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

2019冠状病毒病大流行将持续很长时间。各国政府和其他国际机构要成功地管理艾滋病毒/艾滋病，就需要有统计工具来实时监测艾滋病毒/艾滋病在空间和时间上的演变。人们对新型冠状病毒如何随着时间的推移在城市地区传播知之甚少，例如，集群的数量是多是少，它们在空间上有多大，以及它们的增长速度有多快。了解当地现象还可以支持其他研究项目，并为未来的封锁政策提供证据，例如需要如何进行局部封锁（全市范围还是社区范围）以及封锁多长时间。地方当局也可能利用这些证据来支持具有高度针对性的部分封锁政策（例如对不同地区区别适用全国新冠肺炎警戒级别）。确定病例位置的数据源可用于预测COVID-19病例随时间和空间的传播，这将有助于实施本地化政策以遏制病毒传播。该项目的目的是为此调整统计方法，并开发用于实施的软件。该项目将开发用于COVID-19实时监测的软件，该软件可用于任何地理参考和时间戳数据。我们将使用新型冠状病毒肺炎（COVID-19，即2019冠状病毒病）的医院就诊和入院数据来开发、校准和测试我们的软件和模型。我们将建立在共同申请人开发的最先进的地质统计软件的基础上，根据现有的数据来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.