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Dose-response model function for listeriosis

DR.Rd

The DR() 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, the EFSA dose-response models or the model developed within this project (EFSAMV,EFSAV,EFSALV) (see References).

Usage

DR(
  Dose,
  model = "JEMRA",
  population = 1,
  Poisson = FALSE,
  method = "integrate",
  ...
)

Arguments

Dose

average dose in CFU/serving assuming a Poisson distribution from serving to serving (scalar or vector)

model

either JEMRA, Pouillot, Fritsch, EFSA, EFSAMV,EFSAV or EFSALV

population

considered population (scalar or vector) (see below)

Poisson

if TRUE, assume that Dose is the mean of a Poisson distribution. (actual LogNormal Poisson). If FALSE (default), assume that Dose is the actual number of bacteria.

method

either integrate or cubature to specify the integration method.

...

further arguments to pass to the DRLogNormPoisson() function

Value

A vector of size Dose (if population is a scalar) or a matrix of dimension (length of the Dose vector times length of the population vector)

Details

For the Pouillot, Fritsch, EFSA EFSAMV,EFSAV or EFSALV model, it integrates a log10Normal Poisson distribution Pouillot et al. (2015) . or a log10Normal-binomial distribution. For the JEMRA model an exponential dose-response model or a binomial model is used.

This function is slow. Use DRQuick() in production.

ModelPopulationCharacteristics
JEMRA0Marginal over subpopulations
JEMRA1Healthy population
JEMRA2Increased susceptibility
Pouillot0Marginal over subpopulations
Pouillot1Less than 65 years old
Pouillot2More than 65 years old
Pouillot3Pregnancy
Pouillot4Nonhematological Cancer
Pouillot5Hematological cancer
Pouillot6Renal or Liver failure
Pouillot7Solid organ transplant
Pouillot8Inflammatory diseases
Pouillot9HIV/AIDS
Pouillot10Diabetes
Pouillot11Hear diseases
Fritsch0Marginal over subpopulations
Fritsch1Highly virulent
Fritsch2Medium virulent
Fritsch3Hypovirulent
EFSA-EFSALV-EFSAV-EFSAMV0Marginal over subpopulations
EFSA-EFSALV-EFSAV-EFSAMV1Female 1-4 yo
EFSA-EFSALV-EFSAV-EFSAMV2Male 1-4 yo
EFSA-EFSALV-EFSAV-EFSAMV3Female 5-14 yo
EFSA-EFSALV-EFSAV-EFSAMV4Male 5-14 yo
EFSA-EFSALV-EFSAV-EFSAMV5Female 15-24 yo
EFSA-EFSALV-EFSAV-EFSAMV6Male 15-24 yo
EFSA-EFSALV-EFSAV-EFSAMV7Female 25-44 yo
EFSA-EFSALV-EFSAV-EFSAMV8Male 25-44 yo
EFSA-EFSALV-EFSAV-EFSAMV9Female 45-64 yo
EFSA-EFSALV-EFSAV-EFSAMV10Male 45-64 yo
EFSA-EFSALV-EFSAV-EFSAMV11Female 65-74 yo
EFSA-EFSALV-EFSAV-EFSAMV12Male 65-74 yo
EFSA-EFSALV-EFSAV-EFSAMV13Female >75 yo
EFSA-EFSALV-EFSAV-EFSAMV14Male >75 yo

See the parameters in the JEMRA report.

method = "cubature" will use the hcubature function that is much slower but guarantees a tolerance of \(1E-5\).

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 .

Author

Regis Pouillot

Examples

DR(5:10, "Pouillot", 1:11)
#>      Less than 65 years old, no known underlying condition
#> [1,]                                          4.077694e-11
#> [2,]                                          4.893216e-11
#> [3,]                                          5.708732e-11
#> [4,]                                          6.524243e-11
#> [5,]                                          7.339749e-11
#> [6,]                                          8.155250e-11
#>      More than 65 years old, no known underlying condition    Pregnancy
#> [1,]                                          7.768332e-10 1.046297e-08
#> [2,]                                          9.321587e-10 1.255169e-08
#> [3,]                                          1.087472e-09 1.463939e-08
#> [4,]                                          1.242774e-09 1.672614e-08
#> [5,]                                          1.398066e-09 1.881197e-08
#> [6,]                                          1.553346e-09 2.089694e-08
#>      Nonhematological Cancer Hematological cancer Renal or Liver failure
#> [1,]            4.074294e-09         4.989462e-08           1.443598e-08
#> [2,]            4.888381e-09         5.982261e-08           1.731652e-08
#> [3,]            5.702257e-09         6.973809e-08           2.019534e-08
#> [4,]            6.515934e-09         7.964192e-08           2.307254e-08
#> [5,]            7.329420e-09         8.953481e-08           2.594821e-08
#> [6,]            8.142723e-09         9.941735e-08           2.882241e-08
#>      Solid organ transplant Inflammatory diseases     HIV/AIDS     Diabetes
#> [1,]           1.619420e-08          4.365476e-09 3.389244e-09 3.893770e-10
#> [2,]           1.942499e-08          5.237701e-09 4.066531e-09 4.672406e-10
#> [3,]           2.265369e-08          6.109690e-09 4.743665e-09 5.451009e-10
#> [4,]           2.588045e-08          6.981455e-09 5.420653e-09 6.229577e-10
#> [5,]           2.910535e-08          7.853005e-09 6.097502e-09 7.008114e-10
#> [6,]           3.232850e-08          8.724350e-09 6.774215e-09 7.786619e-10
#>      Hear diseases
#> [1,]  2.632604e-10
#> [2,]  3.159067e-10
#> [3,]  3.685514e-10
#> [4,]  4.211944e-10
#> [5,]  4.738358e-10
#> [6,]  5.264757e-10
DR(5:10, "Pouillot", 0)
#>              [,1]
#> [1,] 6.210723e-10
#> [2,] 7.450437e-10
#> [3,] 8.689530e-10
#> [4,] 9.928042e-10
#> [5,] 1.116600e-09
#> [6,] 1.240344e-09