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Infer the prevalence of disease from incidence estimates and population size.

Source: R/infer-prevalence.R
infer_prevalence.Rd

[Experimental]

Log-scaled incidence estimates are used to generate samples of incidence. These are convolved with the duration of infection (as the probability still infected) on any given day (as a set of discrete distributions) and the result is evaluated as a fraction of population. The result is fitted to a logit-normal (using moments) and quantiles produced from samples. If test sensitivity is used instead of duration of infection then the prevalence estimate will be the expected proportion of test positives by screening test rather than a true prevalence. Differences between this and the proportion of test positives observed may be due to ascertainment bias or test sensitivity misspecification.

Usage

infer_prevalence(
  modelled = i_incidence_model,
  pop = i_population_data,
  ip = i_discrete_ip,
  bootstraps = 1000,
  seed = Sys.time(),
  ...
)

Arguments

modelled

Model output from processing the raw dataframe with something like poission_locfit_model - a dataframe with columns:

  • time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

  • incidence.fit (double) - an estimate of the incidence rate on a log scale

  • incidence.se.fit (positive_double) - the standard error of the incidence rate estimate on a log scale

  • incidence.0.025 (positive_double) - lower confidence limit of the incidence rate (true scale)

  • incidence.0.5 (positive_double) - median estimate of the incidence rate (true scale)

  • incidence.0.975 (positive_double) - upper confidence limit of the incidence rate (true scale)

Any grouping allowed.

pop

The population data must be grouped in the same way as modelled. - a dataframe with columns:

  • population (positive_integer) - Size of population

Any grouping allowed.

ip

A discrete distribution representing the duration of infection (or probability of detection of infection). This will not sum to one. - a dataframe with columns:

  • boot (anything + default(1)) - a bootstrap identifier

  • probability (proportion) - the probability of new event during this period.

  • tau (integer + complete) - the days since the index event.

Minimally grouped by: boot (and other groupings allowed).

A default value is defined.

bootstraps

the number of samples to take at each time point. This will be rounded up to a whole multiple of the infection duration distribution length.

seed

a random number seed for reproducibility

...

not used

Value

the modelled input with additional proportion columns

Examples

tmp = ggoutbreak::test_poisson_rt_smooth %>%
  ggoutbreak::poisson_locfit_model(window=14) %>%
  ggoutbreak::infer_prevalence(
     pop = 10000,
     ip = ggoutbreak::test_ip
  )

if(interactive()) {
  plot_prevalence(tmp)
}