Updates

Model and report changes

  1. The model now accounts for the ongoing immunisation programme, stratifying the population of people still susceptible to infection with the virus according to their immunisation status (unimmunised/1 dose/2 doses). We use data on the daily proportions of the population getting immunised to inform this splitting of the population, assuming that it takes three weeks for vaccine-derived immunity to develop. Vaccine efficacy is assumed against both infection and death, using values for the efficacy in agreement with those found here. We have a changepoint in the vaccine efficacy on the 10th May, which marks a transition from alpha being the dominant variant, to delta.
  2. The model also accounts for a different susceptibility to infection in each adult age group (no prior information is used); and for the under-15s, (using prior information from Viner et al, 2020, which estimates children to be less likely to acquire infection when in contact with an infectious individual).
  3. The model has the ability to incorporate estimates of community prevalence, by region and age group, from the Office of National Statistics COVID-19 Infection Survey (see Data Sources for details). These are included weekly since the outset of the Survey in May 2020 for the age groups >4 years to inform trends in incidence that are too recent to be captured by the data on deaths.
  4. The geographical definition has been changed from the seven NHS regions (map) to the nine regions typically used in government (map). This new spatial definition more appropriately reflects the existing regional heterogeneity.
  5. The underlying probability of an unvaccinated individual dying following infection with SARS-CoV2 (the infection-fatality rate, IFR) is allowed to change gradually over the course of 30 days every (approximately) 100 days. This is designed to reflect fluctuations due to seasonal effects, demand on healthcare services or the emergence of new virus variants of differing severity.
  6. The ‘Epidemic summary’ only reports the current value for the IFR by age. To visualise how this has changed over time in our model, see the IFR tab in the ‘Infections and Deaths’ section of the report. The quantity that is now plotted under this tab is the probability of dying if infected, taking into account the impact of the immunisation programme.

Updated findings

  1. The estimate of the daily number of new infections on the 4th September across England is 37,000 (30,100–45, 400, 95% credible interval). The daily infection rate is estimated to be 66 per 100K population per day nationally. The highest rate is in the South West (SW) with 116 infections per 100K corresponding to 6,470 new daily infections. The next highest rates of infection are in the North East (NE) with 102 infections per 100K (2,700 daily infections). The West Midlands (WM) has around 90 while East Midlands (EM) and East of England (EE) both have around 80 new infections per 100k each day. Yorkshire and Humberside (YH) is next with 66, while the South East (SE), London (GL) and North West (NW) have the lowest daily infection rates between 46 and 26 per 100k. Note that a substantial proportion of these infections will be asymptomatic.
  2. The number of deaths occurring daily has continued to increase but it is predicted to start flattening such that we forecast between 63 and 121 deaths on the 25th of September.
  3. The probability of Rt exceeding 1 has decreased in most regions. It is highest at 62% in the SW followed by values of around 50% in the EE and WM; it is around 30% or lower in the remaining regions, being particularly low in NW and GL (3% and 1% respectively).
  4. The growth rate for England is estimated at 0.00 (-0.01– 0.01) per day. This means that, nationally, the number of infections remains flat. This pattern is still the result of heterogeneity in transmission in the different regions: in the SW incidence is increasing; it has peaked in the NE and it is flat or decreasing in the others, with some potential for sustained transmission.
  5. This week we estimate NE to be the region with the highest attack rate, that is the proportion of the regional populations who have ever been infected, at 39% followed by GL and the WM with 38% and 35% respectively. The SW continues to have the lowest attack rate at 23%. These attack rates are broadly consistent with our previous published report.
  6. Note that the deaths data used are only very weakly informative on Rt over the last two weeks. Therefore, the estimate for current incidence, Rt and the forecast of daily numbers of deaths are likely to be subject to some revision.

Interpretation

The plots of the estimated Rt over the most recent weeks continues to show Rt plateauing just below 1.0 in most regions. This plateau largely reflects low-levels of mobility and social interaction over the summer school holiday period, resulting in reduced rates of infectious contact. The incidence of deaths has been gradually increasing since mid-June, although the actual numbers remain low in comparison to the peaks of the first two waves of infection. Similar to our most recent publication, our projections for the number of deaths suggest that we are now close to the peak of the current summer wave.

Plots of the IFR over time show that from the end of January we estimate a decreasing IFR in all adult age groups, but most steeply in the older ages. This drop indicates the benefits of immunisation against death over and above the benefits against infection. Following this drop, over the last month there has been a slight increase to 3.3% (3.0%–3.6%) in the over-75s and 0.26% (0.24%–0.28%) overall.

For context, in addition to the data used here, the numbers of reported new positive tests have remained stable over the holiday period. This is, however, highly dependent on the targeting of testing and the public’s testing behaviour and, therefore, is difficult to interpret. Hospital admissions are showing a similar pattern nationally, with increases only evident in some regions. Prevalence of infection, as estimated by the ONS Coronavirus Infections Survey, remains high, staying constant at around 1.40% in England.

Summary

Real-time tracking of an epidemic, as data accumulate over time, is an essential component of a public health response to a new outbreak. A team of statistical modellers at the MRC Biostatistics Unit (BSU), University of Cambridge, are working to provide regular now-casts and forecasts of COVID-19 infections and deaths. This information feeds directly to the SAGE sub-group, Scientific Pandemic Influenza sub-group on Modelling (SPI-M), and to regional Public Health England (PHE) teams.

Methods

We fit a transmission model (Birrell et al. 2020) to a number of data sources (see ‘Data Sources’), to reconstruct the number of new COVID-19 infections over time in different age groups and NHS regions, estimate a measure of ongoing transmission and predict the number of new COVID-19 deaths.

Data sources

We use:

  1. Data on COVID-19 confirmed deaths from the Public Health England (PHE) line-listing This consists of a combination of deaths notified to:
    • the Demographics Batch Service (DBS), a mechanism that allows PHE to submit a file of patient information to the National Health Service spine for tracing against the personal demographics service (PDS). PHE submit a line list of patients diagnosed with COVID-19 to DBS daily. The file is returned with a death flag and date of death updated (started 20th March, 2020).
    • NHS England, who report data from NHS trusts relating to patients who have died after admission to hospital or within emergency department settings.
    • Health Protection Teams (HPTs), resulting from a select survey created by PHE to capture deaths occurring outside of hospital settings, e.g. care homes (started 23rd March, 2020)
  2. Data on antibody prevalence in blood samples from a PHE survey of NHS Blood Transfusion (NHSBT) donors.

Data are stratified into eight age groups: <1, 1-4, 5-14, 15-24, 25-44, 45-64, 65-74, 75+, and the NHS England regions (North East and Yorkshire, North West, Midlands, East of England, London, South East, South West).

  1. Published information on the the natural history of COVID-19 (Verity et al., 2020; Li et al, 2020)
  2. Information on contacts between different age groups from:
    • A Survey that describes relative rates of contacts between different age groups (Mossong et al. 2008).
    • Google Community Mobility reports, informing the changes in people’s mobility over the course of the pandemic, particularly after the March 23rd lockdown measures.
    • The ONS’ time use survey, which in conjunction with the google mobility study, allows estimation of the changing exposure to infection risk over time.
    • Data from the Department for Education describing the proportion of children currently attending school.
  3. Daily data on the numbers of people getting immunised by age-group and region. These data are derived from the National Immunisation Management Service (NIMS). These data includes all COVID-19 immunisations administered at hospital hubs, local immunisation service sites such as GP practices, and dedicated immunisation centres.

Epidemic summary

Current \(R_t\)

Value of \(R_t\), the average number of secondary infections due to a typical infection today.

Number of infections

Attack rate

The percentage of a given group that has been infected.

By region

By age

Current IFR

Change in infections incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 -0.01 0.01
East of England 0.00 -0.03 0.02
East Midlands -0.01 -0.03 0.02
London -0.03 -0.06 0.00
North East -0.01 -0.03 0.02
North West -0.03 -0.06 0.00
South East -0.01 -0.03 0.01
South West 0.00 -0.03 0.03
West Midlands 0.00 -0.03 0.03
Yorkshire and The Humber -0.01 -0.04 0.01

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 209.81 50.12 NA
East of England NA 25.93 NA
East Midlands 77.46 19.55 NA
London 20.56 10.85 184.89
North East 120.69 19.51 NA
North West 22.10 11.10 NA
South East 72.91 19.80 NA
South West NA 27.25 NA
West Midlands NA 22.48 NA
Yorkshire and The Humber 48.34 17.09 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 104.71 NA
East of England 1227.64 30.82 NA
East Midlands NA 39.73 NA
London NA NA NA
North East NA 32.88 NA
North West NA 8222.36 NA
South East NA 51.40 NA
South West 155.63 24.67 NA
West Midlands 5710.51 26.34 NA
Yorkshire and The Humber NA 89.38 NA

Change in deaths incidence

Growth rates

NB: negative growth rates are rates of decline. Values are daily changes.

Region Median 95% CrI (lower) 95% CrI (upper)
England 0.00 -0.01 0.01
East of England 0.01 -0.01 0.03
East Midlands 0.00 -0.02 0.02
London -0.02 -0.03 0.00
North East 0.00 -0.02 0.02
North West -0.02 -0.03 0.01
South East 0.00 -0.02 0.02
South West 0.02 0.00 0.04
West Midlands 0.00 -0.02 0.03
Yorkshire and The Humber -0.01 -0.02 0.01

Halving times

Halving times in days, if a region shows growth than value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England 688.83 92.01 NA
East of England NA 62.98 NA
East Midlands NA 37.62 NA
London 35.11 19.99 478.44
North East 455.91 33.41 NA
North West 40.94 20.57 NA
South East NA 42.10 NA
South West NA 140.34 NA
West Midlands NA 42.00 NA
Yorkshire and The Humber 112.73 31.23 NA

Doubling times

Doubling times in days, if a region shows decline then the value will be NA.

Region Median 95% CrI (lower) 95% CrI (upper)
England NA 126.02 NA
East of England 94.45 24.14 NA
East Midlands 2037.89 31.12 NA
London NA NA NA
North East NA 32.58 NA
North West NA 133.18 NA
South East 1502.83 37.96 NA
South West 44.66 17.61 NA
West Midlands 207.68 25.88 NA
Yorkshire and The Humber NA 54.62 NA

Infections and deaths

The shaded areas show periods of national lockdown, the green lines the dates (once confirmed) of the steps in the roadmap in the UK Governement’s COVID-19 Response – Spring 2021, and the red line shows the date these results were produced (04 Sep).

Infection incidence

By region

By age

Cumulative infections

By region

By age

Deaths incidence

By region

By age

Cumulative deaths

By region

By age

IFR

Prob \(R_t > 1\)

The figure below shows the probability that \(R_t\) is greater than 1 (ie: the number of infections is growing) in each region over time. Clicking the regions in the legend allows lines to be added or removed from the figure.

\(R_t\)

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