Dose-response model function for listeriosis
DR.RdThe 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 the "WHO Risk assessment of Listeria monocytogenes in foods" project (EFSAMV, EFSAV, EFSALV) (see References).
Arguments
- Dose
average dose in
CFU/servingassuming a Poisson distribution from serving to serving (scalar or vector) or exact dose ifPoissonisTRUE- model
either
JEMRA,Pouillot,Fritsch,EFSA,EFSAMV,EFSAVorEFSALV- population
considered population (scalar or vector) (see below)
- Poisson
if
TRUE, assume thatDoseis the mean of a Poisson distribution. (actual LogNormal Poisson). IfFALSE(default), assume thatDoseis the actual number of bacteria.- method
either
integrateorcubatureto 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.
The marginal dose-Response over all sub-populations (population: 0) is obtained by weighting the estimated probabilities according to the proportion of each sub-populations as provided by (FAO-WHO 2004) (82.5% healthy, 17.5% with increased susceptibility), Pouillot et al. (2015) , (their Table I, based on frequency of each sub-populations in France), (Fritsch et al. 2018) , and (EFSA 2018) (their Table 2, based on the number of eating occasions per sub-population).
This function is slow. Use DRQuick() in production.
| Model | Population | Characteristics |
| JEMRA | 0 | Marginal over subpopulations |
| JEMRA | 1 | Healthy population |
| JEMRA | 2 | Increased susceptibility |
| Pouillot | 0 | Marginal over subpopulations |
| Pouillot | 1 | Less than 65 years old |
| Pouillot | 2 | More than 65 years old |
| Pouillot | 3 | Pregnancy |
| Pouillot | 4 | Nonhematological Cancer |
| Pouillot | 5 | Hematological cancer |
| Pouillot | 6 | Renal or Liver failure |
| Pouillot | 7 | Solid organ transplant |
| Pouillot | 8 | Inflammatory diseases |
| Pouillot | 9 | HIV/AIDS |
| Pouillot | 10 | Diabetes |
| Pouillot | 11 | Hear diseases |
| Fritsch | 0 | Marginal over virulence |
| Fritsch | 1 | Highly virulent |
| Fritsch | 2 | Medium virulent |
| Fritsch | 3 | Hypovirulent |
| EFSA-EFSALV-EFSAV-EFSAMV | 0 | Marginal over subpopulations |
| EFSA-EFSALV-EFSAV-EFSAMV | 1 | Female 1-4 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 2 | Male 1-4 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 3 | Female 5-14 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 4 | Male 5-14 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 5 | Female 15-24 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 6 | Male 15-24 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 7 | Female 25-44 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 8 | Male 25-44 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 9 | Female 45-64 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 10 | Male 45-64 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 11 | Female 65-74 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 12 | Male 65-74 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 13 | Female >75 yo |
| EFSA-EFSALV-EFSAV-EFSAMV | 14 | Male >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 .
c
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)
#> Marginal over subpopulations
#> [1,] 6.210723e-10
#> [2,] 7.450437e-10
#> [3,] 8.689530e-10
#> [4,] 9.928042e-10
#> [5,] 1.116600e-09
#> [6,] 1.240344e-09