Package index • EpiSewer

EpiSewer

sewer_data(): Specify observation data

sewer_assumptions(): Specify modeling assumptions

EpiSewer(): Estimate epidemiological parameters from wastewater measurements

Measurements module

Modeling functions for the measurement process

model_measurements(): Model the measurement process

concentrations_observe(): Observe concentration measurements

noise_estimate(): Estimate measurement noise

noise_estimate_constant_var(): Estimate measurement noise with constant variance

noise_estimate_dPCR(): Estimate measurement noise for digital PCR data

LOD_assume(): Assume a limit of detection

LOD_estimate_dPCR(): Estimate a limit of detection model for digital PCR data

LOD_none(): Do not model a limit of detection

Sampling module

Modeling functions for the sampling process

model_sampling(): Model the sampling process

outliers_estimate(): Model outliers via an extreme value distribution

outliers_none(): Do not model outliers

sample_effects_estimate_matrix(): Estimate sample effects using a design matrix

sample_effects_estimate_weekday(): Estimate weekday sample effects

sample_effects_none(): Do not model sample effects

Sewage module

Modeling functions for the sewage process

model_sewage(): Model the sewage process

flows_assume(): Assume a constant wastewater flow

flows_observe(): Observe wastewater flows

residence_dist_assume(): Assume a sewer residence time distribution

Shedding module

Modeling functions for the shedding process

model_shedding(): Model the shedding process

shedding_dist_assume(): Assume a shedding load distribution

incubation_dist_assume(): Assume an incubation period distribution

load_per_case_assume(): Assume the average load per case

load_per_case_calibrate(): Calibrate the average load per case using case count data

load_variation_estimate(): Estimate individual-level load variation

load_variation_none(): Do not model individual-level load variation

Infections module

Modeling functions for the infection process

model_infections(): Model the infection process

R_estimate_splines(): Estimate Rt via smoothing splines

R_estimate_rw(): Estimate Rt via a random walk

R_estimate_ets(): Estimate Rt via exponential smoothing

R_estimate_approx(): Estimate Rt using an approximation of the generative renewal model

generation_dist_assume(): Assume a generation time distribution

seeding_estimate_constant(): Estimate constant seeding infections

seeding_estimate_rw(): Estimate seeding infections using a random walk model

infection_noise_estimate(): Estimate infection noise

infection_noise_none(): Do not model infection noise

Forecast module

Modeling functions for specifying forecasts

model_forecast(): Forecasting module

horizon_assume(): Specify the forecast horizon

horizon_none(): Do not produce forecasts

Model fitting

Functions to specify and run the sampling of EpiSewer models

set_fit_opts(): Configure the model fitting

sampler_stan_mcmc(): Use the stan MCMC sampler

model_stan_opts(): Specify details of the stan model

set_results_opts(): Configure results returned after model fitting

sewer_compile(): Compile EpiSewer models

run(): Fit an EpiSewer model.

Plotting

Functions for plotting

plot_R(): Plot the effective reproduction number

plot_growth_report(): Plot a growth report

plot_growth_rate(): Plot the epidemic growth rate

plot_doubling_time(): Plot the epidemic doubling time

plot_infections(): Plot infections

plot_load(): Plot the estimated load

plot_concentration(): Plot predicted concentration

plot_prior_posterior(): Visually compare prior and posterior of a model parameter

plot_LOD(): Plot limit of detection

plot_sample_effects(): Plot estimated sample effects

Specification

Helper functions for specifying distributions and other assumptions

get_discrete_gamma(): Get PMF of a discretized Gamma distribution

get_discrete_gamma_shifted(): Get PMF of a discretized shifted Gamma distribution

get_discrete_lognormal(): Get PMF of a discretized lognormal distribution

suggest_load_per_case(): Suggest load per case assumption using wastewater data and case numbers

Preprocessing

Functions for preprocessing data

mark_outlier_spikes_median() experimental: Mark outlier spikes in a measurement time series

Utilities

Utility functions

component_functions(): Get a list of modeling functions for a component

modeldata_init(): Construct an unspecified EpiSewer model

get_checksums(): Get checksums that uniquely identify an EpiSewer job.

summarize_fit(): Summarize parameters of interest