ears-c1's Introduction

EARS-C1

Implementation of EARS-C1 algorithm in Julia. To run just include the function in your code:

#Setup in Julia
include("EARS_C1.jl")

and run the function over any list of cases observed at the same time step:

#Running in Julia
cases = [1,2,1,0,0,1,2,3,1,1,0,0,0,1,2,1,1,1,1,0,0,0,1,1]
detected = EARS_C1(cases, initial_date = 1)

You can also run the algorithm from R using JuliaCall:

#Setup in R
library(JuliaCall)
julia <- julia_setup()
julia_command('include("EARS_C1.jl")')

and then call the function:

cases    <- rpois(100,20)                #Simulate cases
julcases <- julia_eval("EARS_C1")(cases) #Evaluate function

The algorithm is faster than current implementation in the surveillance package:

library(surveillance)
library(microbenchmark)
#Comparación de tiempos
mb <- microbenchmark(
  cases    <- rpois(100, 20),
  julcases <- julia_eval("EARS_C1")(cases),
  surv     <- earsC(as(ts(cases, start = 1),"sts"), 
                    control = list(method = "C1", alpha = 0.05, baseline = 7, minSigma = 0)),
  times = 1000
)

With results 10 times faster:

expr	min	lq	mean	median	uq	max	neval
cases <- rpois(100, 20)	12.356	18.329	24.32397	23.7460	26.8080	99.146	1000
julcases <- julia_eval("EARS_C1")(cases)	425.427	573.103	766.03114	654.5255	749.8775	53798.048	1000
surv <- earsC(as(ts(cases, start = 1), "sts"), control = list(method = "C1", alpha = 0.05, baseline = 7, minSigma = 0))	4400.425	5103.316	6045.99175	5582.3825	6040.5920	45565.106	1000

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