This function provides the marginal probability of invasive listeriosis
in a given population for a given Dose in CFU using the
JEMRA, the Pouillot, the Fritsch or the EFSA dose-response models
or the model developed within this project (EFSAMV,EFSAV,EFSALV) (see References).
Usage
DRForModel(data = list(), model = "JEMRA", population = 1, Poisson = FALSE)Arguments
- data
a list of (a minima):
N(
CFU) A matrix containing the numbers of L. monocytogenes per portion of defrosted or frozen vegetables.
- model
either
JEMRA,Pouillot,Fritsch,EFSA,EFSAMV,EFSAVorEFSALV- population
considered population (scalar).
- Poisson
if
TRUE, assume thatDoseis the mean of a Poisson distribution. (actual LogNormal Poisson). IfFALSE(default), assume thatDoseis the actual number of bacteria.
Value
the data object with added:
RiskA matrix of risk, of size similar to
NModelthe Model
Populationthe Population
Details
see doseresponsemodels::DR() or doseresponsemodels::DRQuick() for details on population and models.
Note
This function uses (for all model but JEMRA) a linear approximation (approxfun)
from the exact doseresponsemodels::DR() model evaluated on \(Dose = c(0,10^{seq(-5,12,length=1701)})\)
(if Poisson=TRUE) or \(c(0,10^{seq(0,12,length=2000)})\) (if Poisson=FALSE).
Any Dose lower or higher than these ranges will lead to NA.
References
EFSA (2018). “Scientific opinion on the Listeria monocytogenes contamination of ready-to-eat foods and the risk from human health in the EU.” EFSA Journal, 16(1), 5134. FAO-WHO (2004). “Risk assessment of Listeria monocytogenes in ready-to-eat foods: Technical report.” World Health Organization and Food and Agriculture Organization of the United Nations. Fritsch L, Guillier L, Augustin J (2018). “Next generation quantitative microbiological risk assessment: Refinement of the cold smoked salmon-related listeriosis risk model by integrating genomic data.” Microbial Risk Analysis, 10, 20–27. doi:10.1016/j.mran.2018.06.003 . Pouillot R, Hoelzer K, Chen Y, Dennis SB (2015). “Listeria monocytogenes dose response revisited–incorporating adjustments for variability in strain virulence and host susceptibility.” Risk Analysis, 35(1), 90–108. doi:10.1111/risa.12235 .
Examples
data <- list(N = matrix(10^runif(100, 0, 5), ncol = 25))
DRForModel(data, "JEMRA", 1)
#> $N
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 1008.798508 308.23854 23604.961899 102.716732 87.80244 4748.95434
#> [2,] 6.110014 28.10841 7.493818 9.513694 75463.88788 9.54517
#> [3,] 1.088923 4617.53223 1.483224 104.157510 28.14890 79927.89459
#> [4,] 214.766968 7287.98644 39.987905 2.081169 2465.48371 5100.44003
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 1.808137 1.432688 248.3835 7.974323 1615.834000 7053.00224
#> [2,] 447.777552 13.421859 144.8293 12.147849 2001.804892 89858.99590
#> [3,] 3013.834464 31.926700 3405.4083 2516.602293 3.020792 71220.41737
#> [4,] 2771.915691 1521.696615 55320.0905 312.052778 6729.779266 88.29046
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] 202.270395 293.89018 2.118754 708.312254 54804.93775 171.203384
#> [2,] 37.688492 7880.30687 59.024728 47.708461 515.70583 72.037050
#> [3,] 7.471013 10.49281 13365.866073 957.886261 528.46590 1.381354
#> [4,] 454.849481 3689.66040 23.393286 9.069642 24.71643 213.764683
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 89.157306 19293.80702 20.27014 100.832590 96682.011294 3902.55130
#> [2,] 1.259871 83.95538 28.20011 11.637603 5.561313 16.09052
#> [3,] 76.710469 436.09909 251.40613 2284.725761 391.546348 543.52164
#> [4,] 630.324517 1007.36676 39813.25916 1.963679 17005.925532 14928.25607
#> [,25]
#> [1,] 1.379686
#> [2,] 222.290805
#> [3,] 10675.793227
#> [4,] 11755.918060
#>
#> $Risk
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.385581e-11 7.289169e-12 5.582043e-10 2.429057e-12 2.076339e-12
#> [2,] 1.444400e-13 6.646905e-13 1.771916e-13 2.249312e-13 1.784551e-09
#> [3,] 2.575717e-14 1.091942e-10 3.508305e-14 2.463141e-12 6.656897e-13
#> [4,] 5.078715e-12 1.723445e-10 9.455770e-13 4.918288e-14 5.830314e-11
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 1.123021e-10 4.274359e-14 3.386180e-14 5.873746e-12 1.886269e-13
#> [2,] 2.257083e-13 1.058897e-11 3.174128e-13 3.424927e-12 2.872147e-13
#> [3,] 1.890115e-09 7.127043e-11 7.549517e-13 8.053025e-11 5.951195e-11
#> [4,] 1.206140e-10 6.554957e-11 3.598466e-11 1.308196e-09 7.379319e-12
#> [,11] [,12] [,13] [,14] [,15]
#> [1,] 3.821088e-11 1.667876e-10 4.783285e-12 6.949885e-12 5.007106e-14
#> [2,] 4.733813e-11 2.124963e-09 8.912870e-13 1.863515e-10 1.395772e-12
#> [3,] 7.138734e-14 1.684203e-09 1.766365e-13 2.481348e-13 3.160726e-10
#> [4,] 1.591441e-10 2.087885e-12 1.075617e-11 8.725221e-11 5.532241e-13
#> [,16] [,17] [,18] [,19] [,20]
#> [1,] 1.675005e-11 1.296014e-09 4.048539e-12 2.108425e-12 4.562551e-10
#> [2,] 1.128209e-12 1.219524e-11 1.703526e-12 2.975398e-14 1.985301e-12
#> [3,] 2.265188e-11 1.249700e-11 3.264056e-14 1.813993e-12 1.031275e-11
#> [4,] 2.144951e-13 5.845324e-13 5.055067e-12 1.490574e-11 2.382194e-11
#> [,21] [,22] [,23] [,24] [,25]
#> [1,] 4.793943e-13 2.384426e-12 2.286312e-09 9.228651e-11 3.264056e-14
#> [2,] 6.669110e-13 2.752243e-13 1.315614e-13 3.804734e-13 5.256684e-12
#> [3,] 5.945244e-12 5.402867e-11 9.259149e-12 1.285305e-11 2.524585e-10
#> [4,] 9.414940e-10 4.640732e-14 4.021519e-10 3.530197e-10 2.780011e-10
#>
#> $lotMeanRisk
#> [1] 2.034299e-10 1.674120e-10 1.838876e-10 1.621413e-10
#>
#> $servingRisk
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.385581e-11 7.289169e-12 5.582043e-10 2.429057e-12 2.076339e-12
#> [2,] 1.444400e-13 6.646905e-13 1.771916e-13 2.249312e-13 1.784551e-09
#> [3,] 2.575717e-14 1.091942e-10 3.508305e-14 2.463141e-12 6.656897e-13
#> [4,] 5.078715e-12 1.723445e-10 9.455770e-13 4.918288e-14 5.830314e-11
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 1.123021e-10 4.274359e-14 3.386180e-14 5.873746e-12 1.886269e-13
#> [2,] 2.257083e-13 1.058897e-11 3.174128e-13 3.424927e-12 2.872147e-13
#> [3,] 1.890115e-09 7.127043e-11 7.549517e-13 8.053025e-11 5.951195e-11
#> [4,] 1.206140e-10 6.554957e-11 3.598466e-11 1.308196e-09 7.379319e-12
#> [,11] [,12] [,13] [,14] [,15]
#> [1,] 3.821088e-11 1.667876e-10 4.783285e-12 6.949885e-12 5.007106e-14
#> [2,] 4.733813e-11 2.124963e-09 8.912870e-13 1.863515e-10 1.395772e-12
#> [3,] 7.138734e-14 1.684203e-09 1.766365e-13 2.481348e-13 3.160726e-10
#> [4,] 1.591441e-10 2.087885e-12 1.075617e-11 8.725221e-11 5.532241e-13
#> [,16] [,17] [,18] [,19] [,20]
#> [1,] 1.675005e-11 1.296014e-09 4.048539e-12 2.108425e-12 4.562551e-10
#> [2,] 1.128209e-12 1.219524e-11 1.703526e-12 2.975398e-14 1.985301e-12
#> [3,] 2.265188e-11 1.249700e-11 3.264056e-14 1.813993e-12 1.031275e-11
#> [4,] 2.144951e-13 5.845324e-13 5.055067e-12 1.490574e-11 2.382194e-11
#> [,21] [,22] [,23] [,24] [,25]
#> [1,] 4.793943e-13 2.384426e-12 2.286312e-09 9.228651e-11 3.264056e-14
#> [2,] 6.669110e-13 2.752243e-13 1.315614e-13 3.804734e-13 5.256684e-12
#> [3,] 5.945244e-12 5.402867e-11 9.259149e-12 1.285305e-11 2.524585e-10
#> [4,] 9.414940e-10 4.640732e-14 4.021519e-10 3.530197e-10 2.780011e-10
#>
#> $Model
#> [1] "JEMRA"
#>
#> $Population
#> [1] 1
#>
DRForModel(data, "EFSAMV", 10)
#> $N
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 1008.798508 308.23854 23604.961899 102.716732 87.80244 4748.95434
#> [2,] 6.110014 28.10841 7.493818 9.513694 75463.88788 9.54517
#> [3,] 1.088923 4617.53223 1.483224 104.157510 28.14890 79927.89459
#> [4,] 214.766968 7287.98644 39.987905 2.081169 2465.48371 5100.44003
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 1.808137 1.432688 248.3835 7.974323 1615.834000 7053.00224
#> [2,] 447.777552 13.421859 144.8293 12.147849 2001.804892 89858.99590
#> [3,] 3013.834464 31.926700 3405.4083 2516.602293 3.020792 71220.41737
#> [4,] 2771.915691 1521.696615 55320.0905 312.052778 6729.779266 88.29046
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] 202.270395 293.89018 2.118754 708.312254 54804.93775 171.203384
#> [2,] 37.688492 7880.30687 59.024728 47.708461 515.70583 72.037050
#> [3,] 7.471013 10.49281 13365.866073 957.886261 528.46590 1.381354
#> [4,] 454.849481 3689.66040 23.393286 9.069642 24.71643 213.764683
#> [,19] [,20] [,21] [,22] [,23] [,24]
#> [1,] 89.157306 19293.80702 20.27014 100.832590 96682.011294 3902.55130
#> [2,] 1.259871 83.95538 28.20011 11.637603 5.561313 16.09052
#> [3,] 76.710469 436.09909 251.40613 2284.725761 391.546348 543.52164
#> [4,] 630.324517 1007.36676 39813.25916 1.963679 17005.925532 14928.25607
#> [,25]
#> [1,] 1.379686
#> [2,] 222.290805
#> [3,] 10675.793227
#> [4,] 11755.918060
#>
#> $Risk
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.438654e-08 7.451311e-09 5.706194e-07 2.483059e-09 2.122523e-09
#> [2,] 1.477026e-10 6.794884e-10 1.811544e-10 2.299826e-10 1.824218e-06
#> [3,] 2.632346e-11 1.116234e-07 3.585523e-11 2.517888e-09 6.804673e-10
#> [4,] 5.191744e-09 1.761784e-07 9.666616e-10 5.030986e-11 5.960019e-08
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 1.148004e-07 4.370963e-11 3.463358e-11 6.004385e-09 1.927701e-10
#> [2,] 2.307435e-10 1.082451e-08 3.244580e-10 3.501080e-09 2.936603e-10
#> [3,] 1.932126e-06 7.285592e-08 7.717912e-10 8.232175e-08 6.083592e-08
#> [4,] 1.232971e-07 6.700782e-08 3.678525e-08 1.337281e-06 7.543516e-09
#> [,11] [,12] [,13] [,14] [,15]
#> [1,] 3.906091e-08 1.704979e-07 4.889654e-09 7.104456e-09 5.121844e-11
#> [2,] 4.839130e-08 2.172189e-06 9.110759e-10 1.904970e-07 1.426855e-09
#> [3,] 7.302416e-11 1.721641e-06 1.806031e-10 2.536515e-10 3.231033e-07
#> [4,] 1.626844e-07 2.134320e-09 1.099546e-08 8.919321e-08 5.655057e-10
#> [,16] [,17] [,18] [,19] [,20]
#> [1,] 1.712263e-08 1.324828e-06 4.138644e-09 2.155275e-09 4.664033e-07
#> [2,] 1.153297e-09 1.246659e-08 1.741413e-09 3.045593e-11 2.029525e-09
#> [3,] 2.315579e-08 1.277505e-08 3.339263e-11 1.854387e-09 1.054219e-08
#> [4,] 2.192482e-10 5.974913e-10 5.167515e-09 1.523737e-08 2.435193e-08
#> [,21] [,22] [,23] [,24] [,25]
#> [1,] 4.900073e-10 2.437512e-09 2.337120e-06 9.433959e-08 3.335231e-11
#> [2,] 6.817052e-10 2.813257e-10 1.344383e-10 3.889698e-10 5.373624e-09
#> [3,] 6.077453e-09 5.523058e-08 9.465181e-09 1.313901e-08 2.580743e-07
#> [4,] 9.624301e-07 4.746969e-11 4.110969e-07 3.608721e-07 2.841849e-07
#>
#> $lotMeanRisk
#> [1] 2.079524e-07 1.711331e-07 1.879758e-07 1.657472e-07
#>
#> $servingRisk
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2.438654e-08 7.451311e-09 5.706194e-07 2.483059e-09 2.122523e-09
#> [2,] 1.477026e-10 6.794884e-10 1.811544e-10 2.299826e-10 1.824218e-06
#> [3,] 2.632346e-11 1.116234e-07 3.585523e-11 2.517888e-09 6.804673e-10
#> [4,] 5.191744e-09 1.761784e-07 9.666616e-10 5.030986e-11 5.960019e-08
#> [,6] [,7] [,8] [,9] [,10]
#> [1,] 1.148004e-07 4.370963e-11 3.463358e-11 6.004385e-09 1.927701e-10
#> [2,] 2.307435e-10 1.082451e-08 3.244580e-10 3.501080e-09 2.936603e-10
#> [3,] 1.932126e-06 7.285592e-08 7.717912e-10 8.232175e-08 6.083592e-08
#> [4,] 1.232971e-07 6.700782e-08 3.678525e-08 1.337281e-06 7.543516e-09
#> [,11] [,12] [,13] [,14] [,15]
#> [1,] 3.906091e-08 1.704979e-07 4.889654e-09 7.104456e-09 5.121844e-11
#> [2,] 4.839130e-08 2.172189e-06 9.110759e-10 1.904970e-07 1.426855e-09
#> [3,] 7.302416e-11 1.721641e-06 1.806031e-10 2.536515e-10 3.231033e-07
#> [4,] 1.626844e-07 2.134320e-09 1.099546e-08 8.919321e-08 5.655057e-10
#> [,16] [,17] [,18] [,19] [,20]
#> [1,] 1.712263e-08 1.324828e-06 4.138644e-09 2.155275e-09 4.664033e-07
#> [2,] 1.153297e-09 1.246659e-08 1.741413e-09 3.045593e-11 2.029525e-09
#> [3,] 2.315579e-08 1.277505e-08 3.339263e-11 1.854387e-09 1.054219e-08
#> [4,] 2.192482e-10 5.974913e-10 5.167515e-09 1.523737e-08 2.435193e-08
#> [,21] [,22] [,23] [,24] [,25]
#> [1,] 4.900073e-10 2.437512e-09 2.337120e-06 9.433959e-08 3.335231e-11
#> [2,] 6.817052e-10 2.813257e-10 1.344383e-10 3.889698e-10 5.373624e-09
#> [3,] 6.077453e-09 5.523058e-08 9.465181e-09 1.313901e-08 2.580743e-07
#> [4,] 9.624301e-07 4.746969e-11 4.110969e-07 3.608721e-07 2.841849e-07
#>
#> $Model
#> [1] "EFSAMV"
#>
#> $Population
#> [1] 10
#>