Dose-response modelling

1. Load the required R packages

library(kwb.qmra)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(knitr)
library(ggplot2)
library(ggrepel)

2. Dose-response database from QMRAwiki

2.1 Download

dr.db <- kwb.qmra::dr.db_download()
## Rows: 37 Columns: 18
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (12): Agent, Best fit model*, Optimized parameter(s), Host type, Agent s...
## dbl  (6): LD50/ID50, # of doses, k, alpha, N50, PathogenID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2.2 Visualise

Database:

dr.db2 <- dr.db %>% dplyr::select(PathogenGroup,
                         PathogenName, 
                        `Agent strain`,
                        `Best fit model*`,
                        `Optimized parameter(s)`, 
                        `LD50/ID50`,
                        `Host type`,
                         Route,
                        `Dose units`,
                         Response, 
                         Reference)  %>% 
                dplyr::arrange(PathogenGroup, PathogenName) 

caption <- "Table 1: Best-fit dose-response parameters ([QMRAwiki, 2016](http://qmrawiki.canr.msu.edu/index.php/?title=Table_of_Recommended_Best-Fit_Parameters))"
knitr::kable(dr.db2,caption = caption )
Table 1: Best-fit dose-response parameters (QMRAwiki, 2016)
PathogenGroup PathogenName Agent strain Best fit model* Optimized parameter(s) LD50/ID50 Host type Route Dose units Response Reference
Bacteria Bacillus anthracis Vollum exponential k = 1.65E-05 4.20e+04 guinea pig inhalation spores death Druett 1953
Bacteria Burkholderia pseudomallei KHW,316c beta-Poisson a = 3.28E-01 , N50 = 5.43E+03 5.43e+03 C57BL/6 mice and diabetic rat intranasal,intraperitoneal CFU death Liu, Koo et al. 2002 and Brett and Woods 1996
Bacteria Campylobacter jejuni and Campylobacter coli strain A3249 beta-Poisson a= 1.44E-01 , N50 = 8.9E+02 8.90e+02 human oral (in milk) CFU infection Black et al 1988
Bacteria Coxiella burnetii phase I Ohio beta-Poisson a= 3.57E-01 , N50 = 4.93E+08 4.93e+08 C57BL/1OScN mice intraperitoneal PFU death Williams et al, 1982
Bacteria Escherichia coli EIEC 1624 beta-Poisson a = 1.55E-01 , N50 = 2.11E+06 2.11e+06 human oral (in milk) CFU positive stool isolation DuPont et al. (1971)
Bacteria Escherichia coli enterohemorrhagic (EHEC) EHEC O157:H7, strain 86-24 exponential k=2.18E-04 3.18e+03 pig oral (in food) CFU shedding in feces Cornick & Helgerson (2004)
Bacteria Francisella tularensis SCHU S-4 exponential k = 4.73E-02 1.46e+01 monkeys inhalation CFU death Day and Berendt, 1972
Bacteria Legionella pneumophila Philadelphia 1 exponential k = 5.99E-02 1.16e+01 guinea pig inhalation CFU infection Muller et al. (1983)
Bacteria Listeria monocytogenes (Death as response) F5817 exponential k = 1.15E-05 6.05e+04 C57B1/6J mice oral CFU death Golnazarian, Donnelly et al. 1989
Bacteria Listeria monocytogenes (Infection) F5817 beta-Poisson a = 2.53E-01 , N50 = 2.77E+02 2.77e+02 C57Bl/6J mice oral CFU infection Golnazarian
Bacteria Listeria monocytogenes (Stillbirths) NA beta-Poisson a = 4.22E-02 , N50 = 1.78E+09 1.78e+09 pooled oral CFU stillbirths Smith, Williams2007
Bacteria Mycobacterium avium sub sp. Paratuberculosis Bovine exponential k = 6.93E-04 1.00e+03 deer oral CFU infection O’Brien et al(1976)
Bacteria Pseudomonas aeruginosa (Contact lens) NA beta-Poisson a = 1.9E-01 , N50 = 1.85E+04 1.85e+04 white rabbit contact lens CFU corneal ulceration Lawin-Brussel et al. (1993)
Bacteria Pseudomonas aeruginosa (bacterimia) ATCC 19660 exponential k = 1.05E-04 6.61e+03 Swiss webster mice (5day old) injected in eyelids CFU death Hazlett, Rosen et al. 1978
Bacteria Rickettsia rickettsi R1 and Sheila Smith beta-Poisson a= 7.77E-01 , N50 = 2.13E+01 2.13e+01 Pooled data NA CFU morbidity Saslaw and Carlisle 1966 and Dupont, Hornick et al. 1973
Bacteria Salmonella Typhi Quailes beta-Poisson a = 1.75E-01 , N50 = 1.11E+06 1.11e+06 human oral, in milk CFU disease Hornick et al. (1966),Hornick et al. (1970)
Bacteria Salmonella anatum strain I beta-Poisson a= 3.18E-01 , N50 = 3.71E+04 3.71e+04 human oral, with eggnog CFU positive stool culture McCullough and Elsele,1951
Bacteria Salmonella meleagridis strain I beta-Poisson a= 3.89E-01 , N50 = 1.68E+04 1.68e+04 human oral, with eggnog CFU infection McCullough and Eisele 1951,2
Bacteria Salmonella nontyphoid strain 216 and 219 beta-Poisson a= 2.1E-01 , N50 = 4.98E+01 4.98e+01 mice intraperitoneal CFU death Meynell and Meynell,1958
Bacteria Salmonella serotype newport Salmonella newport exponential k = 3.97E-06 1.74e+05 human oral CFU infection McCullough and Elsele,1951
Bacteria Shigella 2a (strain 2457T) beta-Poisson a= 2.65E-01 , N50 = 1.48E+03 1.48e+03 human oral (in milk) CFU positive stool isolation DuPont et al. (1972b)
Bacteria Staphylococcus aureus NA exponential k = 7.64E-08 9.08e+06 human subcutaneous CFU/cm2 infection Rose and Haas 1999
Bacteria Vibrio cholerae Inaba 569B beta-Poisson a= 2.50E-01 , N50 = 2.43E+02 2.43e+02 human oral (with NaHCO3) CFU infection Hornick et al., (1971)
Bacteria Yersinia pestis CO92 exponential k = 1.63E-03 4.26e+02 mice intranasal CFU death Lathem et al. 2005
Protozoa Cryptosporidium parvum and Cryptosporidium hominis TAMU isolate exponential k = 5.72E-02 1.21e+01 human oral oocysts infection Messner et al. 2001
Protozoa Endamoeba coli From an infected human beta-Poisson a = 1.01E-01 , N50 = 3.41E+02 3.41e+02 human oral Cysts infection Rendtorff 1954
Protozoa Giardia duodenalis From an infected human exponential k = 1.99E-02 3.48e+01 human oral Cysts infection Rendtorff 1954
Protozoa Naegleria fowleri LEE strain exponential k = 3.42E-07 2.03e+06 mice intravenous no of trophozoites death Adams et al. 1976 & Haggerty and John 1978
Viruses Adenovirus type 4 exponential k = 6.07E-01 1.14e+00 human inhalation TCID50 infection Couch, Cate et al. 1966
Viruses Echovirus strain 12 beta-Poisson a = 1.06E+00 , N50 = 9.22E+02 9.22e+02 human oral PFU infection Schiff et al.,1984
Viruses Enteroviruses porcine, PE7-05i exponential k = 3.74E-03 1.85e+02 pig oral PFU infection Cliver, 1981
Viruses Influenza H1N1,A/California/10/78 attenuated strain, H3N2,A/Washington/897/80 attenuated strain beta-Poisson a = 5.81E-01 , N50 =9.45E+05 9.45e+05 human intranasal TCID50 infection Murphy et al., 1984 & Murphy et al., 1985
Viruses Lassa virus Josiah strain exponential k = 2.95E+00 2.35e-01 guinea pig subcutaneous PFU death Jahrling et al., 1982
Viruses Poliovirus type 1,attenuated exponential k = 4.91E-01 1.41e+00 human oral (capsule) PD50 (mouse paralytic doses) alimentary infection Koprowski
Viruses Rhinovirus type 39 beta-Poisson a = 2.21E-01 , N50 = 1.81E+00 1.81e+00 human intranasal TCID50 doses infection Hendley et al., 1972
Viruses Rotavirus CJN strain (unpassaged) beta-Poisson a = 2.53E-01 , N50 = 6.17E+00 6.17e+00 human oral FFU infection Ward et al, 1986
Viruses SARS rSARS-CoV exponential k = 2.46E-03 2.82e+02 mice hACE-2 and A/J intranasal PFU death DeDiego et al., 2008 & De Albuquerque et al., 2006 
#DT::datatable(doseresponse, caption = caption)

Dose response for all microbial parameters:

dr.model <- kwb.qmra::dr.db_model(dr.db = dr.db)


ggplot( dr.model, aes(x = dose, 
                      y = infectionProbability, 
                      col = PathogenGroup)) + 
  geom_point() + 
  scale_x_log10() + 
  theme_bw()

Dose for all microbial parameters with 50% infection probability

## `summarise()` has grouped output by 'PathogenID', 'PathogenGroup'. You can
## override using the `.groups` argument.
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Figure 3: Dose for all microbial parameters with 50% infection probability

Figure 3: Dose for all microbial parameters with 50% infection probability