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Cell[CellGroupData[{
Cell[TextData[{
 "Superimposing evolutionary changes upon case trends in Canada\n",
 StyleBox["Supplementary Material:",
  FontSize->18,
  FontWeight->"Bold"],
 StyleBox[" Endemic does not mean constant as SARS-CoV-2 continues to evolve\n\
Sarah P. Otto, Ailene MacPherson, & Caroline Colijn\n",
  FontSize->18],
 StyleBox["Evolution",
  FontSize->18,
  FontSlant->"Italic"],
 StyleBox[" (2024)",
  FontSize->18]
}], "Title",
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Cell[TextData[StyleBox["Date of data download: September 23, 2023",
 FontSize->16]], "Chapter",
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Cell["\<\
duotang: Lineage evolution from December 1 2021 - August 31 2022\
\>", "Section",
 CellChangeTimes->{{3.9044746834360743`*^9, 3.904474703492754*^9}, {
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Cell["\<\
####################
#Ran duotang in RStudio, adjusting dates and lineages of interest to estimate \
the selection coefficients and lineage frequencies for different periods of \
time.
#Version : 22 September 2023
#Obtained from https://github.com/CoVaRR-NET/duotang
####################

####################
#Notes to run duotang.Rmd in RStudio : 
#Set directories and dates needed to run duotang.Rmd
####################
setwd (\"~/Dropbox/COVID19/CoVaRRNet_Pillar6_Notebook\")
params <- list ()
params$datadir <-\"data_needed/\"
params$datestamp <-\"2023-09-02\"

#Desired date range
mystartdate <-\[OpenCurlyDoubleQuote]2021-12-01\[CloseCurlyDoubleQuote]
myenddate <-\[OpenCurlyDoubleQuote]2022-08-31\[CloseCurlyDoubleQuote]
params$datestamp <- myenddate

#Enter duotang.Rmd through \[OpenCurlyDoubleQuote]meta$month\
\[CloseCurlyDoubleQuote] then adjust meta to trim data after the desired date

  meta <- meta %>% filter(
     sample_collection_date >= mystartdate,
     sample_collection_date <= myenddate
    )
 
 #Use as a check for size of data set later:
 nrow(meta)  
 
 #Replace startdate with
   startdate <- as.Date(mystartdate)
 #Set enddate
  maxdate<-as.Date(myenddate)

 #Skipping the startdate line in duotang.Rmd, continue entering commands \
until \[OpenCurlyDoubleQuote]## 2. LOAD epidemiological data (PHAC)\
\[CloseCurlyDoubleQuote]

####################
#Adjust code first in plot_selection_estimator.R and save
####################
#Hard code refdate to the middle of the time period (avoids low counts of \
some variants setting time to a different point)
#and allows a consistent time at which the allele frequencies are measured \
across provinces (here the middate
  refdate<-135
  
 #which corresponds to April 15, 2022 for t=0
 
####################
#Adapting code from selection.Rmd do the following (only):
####################
source(\[OpenCurlyDoubleQuote]scripts/plot_selection_estimator.R\
\[CloseCurlyDoubleQuote])

#define starting parameters (p,s) for making the selection estimates
startpar <- list(p=c(0.1, 0.05, 0.01), s=c(0.05, 0.05, 0.01))

#filter samples with variants of interest
setAll=getAllStrictoLineages(meta) 
sublineages_BA45 <- c(getStrictoSubLineages(\[OpenCurlyDoubleQuote]BA.5*\
\[CloseCurlyDoubleQuote],meta), \
getStrictoSubLineages(\[OpenCurlyDoubleQuote]BA.4*\[CloseCurlyDoubleQuote],\
meta) )

sublineages_BA2 <- getStrictoSubLineages(\[OpenCurlyDoubleQuote]BA.2*\
\[CloseCurlyDoubleQuote],meta)

#The rest in this period are primarily BA.1:
sublineages_BA1<-setAll[!setAll %in% c(sublineages_BA2, sublineages_BA45)] \
#the rest

sublineages_BA11  <- getStrictoSubLineages(\[OpenCurlyDoubleQuote]BA.1.1*\
\[CloseCurlyDoubleQuote],meta) 
sublineages_BA1 <- sublineages_BA1[!sublineages_BA1 %in% c(sublineages_BA11)] \
#BA.1 Other

reference <- sublineages_BA1
#define the mutants
mutants = list(sublineages_BA11, sublineages_BA2, sublineages_BA45)
#this list defines the mutants we are looking at for plotting. The order of \
mutants listed will be respected on the respective plots. 
#!!!last element is always the reference.
mutantNames = list(\[OpenCurlyDoubleQuote]BA.1.1*\[CloseCurlyDoubleQuote],  \
\[OpenCurlyDoubleQuote]BA.2*\[CloseCurlyDoubleQuote], \
\[OpenCurlyDoubleQuote]BA.4/5*\[CloseCurlyDoubleQuote], \
\[OpenCurlyDoubleQuote]BA.1*\[CloseCurlyDoubleQuote]) 
col <- c(\[OpenCurlyDoubleQuote]BA.1.1\[CloseCurlyDoubleQuote]=\
\[OpenCurlyDoubleQuote]#CC5500\[CloseCurlyDoubleQuote],\[OpenCurlyDoubleQuote]\
BA.2\[CloseCurlyDoubleQuote]=pal[\[OpenCurlyDoubleQuote]Omicron BA.2\
\[CloseCurlyDoubleQuote]],\[OpenCurlyDoubleQuote]BA.4/5\
\[CloseCurlyDoubleQuote]=pal[\[OpenCurlyDoubleQuote]Omicron BA.5\
\[CloseCurlyDoubleQuote]],\[OpenCurlyDoubleQuote]BA.1\[CloseCurlyDoubleQuote]=\
pal[\[OpenCurlyDoubleQuote]Omicron BA.1\[CloseCurlyDoubleQuote]]) #define 

#check that all lineages are present once:
nrow(setAll)-nrow(reference)-nrow(Reduce(union,mutants))

####################
#Switch to code from plot_selection_estimator.R and enter one region at a \
time then the following code
####################
region<-\[OpenCurlyDoubleQuote]Ontario\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]Alberta\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]British Columbia\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]Quebec\[CloseCurlyDoubleQuote]
method<-\[OpenCurlyQuote]BFGS\[CloseCurlyQuote]
collapseMutants<-T
sample_collection_date<-max(meta$sample_collection_date)

#Enter
  est <- .make.estimator(region, startdate, reference, mutants)
  toplot <- est$toplot
  toplot$tot <- apply(toplot[which(!is.element(names(toplot), c(\
\[OpenCurlyQuote]time\[CloseCurlyQuote], \[OpenCurlyQuote]date\
\[CloseCurlyQuote])))], 1, sum)
  
  fit <- .fit.model(est, startpar, method=method)
  fit
  
  # To visualize fits, enter code for \
\[OpenCurlyDoubleQuote]sub.plot.selection.estimate <- \
function(region,maxdate=NA){\[OpenCurlyDoubleQuote] in selection.Rmd then \
enter:
  sub.plot.selection.estimate(region, maxdate)  

# Check that all sequences are accounted for
  nrow(meta %>% filter(province == \
region))-(sum(toplot$n1)+sum(toplot$n2)+sum(toplot$n3)+sum(toplot$n4))\
\>", "Text",
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ONTARIO
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ALBERTA
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BRITISH COLUMBIA
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duotang: Lineage evolution from September 1 2022 - May 31 2023\
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####################
#Ran duotang in RStudio, adjusting dates and lineages of interest to estimate \
the selection coefficients and lineage frequencies for different periods of \
time.
#Version : 22 September 2023
#Obtained from https://github.com/CoVaRR-NET/duotang
####################

####################
#Notes to run duotang.Rmd in RStudio : 
#Set directories and dates needed to run duotang.Rmd
####################
setwd (\"~/Dropbox/COVID19/CoVaRRNet_Pillar6_Notebook\")
params <- list ()
params$datadir <-\"data_needed/\"
params$datestamp <-\"2023-09-02\"

#Desired date range
mystartdate <-\[OpenCurlyDoubleQuote]2022-09-01\[CloseCurlyDoubleQuote]
myenddate <-\[OpenCurlyDoubleQuote]2023-05-31\[CloseCurlyDoubleQuote]
params$datestamp <- myenddate

#Enter duotang.Rmd through \[OpenCurlyDoubleQuote]meta$month\
\[CloseCurlyDoubleQuote] then adjust meta to trim data after the desired date

  meta <- meta %>% filter(
     sample_collection_date >= mystartdate,
     sample_collection_date <= myenddate
    )
 
 #Use as a check for size of data set later:
 nrow(meta)  
    
 #Replace startdate with
   startdate <- as.Date(mystartdate)
 #Set enddate
  maxdate<-as.Date(myenddate)

 #Skipping the startdate line in duotang.Rmd, continue entering commands \
until \[OpenCurlyDoubleQuote]## 2. LOAD epidemiological data (PHAC)\
\[CloseCurlyDoubleQuote]

####################
#Adjust code first in plot_selection_estimator.R and save
####################
#Hard code refdate to the middle of the time period (avoids low counts of \
some variants setting time to a different point)
#and allows a consistent time at which the allele frequencies are measured \
across provinces (here the middate
  refdate<-135
  
#which corresponds to Jan 14, 2023 for t=0

####################
#Adapting code from selection.Rmd do the following (only):
####################
source(\[OpenCurlyDoubleQuote]scripts/plot_selection_estimator.R\
\[CloseCurlyDoubleQuote])

#define starting parameters (p,s) for making the selection estimates
startpar <- list(p=c(0.1, 0.05, 0.01), s=c(0.05, 0.05, 0.01))
  
#filter samples with variants of interest
setAll=getAllStrictoLineages(meta) 
sublineages_XBB <- getStrictoSubLineages(\[OpenCurlyDoubleQuote]XBB*\
\[CloseCurlyDoubleQuote],meta) #XBB variants

sublineages_XBB15  <- getStrictoSubLineages(\[OpenCurlyDoubleQuote]XBB.1.5*\
\[CloseCurlyDoubleQuote],meta) #XBB1.5 variants
sublineages_XBBOther <- sublineages_XBB[!sublineages_XBB %in% \
c(sublineages_XBB15)] #XBB Other

sublineages_BQ <- getStrictoSubLineages(\[OpenCurlyDoubleQuote]BQ*\
\[CloseCurlyDoubleQuote],meta)

#The rest in this period are primarily BA.5 & BA.4:
sublineages_Other<-setAll[!setAll %in% c(sublineages_BQ, sublineages_XBB15, \
sublineages_XBBOther)] #the rest

reference <- sublineages_Other
#define the mutants
mutants = list(sublineages_XBB15, sublineages_XBBOther, sublineages_BQ)
#this list defines the mutants we are looking at for plotting. The order of \
mutants listed will be respected on the respective plots. 
#!!!last element is always the reference.
mutantNames = list(\[OpenCurlyDoubleQuote]XBB.1.5*\[CloseCurlyDoubleQuote],  \
\[OpenCurlyDoubleQuote]XBB.Other*\[CloseCurlyDoubleQuote], \
\[OpenCurlyDoubleQuote]BQ*\[CloseCurlyDoubleQuote], \
\[OpenCurlyDoubleQuote]BA.5*\[CloseCurlyDoubleQuote]) #
col <- c(\[OpenCurlyDoubleQuote]XBB.1.5*\[CloseCurlyDoubleQuote]=\
\[OpenCurlyDoubleQuote]#00eeff\[CloseCurlyDoubleQuote],\[OpenCurlyDoubleQuote]\
XBB.Other\[CloseCurlyDoubleQuote]=pal[\[OpenCurlyDoubleQuote]XBB\
\[CloseCurlyDoubleQuote]],\[OpenCurlyDoubleQuote]BQ\[CloseCurlyDoubleQuote] = \
pal[\[OpenCurlyDoubleQuote]Omicron BQ\[CloseCurlyDoubleQuote]]) #define \
custom color for XBB here because otherwise it will be black cuz recombinant.

#check that all lineages are present once:
nrow(setAll)-nrow(reference)-nrow(Reduce(union,mutants))

####################
#Switch to code from plot_selection_estimator.R and enter one region at a \
time then the following code
####################
region<-\[OpenCurlyDoubleQuote]Ontario\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]Alberta\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]British Columbia\[CloseCurlyDoubleQuote]
region<-\[OpenCurlyDoubleQuote]Quebec\[CloseCurlyDoubleQuote]
method<-\[OpenCurlyQuote]BFGS\[CloseCurlyQuote]
collapseMutants<-T
sample_collection_date<-max(meta$sample_collection_date)

#Enter
  est <- .make.estimator(region, startdate, reference, mutants)
  toplot <- est$toplot
  toplot$tot <- apply(toplot[which(!is.element(names(toplot), c(\
\[OpenCurlyQuote]time\[CloseCurlyQuote], \[OpenCurlyQuote]date\
\[CloseCurlyQuote])))], 1, sum)
  
  fit <- .fit.model(est, startpar, method=method)
  fit
  
  # To visualize fits, enter code for \
\[OpenCurlyDoubleQuote]sub.plot.selection.estimate <- \
function(region,maxdate=NA){\[OpenCurlyDoubleQuote] in selection.Rmd then \
enter:
  sub.plot.selection.estimate(region, maxdate)  
  
  # Check that all sequences are accounted for
  nrow(meta %>% filter(province == \
region))-(sum(toplot$n1)+sum(toplot$n2)+sum(toplot$n3)+sum(toplot$n4))\
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ONTARIO
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$confint
         2.5 %      97.5 %
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p2 0.006643921 0.008606496
p3 0.627474991 0.642240743
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ALBERTA
$fit
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$confint
         2.5 %     97.5 %
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p2 0.009555296 0.01476919
p3 0.722050410 0.74815387
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$confint
         2.5 %      97.5 %
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BRITISH COLUMBIA
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$confint
         2.5 %      97.5 %
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Canadian population size by 70+ and all ages on 1 July 2022 (from \
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GEO\tTotal\t70+
Alberta\t4543111\t439109
British Columbia\t5319324\t726247
Canada\t38929902\t5022509
Manitoba\t1409223\t159565
New Brunswick\t812061\t125512
Newfoundland and Labrador\t525972\t83596
Northwest Territories\t45605\t2507
Nova Scotia\t1019725\t152435
Nunavut\t40526\t1076
Ontario\t15109416\t1918812
Prince Edward Island\t170688\t23926
Quebec\t8695659\t1250508
Saskatchewan\t1194803\t135543
Yukon\t43789\t3673\
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BC - 70+ case counts and genomic frequencies (Dec 2021-May 2023)\
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http://www.bccdc.ca/Health-Info-Site/Documents/BCCDC_COVID19_Dashboard_Case_\
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Processing BC Case Data [ENTER this or the next folder - uploading & \
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Cell["\<\
The reported cases among those under and over 70+ will be reported per \
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