Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters
Clean Nowcasts for a Supplied Date
Clean Regions
Collapse nonparametric distributions in a <dist_spec>
Construct Output
Convert mean and sd to log mean for a log normal distribution
Convert mean and sd to log standard deviation for a log normal distrib...
Internal function for converting parameters to natural parameters.
Create Initial Conditions Generating Function
Fit a Stan Model using an approximate method
Fit a Stan Model using the NUTs sampler
Fix the parameters of a <dist_spec>
Forecast infections from a given fit and trajectory of the time-varyin...
Get a Literature Distribution for the Generation Time
Get a Literature Distribution for the Incubation Period
Get parameters of a parametric distribution
Get the probability mass function of a nonparametric distribution
Get a Single Raw Result
Internal function for generating a dist_spec given parameters and a ...
Plot method for estimate_truncation
Plot EpiNow2 Credible Intervals
Returns the standard deviation of one or more delay distribution
Stan Variational Bayes Options
Summarise rt and cases
Summarise Real-time Results
Summary output from epinow
Create delay variables for stan
Adds a day of the week vector
Adjust from Case Counts by Infection Date to Date of Report
Allocate Delays into Required Stan Format
Allocate Empty Parameters to a List
Applies a threshold to all nonparametric distributions in a <dist_spec...
Back Calculation Options
Fit a Subsampled Bootstrap to Integer Values and Summarise Distributio...
Combines multiple delay distributions for further processing
Calculate Credible Interval
Calculate Credible Intervals
Calculate All Summary Measures
Calculate Summary Statistics
Validate data input
Validate probability distribution for passing to stan
Convolve and scale a time series
Copy Results From Dated Folder to Latest
Create Back Calculation Data
Create Clean Reported Cases
Create complete cases
Create initial conditions for delays
Construct the Required Future Rt assumption
Create Gaussian Process Data
Create Observation Model Settings
Create Time-varying Reproduction Number Data
Create Delay Shifted Cases
Create a List of Stan Arguments
Create Stan Data Required for estimate_infections
Delay Distribution Options
Discretise a <dist_spec>
Fit an Integer Adjusted Exponential, Gamma or Lognormal distributions
Distribution Skeleton
Specify a distribution.
Get the distribution of a dist_spec()
Probability distributions
Real-time Rt Estimation, Forecasting and Reporting
EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiologic...
Load and compile an EpiNow2 cmdstanr model
Load an EpiNow2 rstan model.
Return a stan model object for the appropriate backend
Estimate a Delay Distribution
Estimate Infections, the Time-Varying Reproduction Number and the Rate...
Estimate a Secondary Observation from a Primary Observation
Estimate Truncation of Observed Data
Estimate Cases by Report Date
Expose internal package stan functions in R
Extract Credible Intervals Present
Filter Options for a Target Region
Fit a model using the chosen backend.
Generate initial conditions from a Stan fit
Extract Samples for a Parameter from a Stan model
Extract Parameter Samples from a Stan Model
Extract parameter names
Extract all samples from a stan fit
Extract a single element of a composite <dist_spec>
Extract a Parameter Summary from a Stan Object
Extract Samples from a Parameter with a Single Dimension
Forecast Secondary Observations Given a Fit from estimate_secondary
Format Posterior Samples
Generate a Gamma Distribution Definition Based on Parameter Estimates
Generation Time Distribution Options
Get a Literature Distribution
Perform checks for <dist_spec> get_... functions
Get Combined Regional Results
Get Folders with Results
Get Regions with Most Reported Cases
Estimate seeding time from delays and generation time
Approximate Gaussian Process Settings
Convert Growth Rates to Reproduction numbers.
Generate initial conditions by fitting to cumulative cases
Generate a Log Normal Distribution Definition Based on Parameter Estim...
Get the lower bounds of the parameters of a distribution
Format Credible Intervals
Categorise the Probability of Change for Rt
Match User Supplied Arguments with Supported Options
Returns the maximum of one or more delay distribution
Returns the mean of one or more delay distribution
Get the names of the natural parameters of a distribution
Observation Model Options
Return an _opts List per Region
Plot PMF and CDF for a dist_spec object
Plot method for epinow
Plot method for estimate_infections
Plot method for estimate_secondary
Plot Estimates
Plot a Summary of the Latest Results
Creates a delay distribution as the sum of two other delay distributio...
Prints the parameters of one or more delay distributions
Process regional estimate
Process all Region Estimates
Convert Reproduction Numbers to Growth Rates
Provide Summary Statistics for Estimated Infections and Rt
Real-time Rt Estimation, Forecasting and Reporting by Region
Summarise Regional Runtimes
Regional Summary Output
Report case counts by date of report
Report plots
Rstan Options
Rstan Sampling Options
Rstan Variational Bayes Options
Time-Varying Reproduction Number Options
Run epinow with Regional Processing Code
Approximate Sampling a Distribution using Counts
Save Estimated Infections
Save Observed Data
Setup Target Folder for Saving
Secondary Reports Options
Set to Single Threading
Setup Default Logging
Convert to Data Table
Set up Future Backend
Setup Logging
Simulate infections using the renewal equation
Simulate secondary observations from primary observations
Stan Laplace algorithm Options
Stan Options
Stan pathfinder algorithm Options
Stan Sampling Options
Summary output from estimate_infections
Truncation Distribution Options
Updates Forecast Horizon Based on Input Data and Target
Update estimate_secondary default priors
Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.
Useful links