Nowcasting positive test counts with reporting lag
Research team
Dr Radka Jersakova, The Alan Turing Institute
Dr James Lomax, National Cyber Security Centre and The Alan Turing Institute
To monitor the current state of COVID-19, the UK government tracks the number of positive tests in each local authority. Since it takes time to process PCR swab tests, there is a delay of up to five days before all positive test results are reported.
The goal of this project is to 'nowcast' the number of daily positive test counts up to the present date. A 'nowcast' is a prediction informed by analysis of data currently available. Using statistical models we can infer the expected final count using the incomplete data as it arrives. This estimate can be used to make up for the lag in data reporting and aid decision making.
Timeframe
From August 2020
Spatial and temporal modelling of incidence and prevalence of COVID-19
PI
Professor Marta Blangiardo, Imperial College London
Research team
Tullia Padellini, Imperial College London
Annie Mallon, MRC Harwell
Ruairidh King, MRC Harwell
Luis Santos, MRC Harwell
Our goal is to estimate the prevalence of COVID-19, combining several sources of data, accounting for their biases and uncertainty.
We aim at predicting the burden of the disease by integrating two different type of information on the number of cases: direct estimates (such as randomized surveys and testing programs) and indirect estimates (such as hospital admissions). We provide a flexible modelling framework, which is adjusted for known risk factors and accounts for spatial as well as temporal dependencies in our data.
Timeframe
From November 2020
Estimating COVID-19 prevalence and transmission from multiple sources: de-biasing Pillar 2 data
Research team
Brieuc Lehmann, University of Oxford
George Nicholson, University of Oxford
Annie Mallon, MRC Harwell
Ruairidh King, MRC Harwell
Luis Santos, MRC Harwell
Our goal is to estimate COVID-19 prevalence and transmission rates at a fine-scale level, such as local authority, by harnessing data from multiple testing sources. We are designing a statistical model to adaptively adjust for biases and coherently combine information across multiple data streams.
Background
The daily or weekly number of positive COVID-19 tests in a region is widely used as a proxy for the local number of infected individuals.
Multiple testing sources
Positive test numbers arise from:
- Randomized surveillance (REACT study, ONS survey).
- Pillar 2 testing focused on testing symptomatic individuals.
- Local mass testing at the level of cities, universities, care homes etc.
Testing bias
Tests results are subject to sampling and operational influences:
- Ascertainment bias: symptomatic individuals are prioritized for testing, so the rate of positive tests is greater than the actual disease prevalence in the population.
- False positive/negative test results: tests for COVID-19 infection, such as PCR and lateral flow, vary in sensitivity and specificity.
- Weekday effects: the numbers of tests performed depends strongly on the day of the week.
Timeframe
From November 2020
Effectiveness of Non-Pharmaceutical Interventions (NPIs)
This project will seek to understand the effect of various non-pharmaceutical interventions on a range of outcomes which will be specified as the project executes. The ultimate research goal is to provide a deep understanding of the menu of policy options available to the government to help stop the spread of the virus.
Read more about this project. Please note that applications for the open call have now closed.