Package index • EpiSewer

EpiSewer

sewer_data(): Specify observation data
sewer_assumptions(): Specify modeling assumptions
EpiSewer(): Estimate epidemiological parameters from wastewater measurements

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

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

Modeling functions for the sewage process

Modeling functions for the shedding process

model_shedding(): Model the shedding process
shedding_dist_assume(): Assume a shedding load distribution
shedding_dist_estimate(): Estimate an uncertain 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

Modeling functions for the infection process

model_infections(): Model the infection process
R_estimate_gp(): Estimate Rt using Gaussian processes
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_piecewise(): Estimate Rt via piecewise constant changepoint model
R_estimate_smooth_derivative(): Estimate Rt with a smooth derivative
R_estimate_changepoint_splines(): Estimate Rt via a changepoint spline model
R_estimate_approx() deprecated: 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_growth(): Estimate seeding infections with a time-varying growth rate
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

Modeling functions for specifying forecasts

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
sampler_stan_pathfinder(): Use stan's pathfinder variational inference algorithm
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.

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

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
qexpgamma(): Exponential-Gamma distribution

Functions for preprocessing data

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

Utility functions