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Epidemiologinen malli
=== Laskenta ===
 
R-program code for the replcement model as in  File S1 of
Nurhonen M, Auranen K: Optimal serotype compositions for pneumococcal conjugate vaccination under serotype replacement [[http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003477]
 
 
==== Esimerkkejä ====
 
* VT: vaccine serotypes, i.e. pneumococcus serotypes that are found in a vaccine.
* NVT: non-vaccine serotypes, i.e. pneumococcus serotypes that are not found in the vaccine.
* [http://fi.opasnet.org/fi_wiki/index.php?title=Toiminnot:RTools&id=P6CXptUReneZ8YNh Example model run]
 
{{comment|# |Yritin selvittää kuinka alla oleva koodi toimii. Huomasin että vaihtaessa arvoja ajoon, niin vaikuttaisi siltä että  ajon tulokset eivät kuitenkaan muutu?  |--[[Käyttäjä:Jaakko|Jaakko]] ([[Keskustelu käyttäjästä:Jaakko|keskustelu]]) 5. kesäkuuta 2014 kello 14.13 (UTC)}}
{{comment|# |Taulukot vastaavat näköjään aina lähtömallia, joka on PCv13, mutta pylväskuviossa muutettu malli on mukana.| --[[Käyttäjä:Mnud|Markku N.]] ([[Keskustelu käyttäjästä:Mnud|keskustelu]]) 9. kesäkuuta 2014 kello 09.18 (UTC)}}
 
<rcode embed=1 graphics=1 variables="
name:q_user|description:Proportion of VT carriage removed by vaccination|default:1|
name:p_user|description:Proportion of VT carriage removed by vaccination that is replaced by NVT|default:1|
name:adultcarriers|description:The true amount of more than 5-year-old carriers is n times larger than estimated. What is n?|default:1|
name:servac_user|description:Serotypes in a vaccine to look at|type:checkbox|options:
'19F';19F;'23F';23F;'6B';6B;'14';14;'9V';9V;'4';4;'18C';18C;'1';1;'7';7;
'6A';6A;'19A';19A;'3';3;'8';8;'9N';9N;'10';10;'11';11;'12';12;'15';15;
'16';16;'20';20;'22';22;'23A';23;'33';33;'35';35;'38';38;'6C';6C;'Oth';Other|
default:'19F';'23F';'6B';'14';'9V';'4';'18C';'1';'7';'6A';'19A';'3'
">
library(OpasnetUtils)
library(ggplot2)
 
objects.latest("Op_fi4305", code_name = "alusta") # [[Pneumokokkirokote]]
objects.latest("Op_en6007", code_name = "answer") # [[OpasnetUtils/Drafts]]
 
openv.setN(100)
 
## Read the annual IPD and carriage incidence data.
## The 0 entries in IPD and carriage data are replaced by small values.
serotypes<-c(
"19F", "23F", "6B", "14", "9V", "4", "18C", "1", "7",
"6A", "19A", "3", "8", "9N", "10", "11", "12", "15",
"16", "20", "22", "23A", "33", "35", "38", "6C", "Oth")
car_under5<-c(
156030, 156030, 126990, 41200, 22290, 12830, 10130, 10, 14180,
54940, 24320, 12160, 1350, 20940, 4050, 72270, 10, 33100,
3380, 1350, 12160, 3380, 680, 30400, 4050, 27470, 24320 )
car_over5<-c(
168100, 314800, 256700, 209800, 114100, 62500, 200700, 100, 100,
158800, 54900, 30800, 8800, 8800, 20800, 97700, 100, 100,
191900, 25200, 72500, 22000, 100, 71300, 100, 79400, 330100 )
ipd_under5<-c(
7.78, 7.88, 24.39, 20.76, 2.91, 2.91, 6.64, 0.31, 3.02,
3.94, 9.88, 1.25, 0.10, 0.83, 0.41, 0.42, 0.21, 1.98,
0.21, 0.01, 0.93, 0.10, 0.42, 0.31, 0.42, 0.01, 0.73 )
ipd_over5<-c(
28.51, 53.72, 29.53, 99.43, 43.07, 76.99, 24.39, 6.58, 46.88,
17.42, 20.54, 55.04, 11.21, 25.20, 6.28, 12.76, 13.89, 9.18,
4.73, 3.29, 29.03, 4.40, 5.64, 12.41, 1.43, 5.50, 11.20 )
 
## Combine the data into 2 matrices of dimension 27*2:
IPD<-cbind(ipd_under5, ipd_over5)
Car<-cbind(car_under5, car_over5)
 
## Row numbers corresponding to the 3 different PCV formulations
## in matrices IPD and Car. Note: there is no serotype 5 in our data.
pcv7rows<-seq(7); pcv10rows<-seq(9); pcv13rows<-seq(12)
 
 
## Example S1.2A: Calculate the predicted incidence of IPD for the non-vaccine
## types(NVTs) under PCV13. The predictions are calculated separately for the
## two age classes. These are the values reported on the bottom panel in
## Figure 2 (there given as per 100K incidences).
postvacc <-Vaccination(IPD,Car,VT_rows=pcv13rows,p=1,q=1)
 
cat("Incidence of invasive pneumococcal disease after vaccination program (p=1, q=1).\n")
oprint(cbind(serotypes,postvacc[[1]]))
cat("Number of carriers after vaccination program.\n")
oprint(cbind(serotypes,postvacc[[2]]))
 
## Example S1.2B: Decrease in IPD incidence after adding a single new serotype
## to PCV13 separately for the two age categories.
next_under5<-NextVT(IPD[,1],Car[,1], VT_rows=pcv13rows,p=1)
next_over5 <-NextVT(IPD[,2],Car[,2], VT_rows=pcv13rows,p=1)
cat("Next serotype to add to a vaccine (p=1).\n")
oprint(rbind(serotypes, next_under5, next_over5))
# Nämä taulukot kannattaisi transposata niin näyttäisivät siistimmiltä.
 
## Example S1.3A: The optimal sequence for under 5 year olds when replacement is 100%.
## The output shows the decreases in IPD incidence for each step,
## corresponding to Figure 5(C). The last serotype (row 27, the category "Other")
## is excluded from any vaccine composition but is taken into account as a
## replacing serotype at each stage.
opt<-OptimalSequence(IPD[,1],Car[,1],VT_rows=0,Excluded_rows=27,p=1.0,HowmanyAdded=20)
 
cat("Optimal sequence of serotypes to add to a vaccine (p=1, HowmanyAdded=20).\n")
oprint(rbind(serotypes[opt[1,]],opt[2,]))
 
## Example S1.3B: The optimal sequence for the whole population when
## replacement is 50% and the current composition includes the PCV7 serotypes.
opt<-OptimalSequence(IPD,Car, VT_rows=pcv7rows,Excluded_rows=length(serotypes),
p=0.5,HowmanyAdded=17)
 
cat("Optimal sequence of serotypes to add to a vaccine (p=0.5, HownamyAdded=17).\n")
oprint(rbind(serotypes[opt[1,]],opt[2,]))
 
###################################
 
## Read the annual IPD and carriage incidence data.
## The 0 entries in IPD and carriage data are replaced by small values.
 
IPD <- Ovariable("IPD", ddata = "Op_fi4305.pneumokokki_vaestossa")
IPD@data <- IPD@data[IPD@data$Observation == "Incidence" , colnames(IPD@data) != "Observation"]
 
Car <- Ovariable("Car", ddata = "Op_fi4305.pneumokokki_vaestossa")
Car@data <- Car@data[Car@data$Observation == "Carrier" , colnames(Car@data) != "Observation"]
 
servac <- Ovariable("servac", data = data.frame(
Vaccine = rep(c("Current", "New"), each = length(serotypes)),
Serotype = serotypes,
Result = as.numeric(c(
serotypes %in% c("19F", "23F", "6B", "14", "9V", "4", "18C", "1", "7","6A", "19A", "3"),
serotypes %in% servac_user
))
))
 
p <- Ovariable("p", data = data.frame(Result = p_user))
q <- EvalOutput(Ovariable("q", data = data.frame(Result = q_user)))
# EvalOutput must be used because q is mentioned twice in the code and there will otherwise be a merge mismatch.
 
# The true number of adult carriers may actually be larger than estimated. This adjusts for that.
Car <- Car * Ovariable("adjust", data = data.frame(Age = c("Under 5", "Over 5"), Result = c(1, adultcarriers)))
 
VacCar <- EvalOutput(VacCar)
VacIPD <- EvalOutput(VacIPD)
 
cat("Number of carriers\n")
oprint(summary(VacCar))
cat("Incidence of invasive pneumococcal disease.\n")
oprint(summary(VacIPD))
 
if("Iter" %in% colnames(VacCar@output)) N <- max(VacCar@output$Iter) else N <- 1
 
ggplot(VacCar@output, aes(x = Serotype, weight = result(VacCar) / N, fill = Vaccine)) + geom_bar(position = "dodge") + theme_gray(base_size = 24) +
labs(title = "Carriers", y = "Number of carriers in Finland")
 
ggplot(VacIPD@output, aes(x = Serotype, weight = result(VacIPD) / N, fill = Vaccine)) + geom_bar(position = "dodge") + theme_gray(base_size = 24) +
labs(title = "Incidence of invasive pneumococcal disease", y = "Number of cases / 100000 py")
 
ggplot(VacIPD@output, aes(x = Vaccine, weight = result(VacIPD) / N, fill = Age)) + geom_bar(position = "stack") + theme_gray(base_size = 24) +
labs(title = "Incidence of invasive pneumococcal disease", y = "Number of cases / 100000 py")
 
</rcode>
 
=== Funktioiden alustus ===
 
<rcode name="alusta" label="Alusta funktiot" embed=1>
 
library(OpasnetUtils)
 
#S1.4. The R-functions
###############################################################################
##
## R code for the core methods introduced in
## Markku Nurhonen and Kari Auranen:
## "Optimal serotype compositions for pneumococcal conjugate
## vaccination under serotype replacement",
## PLoS Computational Biology, 2014.
##
###############################################################################
## List of arguments common to most functions:
##
## IPD = matrix of IPD incidences by age class (columns) and serotype (rows)
## Car = corresponding matrix of carriage incidences
## VT_rows = vector of the row numbers in matrices IPD and Car
## corresponding to vaccine types (VT_rows=0 for no vaccination)
## p = proportion of lost VT carriage which is replaced by NVT carriage
## q = proportion of VT carriage lost either due to elimination or replacement
##
## This code includes 4 functions:
## Vaccination, NextVT, OptimalSequence and OptimalVacc.
##
 
Vaccination<-function(IPD,Car,VT_rows,p,q) {
##
## Result:
## A list of 2 matrices: IPD and carriage incidences
## after vaccination (corresponding to matrices IPD and Car).
## [Markku Nurhonen 2013]
##
if (VT_rows[1]>0) {
IPD<-as.matrix(IPD); Car<-as.matrix(Car)
# Post vaccination carriage incidences
Car_Total<-t(matrix(apply(Car,2,sum),dim(Car)[2],dim(Car)[1]))
Car2<-Car; Car2[VT_rows,]<-0
Car_NVT<-t(matrix(apply(Car2,2,sum),dim(Car2)[2],dim(Car2)[1]))
Car_VT<-Car_Total-Car_NVT
CarNew<-q*(1+p*Car_VT/Car_NVT)*Car2+(1-q)*Car
# Post vaccination IPD incidences
NVT_rows<-seq(dim(IPD)[1])[-1*VT_rows]
# CCR=Case-to-carrier ratios
CCR<-IPD/Car ; IPDNew<-0*IPD
# Apply the equation appearing above
# equation (1) in text for each serotype.
# First term applies to NVTs.
IPDNew[VT_rows,]<-(1-q)*IPD[VT_rows,]
# Second term applies to NVTs.
IPDNew[NVT_rows,]<-((Car_NVT+p*q*Car_VT)*(Car/Car_NVT)*CCR)[NVT_rows,]
}
else {
IPDNew<-IPD; CarNew<-Car
}
list(IPDNew,CarNew)
}
 
NextVT<-function(IPD,Car,VT_rows,p) {
##
## Result:
## A vector of decreases in IPD due to adding a serotype
## to the vaccine. If VT_rows=0, initially no vaccination.
## For row indexes incuded in VT_rows, the result is 0.
## [Markku Nurhonen 2013]
##
IPD<-as.matrix(IPD); Car<-as.matrix(Car)
## VaccMat = IPD and Car matrices after vaccination
VaccMat<-Vaccination(IPD,Car,VT_rows,p,1)
IPD<-VaccMat[[1]]; Car<-VaccMat[[2]]
## Total_IPD,Total_Car = Matrices corresponding to
## overall IPD and carriage in each age class.
Total_IPD<-t(matrix(apply(IPD,2,sum),dim(IPD)[2],dim(IPD)[1]))
Total_Car<-t(matrix(apply(Car,2,sum),dim(Car)[2],dim(Car)[1]))
## Effect = decrease in IPD when one serotype is added to the vaccine.
## See equation (3) in text.
Effect<-(Total_IPD-IPD)*((IPD/(Total_IPD-IPD))-(p*Car/(Total_Car-Car)))
## Special case when only one NVT remains.
IPD_nonzero<-which(apply(IPD,1,sum)!=0)
if (length(IPD_nonzero)==1) {Effect[IPD_nonzero,]<-IPD[IPD_nonzero,]}
 
## Result is obtained after summation over age classes.
apply(Effect,1,sum)
}
 
OptimalSequence<-function(IPD,Car,VT_rows,Excluded_rows,p,HowmanyAdded) {
##
## Starting from VTs indicated by the vector VT_rows
## (VT_rows=0, for no vaccination) sequentially add new VTs
## to the vaccine composition s.t. at each step the optimal
## serotype (corresponding to largest decrease in IPD) is added.
##
## Excluded_rows = Vector of indexes of the rows in matrices
## IPD and Car corresponding to serotypes that are not to
## be included in a vaccine composition, e.g. a row
## corresponding to a group of serotypes labelled "Other".
## Enter Excluded_rows=0 for no excluded serotypes.
## HowmanyAdded = number of VTs to be added.
##
## Result:
## Matrix of dimension 2*HowmanyAdded with 1st row indicating
## the row numbers of added serotypes in the order they appear
## in the sequence. The 2nd row lists the decreases in IPD
## due to addition of each type. [Markku Nurhonen 2013]
##
IPD<-as.matrix(IPD); Car<-as.matrix(Car)
## First check the maximum possible number of added VTs.
VT_howmany<-length(VT_rows)
if (VT_rows[1]==0) {VT_howmany<-0}
Excluded_howmany<-length(Excluded_rows)
if (Excluded_rows[1]==0) {Excluded_howmany<-0}
HowmanyAdded<-min(HowmanyAdded,dim(IPD)[1]-(VT_howmany+Excluded_howmany))
BestVTs<-BestEffects<-rep(0,HowmanyAdded)
## Sequential procedure: at each step find the best additional VT.
for (i in 1:HowmanyAdded) {
## Effects = Decrease in IPD after addition of each serotype
Effects<-NextVT(IPD,Car,VT_rows,p)
## Set Effects for VTs and excluded types equal to small values
## so that none of these will be selected as the next VT.
minvalue<- -2*max(abs(Effects))
if (Excluded_howmany>0) {Effects[Excluded_rows]<-minvalue}
if (VT_rows[1]>0) {Effects[VT_rows]<-minvalue}
## BestVTs[i] = Index of serotype with maximum decrease in IPD.
BestVTs[i]<-order(-1*Effects)[1]
## BestEffects[i] = Decrese in IPD due to addition of BestVTs[i]
## to the vaccine.
BestEffects[i]<-Effects[BestVTs[i]]
VT_rows<-c(VT_rows,BestVTs[i])
if (VT_rows[1]==0) {VT_rows<-VT_rows[-1]}
VaccMat<-Vaccination(IPD,Car,VT_rows,p,1)
IPD<-VaccMat[[1]]; Car<-VaccMat[[2]]
}
t(matrix(c(BestVTs,BestEffects),HowmanyAdded,2))
}
 
OptimalVacc<-function(IPD,Car,VT_rows,p,q,HowmanyAdded) {
##
## Result:
## A list of 3 elements: (1) Row numbers of serotypes in the optimal
## vaccine composition (2)-(3) IPD and carriage incidences
## by serotype and age class corresponding to the optimal
## vaccine formed using the sequential procedure in the
## function OptimalSequence. [Markku Nurhonen 2013]
##
Additional_VTs<-OptimalSequence(IPD,Car,VT_rows,p,HowmanyAdded)[1,]
All_VTs<-c(VT_rows,Additional_VTs)
if (All_VTs[1]==0) All_VTs<-All_VTs[-1]
VaccMat<-Vaccination(IPD,Car,All_VTs,p,q)
list(All_VTs,VaccMat[[1]],VaccMat[[2]])
}
 
VacCar <- Ovariable("VacCar",
dependencies = data.frame(Name = c(
"IPD", # incidence of pneumococcus disease
"Car", # number of carriers of pneumococcus
"servac", # ovariable of serotypes in vaccine (1 for serotypes in a vaccine, otherwise result is 0)
"p", # proportion of eliminated VT carriage that is replaced by NVT carriage
"q" # proportion of of VT carriage eliminated by vaccine
)),
formula = function(...) {
## Result:
## An ovariable of carriage incidences
## after vaccination (corresponding to Car).
## [Markku Nurhonen 2013, Jouni Tuomisto 2014]
# Post vaccination carriage incidences
# Sum over serotypes and drop extra columns
Car_Total<- unkeep(oapply(Car, cols = "Serotype", FUN = sum) * 1, prevresults = TRUE)
# Car2 is a temporary ovariable with NVT carriers only
Car2 <- unkeep(Car * (1 - servac), prevresults = TRUE) # Take only NVT carriers
Car_NVT <- oapply(Car2, cols = "Serotype", FUN = sum) # Carriers of serotypes not in vaccine (NVT)
Car_VT <- Car_Total - Car_NVT # Carriers of vaccine serotypes
 
CarNew <- q * (1 + p * Car_VT / Car_NVT) * Car2 + (1 - q) * Car
 
return(CarNew)
}
)
VacIPD <- Ovariable("VacIPD",
dependencies = data.frame(Name = c(
"IPD", # incidence of pneumococcus disease
"Car", # number of carriers of pneumococcus
"servac", # ovariable of serotypes in vaccine (1 for serotypes in a vaccine, otherwise result is 0)
"p", # proportion of eliminated VT carriage that is replaced by NVT carriage
"q" # proportion of of VT carriage eliminated by vaccine
)),
formula = function(...) {
## Result:
## An ovariable of IPD incidence
## after vaccination (corresponding to ovariable IPD).
## [Markku Nurhonen 2013, Jouni Tuomisto 2014]
# Post vaccination carriage incidences (same code as in VacCar)
Car_Total <- unkeep(oapply(Car, cols = "Serotype", FUN = sum) * 1, prevresults = TRUE) # Sums over serotypes
Car2 <- unkeep(Car * (1 - servac), prevresults = TRUE)
Car_NVT <- oapply(Car2, cols = "Serotype", FUN = sum) # Carriers of serotypes not in vaccine (NVT)
Car_VT <- Car_Total - Car_NVT # Carriers of vaccine serotypes
CarNew <- q * (1 + p * Car_VT / Car_NVT) * Car2 + (1 - q) * Car
# Post vaccination IPD incidences
# CCR=Case-to-carrier ratios
CCR <- IPD / Car
 
# Apply the equation appearing above
# equation (1) in text for each serotype.
# First term applies to VTs.
IPDNewVT <- (1 - q) * IPD * servac
 
# Second term applies to NVTs.
IPDNewNVT <- (Car_NVT + p * q * Car_VT) * (Car / Car_NVT) * CCR * (1 - servac)
 
IPDNew <- IPDNewVT + IPDNewNVT
 
return(IPDNew)
}
)
 
objects.store(Vaccination, NextVT, OptimalSequence, OptimalVacc, VacCar, VacIPD)
 
cat("Funktiot Vaccination, NextVT, OptimalSequence, OptimalVacc sekä ovariablet VacCar, VacIPD tallennettu. \n")
 
</rcode>
 
=== Data ===
 
Pneumokokin esiintyvyys suomalaisessa väestössä. Carrier: (oireettomien) kantajien lukumäärä, Incidence: invasiivisen pneumokokkitaudin ilmaantuvuus 100000 henkilövuotta kohti.
 
<t2b name='Pneumokokki väestössä' index='Serotype,Age,Observation' locations='Carrier,Incidence' unit='#, #/100000py'>
19F|Under 5|156030|7.78
23F|Under 5|156030|7.88
6B|Under 5|126990|24.39
14|Under 5|41200|20.76
9V|Under 5|22290|2.91
4|Under 5|12830|2.91
18C|Under 5|10130|6.64
1|Under 5|10|0.31
7|Under 5|14180|3.02
6A|Under 5|54940|3.94
19A|Under 5|24320|9.88
3|Under 5|12160|1.25
8|Under 5|1350|0.1
9N|Under 5|20940|0.83
10|Under 5|4050|0.41
11|Under 5|72270|0.42
12|Under 5|10|0.21
15|Under 5|33100|1.98
16|Under 5|3380|0.21
20|Under 5|1350|0.01
22|Under 5|12160|0.93
23A|Under 5|3380|0.1
33|Under 5|680|0.42
35|Under 5|30400|0.31
38|Under 5|4050|0.42
6C|Under 5|27470|0.01
Oth|Under 5|24320|0.73
19F|Over 5|168100|28.51
23F|Over 5|314800|53.72
6B|Over 5|256700|29.53
14|Over 5|209800|99.43
9V|Over 5|114100|43.07
4|Over 5|62500|76.99
18C|Over 5|200700|24.39
1|Over 5|100|6.58
7|Over 5|100|46.88
6A|Over 5|158800|17.42
19A|Over 5|54900|20.54
3|Over 5|30800|55.04
8|Over 5|8800|11.21
9N|Over 5|8800|25.2
10|Over 5|20800|6.28
11|Over 5|97700|12.76
12|Over 5|100|13.89
15|Over 5|100|9.18
16|Over 5|191900|4.73
20|Over 5|25200|3.29
22|Over 5|72500|29.03
23A|Over 5|22000|4.4
33|Over 5|100|5.64
35|Over 5|71300|12.41
38|Over 5|100|1.43
6C|Over 5|79400|5.5
Oth|Over 5|330100|11.2
</t2b>

Versio 10. kesäkuuta 2014 kello 11.46

Laskenta

R-program code for the replcement model as in File S1 of Nurhonen M, Auranen K: Optimal serotype compositions for pneumococcal conjugate vaccination under serotype replacement [[1]


Esimerkkejä

  • VT: vaccine serotypes, i.e. pneumococcus serotypes that are found in a vaccine.
  • NVT: non-vaccine serotypes, i.e. pneumococcus serotypes that are not found in the vaccine.
  • Example model run

----#: . Yritin selvittää kuinka alla oleva koodi toimii. Huomasin että vaihtaessa arvoja ajoon, niin vaikuttaisi siltä että ajon tulokset eivät kuitenkaan muutu? --Jaakko (keskustelu) 5. kesäkuuta 2014 kello 14.13 (UTC) (type: truth; paradigms: science: comment) ----#: . Taulukot vastaavat näköjään aina lähtömallia, joka on PCv13, mutta pylväskuviossa muutettu malli on mukana. --Markku N. (keskustelu) 9. kesäkuuta 2014 kello 09.18 (UTC) (type: truth; paradigms: science: comment)

Proportion of VT carriage removed by vaccination:

Proportion of VT carriage removed by vaccination that is replaced by NVT:

The true amount of more than 5-year-old carriers is n times larger than estimated. What is n?:

Serotypes in a vaccine to look at:
19F
23F
6B
14
9V
4
18C
1
7
6A
19A
3
8
9N
10
11
12
15
16
20
22
23
33
35
38
6C
Other

+ Näytä koodi

Funktioiden alustus

+ Näytä koodi

Data

Pneumokokin esiintyvyys suomalaisessa väestössä. Carrier: (oireettomien) kantajien lukumäärä, Incidence: invasiivisen pneumokokkitaudin ilmaantuvuus 100000 henkilövuotta kohti.

Pneumokokki väestössä(#, #/100000py)
ObsSerotypeAgeCarrierIncidence
119FUnder 51560307.78
223FUnder 51560307.88
36BUnder 512699024.39
414Under 54120020.76
59VUnder 5222902.91
64Under 5128302.91
718CUnder 5101306.64
81Under 5100.31
97Under 5141803.02
106AUnder 5549403.94
1119AUnder 5243209.88
123Under 5121601.25
138Under 513500.1
149NUnder 5209400.83
1510Under 540500.41
1611Under 5722700.42
1712Under 5100.21
1815Under 5331001.98
1916Under 533800.21
2020Under 513500.01
2122Under 5121600.93
2223AUnder 533800.1
2333Under 56800.42
2435Under 5304000.31
2538Under 540500.42
266CUnder 5274700.01
27OthUnder 5243200.73
2819FOver 516810028.51
2923FOver 531480053.72
306BOver 525670029.53
3114Over 520980099.43
329VOver 511410043.07
334Over 56250076.99
3418COver 520070024.39
351Over 51006.58
367Over 510046.88
376AOver 515880017.42
3819AOver 55490020.54
393Over 53080055.04
408Over 5880011.21
419NOver 5880025.2
4210Over 5208006.28
4311Over 59770012.76
4412Over 510013.89
4515Over 51009.18
4616Over 51919004.73
4720Over 5252003.29
4822Over 57250029.03
4923AOver 5220004.4
5033Over 51005.64
5135Over 57130012.41
5238Over 51001.43
536COver 5794005.5
54OthOver 533010011.2
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