Simulate Point-Source Epidemics
Simulation of epidemics which were introduced by point sources.
The basis of this programme is a combination of a Hidden Markov Model
(to get random timepoints for outbreaks) and a simple model
(compare sim.seasonalNoise
) to simulate the baseline.
sim.pointSource(p = 0.99, r = 0.01, length = 400, A = 1, alpha = 1, beta = 0, phi = 0, frequency = 1, state = NULL, K)
p |
probability to get a new outbreak at time i if there was one at time i-1, default 0.99. |
r |
probability to get no new outbreak at time i if there was none at time i-1, default 0.01. |
length |
number of weeks to model, default 400. |
A |
amplitude (range of sinus), default = 1. |
alpha |
parameter to move along the y-axis (negative values not allowed) with alpha > = A, default = 1. |
beta |
regression coefficient, default = 0. |
phi |
factor to create seasonal moves (moves the curve along the x-axis), default = 0. |
frequency |
factor to determine the oscillation-frequency, default = 1. |
state |
use a state chain to define the status at this timepoint (outbreak or not). If not given a Markov chain is generated by the programme, default NULL. |
K |
additional weigth for an outbreak which influences the distribution parameter mu, default = 0. |
a disProg
(disease progress) object including a list of the
observed, the state chain and nearly all input parameters.
M. Höhle, A. Riebler, C. Lang
set.seed(123) disProgObj <- sim.pointSource(p = 0.99, r = 0.5, length = 208, A = 1, alpha = 1, beta = 0, phi = 0, frequency = 1, state = NULL, K = 2) plot(disProgObj) ## with predefined state chain state <- rep(c(0,0,0,0,0,0,0,0,1,1), 20) disProgObj <- sim.pointSource(state = state, K = 1.2) plot(disProgObj) ## simulate epidemic, send to RKI 1 system, plot, and compute quality values testSim <- function (..., K = 0, range = 200:400) { disProgObj <- sim.pointSource(..., K = K) survResults <- algo.call(disProgObj, control = list(list(funcName = "rki1", range = range))) plot(survResults[[1]], "RKI 1", "Simulation") algo.compare(survResults) } testSim(K = 2) testSim(r = 0.5, K = 5) # larger and more frequent outbreaks
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