In KWB-R/kwb.qmra: QMRA (quantitative microbial risk assessment)

library(kwb.qmra)
library(dplyr)
dr.db <- dr.db_download()


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

library(knitr)
library(ggplot2)
library(ggrepel)
library(shiny)

Dose-response model database

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.db,caption = caption )
#DT::datatable(doseresponse, caption = caption)

Example plot

Play with the model parameters

inputPanel(sliderInput(inputId = "k", 
                       label = "K (exponential model)",
                       min = 1E-5,
                       max = 0.1, step = 0.00001,
                       value = 1E-3),
           sliderInput(inputId = "alpha", 
                       label = "alpha (beta-poission model)",
                       min =  0,
                       max = 1, step = 0.01,
                       value = 0.5),
          sliderInput(inputId = "N50", 
                       label = "log10(N50) (beta-poission model)",
                       min =  0.3,
                       max = 9.3, step = 0.1,
                       value = 4)
           )

p1 <- reactive(plyr::rbind.fill(dr.expo(k = as.numeric(input$k)),
                                dr.betapoisson(alpha = as.numeric(input$alpha),
                                               N50 = 10^(as.numeric(input$N50)))))

renderPlot({
   print(ggplot(p1(), aes(x = dose, 
                y = infectionProbability, 
                col = model)) + 
   geom_point() +
   scale_x_log10() + 
   theme_bw() + 
   ggtitle(sprintf("Expo (k = %1.5f), Beta-poisson (alpha = %1.2f, N50 = %10.0f)", as.numeric(input$k), 
                   as.numeric(input$alpha), 
                   10^as.numeric(input$N50))))
  })

Dose response for all microbial parameters

dr.db <- dr.db_download()
dr.model <- dr.db_model(dr.db = dr.db)


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

Dose for all microbial parameters with 50% infection probability

tt <- dr.model  %>%  
  filter(infectionProbability > 0.49,
         infectionProbability < 0.51) %>%  
  group_by(ID, Group, AgentName)  %>% 
  summarise(infectionProbability = round(median(infectionProbability),2), 
            dose = median(dose)) %>%  
  ungroup() %>% 
  dplyr::arrange(dose)

ggplot(tt, aes(Group, dose, col = Group)) + 
  geom_point(position = position_jitter(w = 0, h = 0)) + 
  geom_text_repel(aes(label = AgentName)) +
scale_y_log10() + 
  theme_bw() +
  guides(fill=FALSE) +
  ylab("Dose with 50% infection probability")