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

  • Funded by Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)
  • Total publications:3 publications

Grant number: MR/V028421/1

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2020
  • Known Financial Commitments (USD)

    $22,356.99
  • Funder

    Department of Health and Social Care / National Institute for Health and Care Research (DHSC-NIHR), UK Research and Innovation (UKRI)
  • Principle Investigator

    Pending
  • Research Location

    United Kingdom, Europe
  • Lead Research Institution

    University of Birmingham
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease transmission dynamics

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

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.

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