A Framework for Data-Driven Stochastic Disease Spread Simulations
Definition of the SIR model
Definition of the SEIR model
Set the select matrix for a SimInf_model object
Approximate Bayesian computation
Coerce to data frame
Coerce events to a data frame
Coerce to data frame
Box plot of number of individuals in each compartment
Extract the C code from a SimInf_model object
Run more generations of ABC SMC
Create a distance matrix between nodes for spatial models
Convert an edge list with properties to a matrix
Extract the events from a SimInf_model object
Example data to initialize events for the SEIR model
Example data to initialize events for the SIR model
Example data to initialize events for the SIS model
Example data to initialize events for the SISe model
Set a global data parameter for a SimInf_model object
Extract global data from a SimInf_model object
Extract individuals from SimInf_indiv_events
Determine in-degree for each node in a model
Extract the select matrix from a SimInf_model object
Individual events
Extract local data from a node
Log likelihood
Model parser to define new models to run in SimInf
Determine the number of generations
Determine the number of nodes in a model
Transform individual events to node events for a model
Determine out-degree for each node in a model
Create a package skeleton from a SimInf_model
Scatterplot of number of individuals in each compartment
Bootstrap particle filter
Display the ABC posterior distribution
Display the distribution of scheduled events over time
Display the distribution of individual events over time
Diagnostic plot of a particle filter object
Display the outcome from a simulated trajectory
Create an SEIR model
Calculate prevalence from a model object with trajectory data
Extract prevalence from running a particle filter
Generic function to calculate prevalence from trajectory data
Set a template for where to record result during a simulation
Run the SimInf stochastic simulation algorithm
Specify the number of threads that SimInf should use
Set the shift matrix for a SimInf_model object
Extract the shift matrix from a SimInf_model object
Print summary of a SimInf_abc object
Brief summary of SimInf_events
Print summary of a SimInf_indiv_events object
Brief summary of SimInf_model
Brief summary of a SimInf_pfilter object
A Framework for Data-Driven Stochastic Disease Spread Simulations
Class "SimInf_abc"
Class "SimInf_events"
Create a SimInf_events object
Class "SimInf_indiv_events"
Class "SimInf_model"
Create a SimInf_model
Class "SimInf_pfilter"
Create an SIR model
Definition of the SIS model
Create an SIS model
Definition of the SISe model
Create a SISe model
Definition of the SISe_sp model
Create a SISe_sp model
Definition of the SISe3 model
Create a SISe3 model
Definition of the SISe3_sp model
Create an SISe3_sp model
Detailed summary of a SimInf_abc object
Detailed summary of a SimInf_events object
Detailed summary of a SimInf_indiv_events object
Detailed summary of a SimInf_model object
Detailed summary of a SimInf_pfilter object
Extract data from a simulated trajectory
Extract filtered trajectory from running a particle filter
Generic function to extract data from a simulated trajectory
Update the initial compartment state u0 in each node
Get the initial compartment state
Example data to initialize the SEIR model
Example data to initialize the SIR model
Example data to initialize the SIS model
Example data to initialize the SISe model
Update the initial continuous state v0 in each node
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. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.