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Synthetic dataset of stochastic SIR system with known Rt

Source: R/data.R
stochastic_sir_rt.Rd

A dataset from Gostic, Katelyn M., et al. "Practical considerations for measuring the effective reproductive number, Rt." PLoS Computational Biology 16.12 (2020): e1008409. The data are simulated from a stochastic SEIR compartmental model.

Usage

stochastic_sir_rt

Format

stochastic_sir_rt A data frame with 301 rows and 12 columns:

time

Timestep of the discrete-time stochastic SEIR simulation

date

Added from the original Gostic, 2020 dataset. A date corresponding to the assigned time. Arbitrarily starts on January 1st, 2023.

S, E, I, R

The realized state of the stochastic SEIR system

dS, dEI, DIR

The stochastic transition between compartments

incidence

The true incidence in the I compartment at time t

obs_cases

The observed number of cases at time t from forward-convolved incidence.

obs_incidence

Added from the original Gostic, 2020 dataset. The incidence column with added negative-binomial observation noise. Created with set.seed(123456) and the call rnbinom(299, mu = [["incidence"]], size = 10) Useful for testing.

true_r0

The initial R0 of the system (i.e., 2)

true_rt

The known, true Rt of the epidemic system

Source

https://github.com/cobeylab/Rt_estimation/tree/d9d8977ba8492ac1a3b8287d2f470b313bfb9f1d # nolint https://github.com/CDCgov/cfa-epinow2-pipeline/pull/17 # nolint

Details

This synthetic dataset has a number of desirable properties:

  1. The force of infection changes depending on the Rt, allowing for sudden changes in the Rt. This allows for modeling of sudden changes in infection dynamics, which might otherwise be difficult to capture. Rt estimation framework

  2. The realized Rt is known at each timepoint

  3. The dataset incorporates a simple generation interval and a reporting delay.

Gostic et al. benchmark the performance of a number of Rt estimation frameworks, providing practical guidance on how to use this dataset to evaluate Rt estimates.

In practice, we've found that the amount of observation noise in the incidence and/or observed cases is often undesirably low for testing. Many empirical datasets are much noisier. As a result, models built with these settings in mind can perform poorly on this dataset or fail to converge. We manually add observation noise with rnbinom(299, mu = stochastic_sir_rt[["obs_cases"]], size = 10) and the random seed 123456 and store it in the obs_incidence column.