by the pandemic, it is important to only enact NPIs when strictly necessary and to deploy vaccinations strategically for maximum impact. However, the current state of the pandemic is only reflected in case numbers and hospital admissions with a significant time lag and can change quickly, and the impact of new measures is often not immediately clear. This has been a barrier to timely and effective decision making.Real-time modelling aims to alleviate these issues by providing information on the current state of the pandemic ('now-casting'), its trajectory (forecasting) and on the effect of control strategies (scenario modelling). This has been key in aiding decision-making around COVID-19 interventions and much progress has been made to improve methods for real-time modelling. A key challenge with real-time modelling of COVID-19 lies in its novelty: there is little historical data and a relatively limited body of evidence about the pathogen and the disease. There are many uncertainties around transmission, case ascertainment, effective control strategies, the impact of human behaviour, and the impact of new variants. This makes it hard to know a priori which modelling approaches and which data streams are most suitable for modelling. A promising approach to this challenge is to ensemble multiple base models into a single forecast. In this project, we will compare and expand methods for real-time modelling and forecasting of Covid-19 and assess their utility for evaluating interventions and real-time decision making. This will be done using a combination of mathematical modelling and statistical analysis applied to subnational time series of Covid-19 cases, hospitalisations, vaccinations, and deaths from official sources such as the UK Government COVID-19 data dashboard and the Office for National Statistics. We will also explore wider novel data streams which may be incorporated to improve models, such as Google mobility data and CoMix study data to estimate changes in social behaviour, and data from prominent studies such as REACT and SIREN which have produced useful findings and data on seroprevalence and risk of reinfection respectively.This project will develop the student's quantitative skills, including statistics, mathematical modelling, predictive evaluation, and computational skills such as advanced programming in R. The student will learn to apply these skills to a variety of data sources as described above. The student will also develop interdisciplinary skills at multiple interfaces, for example the application of computational skills to biomedical and epidemiological contexts; the interface between science and political decision-making; and the role of the social sciences such as behavioural science and economics in pandemic response.