Efficient geostatistical sampling to estimate the fraction of the population recovered from Covid-19

新冠肺炎的大流行还有很长的路要走。各国政府和其他国际机构的成功管理将需要统计工具来实时监测大流行在空间和时间上的演变。新冠肺炎是如何随着时间的推移在城市地区扩散的，例如集群是少是多，它们在空间上有多大，以及它们增长的速度，人们知之甚少。了解当地现象还可以支持其他研究项目，并为支持未来的封锁政策提供证据，例如，需要在多大程度上限制当地的封锁(全市范围内的封锁与社区封锁相比)以及封锁多久。地方当局也可以利用这一证据来支持具有高度针对性的局部封锁政策(例如对不同地区不同地适用国家Covid警戒等级)。识别病例位置的数据来源可用于生成新冠肺炎病例在时间和空间上传播的预测，这将有助于实施遏制病毒传播的本地化政策。该项目的目的是将统计方法应用于此目的并开发实现这些方法的软件。该项目将开发用于实时监控新冠肺炎的软件，该软件可以用于任何地理参考和时间戳数据。我们将使用新冠肺炎的就诊人数和入院人数数据来开发、校准和测试我们的软件和模型。我们将建立在共同申请者开发的最先进的地质统计学软件的基础上，根据现有的数据来源，对感兴趣地区的发病率或“R数”进行估计和预测。这些产出还可以支持计划的设计，以便在推出这些计划时对人口进行抽样测试，我们还将为此提供功能。

The Covid-19 pandemic has a long course to run. Its successful management by governments and other international agencies will require statistical tools for real-time monitoring of the evolution of the pandemic over space and time. How covid-19 spreads across an urban area over time, for example whether there are small or large numbers of clusters, how large they are spatially, and how rapidly they grow, is poorly understood. Understanding local phenomena can also support other research programmes and provide evidence to support future lockdown policies, for example how localised lockdowns need to be (city-wide versus neighbourhoods) and for how long. Local authorities may also use this evidence in support of highly targeted partial lockdown policies (such as differential application of the national Covid alert scale for different areas). Data sources that identify the location of cases can be used to generate predictions of the spread of Covid-19 cases over time and space, which will facilitate the implementation of localised policies to contain the spread of the virus. The aim of this project is to adapt statistical methods for this purpose and develop software for their implementation.This project will develop software for the real-time surveillance of Covid-19 that can be used with any georeferenced and time stamped data. We will use data on hospital attendances and admissions for Covid-19 to develop, calibrate, and test our software and models. We will build on state-of-the-art geostatistical software developed by the co-applicants to produce estimates and predictions of incidence or the "R number" across an area of interest based on available data sources. These outputs can also support the design of scheme to sample the population for testing when such programmes are rolled out, for which we will also include functionality.