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

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.

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. The aims of this project are to develop statistical tools and software for real-time covid-19 surveillance using georeferenced and time-stamped data from covid-19 cases or testing programmes.

To summarise our statistical approach, we assume that across our area of interest there is an underlying, unobserved prevalence of people who have had the disease. At a single time-point we observe spatially-referenced data, which may include only cases from routine hospital records, or testing data with dichotomous outcomes. Where the data provide both numerators and denominators we will use the binomial generalised geostatistical model. Where only numerators are available we will use a Poisson approximation to the binomial with small-area population counts as offsets. We will including a set of spatially varying-covariates, and the unobserved process which is specified as a spatio-temporal Gaussian process.

The outputs of our analyses will be predictions of incidence, cumulative incidence, and the effective reproductive rate (the "R number") and their spatial distribution. The output will identify areas where there is a high probability of high reproductive rates (R>1, for example). Unbiased estimates of prevalence at a particular point in time from nationwide testing programmes will be used to correct for the spatial biases that may result from the use of routine, non-randomised data. We will also provide functionality to support random sampling schemes.