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

新冠肺炎大流行还有很长的路要走。各国政府和其他国际机构要成功地管理这一疾病，就需要有统计工具来实时监测这一流行病在空间和时间上的演变。covid-19如何随着时间的推移在城市地区传播，例如，聚集性病例的数量是多是少，它们在空间上有多大，以及它们的增长速度有多快，人们知之甚少。了解当地现象还可以支持其他研究规划，并为支持未来的封锁政策提供证据，例如，局部封锁需要如何实施（全市范围还是社区范围），以及需要多长时间。地方当局还可以利用这一证据来支持高度针对性的部分封锁政策（例如对不同地区区别应用国家Covid警报级别）。确定病例位置的数据来源可用于预测Covid-19病例随时间和空间的传播，这将有助于实施遏制病毒传播的局部政策。这个项目的目的是为了这个目的调整统计方法，并开发软件来实现这些方法。该项目将开发用于Covid-19实时监测的软件，可用于任何地理参考和时间戳数据。我们将利用Covid-19住院人数和入院人数的数据来开发、校准和测试我们的软件和模型。我们将以共同申请人开发的最先进的地质统计软件为基础，根据现有数据源对感兴趣的区域的发生率或“R数”进行估计和预测。这些产出还可以支持方案的设计，以便在推出此类计划时对人口进行抽样测试，为此我们还将包括功能。

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.