A flexible and efficient framework for data-driven stochastic disease spread simulations
The package provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models.
You can use one of the predefined compartment models in SimInf, for
example, SEIR. But you can also define a custom model 'on the fly'
using the model parser method mparse
. The method takes a character
vector of transitions in the form of X -> propensity -> Y
and
automatically generates the C and R code for the model. The left hand
side of the first arrow (->
) is the initial state, the right hand
side of the last arrow (->
) is the final state, and the propensity
is written between the two arrows. The flexibility of the mparse
approach allows for quick prototyping of new models or features. To
illustrate the mparse
functionality, let us consider an SIR model in
a closed population i.e., no births or deaths. Let beta
denote the
transmission rate of spread between a susceptible individual and an
infectious individual and gamma
the recovery rate from infection
(gamma
= 1 / average duration of infection). The model can be
described as:
library(SimInf)
transitions <- c("S -> beta*S*I/(S+I+R) -> I",
"I -> gamma*I -> R")
compartments <- c("S", "I", "R")
The transitions
and compartments
variables together with the
constants beta
and gamma
can now be used to generate a model with
mparse
. The model also needs to be initialised with the initial
condition u0
and tspan
, a vector of time points where the state of
the system is to be returned. Let us create a model that consists of
1000 replicates of a population, denoted a node in SimInf, that each
starts with 99 susceptibles, 5 infected and 0 recovered individuals.
n <- 1000
u0 <- data.frame(S = rep(99, n), I = rep(5, n), R = rep(0, n))
model <- mparse(transitions = transitions,
compartments = compartments,
gdata = c(beta = 0.16, gamma = 0.077),
u0 = u0,
tspan = 1:150)
To generate data from the model and then print some basic information about the outcome, run the following commands:
result <- run(model)
result
#> Model: SimInf_model
#> Number of nodes: 1000
#> Number of transitions: 2
#> Number of scheduled events: 0
#>
#> Global data
#> -----------
#> Parameter Value
#> beta 0.160
#> gamma 0.077
#>
#> Compartments
#> ------------
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> S 1.00 19.00 30.00 40.74 60.00 99.00
#> I 0.00 0.00 4.00 6.87 11.00 47.00
#> R 0.00 28.00 67.00 56.39 83.00 103.00
There are several functions in SimInf to facilitate analysis and
post-processing of simulated data, for example, trajectory
,
prevalence
and plot
. The default plot
will display the median
count in each compartment across nodes as a colored line together with
the inter-quartile range using the same color, but with transparency.
plot(result)
Most modeling and simulation studies require custom data analysis once
the simulation data has been generated. To support this, SimInf
provides the trajectory
method to obtain a data.frame
with the
number of individuals in each compartment at the time points specified
in tspan
. Below is the first 10 lines of the data.frame
with
simulated data.
trajectory(result)
#> node time S I R
#> 1 1 1 98 6 0
#> 2 2 1 98 6 0
#> 3 3 1 98 6 0
#> 4 4 1 99 5 0
#> 5 5 1 97 7 0
#> 6 6 1 98 5 1
#> 7 7 1 99 5 0
#> 8 8 1 99 5 0
#> 9 9 1 97 7 0
#> 10 10 1 97 6 1
...
Finally, let us use the prevalence
method to explore the proportion
of infected individuals across all nodes. It takes a model object and
a formula specification, where the left hand side of the formula
specifies the compartments representing cases i.e., have an attribute
or a disease and the right hand side of the formula specifies the
compartments at risk. Below is the first 10 lines of the data.frame
.
prevalence(result, I ~ S + I + R)
#> time prevalence
#> 1 1 0.05196154
#> 2 2 0.05605769
#> 3 3 0.06059615
#> 4 4 0.06516346
#> 5 5 0.06977885
#> 6 6 0.07390385
#> 7 7 0.07856731
#> 8 8 0.08311538
#> 9 9 0.08794231
#> 10 10 0.09321154
...
See the vignette to learn more about special features that the SimInf R package provides, for example, how to:
use continuous state variables
use the SimInf framework from another R package
incorporate available data such as births, deaths and movements as scheduled events at predefined time-points.
You can install the released version of SimInf
from
CRAN
install.packages("SimInf")
or use the remotes
package to install the development version from
GitHub
library(remotes)
install_github("stewid/SimInf")
We refer to section 3.1 in the vignette for detailed installation instructions.
In alphabetical order: Pavol Bauer , Robin Eriksson , Stefan Engblom , and Stefan Widgren (Maintainer)
Any suggestions, bug reports, forks and pull requests are appreciated. Get in touch.
SimInf is research software. To cite SimInf in publications, please use:
Widgren S, Bauer P, Eriksson R, Engblom S (2019) SimInf: An R Package for Data-Driven Stochastic Disease Spread Simulations. Journal of Statistical Software, 91(12), 1--42. doi: 10.18637/jss.v091.i12
Bauer P, Engblom S, Widgren S (2016) Fast event-based epidemiological simulations on national scales. International Journal of High Performance Computing Applications, 30(4), 438--453. doi: 10.1177/1094342016635723
This software has been made possible by support from the Swedish Research Council within the UPMARC Linnaeus center of Excellence (Pavol Bauer, Robin Eriksson, and Stefan Engblom), the Swedish Research Council Formas (Stefan Engblom and Stefan Widgren), the Swedish Board of Agriculture (Stefan Widgren), the Swedish strategic research program eSSENCE (Stefan Widgren), and in the framework of the Full Force project, supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme (Stefan Widgren).
The SimInf
package uses semantic versioning.
The SimInf
package is licensed under the
GPLv3.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.