Data on the 2020-2022 SARS-CoV-2 epidemic in the UK.

Source: R/data.R

This data set gives:

  1. the UK daily incidence of deaths within 28 days of a positive COVID test (see source and references),

  2. the discrete daily distribution of the serial interval for SARS-CoV-2, assuming a shifted Gamma distribution with mean 6.1 days, standard deviation 4.2 days and shift 1 day (see references).

Format

A list of two elements:

  • incidence: a data.frame containing 672 days of observation,

  • si_distr: a vector containing a set of 30 probabilities.

Source

Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases. 2020 Aug 1;20(8):911–9.

https://coronavirus.data.gov.uk/details/cases

References

Nash RK, Cori A, Nouvellet P. Estimating the epidemic reproduction number from temporally aggregated incidence data: a statistical modelling approach and software tool. (medRxiv pre-print)

Bi Q, Wu Y, Mei S, Ye C, Zou X, Zhang Z, et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. The Lancet Infectious Diseases. 2020 Aug 1;20(8):911–9.

Examples

data("covid_deaths_2020_uk")

## estimate the reproduction number (method "non_parametric_si")
res <- estimate_R(covid_deaths_2020_uk$incidence$Incidence, 
method="non_parametric_si",
          config = make_config(list(si_distr = covid_deaths_2020_uk$si_distr)))
#> Default config will estimate R on weekly sliding windows.
#>     To change this change the t_start and t_end arguments. 
plot(res)

## the second plot produced shows, at each each day,
## the estimate of the reproduction number
## over the 7-day window finishing on that day.