Calibrate the average load per case using case count data

This option calibrates the average total shedding load per case based on the relationship between measured concentrations and case counts. The goal is to obtain a load_per_case that is on the right order of magnitude - this will not be sufficient for accurate prevalence estimation from wastewater, but fully sufficient for monitoring trends and estimating Rt.

Usage

load_per_case_calibrate(
  cases = NULL,
  min_cases = NULL,
  ascertainment_prop = 1,
  measurement_shift = seq(-7, 7),
  shift_weights = 1/(abs(measurement_shift) + 1),
  date_col = "date",
  case_col = "cases",
  signif_fig = 2,
  modeldata = modeldata_init()
)

Arguments

cases

A data.frame of case numbers with each row representing one day. Must have at least a column with dates and a column with case numbers.

min_cases

This is an alternative to supplying a data.frame of cases. If min_cases is specified, then load_per_case is calibrated based on the smallest detected load in the wastewater. EpiSewer uses min_cases = 10 as a default if no case data is supplied (see sewer_assumptions()).

ascertainment_prop

Proportion of all cases that get detected / reported. Can be used to account for underreporting of infections. Default is ascertainment_prop=1, meaning that 100% of infections become confirmed cases.

measurement_shift

The specific timing between wastewater concentrations and case numbers depends on reporting delays and shedding profiles and is typically uncertain. This argument allows to shift the concentration and case number time series relative to each other and to average over several potential lags/leads, as specified by an integer vector. The default is measurement_shift = seq(-7,7), i.e. a shift of concentrations between up to one week before and after case numbers.

shift_weights

Weights for the shifted comparisons. Must be an numeric vector of the same length as measurement_shift. If NULL (default), the weights are chosen to be approximately inversely proportional to the shift distance.

date_col

Name of the date column in the provided cases data.frame.

case_col

Name of the column containing the case numbers.

signif_fig

Significant figures to round to. Since this heuristic only provides crude estimates which should not be over-interpreted, the result gets rounded (this also increases stability when fitting the model at different points in time). Default is rounding to the 2 most significant figures.

modeldata

A modeldata object to which the above model specifications should be added. Default is an empty model given by modeldata_init(). Can also be an already partly specified model returned by other EpiSewer modeling functions.

Value

A modeldata object containing data and specifications of the model to be fitted. Can be passed on to other EpiSewer modeling functions to add further data and model specifications.

The modeldata object also includes information about parameter initialization (.init), meta data (.metainfo), and checks to be performed before model fitting (.checks).

Details

In EpiSewer, the load_per_case serves as a scaling factor describing how many pathogen particles are shed by the average infected individual overall and how much of this is detectable at the sampling site. This depends both on biological factors as well as on the specific sewage system and laboratory quantification. It is therefore almost always necessary to assume the load per case based on a comparison of measured concentrations/loads and case numbers.

If a data.frame of cases is supplied via the cases argument, the average load per case is determined by fitting a linear regression model with loads (computed using concentrations and flows) as dependent variable and case numbers as independent variable. This does not explicitly account for shedding profiles or reporting delays, but the measurement_shift argument allows to average over a set of relative shifts between the two time series.

See also

Other load per case functions: load_per_case_assume()