Parameters

epiparameter

Purpose: Classes and helper functions for working with epidemiological parameters.

Key Functions:

• epiparameter_db(): Load parameters from library (disease, epi_name, author filters)
• epiparameter(): Create custom parameter object
• parameter_tbl(): View parameters as table
• plot(): Visualize parameter distribution
• convert_params_to_summary_stats(): Convert distribution parameters to mean/SD
• convert_summary_stats_to_params(): Convert mean/SD to distribution parameters
• extract_param(): Extract parameters from summary statistics

Supported Distributions: gamma, lognormal, Weibull, normal, negative binomial, geometric

epiparameterDB

Purpose: Database of epidemiological parameters extracted from literature.

Access: Via epiparameter::epiparameter_db() (recommended) or direct JSON access

Coverage: 23 diseases, 125+ parameter sets including incubation periods, serial intervals, generation times, onset-to-hospitalization, onset-to-death

Online Database: View at https://epiverse-trace.github.io/epiparameter/articles/database.html

Typical Workflow

Finding Parameters

# Search by disease and parameter type
param <- epiparameter_db(
  disease = "Ebola Virus Disease",
  epi_name = "incubation period",
  single_epiparameter = TRUE
)

# View all available parameters
all_params <- epiparameter_db()
parameter_tbl(all_params)

Using Parameters

# Extract distribution function
dist_func <- function(x) dgamma(x, shape = param$shape, scale = param$scale)

# Use in analysis
cfr_static(data, delay_density = dist_func)

Creating Custom Parameters

# When parameter not in library
custom_param <- epiparameter(
  disease = "COVID-19",
  epi_name = "onset to hospitalization",
  prob_distribution = create_prob_distribution(
    prob_distribution = "lnorm",
    prob_distribution_params = c(meanlog = 1.5, sdlog = 0.5)
  )
)

Parameter Conversion

# Convert summary statistics to distribution parameters
params <- convert_summary_stats_to_params(
  mean = 10,
  sd = 5,
  distribution = "gamma"
)

Common Parameter Types

• Incubation period: Time from infection to symptom onset
• Serial interval: Time between symptom onset in infector and infectee
• Generation time: Time between infection in infector and infectee
• Onset to hospitalization: Time from symptoms to hospital admission
• Onset to death: Time from symptoms to death
• Hospitalisation to death: Time from admission to death
• Offspring distribution: Number of secondary infections per case

Best Practices

• Always cite the source of parameters (use get_citation())
• Check parameter applicability (population, setting, time period)
• Use single_epiparameter = TRUE when expecting one result
• Visualize distributions with plot() to verify plausibility
• Document parameter choices in analysis reports
• Consider uncertainty in parameter estimates

Contributing Parameters

To add parameters to the library:

1. Use the public Google Sheet
2. Or submit PR to epiparameterDB

Integration with Other Skills

• simulation: Use parameters to simulate realistic outbreaks
• analysis: Use delay distributions for CFR correction
• reporting: Document parameter sources and justifications