Revision 1 R files

R Analysis/README - Tylosin Model R Analysis.md

@@ -17,7 +17,7 @@ scripts:
 
 "TYL sensitivity analysis.R": a sensitivity analysis of the TYL model outputs (from TYL no intervention scenario) to model parameters. Figure 5 saved to "figures/". Returns realized range of model parameters to "results/param_range.xlsx".
 
-
+"TYL intervention vs CON intervention prop res cow_LG Pen.R": analyzing the proportion of resistant enterococci in cattle, comparing TYL intervention groups to CON intervention groups, in the model scenario with a large pen size (N=150 cattle). Also analyzes the concentration of TYL in the large intestine. Does not assess validation because the validation studies use a small pen size. Descriptive results are saved to "results/tyl intestine concentrations LGpen.txt", "results/TYL effect LGpen.txt". LG Pen versions of Figures 2 and 4 are created and saved to "figures/".
 
 --------------------------------
 $platform

R Analysis/results/TYL effect LGpen.txt

@@ -0,0 +1,131 @@
+[1] "TYL_NI, median. Min, max over time and end of simulation"
+[1] 0.0497
+[1] 0.08
+[1] 0.0786
+[1] "TYL_NI, 5th percentile. Min, max over time and end of simulation"
+[1] 0.0016
+[1] 0.0022
+[1] 0.0016
+[1] "TYL_NI, 95th percentile. Min, max over time and end of simulation"
+[1] 0.463
+[1] 0.9919
+[1] 0.9919
+[1] ""
+[1] "change over time in TYL_NI simulations"
+[1] "% with decrease"
+[1] 0.377
+[1] "maximum decrease"
+[1] -0.1538
+[1] "% with increase"
+[1] 0.623
+[1] "% with minimal increase"
+[1] 0.317
+[1] "% with moderate increases"
+[1] 0.165
+[1] "% with substantial increase"
+[1] 0.141
+[1] ""
+[1] "CON NI change over time"
+[1] "% with decrease"
+[1] 0.812
+[1] "maximum decrease"
+[1] -0.2072
+[1] "% with minimal increase"
+[1] 0.087
+[1] "% with moderate increases"
+[1] 0.057
+[1] "% with substantial increase"
+[1] 0.044
+[1] ""
+[1] "difference between TYL and CON at the end of the feeding period"
+[1] "% with decrease"
+[1] 0.048
+[1] "largest decrease"
+[1] 0
+[1] "the largest decrease is 0 percentage points. so 5% of simulations had no difference between TYL and CON"
+[1] "% with minimal increase"
+[1] 0.707
+[1] "% with minimal increase or decrease"
+[1] 0.755
+[1] "% with moderate increase"
+[1] 0.159
+[1] "% with substantial increase"
+[1] 0.086
+[1] "median difference"
+        50%
+3433 0.0125
+[1] ""
+[1] "difference between TYL and CON at the end of the feeding period for different interventions"
+[1] "RWT"
+[1] "% with minimal increase or decrease"
+[1] 0.791
+[1] "% with moderate increase"
+[1] 0.14
+[1] "% with substantial increase"
+[1] 0.069
+[1] "DFM"
+[1] "% with minimal increase or decrease"
+[1] 0.752
+[1] "% with moderate increase"
+[1] 0.162
+[1] "% with substantial increase"
+[1] 0.086
+[1] "AFTP"
+[1] "% with minimal increase or decrease"
+[1] 0.809
+[1] "% with moderate increase"
+[1] 0.144
+[1] "% with substantial increase"
+[1] 0.047
+[1] ""
+[1] "time and magnitude of intervention impact in TYL effect"
+[1] "AFTP"
+`summarise()` ungrouping output (override with `.groups` argument)
+# A tibble: 7 x 9
+  variable     d113      max     min    d143 day.max day.min d143.reduction lrgst.reduction
+  <fct>       <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>          <dbl>           <dbl>
+1 X1.      0        0        0       0          113     113        0               0       
+2 X5.      0.000100 0.000100 0       0          113.    113.       0.000100        0.000100
+3 X25.     0.00210  0.00210  0.00120 0.00120    113     139        0.000900        0.000900
+4 X50.     0.0115   0.0115   0.00760 0.0076     113     142.       0.00385         0.00385 
+5 X75.     0.0816   0.0816   0.0548  0.0548     113     143.       0.0268          0.0268  
+6 X95.     0.650    0.650    0.486   0.486      113     143.       0.164           0.164   
+7 X99.     0.947    0.947    0.802   0.802      113     143.       0.146           0.146   
+[1] "RWT"
+`summarise()` ungrouping output (override with `.groups` argument)
+# A tibble: 7 x 9
+  variable     d113      max     min    d143 day.max day.min d143.reduction lrgst.reduction
+  <fct>       <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>          <dbl>           <dbl>
+1 X1.      0        0        0       0          113     113        0               0       
+2 X5.      0.000100 0.000100 0       0          115.    118.       0.000100        0.000100
+3 X25.     0.00210  0.00210  0.00160 0.00160    114.    143.       0.000500        0.0005  
+4 X50.     0.0115   0.0115   0.0100  0.0100     114.    143.       0.0014          0.0014  
+5 X75.     0.0816   0.0820   0.0672  0.0672     114.    143.       0.0143          0.0144  
+6 X95.     0.650    0.653    0.601   0.601      114.    143.       0.0486          0.0486  
+7 X99.     0.947    0.948    0.923   0.923      114.    143.       0.0243          0.0243  
+[1] ""
+[1] "DFM vs NI. They are essentially the same for proportion resistant"
+[1] "DFM"
+`summarise()` ungrouping output (override with `.groups` argument)
+# A tibble: 7 x 2
+  variable        v
+  <fct>       <dbl>
+1 X1.      0       
+2 X5.      0.000100
+3 X25.     0.00210 
+4 X50.     0.0124  
+5 X75.     0.0973  
+6 X95.     0.732   
+7 X99.     0.964   
+[1] "NI"
+`summarise()` ungrouping output (override with `.groups` argument)
+# A tibble: 7 x 2
+  variable        v
+  <fct>       <dbl>
+1 X1.      0       
+2 X5.      0.000100
+3 X25.     0.00207 
+4 X50.     0.0125  
+5 X75.     0.0935  
+6 X95.     0.734   
+7 X99.     0.962   

R Analysis/results/TYL effect on enterococci concentrations.txt

@@ -23,11 +23,6 @@
 3432 5.095095
           50%
 3432 5.095901
-[1] "ALL, median for TYL, CON at end of treatment"
-          50%
-3432 5.095636
-          50%
-3432 5.096027
 [1] "NI, median for TYL, CON at end of treatment"
           50%
 3432 5.286565

R Analysis/results/TYL effect.txt

@@ -72,31 +72,30 @@
 [1] 0.086
 [1] "AFTP"
 [1] "% with minimal increase or decrease"
-[1] 0.775
-[1] "% with moderate increase"
-[1] 0.146
-[1] "% with substantial increase"
-[1] 0.079
-[1] "#ALL"
-[1] "% with minimal increase or decrease"
 [1] 0.809
 [1] "% with moderate increase"
 [1] 0.144
 [1] "% with substantial increase"
 [1] 0.047
+[1] ""  [1] "time and magnitude of intervention impact in TYL effect"
+[1] "AFTP"
+[1] "RWT"
 [1] ""
+[1] "DFM vs NI. They are essentially the same for proportion resistant"
+[1] "DFM"
+[1] "NI"
 [1] "time and magnitude of intervention impact in TYL effect"
 [1] "AFTP"
-# A tibble: 7 x 9
-  variable     d113     max    min     d143 day.max day.min d143.reduction lrgst.reduction
-  <fct>       <dbl>   <dbl>  <dbl>    <dbl>   <dbl>   <dbl>          <dbl>           <dbl>
-1 X1.      0        0       0      0           113     113        0.              0       
-2 X5.      0.000100 0.0001  0      0.000100    125.    113.      -9.49e-19        0.000100
-3 X25.     0.00210  0.00210 0.0017 0.0017      113     115.       4.00e- 4        0.000400
-4 X50.     0.0115   0.0115  0.0097 0.0101      113     123.       1.35e- 3        0.00175 
-5 X75.     0.0816   0.0816  0.0689 0.0761      113     118.       5.43e- 3        0.0126  
-6 X95.     0.650    0.667   0.557  0.667       143.    119.      -1.70e- 2        0.0931  
-7 X99.     0.947    0.947   0.852  0.918       113     116.       2.92e- 2        0.0955  
+# A tibble: 7 x 11
+  variable     d113      max     min    d143   d120 day.max day.min d143.reduction week.reduction lrgst.reduction
+  <fct>       <dbl>    <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>          <dbl>          <dbl>           <dbl>
+1 X1.      0        0        0       0       0         113     113        0              0               0       
+2 X5.      0.000100 0.000100 0       0       0         113.    113.       0.000100       0.000100        0.000100
+3 X25.     0.00210  0.00210  0.00120 0.00120 0.0014    113     139        0.000900       0.000700        0.000900
+4 X50.     0.0115   0.0115   0.00760 0.0076  0.0088    113     142.       0.00385        0.00265         0.00385 
+5 X75.     0.0816   0.0816   0.0548  0.0548  0.0633    113     143.       0.0268         0.0182          0.0268  
+6 X95.     0.650    0.650    0.486   0.486   0.515     113     143.       0.164          0.135           0.164   
+7 X99.     0.947    0.947    0.802   0.802   0.824     113     143.       0.146          0.123           0.146   
 [1] "RWT"
 # A tibble: 7 x 9
   variable     d113      max     min    d143 day.max day.min d143.reduction lrgst.reduction
@@ -108,17 +107,6 @@
 5 X75.     0.0816   0.0820   0.0672  0.0672     114.    143.       0.0143          0.0144  
 6 X95.     0.650    0.653    0.601   0.601      114.    143.       0.0486          0.0486  
 7 X99.     0.947    0.948    0.923   0.923      114.    143.       0.0243          0.0243  
-[1] "ALL"
-# A tibble: 7 x 11
-  variable     d113      max     min    d143   d120 day.max day.min d143.reduction week.reduction lrgst.reduction
-  <fct>       <dbl>    <dbl>   <dbl>   <dbl>  <dbl>   <dbl>   <dbl>          <dbl>          <dbl>           <dbl>
-1 X1.      0        0        0       0       0         113     113        0              0               0       
-2 X5.      0.000100 0.000100 0       0       0         114.    114.       0.000100       0.000100        0.000100
-3 X25.     0.0021   0.0021   0.00120 0.0012  0.0014    113     140.       0.000900       0.000700        0.0009  
-4 X50.     0.0115   0.0115   0.0076  0.00770 0.0089    113     142.       0.00385        0.00265         0.00395 
-5 X75.     0.0810   0.0810   0.0573  0.0573  0.0637    113     143.       0.0237         0.0174          0.0237  
-6 X95.     0.651    0.651    0.484   0.484   0.513     113     143.       0.166          0.137           0.167   
-7 X99.     0.946    0.946    0.797   0.801   0.824     113     138.       0.145          0.122           0.150   
 [1] ""
 [1] "DFM vs NI. They are essentially the same for proportion resistant"
 [1] "DFM"

R Analysis/results/intervention effects.txt

@@ -21,4 +21,4 @@
 [1] ""
 [1] "AFTP, TYL"
 [1] "% with minimal increase or decrease"
-[1] 0.944
+[1] 0.807

R Analysis/results/tyl intestine concentrations LGpen.txt

@@ -0,0 +1,14 @@
+[1] "descriptive results (median, 5th, 95th percentiles) for peak (max) and end of treatment (index 3433)"
+[1] "median, peak and end"
+[1] 0.6
+[1] 0.4
+[1] ""
+[1] "5th percentile, peak and end"
+[1] 0.25
+[1] 0.17
+[1] ""
+[1] "95th percentile, peak and end"
+[1] 1.13
+[1] 0.75
+[1] "maximum tyl conc still substantially lowr than intermediate MIC (16 ug/mL)"
+[1] 1.6695

R Analysis/scripts/Intervention vs NI prop res cow.R

@@ -34,7 +34,7 @@ lcolor=c("black", "#F8766D", "#7CAE00", "#619CFF", "#7CAE00", "#F8766D", "black"
 #Take the difference between individual simulations because parameter values are the same for each simulation (due to random number seed)
 
 #automate loading data and calculating differences
-TYLdatafiles<-c("TYL_NI_", "TYL_RWT_", "TYL_NI_", "TYL_AFTP_", "TYL_NI_", "TYL_DFM_", "TYL_NI_", "TYL_ALL_") #control (TYL_NI) followed by TYL intervention group.
+TYLdatafiles<-c("TYL_NI_", "TYL_RWT_", "TYL_NI_", "TYL_AFTP_", "TYL_NI_", "TYL_DFM_") #control (TYL_NI) followed by TYL intervention group.
 
 #load in proportion resistant
 for (i in seq(1,length(TYLdatafiles),2)){
@@ -54,7 +54,7 @@ rm(tyl.NI.cow, tyl.I.cow, diff.cow, list, label)
 
 #difference between CON_INT and CON_NI. Note that there is no CON_RWT because TYL can't be withdrawn in CON scenario
 #automate loading data and calculating differences
-CONdatafiles<-c("NoTYL_NI_", "NoTYL_AFTP_", "NoTYL_NI_", "NoTYL_DFM_", "NoTYL_NI_", "NoTYL_ALL_")
+CONdatafiles<-c("NoTYL_NI_", "NoTYL_AFTP_", "NoTYL_NI_", "NoTYL_DFM_")
 
 #load in proportion resistant
 for (i in seq(1,length(CONdatafiles),2)){
@@ -75,7 +75,7 @@ rm(con.NI.cow, con.I.cow, diff.cow, list, label)
 
 #difference between TYL_INT and CON_NI.
 #automate loading data and calculating differences
-datafiles<-c("NoTYL_NI_", "TYL_RWT_", "NoTYL_NI_", "TYL_AFTP_", "NoTYL_NI_", "TYL_DFM_", "NoTYL_NI_", "TYL_ALL_") #control (NoTYL_NI) followed by TYL intervention group.
+datafiles<-c("NoTYL_NI_", "TYL_RWT_", "NoTYL_NI_", "TYL_AFTP_", "NoTYL_NI_", "TYL_DFM_") #control (NoTYL_NI) followed by TYL intervention group.
 
 #load in proportion resistant
 for (i in seq(1,length(datafiles),2)){
@@ -96,7 +96,7 @@ rm(con.NI.cow, tyl.I.cow, diff.cow, list, label)
 
 
 #plotting
-scenarios <- c("RWT", "DFM", "AFTP", "ALL")
+scenarios <- c("RWT", "DFM", "AFTP")
 #create figure of Prop res in cow for all scenarios
 for (i in 1:length(scenarios)){
   TYLdata <- get(paste("TYL_", scenarios[i], "_Prop_res", sep=""))
@@ -146,14 +146,11 @@ grid.arrange(TYLplot.cow.Pr.RWT+
                xlab("")+
                ggtitle("B"), 
              TYLplot.cow.Pr.AFTP+
-               ylab("Difference in Proportion Resistant")+
-               geom_vline(aes(xintercept=113), linetype="longdash", size=1.25)+
-               ggtitle("D"), 
-             TYLplot.cow.Pr.ALL+
                ylab("Difference in Proportion Resistant")+
                xlab("Days")+
                geom_vline(aes(xintercept=113), linetype="longdash", size=1.25)+
-               ggtitle("F"), ncol=1),
+               ggtitle("D"), 
+             ncol=1),
 grid.arrange(ggplot()+
                theme_bw()+
                theme(panel.border=element_blank())+
@@ -164,17 +161,14 @@ grid.arrange(ggplot()+
                xlab("")+
                ggtitle("C"), 
              CONplot.cow.Pr.AFTP+
-               ylab("")+
-               geom_vline(aes(xintercept=113), linetype="longdash", size=1.25)+
-               ggtitle("E"), 
-             CONplot.cow.Pr.ALL+
                ylab("")+
                xlab("Days")+
                geom_vline(aes(xintercept=113), linetype="longdash", size=1.25)+
-               ggtitle("G"), ncol=1),
+               ggtitle("E"),
+             ncol=1),
 ncol=2),
 width=6*2,
-height=5*4,
+height=5*3,
 units="in",
 dpi=320)