A comprehensive inventory of all stochastic parameters that are estimated through Bayesian sampling in the MOSAIC cholera transmission modeling framework. This dataset contains metadata, categorization, and ordering information for parameters that have prior distributions and are part of the parameter estimation process.
Format
A data frame with 46 rows and 14 columns:
- parameter_name
Character. Parameter names as used in configuration files and sampling functions
- display_name
Character. Human-readable display names for plotting and reporting
- description
Character. Detailed descriptions of what each parameter represents
- units
Character. Units of measurement for each parameter
- distribution
Character. Prior distribution type (beta, gamma, lognormal, uniform, normal, gompertz, truncnorm, derived)
- scale
Character. Parameter scale: "global" (same value across all locations) or "location" (varies by location)
- category
Character. Biological/functional category: transmission, environmental, disease, immunity, surveillance, mobility, spatial, initial_conditions, seasonality
- order
Integer. Overall ordering for systematic presentation
- order_scale
Character. Scale-based ordering (01 = global, 02 = location)
- order_category
Character. Category-based ordering within each scale
- order_parameter
Character. Parameter-based ordering within each category
- posterior_distribution
Character. Fitted posterior distribution type for each parameter (NA when not yet estimated)
- posterior_lower
Numeric. Lower bound of the fitted posterior support (NA when not yet estimated)
- posterior_upper
Numeric. Upper bound of the fitted posterior support (NA when not yet estimated)
Details
This inventory focuses exclusively on estimated parameters - those with prior distributions that are sampled during Bayesian parameter estimation. It does not include:
Fixed model constants
Non-stochastic derived quantities
Intermediate calculations
Data inputs (observed cases, demographics, etc.)
The inventory is organized hierarchically:
Global parameters (26): Same value across all locations
Location-specific parameters (20): Vary by geographic location (including
alpha_1, per-location since priors_default v15.16)
The reported case fatality ratio mu_jt is not listed: it is not
sampled but integrated out of the deaths likelihood (see
calc_log_likelihood_deaths_integrated), and its calibrated value
is 3_results/posterior/cfr_posterior.csv.
Categories reflect biological processes in cholera transmission:
transmission: Population mixing, frequency dependence
environmental: V. cholerae survival, shedding, dose-response
disease: Recovery rates, incubation, symptom proportions
immunity: Natural and vaccine-induced protection
surveillance: Reporting rates, delays and the case-reporting PPV switch
mobility: Human movement parameters
spatial: Geographic covariates (WASH coverage)
initial_conditions: Starting compartment proportions
seasonality: Temporal transmission patterns
Usage
This dataset is used by:
sample_parametersfor Bayesian parameter samplingPlotting functions for consistent parameter ordering
Model validation and convergence diagnostics
Documentation and reporting functions
Data Sources
Parameter metadata compiled from:
priors_default- Prior distributionsconfig_default- Configuration templatesLiterature on cholera transmission modeling
Expert knowledge of epidemic processes
Examples
# Load the estimated parameters inventory
data(estimated_parameters)
# View parameter structure
str(estimated_parameters)
#> 'data.frame': 46 obs. of 14 variables:
#> $ parameter_name : chr "alpha_2" "decay_days_short" "decay_days_spread" "decay_days_long" ...
#> $ display_name : chr "Frequency-Driven Transmission" "Minimum V. cholerae Survival" "V. cholerae Survival Spread" "Maximum V. cholerae Survival (derived)" ...
#> $ description : chr "Degree of frequency driven transmission (0-1)" "Minimum V. cholerae survival time in environment" "Spread between min and max V. cholerae survival time (decay_days_long - decay_days_short)" "Maximum V. cholerae survival time in environment (DERIVED = decay_days_short + decay_days_spread)" ...
#> $ units : chr "proportion" "days" "days" "days" ...
#> $ distribution : chr "beta" "truncnorm" "truncnorm" "truncnorm" ...
#> $ scale : chr "global" "global" "global" "global" ...
#> $ category : chr "transmission" "environmental" "environmental" "environmental" ...
#> $ order : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ order_scale : chr "01" "01" "01" "01" ...
#> $ order_category : chr "01" "02" "02" "02" ...
#> $ order_parameter : chr "01" "01" "02" "03" ...
#> $ posterior_distribution: chr "beta" "truncnorm" "truncnorm" "truncnorm" ...
#> $ posterior_lower : num NA NA NA 1.01 NA NA NA NA NA NA ...
#> $ posterior_upper : num NA NA NA 425 NA NA NA NA NA NA ...
#> - attr(*, "creation_date")= Date[1:1], format: "2026-09-30"
#> - attr(*, "version")= chr "1.3.0"
#> - attr(*, "description")= chr "Comprehensive parameter inventory for MOSAIC cholera transmission model. Includes metadata, categorization, and"| __truncated__
# Global vs location-specific parameters
table(estimated_parameters$scale)
#>
#> global location
#> 26 20
# Parameters by biological category
table(estimated_parameters$category)
#>
#> disease environmental immunity initial_conditions
#> 4 13 5 6
#> mobility seasonality spatial surveillance
#> 3 4 1 6
#> transmission
#> 4
# Parameters by distribution type
table(estimated_parameters$distribution)
#>
#> beta gamma lognormal normal truncnorm
#> 18 4 10 5 9
# Transmission parameters
subset(estimated_parameters, category == "transmission")
#> parameter_name display_name
#> 1 alpha_2 Frequency-Driven Transmission
#> 33 alpha_1 Population Mixing
#> 34 beta_j0_tot Total Base Transmission Rate
#> 35 p_beta Human-to-Human Proportion
#> description units
#> 1 Degree of frequency driven transmission (0-1) proportion
#> 33 Population mixing within metapops (0-1, 1 = well-mixed) proportion
#> 34 Total base transmission rate (human + environmental) per day
#> 35 Proportion of total transmission that is human-to-human proportion
#> distribution scale category order order_scale order_category
#> 1 beta global transmission 1 01 01
#> 33 beta location transmission 33 02 02
#> 34 lognormal location transmission 34 02 02
#> 35 beta location transmission 35 02 02
#> order_parameter posterior_distribution posterior_lower posterior_upper
#> 1 01 beta NA NA
#> 33 01 beta NA NA
#> 34 02 lognormal NA NA
#> 35 03 beta NA NA
# Location-specific initial conditions
subset(estimated_parameters,
scale == "location" & category == "initial_conditions")
#> parameter_name display_name
#> 27 prop_S_initial Initial Susceptible Proportion
#> 28 prop_E_initial Initial Exposed Proportion
#> 29 prop_I_initial Initial Infected Proportion
#> 30 prop_R_initial Initial Recovered Proportion
#> 31 prop_V1_initial Initial One-Dose Vaccinated Proportion
#> 32 prop_V2_initial Initial Two-Dose Vaccinated Proportion
#> description
#> 27 Proportion of population susceptible at start (diagnostic reference: sample_parameters() sets S to the simplex residual of the sampled V1/V2/E/I/R, it does not draw this prior)
#> 28 Proportion of population exposed at start
#> 29 Proportion of population infected at start
#> 30 Proportion of population recovered/immune at start
#> 31 Proportion with one vaccine dose at start
#> 32 Proportion with two vaccine doses at start
#> units distribution scale category order order_scale
#> 27 proportion beta location initial_conditions 27 02
#> 28 proportion beta location initial_conditions 28 02
#> 29 proportion beta location initial_conditions 29 02
#> 30 proportion beta location initial_conditions 30 02
#> 31 proportion beta location initial_conditions 31 02
#> 32 proportion beta location initial_conditions 32 02
#> order_category order_parameter posterior_distribution posterior_lower
#> 27 01 01 beta NA
#> 28 01 02 beta NA
#> 29 01 03 beta NA
#> 30 01 04 beta NA
#> 31 01 05 beta NA
#> 32 01 06 beta NA
#> posterior_upper
#> 27 NA
#> 28 NA
#> 29 NA
#> 30 NA
#> 31 NA
#> 32 NA