Load add-on packages - deSolve - contains lsoda function - differential equation solver.

library (EpiModel)
## Loading required package: deSolve
## 
## Attaching package: 'deSolve'
## 
## The following object is masked from 'package:graphics':
## 
##     matplot
## 
## Loading required package: networkDynamic
## Loading required package: network
## network: Classes for Relational Data
## Version 1.13.0 created on 2015-08-31.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
##                     Mark S. Handcock, University of California -- Los Angeles
##                     David R. Hunter, Penn State University
##                     Martina Morris, University of Washington
##                     Skye Bender-deMoll, University of Washington
##  For citation information, type citation("network").
##  Type help("network-package") to get started.
## 
## 
## networkDynamic: version 0.8.0, created on 2015-09-14
## Copyright (c) 2015, Carter T. Butts, University of California -- Irvine
##                     Ayn Leslie-Cook, University of Washington
##                     Pavel N. Krivitsky, University of Wollongong
##                     Skye Bender-deMoll, University of Washington
##                     with contributions from
##                     Zack Almquist, University of California -- Irvine
##                     David R. Hunter, Penn State University
##                     Li Wang
##                     Kirk Li, University of Washington
##                     Steven M. Goodreau, University of Washington
##                     Jeffrey Horner
##                     Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").
## 
## Loading required package: tergm
## Loading required package: statnet.common
## Loading required package: ergm
## 
## ergm: version 3.4.0, created on 2015-06-16
## Copyright (c) 2015, Mark S. Handcock, University of California -- Los Angeles
##                     David R. Hunter, Penn State University
##                     Carter T. Butts, University of California -- Irvine
##                     Steven M. Goodreau, University of Washington
##                     Pavel N. Krivitsky, University of Wollongong
##                     Martina Morris, University of Washington
##                     with contributions from
##                     Li Wang
##                     Kirk Li, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm").
## 
## NOTE: If you use custom ERGM terms based on 'ergm.userterms'
## version prior to 3.1, you will need to perform a one-time update
## of the package boilerplate files (the files that you did not write
## or modify) from 'ergm.userterms' 3.1 or later. See
## help('eut-upgrade') for instructions.
## 
## 
## Attaching package: 'ergm'
## 
## The following objects are masked from 'package:network':
## 
##     as.edgelist, as.edgelist.matrix, as.edgelist.network
## Warning: replacing previous import by 'network::as.edgelist.network' when
## loading 'tergm'
## Warning: replacing previous import by 'network::as.edgelist.matrix' when
## loading 'tergm'
## Warning: replacing previous import by 'network::as.edgelist' when loading
## 'tergm'
## 
## tergm: version 3.3.0, created on 2015-06-14
## Copyright (c) 2015, Pavel N. Krivitsky, University of Wollongong
##                     Mark S. Handcock, University of California -- Los Angeles
##                     with contributions from
##                     David R. Hunter, Penn State University
##                     Steven M. Goodreau, University of Washington
##                     Martina Morris, University of Washington
##                     Nicole Bohme Carnegie, New York University
##                     Carter T. Butts, University of California -- Irvine
##                     Ayn Leslie-Cook, University of Washington
##                     Skye Bender-deMoll
##                     Li Wang
##                     Kirk Li, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("tergm").
## Warning: replacing previous import by 'network::as.edgelist.network' when
## loading 'EpiModel'
## Warning: replacing previous import by 'network::as.edgelist.matrix' when
## loading 'EpiModel'
## Warning: replacing previous import by 'network::as.edgelist' when loading
## 'EpiModel'
param <- param.dcm(inf.prob = 0.2, act.rate = 0.25)
init <- init.dcm(s.num = 500, i.num = 1)
control <- control.dcm(type = "SI", nsteps = 500)
mod <- dcm(param, init, control)
mod
## EpiModel Simulation
## =======================
## Model class: dcm
## 
## Simulation Summary
## -----------------------
## Model type: SI
## No. runs: 1
## No. time steps: 500
## No. groups: 1
## 
## Model Parameters
## -----------------------
## inf.prob = 0.2
## act.rate = 0.25
## 
## Model Output
## -----------------------
## Compartments: s.num i.num num
## Flows: si.flow
plot(mod)

summary(mod, at = 150)
## EpiModel Summary
## =======================
## Model class: dcm
## 
## Simulation Summary
## -----------------------
## Model type: SI
## No. runs: 1
## No. time steps:
## No. groups: 1
## 
## Model Statistics
## ------------------------------
## Time: 150     Run: 1 
## ------------------------------ 
##                 n    pct
## Suscept.  112.845  0.225
## Infect.   388.155  0.775
## Total     501.000  1.000
## S -> I      4.311     NA
## ------------------------------
param <- param.icm(inf.prob = 0.2, act.rate = 0.25)
init <- init.icm(s.num = 500, i.num = 1)
control <- control.icm(type = "SI", nsims = 10, nsteps = 300)
mod <- icm(param, init, control)
## 
## * Starting ICM Simulation
## Sim = 1/10
## Sim = 2/10
## Sim = 3/10
## Sim = 4/10
## Sim = 5/10
## Sim = 6/10
## Sim = 7/10
## Sim = 8/10
## Sim = 9/10
## Sim = 10/10
mod
## EpiModel Simulation
## =======================
## Model class: icm
## 
## Simulation Summary
## -----------------------
## Model type: SI
## No. simulations: 10
## No. time steps: 300
## No. groups: 1
## 
## Model Parameters
## -----------------------
## inf.prob = 0.2
## act.rate = 0.25
## 
## Model Output
## -----------------------
## Compartments: s.num i.num num
## Flows: si.flow
summary(mod, at = 125)
## EpiModel Summary
## =======================
## Model class: icm
## 
## Simulation Details
## -----------------------
## Model type: SI
## No. simulations: 10
## No. time steps: 300
## No. groups: 1
## 
## Model Statistics
## ------------------------------
## Time: 125 
## ------------------------------ 
##            mean      sd    pct
## Suscept.  289.3  63.540  0.577
## Infect.   211.7  63.540  0.423
## Total     501.0   0.000  1.000
## S -> I      5.6   2.875     NA
## ------------------------------
head(as.data.frame(mod, out = "mean"))
##   time s.num i.num num si.flow
## 1    1 500.0   1.0 501     0.0
## 2    2 500.0   1.0 501     0.0
## 3    3 499.9   1.1 501     0.1
## 4    4 499.8   1.2 501     0.1
## 5    5 499.7   1.3 501     0.1
## 6    6 499.7   1.3 501     0.0
tail(as.data.frame(mod, out = "vals", sim = 1))
##     time s.num i.num num si.flow
## 295  295     1   500 501       0
## 296  296     1   500 501       0
## 297  297     1   500 501       0
## 298  298     1   500 501       0
## 299  299     1   500 501       0
## 300  300     1   500 501       0
plot(mod)

plot(mod, y = "i.num", sim.lines = TRUE, mean.smooth = FALSE, qnts.smooth = FALSE)