calibrate: Calibrate via particle filter grid search using time series...
In mrc-ide/squire: SEIR transmission model of COVID-19

calibrate

R Documentation

Calibrate via particle filter grid search using time series of deaths

Description

Calibrate via particle filter grid search using time series of deaths

Usage

calibrate(
  data,
  R0_min,
  R0_max,
  R0_step,
  R0_prior = NULL,
  first_start_date,
  last_start_date,
  day_step,
  Meff_min = 1,
  Meff_max = 1,
  Meff_step = 0.1,
  squire_model = explicit_model(),
  Rt_func = function(R0_change, R0, Meff) {     exp(log(R0) - Meff * (1 - R0_change)) },
  pars_obs = NULL,
  forecast = 0,
  n_particles = 100,
  reporting_fraction = 1,
  treated_deaths_only = FALSE,
  replicates = 100,
  date_R0_change = NULL,
  R0_change = NULL,
  date_ICU_bed_capacity_change = NULL,
  baseline_ICU_bed_capacity = NULL,
  ICU_bed_capacity = NULL,
  date_hosp_bed_capacity_change = NULL,
  baseline_hosp_bed_capacity = NULL,
  hosp_bed_capacity = NULL,
  date_contact_matrix_set_change = NULL,
  baseline_contact_matrix = NULL,
  contact_matrix_set = NULL,
  country = NULL,
  population = NULL,
  dt = 0.1,
  ...
)

Arguments

`data`	Deaths data to fit to. See `example_deaths.csv` and `particle_filter_data()`
`R0_min`	Minimum value of R0 in the search
`R0_max`	Maximum value of R0 in the search
`R0_step`	Step to increment R0 between min and max
`R0_prior`	Prior for R0. Default = NULL, which is a flat prior. Should be provided as a list with first argument the distribution function and the second the function arguments (excluding quantiles which are worked out based on R0_min and R0_max), e.g. 'list("func" = dnorm, args = list("mean"= 3.5, "sd"= 3))'.
`first_start_date`	Earliest start date as 'yyyy-mm-dd'
`last_start_date`	Latest start date as 'yyyy-mm-dd'
`day_step`	Step to increment date in days
`Meff_min`	Minimum value of Meff (Movement effect size) in the search. Default = 1, which is the same as `Meff_max`. If `Meff_max` and `Meff_min` are the same then only a 2d grid search is performed based on R0 and start_date
`Meff_max`	Maximum value of Meff (Movement effect size) in the search. Default = 1, which is the same as `Meff_min`. If `Meff_max` and `Meff_min` are the same then only a 2d grid search is performed based on R0 and start_date
`Meff_step`	Step to increment Meff (Movement effect size) between min and max. Default = 0.1
`squire_model`	A squire model. Default = `explicit_SEIR()`
`Rt_func`	Function for converting R0, Meff and R0_change. Function must have names arguments of R0, Meff and R0_change. Default is linear relationship on the log scale given by `exp(log(R0) - Meff*(1-R0_change))`.
`pars_obs`	list of parameters to use for the comparison function.
`forecast`	Number of days to forecast forward. Default = 0
`n_particles`	Number of particles. Positive Integer. Default = 100
`reporting_fraction`	Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by `data$deaths`
`treated_deaths_only`	Boolean for whether likelihood is based only on deaths that occur from healthcare systems, i.e. are treated. Default = FALSE, which uses all deaths.
`replicates`	Replicates to be run. Default = 100
`date_R0_change`	Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0
`R0_change`	Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0
`date_ICU_bed_capacity_change`	Calendar dates at which ICU bed capacity changes set in `model_params` change. Defaut = NULL, i.e. no change
`baseline_ICU_bed_capacity`	The starting number of ICU beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 3/100 of hospital beds
`ICU_bed_capacity`	Number of ICU beds at each date specified in `date_ICU_bed_capacity_change`. Must be same length as `date_ICU_bed_capacity_change`.
`date_hosp_bed_capacity_change`	Calendar dates at which hospital bed capacity changes set in `model_params` change. Defaut = NULL, i.e. no change
`baseline_hosp_bed_capacity`	The starting number of hospital beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 5/1000 of the population
`hosp_bed_capacity`	Number of hospital beds at each date specified in `date_hosp_bed_capacity_change`. Must be same length as `date_hosp_bed_capacity_change`.
`date_contact_matrix_set_change`	Calendar dates at which the contact matrices set in `model_params` change. Defaut = NULL, i.e. no change
`baseline_contact_matrix`	The starting contact matrix prior to any changes due to interventions or otherwise. Default = NULL, which will use the contact matrix associated with the coutnry provided.
`contact_matrix_set`	List of contact matrices to be used from the dates provided in `date_contact_matrix_set_change`.Must be same length as `date_contact_matrix_set_change`
`country`	Character for country beign simulated. WIll be used to generate `population` and `contact_matrix_set` if unprovided. Either `country` or `population` and `contact_matrix_set` must be provided.
`population`	Population vector (for each age group). Default = NULL, which will cause population to be sourced from `country`
`dt`	Time Step. Default = 0.1
`...`	Further aguments for the model parameter function. If using the `explicit_model` (default) this will be `parameters_explicit_SEEIR`.