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Estimate Infections, the Time-Varying Reproduction Number and the Rate of Growth

Source: R/estimate_infections.R
estimate_infections.Rd

[Maturing] Uses a non-parametric approach to reconstruct cases by date of infection from reported cases. It uses either a generative Rt model or non-parametric back calculation to estimate underlying latent infections and then maps these infections to observed cases via uncertain reporting delays and a flexible observation model. See the examples and function arguments for the details of all options. The default settings may not be sufficient for your use case so the number of warmup samples (stan_args = list(warmup)) may need to be increased as may the overall number of samples. Follow the links provided by any warnings messages to diagnose issues with the MCMC fit. It is recommended to explore several of the Rt estimation approaches supported as not all of them may be suited to users own use cases. See here for an example of using estimate_infections within the epinow wrapper to estimate Rt for Covid-19 in a country from the ECDC data source.

Usage

estimate_infections(
  data,
  generation_time = gt_opts(),
  delays = delay_opts(),
  truncation = trunc_opts(),
  rt = rt_opts(),
  backcalc = backcalc_opts(),
  gp = gp_opts(),
  obs = obs_opts(),
  forecast = forecast_opts(),
  stan = stan_opts(),
  CrIs = c(0.2, 0.5, 0.9),
  weigh_delay_priors = TRUE,
  id = "estimate_infections",
  verbose = interactive(),
  filter_leading_zeros = TRUE,
  zero_threshold = Inf,
  horizon
)

Arguments

data

A <data.frame> of disease reports (confirm) by date (date). confirm must be numeric and date must be in date format. Optionally this can also have a logical accumulate column which indicates whether data should be added to the next data point. This is useful when modelling e.g. weekly incidence data. See also the fill_missing() function which helps add the accumulate column with the desired properties when dealing with non-daily data. If any accumulation is done this happens after truncation as specified by the truncation argument.

generation_time

A call to gt_opts() (or its alias generation_time_opts()) defining the generation time distribution used. For backwards compatibility a list of summary parameters can also be passed.

delays

A call to delay_opts() defining delay distributions and options. See the documentation of delay_opts() and the examples below for details.

truncation

A call to trunc_opts() defining the truncation of the observed data. Defaults to trunc_opts(), i.e. no truncation. See the estimate_truncation() help file for an approach to estimating this from data where the dist list element returned by estimate_truncation() is used as the truncation argument here, thereby propagating the uncertainty in the estimate.

rt

A list of options as generated by rt_opts() defining Rt estimation. Defaults to rt_opts(). To generate new infections using the non-mechanistic model instead of the renewal equation model, use rt = NULL. The non-mechanistic model internally uses the setting rt = rt_opts(use_rt = FALSE, future = "project", gp_on = "R0").

backcalc

A list of options as generated by backcalc_opts() to define the back calculation. Defaults to backcalc_opts().

gp

A list of options as generated by gp_opts() to define the Gaussian process. Defaults to gp_opts(). Set to NULL to disable the Gaussian process.

obs

A list of options as generated by obs_opts() defining the observation model. Defaults to obs_opts().

forecast

A list of options as generated by forecast_opts() defining the forecast opitions. Defaults to forecast_opts(). If NULL then no forecasting will be done.

stan

A list of stan options as generated by stan_opts(). Defaults to stan_opts(). Can be used to override data, init, and verbose settings if desired.

CrIs

Numeric vector of credible intervals to calculate.

weigh_delay_priors

Logical. If TRUE (default), all delay distribution priors will be weighted by the number of observation data points, in doing so approximately placing an independent prior at each time step and usually preventing the posteriors from shifting. If FALSE, no weight will be applied, i.e. delay distributions will be treated as a single parameters.

id

A character string used to assign logging information on error. Used by regional_epinow() to assign errors to regions. Alter the default to run with error catching.

verbose

Logical, defaults to TRUE when used interactively and otherwise FALSE. Should verbose debug progress messages be printed. Corresponds to the "DEBUG" level from futile.logger. See setup_logging for more detailed logging options.

filter_leading_zeros

Logical, defaults to TRUE. Should zeros at the start of the time series be filtered out.

zero_threshold

[Experimental] Numeric defaults to Inf. Indicates if detected zero cases are meaningful by using a threshold number of cases based on the 7-day average. If the average is above this threshold then the zero is replaced using fill.

horizon

Deprecated; use forecast instead to specify the predictive horizon

Value

A list of output including: posterior samples, summarised posterior samples, data used to fit the model, and the fit object itself.

See also

epinow() regional_epinow() forecast_infections() estimate_truncation()

Examples

# \donttest{
# set number of cores to use
old_opts <- options()
options(mc.cores = ifelse(interactive(), 4, 1))

# get example case counts
reported_cases <- example_confirmed[1:60]

# set an example generation time. In practice this should use an estimate
# from the literature or be estimated from data
generation_time <- Gamma(
  shape = Normal(1.3, 0.3),
  rate = Normal(0.37, 0.09),
  max = 14
)
# set an example incubation period. In practice this should use an estimate
# from the literature or be estimated from data
incubation_period <- LogNormal(
  meanlog = Normal(1.6, 0.06),
  sdlog = Normal(0.4, 0.07),
  max = 14
)
# set an example reporting delay. In practice this should use an estimate
# from the literature or be estimated from data
reporting_delay <- LogNormal(mean = 2, sd = 1, max = 10)

# for more examples, see the "estimate_infections examples" vignette
def <- estimate_infections(reported_cases,
  generation_time = gt_opts(generation_time),
  delays = delay_opts(incubation_period + reporting_delay),
  rt = rt_opts(prior = LogNormal(mean = 2, sd = 0.1))
)
# real time estimates
summary(def)
#>                         measure                 estimate
#>                          <char>                   <char>
#> 1:       New infections per day      2268 (1393 -- 3675)
#> 2:   Expected change in reports        Likely decreasing
#> 3:   Effective reproduction no.        0.9 (0.72 -- 1.1)
#> 4:               Rate of growth -0.028 (-0.093 -- 0.039)
#> 5: Doubling/halving time (days)         -25 (18 -- -7.4)
# summary plot
plot(def)

options(old_opts)
# }