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Last active January 29, 2022 12:17
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Example of using EpiNow2 to estimate the Rt of Covid-19 in last 3 months for a country in the WHO dataset. See the documentation for more details and examples of producing estimates for subregional areas:
# packages
# install.packages(c("data.table", "remotes", "EpiNow2"))
# target country (must be present in ECDC data)
country <- "france"
# set number of cores to use fitting the model
# no benefit on runtime if cores > chains which is set to 4 by default
options(mc.cores = 4)
# literature distributions - please reach out if there are others you think should be supported
generation_time <- get_generation_time(disease = "SARS-CoV-2", source = "ganyani")
incubation_period <- get_incubation_period(disease = "SARS-CoV-2", source = "lauer")
# define reporting delay as lognormal with mean of 4 days and sd of 1 day in absence of
# evidence. If data on onset -> report then can use estimate_delay to estimate the delay
reporting_delay <- list(mean = convert_to_logmean(4, 1),
mean_sd = 0.1,
sd = convert_to_logsd(4, 1),
sd_sd = 0.1,
max = 15)
# precleaned ECDC data - alternative is to bring your own
reported_cases <- covidregionaldata::get_national_data(country, source = "who")
reported_cases <- data.table::setDT(reported_cases)
reported_cases <- reported_cases[, .(date, confirm = cases_new)]
# filter to the last 3 months of data (to speed up computation)
reported_cases <- reported_cases[date >= (max(date) - 12*7)]
# estimate Rt and nowcast/forecast cases by date of infection
# on a 4 core computer this example should take between 2 ~ 5 minutes to run
# to see saved logs view the dated logs folder
# to see saved results see the dated results folder
# some data sets may produce divergent transition warnings
# this is not unexpected and is usually fine (if dts < 1% of samples)
# but is an area of research as we seek to optimise the underlying model.
# If you have some prior knowledge of the scaling between observations and
# reports see ?obs_opts for options.
# If you have some prior knowledge on the truncation in your data or multiple
# snapshots of data see ?trunc_opts for options.
# Note that the default settings may not be appropriate for your use case.
# Example configurations are here:
out <- epinow(reported_cases = reported_cases,
generation_time = generation_time,
delays = delay_opts(incubation_period, reporting_delay),
rt = rt_opts(prior = list(mean = 1.5, sd = 0.5)),
# here we define the quality of the gaussian process approximation
# if the fit to data appears poor try increasing basis_prop and
# potentially the boundary_scale (see ?gp_opts for details)
# though this will likely increase runtimes.
gp = gp_opts(basis_prop = 0.2),
# in some instances stan chains can get stuck when estimating in
# these instances consider using the future fitting mode by passing
# `future = TRUE, max_execution_time = 60 * 30` to stan_opts and calling
# `future::plan("multiprocess")` prior to running epinow this will time out
# chains after 30 minutes but still return results from completed chains
stan = stan_opts(),
horizon = 14,
target_folder = "results",
logs = file.path("logs", Sys.Date()),
return_output = TRUE,
verbose = TRUE)
# summary of the latest estimates
# plot estimates
# summary of R estimates
summary(out, type = "parameters", params = "R")
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Hello! On doing the tutorial, I noticed pulling the data from the ECDC only covered as far as last December (Dec 14th 2020), but if you change the source to WHO the data is current to present day - I forked this gist with the change, unsure if that is helpful or if the source is intentionall still ECDC. Thanks for a great tutorial and cool package!

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seabbs commented Dec 1, 2021

Thanks and glad it was useful! To be honest I had entirely forgotten this gist existed... As suggested I have updated to the WHO source which we also use everywhere (the ECDC moved to weekly data late last year).

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